From 2f62026100db78222687211be2d0d2efda78e18d Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Mon, 29 Apr 2024 13:07:53 +0200 Subject: [PATCH 01/12] First draft --- .../models/idefics2/fine_tune_idefics2.ipynb | 782 ++++++++++++++++++ 1 file changed, 782 insertions(+) create mode 100644 src/transformers/models/idefics2/fine_tune_idefics2.ipynb diff --git a/src/transformers/models/idefics2/fine_tune_idefics2.ipynb b/src/transformers/models/idefics2/fine_tune_idefics2.ipynb new file mode 100644 index 00000000000000..39446016911461 --- /dev/null +++ b/src/transformers/models/idefics2/fine_tune_idefics2.ipynb @@ -0,0 +1,782 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load model" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/niels/python_projects/transformers/env/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n", + "`low_cpu_mem_usage` was None, now set to True since model is quantized.\n", + "Loading checkpoint shards: 100%|██████████| 7/7 [00:19<00:00, 2.72s/it]\n" + ] + } + ], + "source": [ + "import torch\n", + "from peft import LoraConfig\n", + "from transformers import BitsAndBytesConfig, Idefics2ForConditionalGeneration\n", + "\n", + "DEVICE = \"cuda:0\"\n", + "USE_LORA = False\n", + "USE_QLORA = True\n", + "\n", + "# Three options for training, from the lowest precision training to the highest precision training:\n", + "# - QLora\n", + "# - Standard Lora\n", + "# - Full fine-tuning\n", + "if USE_QLORA or USE_LORA:\n", + " lora_config = LoraConfig(\n", + " r=8,\n", + " lora_alpha=8,\n", + " lora_dropout=0.1,\n", + " target_modules='.*(text_model|modality_projection|perceiver_resampler).*(down_proj|gate_proj|up_proj|k_proj|q_proj|v_proj|o_proj).*$',\n", + " use_dora=False if USE_QLORA else True,\n", + " init_lora_weights=\"gaussian\"\n", + " )\n", + " if USE_QLORA:\n", + " bnb_config = BitsAndBytesConfig(\n", + " load_in_4bit=True,\n", + " bnb_4bit_quant_type=\"nf4\",\n", + " bnb_4bit_compute_dtype=torch.float16\n", + " )\n", + " model = Idefics2ForConditionalGeneration.from_pretrained(\n", + " \"HuggingFaceM4/idefics2-8b\",\n", + " torch_dtype=torch.float16,\n", + " quantization_config=bnb_config if USE_QLORA else None,\n", + " )\n", + " model.add_adapter(lora_config)\n", + " model.enable_adapters()\n", + "else:\n", + " model = Idefics2ForConditionalGeneration.from_pretrained(\n", + " \"HuggingFaceM4/idefics2-8b\",\n", + " torch_dtype=torch.float16,\n", + " _attn_implementation=\"flash_attention_2\", # Only available on A100 or H100\n", + " ).to(DEVICE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from datasets import load_dataset\n", + "\n", + "dataset = load_dataset(\"naver-clova-ix/cord-v2\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DatasetDict({\n", + " train: Dataset({\n", + " features: ['image', 'ground_truth'],\n", + " num_rows: 800\n", + " })\n", + " validation: Dataset({\n", + " features: ['image', 'ground_truth'],\n", + " num_rows: 100\n", + " })\n", + " test: Dataset({\n", + " features: ['image', 'ground_truth'],\n", + " num_rows: 100\n", + " })\n", + "})" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create PyTorch dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-04-29 11:37:02.572654: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2024-04-29 11:37:03.215649: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", + "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n" + ] + } + ], + "source": [ + "import json\n", + "import random\n", + "from typing import Any, List\n", + "\n", + "from torch.utils.data import Dataset\n", + "from transformers import AutoProcessor\n", + "\n", + "added_tokens = []\n", + "processor = AutoProcessor.from_pretrained(\"HuggingFaceM4/idefics2-8b\", do_image_splitting=False)\n", + "\n", + "\n", + "class CustomDataset(Dataset):\n", + " def __init__(self, dataset, is_train=True, sort_json_key: bool = True,):\n", + " self.dataset = dataset\n", + " self.split = \"train\" if is_train else \"validation\"\n", + " self.sort_json_key = sort_json_key\n", + "\n", + " ground_truth_token_sequences = []\n", + " for sample in self.dataset:\n", + " ground_truth = json.loads(sample[\"ground_truth\"])\n", + " if \"gt_parses\" in ground_truth: # some datasets have multiple ground truths available, e.g. DocVQA\n", + " assert isinstance(ground_truth[\"gt_parses\"], list)\n", + " ground_truth_jsons = ground_truth[\"gt_parses\"]\n", + " else:\n", + " assert \"gt_parse\" in ground_truth and isinstance(ground_truth[\"gt_parse\"], dict)\n", + " ground_truth_jsons = [ground_truth[\"gt_parse\"]]\n", + "\n", + " ground_truth_token_sequences.append(\n", + " [\n", + " self.json2token(\n", + " ground_truth_json,\n", + " update_special_tokens_for_json_key=self.split == \"train\",\n", + " sort_json_key=self.sort_json_key,\n", + " )\n", + " for ground_truth_json in ground_truth_jsons # load json from list of json\n", + " ]\n", + " )\n", + "\n", + " self.ground_truth_token_sequences = ground_truth_token_sequences\n", + "\n", + " def json2token(self, obj: Any, update_special_tokens_for_json_key: bool = True, sort_json_key: bool = True):\n", + " \"\"\"\n", + " Convert an ordered JSON object into a token sequence\n", + " \"\"\"\n", + " if type(obj) == dict:\n", + " if len(obj) == 1 and \"text_sequence\" in obj:\n", + " return obj[\"text_sequence\"]\n", + " else:\n", + " output = \"\"\n", + " if sort_json_key:\n", + " keys = sorted(obj.keys(), reverse=True)\n", + " else:\n", + " keys = obj.keys()\n", + " for k in keys:\n", + " if update_special_tokens_for_json_key:\n", + " self.add_tokens([fr\"\", fr\"\"])\n", + " output += (\n", + " fr\"\"\n", + " + self.json2token(obj[k], update_special_tokens_for_json_key, sort_json_key)\n", + " + fr\"\"\n", + " )\n", + " return output\n", + " elif type(obj) == list:\n", + " return r\"\".join(\n", + " [self.json2token(item, update_special_tokens_for_json_key, sort_json_key) for item in obj]\n", + " )\n", + " else:\n", + " obj = str(obj)\n", + " if f\"<{obj}/>\" in added_tokens:\n", + " obj = f\"<{obj}/>\" # for categorical special tokens\n", + " return obj\n", + " \n", + " def add_tokens(self, list_of_tokens: List[str]):\n", + " \"\"\"\n", + " Add special tokens to tokenizer and resize the token embeddings of the decoder\n", + " \"\"\"\n", + " newly_added_num = processor.tokenizer.add_tokens(list_of_tokens)\n", + " if newly_added_num > 0:\n", + " model.resize_token_embeddings(len(processor.tokenizer))\n", + " added_tokens.extend(list_of_tokens)\n", + " \n", + " def __len__(self):\n", + " return len(self.dataset)\n", + " \n", + " def __getitem__(self, idx):\n", + " example = self.dataset[idx]\n", + " image = example[\"image\"]\n", + " target_sequence = random.choice(self.ground_truth_token_sequences[idx]) # can be more than one, e.g., DocVQA\n", + "\n", + " return image, target_sequence" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "train_dataset = CustomDataset(dataset=dataset[\"train\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(,\n", + " '1,591,600144,6951,346,000100,950-4575,000Nasi Campur Bali1 x125,000Bbk Bengil Nasi1 x37,000MilkShake Starwb1 x24,000Ice Lemon Tea1 x70,000Nasi Ayam Dewata1 x0Free Ice Tea3 x65,000Organic Green Sa1 x18,000Ice Tea1 x29,000Ice Orange1 x85,000Ayam Suir Bali1 x36,000Tahu Goreng2 x36,000Tempe Goreng2 x40,000.Tahu Telor Asin1 x70,000Nasi Goreng Samb1 x366,000Bbk Panggang Sam3 x92,000Ayam Sambal Hija1 x44,000Hot Tea2 x32,000Ice Kopi1 x40,000Tahu Telor Asin1 x0Free Ice Tea1 x44,000Bebek Street1 x18,000Ice Tea Tawar1 x')" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_dataset[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "54" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(added_tokens)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '']\n" + ] + } + ], + "source": [ + "print(added_tokens)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define DataCollator" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "class MyDataCollator:\n", + " def __init__(self, processor):\n", + " self.processor = processor\n", + " self.image_token_id = processor.tokenizer.additional_special_tokens_ids[\n", + " processor.tokenizer.additional_special_tokens.index(\"\")\n", + " ]\n", + "\n", + " def __call__(self, examples):\n", + " texts = []\n", + " images = []\n", + " for example in examples:\n", + " image, ground_truth = example\n", + " messages = [\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": \"Extract JSON.\"},\n", + " {\"type\": \"image\"},\n", + " ]\n", + " },\n", + " {\n", + " \"role\": \"assistant\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": ground_truth}\n", + " ]\n", + " }\n", + " ]\n", + " text = processor.apply_chat_template(messages, add_generation_prompt=False)\n", + " texts.append(text.strip())\n", + " images.append([image])\n", + "\n", + " batch = processor(text=texts, images=images, return_tensors=\"pt\", padding=True)\n", + "\n", + " labels = batch[\"input_ids\"].clone()\n", + " labels[labels == processor.tokenizer.pad_token_id] = self.image_token_id\n", + " batch[\"labels\"] = labels\n", + "\n", + " return batch\n", + "\n", + "data_collator = MyDataCollator(processor)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Verify data (by creating dataloader)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No chat template is set for this tokenizer, falling back to a default class-level template. This is very error-prone, because models are often trained with templates different from the class default! Default chat templates are a legacy feature and will be removed in Transformers v4.43, at which point any code depending on them will stop working. We recommend setting a valid chat template before then to ensure that this model continues working without issues.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "input_ids torch.Size([2, 467])\n", + "attention_mask torch.Size([2, 467])\n", + "pixel_values torch.Size([2, 1, 3, 980, 653])\n", + "pixel_attention_mask torch.Size([2, 1, 980, 653])\n", + "labels torch.Size([2, 467])\n" + ] + } + ], + "source": [ + "from torch.utils.data import DataLoader\n", + "\n", + "train_dataloader = DataLoader(train_dataset, collate_fn=data_collator, batch_size=2, shuffle=True)\n", + "batch = next(iter(train_dataloader))\n", + "for k,v in batch.items():\n", + " print(k,v.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[' User: Extract JSON. \\nAssistant:574,255574,25552,205492,50029,55024,500Grilled Salmon Head1318,000Set Menu Family146,000Tairyos Salmon Maki144,000Tepanyaki Cawanmushi236,000Teppan Seafood Fr Rc124,000Tobiko Gunkan1',\n", + " ' User: Extract JSON. \\nAssistant:45,0005,00050,00028,500316,500CRISPY REAL CHEESE1']" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "processor.batch_decode(batch[\"input_ids\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\n" + ] + } + ], + "source": [ + "from transformers import TrainingArguments, Trainer\n", + "\n", + "training_args = TrainingArguments(\n", + " num_train_epochs=2,\n", + " per_device_train_batch_size=2,\n", + " per_device_eval_batch_size=8,\n", + " gradient_accumulation_steps=8,\n", + " warmup_steps=50,\n", + " learning_rate=1e-4,\n", + " weight_decay=0.01,\n", + " logging_steps=25,\n", + " output_dir=\"idefics2_ft_tutorial\",\n", + " save_strategy=\"steps\",\n", + " save_steps=250,\n", + " save_total_limit=1,\n", + " # evaluation_strategy=\"epoch\",\n", + " fp16=True,\n", + " # push_to_hub_model_id=\"idefics2-8b-docvqa-finetuned-tutorial\",\n", + " remove_unused_columns=False,\n", + " report_to=\"none\",\n", + ")\n", + "\n", + "trainer = Trainer(\n", + " model=model,\n", + " args=training_args,\n", + " data_collator=data_collator,\n", + " train_dataset=train_dataset,\n", + " # eval_dataset=eval_dataset, # You can also evaluate (loss) on the eval set, note that it will incur some additional GPU memory\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" + ] + } + ], + "source": [ + "!echo $CUDA_VISIBLE_DEVICES" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " \n", + " [100/100 11:46, Epoch 2/2]\n", + "
\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
StepTraining Loss
250.840800
500.086300
750.053400
1000.036800

" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "TrainOutput(global_step=100, training_loss=0.25431876003742215, metrics={'train_runtime': 726.1078, 'train_samples_per_second': 2.204, 'train_steps_per_second': 0.138, 'total_flos': 3.1095003208925376e+16, 'train_loss': 0.25431876003742215, 'epoch': 2.0})" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trainer.train()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Inference\n", + "\n", + "Let's see if the model has learned something." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": "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", + "image/png": "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", + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_example = dataset[\"test\"][0]\n", + "test_image = test_example[\"image\"]\n", + "test_image" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "User: Extract JSON.\n", + "Assistant:\n" + ] + } + ], + "source": [ + "# prepare image and prompt for the model\n", + "messages = [\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": \"Extract JSON.\"},\n", + " {\"type\": \"image\"},\n", + " ]\n", + " },\n", + "]\n", + "prompt = processor.apply_chat_template(messages, add_generation_prompt=True)\n", + "print(prompt)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The `seen_tokens` attribute is deprecated and will be removed in v4.41. Use the `cache_position` model input instead.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['User: Extract JSON. \\nAssistant:60.00060.0002.0060.00077305.45560.00060.00060.000- TICKET CP2']\n" + ] + } + ], + "source": [ + "inputs = processor(text=prompt, images=[test_image], return_tensors=\"pt\")\n", + "inputs = {k: v.to(DEVICE) for k, v in inputs.items()}\n", + "\n", + "# Generate\n", + "generated_ids = model.generate(**inputs, max_new_tokens=500)\n", + "generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)\n", + "\n", + "print(generated_texts)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "\n", + "# let's turn that into JSON\n", + "def token2json(tokens, is_inner_value=False, added_vocab=None):\n", + " \"\"\"\n", + " Convert a (generated) token sequence into an ordered JSON format.\n", + " \"\"\"\n", + " if added_vocab is None:\n", + " added_vocab = processor.tokenizer.get_added_vocab()\n", + "\n", + " output = {}\n", + "\n", + " while tokens:\n", + " start_token = re.search(r\"\", tokens, re.IGNORECASE)\n", + " if start_token is None:\n", + " break\n", + " key = start_token.group(1)\n", + " key_escaped = re.escape(key)\n", + "\n", + " end_token = re.search(rf\"\", tokens, re.IGNORECASE)\n", + " start_token = start_token.group()\n", + " if end_token is None:\n", + " tokens = tokens.replace(start_token, \"\")\n", + " else:\n", + " end_token = end_token.group()\n", + " start_token_escaped = re.escape(start_token)\n", + " end_token_escaped = re.escape(end_token)\n", + " content = re.search(\n", + " f\"{start_token_escaped}(.*?){end_token_escaped}\", tokens, re.IGNORECASE | re.DOTALL\n", + " )\n", + " if content is not None:\n", + " content = content.group(1).strip()\n", + " if r\"\"):\n", + " leaf = leaf.strip()\n", + " if leaf in added_vocab and leaf[0] == \"<\" and leaf[-2:] == \"/>\":\n", + " leaf = leaf[1:-2] # for categorical special tokens\n", + " output[key].append(leaf)\n", + " if len(output[key]) == 1:\n", + " output[key] = output[key][0]\n", + "\n", + " tokens = tokens[tokens.find(end_token) + len(end_token) :].strip()\n", + " if tokens[:6] == r\"\": # non-leaf nodes\n", + " return [output] + token2json(tokens[6:], is_inner_value=True, added_vocab=added_vocab)\n", + "\n", + " if len(output):\n", + " return [output] if is_inner_value else output\n", + " else:\n", + " return [] if is_inner_value else {\"text_sequence\": tokens}" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'total': {'total_price': '60.000', 'total_etc': '60.000', 'menuqty_cnt': '2.00', 'changeprice': '60.000', 'cashprice': '7730'}, 'sub_total': {'tax_price': '5.455', 'subtotal_price': '60.000'}, 'menu': {'unitprice': '60.000', 'price': '60.000', 'nm': '- TICKET CP', 'cnt': '2'}}\n" + ] + } + ], + "source": [ + "generated_json = token2json(generated_texts[0])\n", + "print(generated_json)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total {'total_price': '60.000', 'total_etc': '60.000', 'menuqty_cnt': '2.00', 'changeprice': '60.000', 'cashprice': '7730'}\n", + "sub_total {'tax_price': '5.455', 'subtotal_price': '60.000'}\n", + "menu {'unitprice': '60.000', 'price': '60.000', 'nm': '- TICKET CP', 'cnt': '2'}\n" + ] + } + ], + "source": [ + "for key, value in generated_json.items():\n", + " print(key, value)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From cb50af67c5292eabf593512069913820aaa12b58 Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Mon, 29 Apr 2024 13:34:44 +0200 Subject: [PATCH 02/12] Update code snippet --- docs/source/en/model_doc/idefics2.md | 33 +++++++--- docs/source/en/model_doc/mistral.md | 96 ++++++++++++++-------------- 2 files changed, 72 insertions(+), 57 deletions(-) diff --git a/docs/source/en/model_doc/idefics2.md b/docs/source/en/model_doc/idefics2.md index 5b91fcf38cd7b5..a66fa03a88fcea 100644 --- a/docs/source/en/model_doc/idefics2.md +++ b/docs/source/en/model_doc/idefics2.md @@ -27,13 +27,18 @@ images, or simply behave as a pure language model without visual inputs. It impr document understanding, OCR, or visual reasoning. Idefics2 is lightweight (8 billion parameters) and treats images in their native aspect ratio and resolution, which allows for varying inference efficiency. -Tips: +This model was contributed by [amyeroberts](https://huggingface.co/amyeroberts). +The original code can be found [here](https://huggingface.co/HuggingFaceM4/idefics2). + +## Usage tips + - Each sample can contain multiple images, and the number of images can vary between samples. The processor will pad the inputs to the maximum number of images in a batch for input to the model. - The processor has a `do_image_splitting` option. If `True`, each input image will be split into 4 sub-images, and concatenated with the original to form 5 images. This is useful for increasing model performance. Make sure `processor.image_processor.do_image_splitting` is set to `False` if the model was not trained with this option. - `text` passed to the processor should have the `` tokens where the images should be inserted. And `` at the end of each utterance if the text is a chat message. - The processor has its own `apply_chat_template` method to convert chat messages to text that can then be passed as `text` to the processor. Example of how to use the processor on chat messages: + ```python import requests from PIL import Image @@ -56,20 +61,30 @@ messages = [{ }] processor = Idefics2Processor.from_pretrained("HuggingFaceM4/idefics2-8b") -model = Idefics2ForConditionalGeneration.from_pretrained("HuggingFaceM4/idefics2-8b") +model = Idefics2ForConditionalGeneration.from_pretrained("HuggingFaceM4/idefics2-8b", device_map="auto") -text = processor.apply_chat_template(messages) -# "User: What’s the difference between these two images?\n" -print(text) +# at inference time, one needs to pass `add_generation_prompt=True` in order to make sure the model completes the prompt +text = processor.apply_chat_template(messages, add_generation_prompt=True) -inputs = processor(images=images, text=text) +inputs = processor(images=images, text=text, return_tensors="pt").to("cuda") -generated_text = model.generate(**inputs) +generated_text = model.generate(**inputs, max_new_tokens=500) +generated_text = processor.batch_decode(generated_text, skip_special_tokens=True)[0] +print("Generated text:", generated_text) ``` -This model was contributed by [amyeroberts](https://huggingface.co/amyeroberts). -The original code can be found [here](https://huggingface.co/HuggingFaceM4/idefics2). +## Model optimizations + +By default, weights are loaded in float32 (32 bits per parameter). However, one can speed up the model significantly by leveraging Flash-Attention-2. + + + +## Resources + +A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Idefics2. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource. +- A notebook on how to fine-tune Idefics2 on a custom dataset using the [Trainer](../main_classes/trainer.md) can be found [here](https://colab.research.google.com/drive/1NtcTgRbSBKN7pYD3Vdx1j9m8pt3fhFDB?usp=sharing). It supports both full fine-tuning as well as (quantized) LoRa. +- A script regarding how to fine-tune Idefics2 using the TRL library can be found [here](). ## Idefics2Config diff --git a/docs/source/en/model_doc/mistral.md b/docs/source/en/model_doc/mistral.md index 0ab214206165f1..b2c011c19f18bb 100644 --- a/docs/source/en/model_doc/mistral.md +++ b/docs/source/en/model_doc/mistral.md @@ -18,7 +18,7 @@ rendered properly in your Markdown viewer. ## Overview -Mistral was introduced in the [this blogpost](https://mistral.ai/news/announcing-mistral-7b/) by Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed. +Mistral was introduced in [this blogpost](https://mistral.ai/news/announcing-mistral-7b/) by Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed. The introduction of the blog post says: @@ -51,39 +51,39 @@ The Mistral team has released 3 checkpoints: The base model can be used as follows: ```python ->>> from transformers import AutoModelForCausalLM, AutoTokenizer +from transformers import AutoModelForCausalLM, AutoTokenizer ->>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", device_map="auto") ->>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1") +model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", device_map="auto") +tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1") ->>> prompt = "My favourite condiment is" +prompt = "My favourite condiment is" ->>> model_inputs = tokenizer([prompt], return_tensors="pt").to("cuda") ->>> model.to(device) +model_inputs = tokenizer([prompt], return_tensors="pt").to("cuda") +model.to(device) ->>> generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True) ->>> tokenizer.batch_decode(generated_ids)[0] +generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True) +tokenizer.batch_decode(generated_ids)[0] "My favourite condiment is to ..." ``` The instruction tuned model can be used as follows: ```python ->>> from transformers import AutoModelForCausalLM, AutoTokenizer +from transformers import AutoModelForCausalLM, AutoTokenizer ->>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2", device_map="auto") ->>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2") +model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2", device_map="auto") +tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2") ->>> messages = [ -... {"role": "user", "content": "What is your favourite condiment?"}, -... {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, -... {"role": "user", "content": "Do you have mayonnaise recipes?"} -... ] +messages = [ + {"role": "user", "content": "What is your favourite condiment?"}, + {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, + {"role": "user", "content": "Do you have mayonnaise recipes?"} +] ->>> model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda") +model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda") ->>> generated_ids = model.generate(model_inputs, max_new_tokens=100, do_sample=True) ->>> tokenizer.batch_decode(generated_ids)[0] +generated_ids = model.generate(model_inputs, max_new_tokens=100, do_sample=True) +tokenizer.batch_decode(generated_ids)[0] "Mayonnaise can be made as follows: (...)" ``` @@ -104,19 +104,19 @@ Make also sure that you have a hardware that is compatible with Flash-Attention To load and run a model using Flash Attention-2, refer to the snippet below: ```python ->>> import torch ->>> from transformers import AutoModelForCausalLM, AutoTokenizer +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer ->>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", torch_dtype=torch.float16, attn_implementation="flash_attention_2", device_map="auto") ->>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1") +model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", torch_dtype=torch.float16, attn_implementation="flash_attention_2", device_map="auto") +tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1") ->>> prompt = "My favourite condiment is" +prompt = "My favourite condiment is" ->>> model_inputs = tokenizer([prompt], return_tensors="pt").to("cuda") ->>> model.to(device) +model_inputs = tokenizer([prompt], return_tensors="pt").to("cuda") +model.to(device) ->>> generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True) ->>> tokenizer.batch_decode(generated_ids)[0] +generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True) +tokenizer.batch_decode(generated_ids)[0] "My favourite condiment is to (...)" ``` @@ -142,31 +142,31 @@ As the Mistral model has 7 billion parameters, that would require about 14GB of Quantizing a model is as simple as passing a `quantization_config` to the model. Below, we'll leverage the BitsAndyBytes quantization (but refer to [this page](../quantization.md) for other quantization methods): ```python ->>> import torch ->>> from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig ->>> # specify how to quantize the model ->>> quantization_config = BitsAndBytesConfig( -... load_in_4bit=True, -... bnb_4bit_quant_type="nf4", -... bnb_4bit_compute_dtype="torch.float16", -... ) +# specify how to quantize the model +quantization_config = BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_quant_type="nf4", + bnb_4bit_compute_dtype="torch.float16", +) ->>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2", quantization_config=True, device_map="auto") ->>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2") +model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2", quantization_config=True, device_map="auto") +tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2") ->>> prompt = "My favourite condiment is" +prompt = "My favourite condiment is" ->>> messages = [ -... {"role": "user", "content": "What is your favourite condiment?"}, -... {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, -... {"role": "user", "content": "Do you have mayonnaise recipes?"} -... ] +messages = [ + {"role": "user", "content": "What is your favourite condiment?"}, + {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, + {"role": "user", "content": "Do you have mayonnaise recipes?"} +] ->>> model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda") +model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda") ->>> generated_ids = model.generate(model_inputs, max_new_tokens=100, do_sample=True) ->>> tokenizer.batch_decode(generated_ids)[0] +generated_ids = model.generate(model_inputs, max_new_tokens=100, do_sample=True) +tokenizer.batch_decode(generated_ids)[0] "The expected output" ``` From 8e88890b257e4326ffa039a95eeb78c37cd6190b Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Mon, 29 Apr 2024 14:45:30 +0200 Subject: [PATCH 03/12] Debug --- .../models/idefics2/fine_tune_idefics2.ipynb | 302 ++++++++++++++---- .../models/idefics2/modeling_idefics2.py | 13 +- .../models/idefics2/processing_idefics2.py | 4 +- src/transformers/models/idefics2/test.py | 38 +++ src/transformers/trainer.py | 5 + 5 files changed, 291 insertions(+), 71 deletions(-) create mode 100644 src/transformers/models/idefics2/test.py diff --git a/src/transformers/models/idefics2/fine_tune_idefics2.ipynb b/src/transformers/models/idefics2/fine_tune_idefics2.ipynb index 39446016911461..d97ba57e6090d9 100644 --- a/src/transformers/models/idefics2/fine_tune_idefics2.ipynb +++ b/src/transformers/models/idefics2/fine_tune_idefics2.ipynb @@ -30,7 +30,7 @@ "/home/niels/python_projects/transformers/env/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n", "`low_cpu_mem_usage` was None, now set to True since model is quantized.\n", - "Loading checkpoint shards: 100%|██████████| 7/7 [00:19<00:00, 2.72s/it]\n" + "Loading checkpoint shards: 100%|██████████| 7/7 [00:06<00:00, 1.16it/s]\n" ] } ], @@ -128,6 +128,52 @@ "dataset" ] }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": "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", + "image/png": "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", + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_example = dataset[\"train\"][0]\n", + "# check the image\n", + "resized_image = train_example[\"image\"]\n", + "width, height = resized_image.size\n", + "resized_image.resize((int(0.3*width), int(0.3*height)))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'menu': [{'nm': 'Nasi Campur Bali', 'cnt': '1 x', 'price': '75,000'}, {'nm': 'Bbk Bengil Nasi', 'cnt': '1 x', 'price': '125,000'}, {'nm': 'MilkShake Starwb', 'cnt': '1 x', 'price': '37,000'}, {'nm': 'Ice Lemon Tea', 'cnt': '1 x', 'price': '24,000'}, {'nm': 'Nasi Ayam Dewata', 'cnt': '1 x', 'price': '70,000'}, {'nm': 'Free Ice Tea', 'cnt': '3 x', 'price': '0'}, {'nm': 'Organic Green Sa', 'cnt': '1 x', 'price': '65,000'}, {'nm': 'Ice Tea', 'cnt': '1 x', 'price': '18,000'}, {'nm': 'Ice Orange', 'cnt': '1 x', 'price': '29,000'}, {'nm': 'Ayam Suir Bali', 'cnt': '1 x', 'price': '85,000'}, {'nm': 'Tahu Goreng', 'cnt': '2 x', 'price': '36,000'}, {'nm': 'Tempe Goreng', 'cnt': '2 x', 'price': '36,000'}, {'nm': 'Tahu Telor Asin', 'cnt': '1 x', 'price': '40,000.'}, {'nm': 'Nasi Goreng Samb', 'cnt': '1 x', 'price': '70,000'}, {'nm': 'Bbk Panggang Sam', 'cnt': '3 x', 'price': '366,000'}, {'nm': 'Ayam Sambal Hija', 'cnt': '1 x', 'price': '92,000'}, {'nm': 'Hot Tea', 'cnt': '2 x', 'price': '44,000'}, {'nm': 'Ice Kopi', 'cnt': '1 x', 'price': '32,000'}, {'nm': 'Tahu Telor Asin', 'cnt': '1 x', 'price': '40,000'}, {'nm': 'Free Ice Tea', 'cnt': '1 x', 'price': '0'}, {'nm': 'Bebek Street', 'cnt': '1 x', 'price': '44,000'}, {'nm': 'Ice Tea Tawar', 'cnt': '1 x', 'price': '18,000'}], 'sub_total': {'subtotal_price': '1,346,000', 'service_price': '100,950', 'tax_price': '144,695', 'etc': '-45'}, 'total': {'total_price': '1,591,600'}}\n" + ] + } + ], + "source": [ + "import json\n", + "\n", + "ground_truth = json.loads(train_example[\"ground_truth\"])\n", + "print(ground_truth[\"gt_parse\"])" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -137,22 +183,21 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2024-04-29 11:37:02.572654: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "2024-04-29 14:44:30.749694: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2024-04-29 11:37:03.215649: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", + "2024-04-29 14:44:31.399103: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n" ] } ], "source": [ - "import json\n", "import random\n", "from typing import Any, List\n", "\n", @@ -164,9 +209,9 @@ "\n", "\n", "class CustomDataset(Dataset):\n", - " def __init__(self, dataset, is_train=True, sort_json_key: bool = True,):\n", - " self.dataset = dataset\n", - " self.split = \"train\" if is_train else \"validation\"\n", + " def __init__(self, hf_dataset, split, sort_json_key: bool = True,):\n", + " self.dataset = hf_dataset[split]\n", + " self.split = split\n", " self.sort_json_key = sort_json_key\n", "\n", " ground_truth_token_sequences = []\n", @@ -246,16 +291,39 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'train': SplitInfo(name='train', num_bytes=1840803702, num_examples=800, shard_lengths=[400, 400], dataset_name='cord-v2'),\n", + " 'validation': SplitInfo(name='validation', num_bytes=242513269, num_examples=100, shard_lengths=None, dataset_name='cord-v2'),\n", + " 'test': SplitInfo(name='test', num_bytes=235013906, num_examples=100, shard_lengths=None, dataset_name='cord-v2')}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset[\"train\"].info.splits" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "train_dataset = CustomDataset(dataset=dataset[\"train\"])" + "train_dataset = CustomDataset(hf_dataset=dataset, split=\"train\")\n", + "eval_dataset = CustomDataset(hf_dataset=dataset, split=\"validation\")" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -265,7 +333,7 @@ " '1,591,600144,6951,346,000100,950-4575,000Nasi Campur Bali1 x125,000Bbk Bengil Nasi1 x37,000MilkShake Starwb1 x24,000Ice Lemon Tea1 x70,000Nasi Ayam Dewata1 x0Free Ice Tea3 x65,000Organic Green Sa1 x18,000Ice Tea1 x29,000Ice Orange1 x85,000Ayam Suir Bali1 x36,000Tahu Goreng2 x36,000Tempe Goreng2 x40,000.Tahu Telor Asin1 x70,000Nasi Goreng Samb1 x366,000Bbk Panggang Sam3 x92,000Ayam Sambal Hija1 x44,000Hot Tea2 x32,000Ice Kopi1 x40,000Tahu Telor Asin1 x0Free Ice Tea1 x44,000Bebek Street1 x18,000Ice Tea Tawar1 x')" ] }, - "execution_count": 7, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -276,7 +344,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -285,7 +353,7 @@ "54" ] }, - "execution_count": 8, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -296,7 +364,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -320,7 +388,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -375,7 +443,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -389,11 +457,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "input_ids torch.Size([2, 467])\n", - "attention_mask torch.Size([2, 467])\n", + "input_ids torch.Size([2, 247])\n", + "attention_mask torch.Size([2, 247])\n", "pixel_values torch.Size([2, 1, 3, 980, 653])\n", "pixel_attention_mask torch.Size([2, 1, 980, 653])\n", - "labels torch.Size([2, 467])\n" + "labels torch.Size([2, 247])\n" ] } ], @@ -408,17 +476,17 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[' User: Extract JSON. \\nAssistant:574,255574,25552,205492,50029,55024,500Grilled Salmon Head1318,000Set Menu Family146,000Tairyos Salmon Maki144,000Tepanyaki Cawanmushi236,000Teppan Seafood Fr Rc124,000Tobiko Gunkan1',\n", - " ' User: Extract JSON. \\nAssistant:45,0005,00050,00028,500316,500CRISPY REAL CHEESE1']" + "[' User: Extract JSON. \\nAssistant:45,901[45,901]04,17341,72841,728SUKIYAKI BEEF RICE DEALJAVA1',\n", + " ' User: Extract JSON. \\nAssistant:56,000056,00056,00028,00056,000ALMOND CREAM CHEESE2 x']" ] }, - "execution_count": 12, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -436,13 +504,81 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import Levenshtein\n", + "import numpy as np\n", + "\n", + "\n", + "def normalized_levenshtein(s1, s2):\n", + " len_s1, len_s2 = len(s1), len(s2)\n", + " distance = Levenshtein.distance(s1, s2)\n", + " return distance / max(len_s1, len_s2)\n", + "\n", + "def similarity_score(a_ij, o_q_i, tau=0.5):\n", + " nl = normalized_levenshtein(a_ij, o_q_i)\n", + " return 1 - nl if nl < tau else 0\n", + "\n", + "def postprocess_text(preds, labels):\n", + " preds = [pred.strip() for pred in preds]\n", + " labels = [[label.strip()] for label in labels]\n", + "\n", + " return preds, labels\n", + "\n", + "def compute_metrics(eval_preds):\n", + " # Get the predicted and ground truth token sequences\n", + " preds, labels = eval_preds\n", + "\n", + " print(\"Predictions:\")\n", + " for pred in preds:\n", + " print(pred.shape)\n", + " print(\"Labels:\")\n", + " for label in labels:\n", + " print(label.shape)\n", + "\n", + " if isinstance(preds, tuple):\n", + " preds = preds[0]\n", + " # Replace -100s used for padding as we can't decode them\n", + " preds = np.where(preds != -100, preds, processor.tokenizer.pad_token_id)\n", + " decoded_preds = processor.batch_decode(preds, skip_special_tokens=True)\n", + " labels = np.where(labels != -100, labels, processor.tokenizer.pad_token_id)\n", + " decoded_labels = processor.batch_decode(labels, skip_special_tokens=True)\n", + "\n", + " # Some simple post-processing\n", + " decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)\n", + "\n", + " assert len(labels) == len(decoded_preds), \"Length of labels and decoded predictions must match.\"\n", + "\n", + " N = len(labels)\n", + " total_score = 0\n", + "\n", + " for i in range(N):\n", + " a_i = labels[i]\n", + " o_q_i = decoded_preds[i]\n", + " if o_q_i == \"\":\n", + " print(\"Warning: Skipped an empty prediction.\")\n", + " max_score = 0\n", + " else:\n", + " max_score = max(similarity_score(a_ij, o_q_i) for a_ij in a_i)\n", + "\n", + " total_score += max_score\n", + "\n", + " return {\"levenshtein\": total_score / N}" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ + "/home/niels/python_projects/transformers/src/transformers/training_args.py:1463: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n", + " warnings.warn(\n", "Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\n" ] } @@ -460,6 +596,8 @@ " weight_decay=0.01,\n", " logging_steps=25,\n", " output_dir=\"idefics2_ft_tutorial\",\n", + " evaluation_strategy=\"steps\",\n", + " eval_steps=1,\n", " save_strategy=\"steps\",\n", " save_steps=250,\n", " save_total_limit=1,\n", @@ -468,20 +606,25 @@ " # push_to_hub_model_id=\"idefics2-8b-docvqa-finetuned-tutorial\",\n", " remove_unused_columns=False,\n", " report_to=\"none\",\n", + " eval_do_concat_batches=False,\n", ")\n", "\n", + "# important: we need to disable caching during training\n", + "model.config.use_cache = False\n", + "\n", "trainer = Trainer(\n", " model=model,\n", " args=training_args,\n", " data_collator=data_collator,\n", " train_dataset=train_dataset,\n", - " # eval_dataset=eval_dataset, # You can also evaluate (loss) on the eval set, note that it will incur some additional GPU memory\n", + " eval_dataset=eval_dataset, # You can also evaluate (loss) on the eval set, note that it will incur some additional GPU memory,\n", + " compute_metrics=compute_metrics,\n", ")" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -508,7 +651,27 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.config.use_cache" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [ { @@ -517,33 +680,18 @@ "\n", "

\n", " \n", - " \n", - " [100/100 11:46, Epoch 2/2]\n", + " \n", + " [ 2/100 : < :, Epoch 0.02/2]\n", "
\n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", "
StepTraining LossValidation Loss
250.840800
500.086300
750.053400
1000.036800

" ], @@ -555,14 +703,32 @@ "output_type": "display_data" }, { - "data": { - "text/plain": [ - "TrainOutput(global_step=100, training_loss=0.25431876003742215, metrics={'train_runtime': 726.1078, 'train_samples_per_second': 2.204, 'train_steps_per_second': 0.138, 'total_flos': 3.1095003208925376e+16, 'train_loss': 0.25431876003742215, 'epoch': 2.0})" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "input_ids torch.Size([8, 285])\n", + "attention_mask torch.Size([8, 285])\n", + "pixel_values torch.Size([8, 1, 3, 980, 735])\n", + "pixel_attention_mask torch.Size([8, 1, 980, 735])\n", + "labels torch.Size([8, 285])\n", + "Loss: tensor(2.5280, device='cuda:0')\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "'tuple' object has no attribute 'shape'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[20], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/python_projects/transformers/src/transformers/trainer.py:1875\u001b[0m, in \u001b[0;36mTrainer.train\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m 1873\u001b[0m hf_hub_utils\u001b[38;5;241m.\u001b[39menable_progress_bars()\n\u001b[1;32m 1874\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1875\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minner_training_loop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1876\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1877\u001b[0m \u001b[43m \u001b[49m\u001b[43mresume_from_checkpoint\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresume_from_checkpoint\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1878\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrial\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrial\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1879\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1880\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/python_projects/transformers/src/transformers/trainer.py:2281\u001b[0m, in \u001b[0;36mTrainer._inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m 2278\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mepoch \u001b[38;5;241m=\u001b[39m epoch \u001b[38;5;241m+\u001b[39m (step \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;241m+\u001b[39m steps_skipped) \u001b[38;5;241m/\u001b[39m steps_in_epoch\n\u001b[1;32m 2279\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallback_handler\u001b[38;5;241m.\u001b[39mon_step_end(args, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol)\n\u001b[0;32m-> 2281\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_maybe_log_save_evaluate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtr_loss\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgrad_norm\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepoch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2282\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 2283\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallback_handler\u001b[38;5;241m.\u001b[39mon_substep_end(args, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol)\n", + "File \u001b[0;32m~/python_projects/transformers/src/transformers/trainer.py:2665\u001b[0m, in \u001b[0;36mTrainer._maybe_log_save_evaluate\u001b[0;34m(self, tr_loss, grad_norm, model, trial, epoch, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m 2663\u001b[0m metrics \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 2664\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol\u001b[38;5;241m.\u001b[39mshould_evaluate:\n\u001b[0;32m-> 2665\u001b[0m metrics \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mevaluate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mignore_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2666\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_report_to_hp_search(trial, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mglobal_step, metrics)\n\u001b[1;32m 2668\u001b[0m \u001b[38;5;66;03m# Run delayed LR scheduler now that metrics are populated\u001b[39;00m\n", + "File \u001b[0;32m~/python_projects/transformers/src/transformers/trainer.py:3513\u001b[0m, in \u001b[0;36mTrainer.evaluate\u001b[0;34m(self, eval_dataset, ignore_keys, metric_key_prefix)\u001b[0m\n\u001b[1;32m 3510\u001b[0m start_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m 3512\u001b[0m eval_loop \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprediction_loop \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39muse_legacy_prediction_loop \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mevaluation_loop\n\u001b[0;32m-> 3513\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43meval_loop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3514\u001b[0m \u001b[43m \u001b[49m\u001b[43meval_dataloader\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3515\u001b[0m \u001b[43m \u001b[49m\u001b[43mdescription\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mEvaluation\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3516\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# No point gathering the predictions if there are no metrics, otherwise we defer to\u001b[39;49;00m\n\u001b[1;32m 3517\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# self.args.prediction_loss_only\u001b[39;49;00m\n\u001b[1;32m 3518\u001b[0m \u001b[43m \u001b[49m\u001b[43mprediction_loss_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute_metrics\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 3519\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3520\u001b[0m \u001b[43m \u001b[49m\u001b[43mmetric_key_prefix\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmetric_key_prefix\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3521\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3523\u001b[0m total_batch_size \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39meval_batch_size \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39mworld_size\n\u001b[1;32m 3524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmetric_key_prefix\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_jit_compilation_time\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m output\u001b[38;5;241m.\u001b[39mmetrics:\n", + "File \u001b[0;32m~/python_projects/transformers/src/transformers/trainer.py:3707\u001b[0m, in \u001b[0;36mTrainer.evaluation_loop\u001b[0;34m(self, dataloader, description, prediction_loss_only, ignore_keys, metric_key_prefix)\u001b[0m\n\u001b[1;32m 3704\u001b[0m \u001b[38;5;28mprint\u001b[39m(k,v\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m 3706\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLoss:\u001b[39m\u001b[38;5;124m\"\u001b[39m, loss)\n\u001b[0;32m-> 3707\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mShape of logits:\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[43mlogits\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m)\n\u001b[1;32m 3708\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mShape of labels:\u001b[39m\u001b[38;5;124m\"\u001b[39m, labels\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m 3710\u001b[0m \u001b[38;5;66;03m# Update containers\u001b[39;00m\n", + "\u001b[0;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'shape'" + ] } ], "source": [ @@ -580,7 +746,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -604,7 +770,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -633,7 +799,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -664,7 +830,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -728,7 +894,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -746,7 +912,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -773,8 +939,22 @@ } ], "metadata": { + "kernelspec": { + "display_name": "env", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" } }, "nbformat": 4, diff --git a/src/transformers/models/idefics2/modeling_idefics2.py b/src/transformers/models/idefics2/modeling_idefics2.py index 28cd6155548ac7..58f4eb0aab2891 100644 --- a/src/transformers/models/idefics2/modeling_idefics2.py +++ b/src/transformers/models/idefics2/modeling_idefics2.py @@ -1348,9 +1348,6 @@ class Idefics2PreTrainedModel(PreTrainedModel): _supports_flash_attn_2 = True def _init_weights(self, module): - # important: this ported version of Idefics2 isn't meant for training from scratch - only - # inference and fine-tuning - so the proper init weights code has been removed - the original codebase - # https://github.com/haotian-liu/LLaVA/tree/main/idefics2 should serve for that purpose std = ( self.config.text_config.initializer_range if hasattr(self.config, "initializer_range") @@ -1442,13 +1439,13 @@ def _autoset_attn_implementation( Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. - pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)): + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_patches, num_channels, height, width)): The tensors corresponding to the input images. Pixel values can be obtained using - [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details ([]`LlavaProcessor`] uses - [`CLIPImageProcessor`] for processing images). - pixel_attention_mask (`torch.Tensor` of shape `(batch_size, image_size, image_size)`, *optional*): + [`AutoImageProcessor`]. See [`Idefics2ImageProcessor.__call__`] for details ([]`Idefics2Processor`] uses + [`Idefics2ImageProcessor`] for processing images). + pixel_attention_mask (`torch.Tensor` of shape `(batch_size, num_patches, height, width)`, *optional*): Mask to avoid performing attention on padding pixel indices. - image_hidden_states (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`): + image_hidden_states (`torch.FloatTensor` of shape `(batch_size, num_latents, hidden_size)`): The hidden states of the image encoder after modality projection and perceiver resampling. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see diff --git a/src/transformers/models/idefics2/processing_idefics2.py b/src/transformers/models/idefics2/processing_idefics2.py index 7b98519928f55e..ea8a706983724f 100644 --- a/src/transformers/models/idefics2/processing_idefics2.py +++ b/src/transformers/models/idefics2/processing_idefics2.py @@ -118,8 +118,8 @@ def __call__( >>> from transformers import Idefics2Processor >>> from transformers.image_utils import load_image - >>> processor = Idefics2Processor.from_pretrained("HuggingFaceM4/idefics2-8b", image_seq_len=2) - >>> processor.image_processor.do_image_splitting = False # Force as False to simplify the example + >>> # We specify `do_image_splitting=False` to reduce memory usage + >>> processor = Idefics2Processor.from_pretrained("HuggingFaceM4/idefics2-8b", image_seq_len=2, do_image_splitting=False) >>> url1 = "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" >>> url2 = "https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg" diff --git a/src/transformers/models/idefics2/test.py b/src/transformers/models/idefics2/test.py new file mode 100644 index 00000000000000..f71d79ce5e673f --- /dev/null +++ b/src/transformers/models/idefics2/test.py @@ -0,0 +1,38 @@ +import requests +from PIL import Image + +from transformers import Idefics2ForConditionalGeneration, Idefics2Processor + + +url_1 = "http://images.cocodataset.org/val2017/000000039769.jpg" +url_2 = "http://images.cocodataset.org/val2017/000000219578.jpg" + +image_1 = Image.open(requests.get(url_1, stream=True).raw) +image_2 = Image.open(requests.get(url_2, stream=True).raw) +images = [image_1, image_2] + +messages = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "What’s the difference between these two images?"}, + {"type": "image"}, + {"type": "image"}, + ], + } +] + +processor = Idefics2Processor.from_pretrained("HuggingFaceM4/idefics2-8b", do_image_splitting=False) +model = Idefics2ForConditionalGeneration.from_pretrained("HuggingFaceM4/idefics2-8b", device_map="auto") + +# at inference time, one needs to pass `add_generation_prompt=True` in order to make sure the model completes the prompt +text = processor.apply_chat_template(messages, add_generation_prompt=True) + +inputs = processor(images=images, text=text, return_tensors="pt").to("cuda") + +for k, v in inputs.items(): + print(k, v.shape) + +generated_text = model.generate(**inputs, max_new_tokens=500) +generated_text = processor.batch_decode(generated_text, skip_special_tokens=True)[0] +print("Generated text:", generated_text) diff --git a/src/transformers/trainer.py b/src/transformers/trainer.py index 1d3c164984ea1c..0c20d0556e91e5 100755 --- a/src/transformers/trainer.py +++ b/src/transformers/trainer.py @@ -3700,6 +3700,11 @@ def evaluation_loop( if is_torch_xla_available(): xm.mark_step() + for k,v in inputs.items(): + print(k,v.shape) + + print("Loss:", loss) + # Update containers if loss is not None: losses = self.gather_function((loss.repeat(batch_size))) From 72a98c5b5bcb96505c16f09d0db9047782642e81 Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Mon, 29 Apr 2024 15:57:57 +0200 Subject: [PATCH 04/12] Use Seq2SeqTrainer --- .../models/idefics2/fine_tune_idefics2.ipynb | 175 ++--------- .../models/idefics2/fine_tune_idefics2.py | 293 ++++++++++++++++++ src/transformers/trainer_seq2seq.py | 3 + 3 files changed, 328 insertions(+), 143 deletions(-) create mode 100644 src/transformers/models/idefics2/fine_tune_idefics2.py diff --git a/src/transformers/models/idefics2/fine_tune_idefics2.ipynb b/src/transformers/models/idefics2/fine_tune_idefics2.ipynb index d97ba57e6090d9..244f34181979ac 100644 --- a/src/transformers/models/idefics2/fine_tune_idefics2.ipynb +++ b/src/transformers/models/idefics2/fine_tune_idefics2.ipynb @@ -30,7 +30,7 @@ "/home/niels/python_projects/transformers/env/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n", "`low_cpu_mem_usage` was None, now set to True since model is quantized.\n", - "Loading checkpoint shards: 100%|██████████| 7/7 [00:06<00:00, 1.16it/s]\n" + "Loading checkpoint shards: 100%|██████████| 7/7 [00:06<00:00, 1.12it/s]\n" ] } ], @@ -190,9 +190,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-04-29 14:44:30.749694: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "2024-04-29 14:53:34.445416: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2024-04-29 14:44:31.399103: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", + "2024-04-29 14:53:35.100124: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n" ] } @@ -323,7 +323,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -344,7 +344,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -364,7 +364,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -388,7 +388,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -443,7 +443,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -457,11 +457,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "input_ids torch.Size([2, 247])\n", - "attention_mask torch.Size([2, 247])\n", + "input_ids torch.Size([2, 387])\n", + "attention_mask torch.Size([2, 387])\n", "pixel_values torch.Size([2, 1, 3, 980, 653])\n", "pixel_attention_mask torch.Size([2, 1, 980, 653])\n", - "labels torch.Size([2, 247])\n" + "labels torch.Size([2, 387])\n" ] } ], @@ -476,14 +476,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[' User: Extract JSON. \\nAssistant:45,901[45,901]04,17341,72841,728SUKIYAKI BEEF RICE DEALJAVA1',\n", - " ' User: Extract JSON. \\nAssistant:56,000056,00056,00028,00056,000ALMOND CREAM CHEESE2 x']" + "[' User: Extract JSON. \\nAssistant:15.00015.000@15.00015.000Es Kopi Susu1x',\n", + " ' User: Extract JSON. \\nAssistant:70,00070,000070,00070,000Si1ky Lychee2xSi1ky Mango1xSi1ky Cotton Candy1xSi1ky Green Tea1xSi1ky Chocolate1x70,000Puyo 6 (Package)1x']" ] }, "execution_count": 15, @@ -504,7 +504,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -531,12 +531,13 @@ " # Get the predicted and ground truth token sequences\n", " preds, labels = eval_preds\n", "\n", - " print(\"Predictions:\")\n", - " for pred in preds:\n", - " print(pred.shape)\n", - " print(\"Labels:\")\n", - " for label in labels:\n", - " print(label.shape)\n", + " print(\"Type of preds:\", type(preds))\n", + " print(\"Type of first prediction:\", type(preds))\n", + " print(\"Type of prediction:\", type(preds[0]))\n", + "\n", + " print(\"Type of labels:\", type(labels))\n", + " print(\"Type of first labels:\", type(labels))\n", + " print(\"Type of labels:\", type(labels[0]))\n", "\n", " if isinstance(preds, tuple):\n", " preds = preds[0]\n", @@ -570,7 +571,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -624,7 +625,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -651,7 +652,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -671,66 +672,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "

\n", - " \n", - " \n", - " [ 2/100 : < :, Epoch 0.02/2]\n", - "
\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
StepTraining LossValidation Loss

" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "input_ids torch.Size([8, 285])\n", - "attention_mask torch.Size([8, 285])\n", - "pixel_values torch.Size([8, 1, 3, 980, 735])\n", - "pixel_attention_mask torch.Size([8, 1, 980, 735])\n", - "labels torch.Size([8, 285])\n", - "Loss: tensor(2.5280, device='cuda:0')\n" - ] - }, - { - "ename": "AttributeError", - "evalue": "'tuple' object has no attribute 'shape'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[20], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/python_projects/transformers/src/transformers/trainer.py:1875\u001b[0m, in \u001b[0;36mTrainer.train\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m 1873\u001b[0m hf_hub_utils\u001b[38;5;241m.\u001b[39menable_progress_bars()\n\u001b[1;32m 1874\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1875\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minner_training_loop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1876\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1877\u001b[0m \u001b[43m \u001b[49m\u001b[43mresume_from_checkpoint\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresume_from_checkpoint\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1878\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrial\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrial\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1879\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1880\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/python_projects/transformers/src/transformers/trainer.py:2281\u001b[0m, in \u001b[0;36mTrainer._inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m 2278\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mepoch \u001b[38;5;241m=\u001b[39m epoch \u001b[38;5;241m+\u001b[39m (step \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;241m+\u001b[39m steps_skipped) \u001b[38;5;241m/\u001b[39m steps_in_epoch\n\u001b[1;32m 2279\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallback_handler\u001b[38;5;241m.\u001b[39mon_step_end(args, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol)\n\u001b[0;32m-> 2281\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_maybe_log_save_evaluate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtr_loss\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgrad_norm\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepoch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2282\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 2283\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallback_handler\u001b[38;5;241m.\u001b[39mon_substep_end(args, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol)\n", - "File \u001b[0;32m~/python_projects/transformers/src/transformers/trainer.py:2665\u001b[0m, in \u001b[0;36mTrainer._maybe_log_save_evaluate\u001b[0;34m(self, tr_loss, grad_norm, model, trial, epoch, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m 2663\u001b[0m metrics \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 2664\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol\u001b[38;5;241m.\u001b[39mshould_evaluate:\n\u001b[0;32m-> 2665\u001b[0m metrics \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mevaluate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mignore_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2666\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_report_to_hp_search(trial, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mglobal_step, metrics)\n\u001b[1;32m 2668\u001b[0m \u001b[38;5;66;03m# Run delayed LR scheduler now that metrics are populated\u001b[39;00m\n", - "File \u001b[0;32m~/python_projects/transformers/src/transformers/trainer.py:3513\u001b[0m, in \u001b[0;36mTrainer.evaluate\u001b[0;34m(self, eval_dataset, ignore_keys, metric_key_prefix)\u001b[0m\n\u001b[1;32m 3510\u001b[0m start_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m 3512\u001b[0m eval_loop \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprediction_loop \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39muse_legacy_prediction_loop \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mevaluation_loop\n\u001b[0;32m-> 3513\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43meval_loop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3514\u001b[0m \u001b[43m \u001b[49m\u001b[43meval_dataloader\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3515\u001b[0m \u001b[43m \u001b[49m\u001b[43mdescription\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mEvaluation\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3516\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# No point gathering the predictions if there are no metrics, otherwise we defer to\u001b[39;49;00m\n\u001b[1;32m 3517\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# self.args.prediction_loss_only\u001b[39;49;00m\n\u001b[1;32m 3518\u001b[0m \u001b[43m \u001b[49m\u001b[43mprediction_loss_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute_metrics\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 3519\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3520\u001b[0m \u001b[43m \u001b[49m\u001b[43mmetric_key_prefix\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmetric_key_prefix\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3521\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3523\u001b[0m total_batch_size \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39meval_batch_size \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39mworld_size\n\u001b[1;32m 3524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmetric_key_prefix\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_jit_compilation_time\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m output\u001b[38;5;241m.\u001b[39mmetrics:\n", - "File \u001b[0;32m~/python_projects/transformers/src/transformers/trainer.py:3707\u001b[0m, in \u001b[0;36mTrainer.evaluation_loop\u001b[0;34m(self, dataloader, description, prediction_loss_only, ignore_keys, metric_key_prefix)\u001b[0m\n\u001b[1;32m 3704\u001b[0m \u001b[38;5;28mprint\u001b[39m(k,v\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m 3706\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLoss:\u001b[39m\u001b[38;5;124m\"\u001b[39m, loss)\n\u001b[0;32m-> 3707\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mShape of logits:\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[43mlogits\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m)\n\u001b[1;32m 3708\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mShape of labels:\u001b[39m\u001b[38;5;124m\"\u001b[39m, labels\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m 3710\u001b[0m \u001b[38;5;66;03m# Update containers\u001b[39;00m\n", - "\u001b[0;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'shape'" - ] - } - ], + "outputs": [], "source": [ "trainer.train()" ] @@ -748,20 +692,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": "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", - "image/png": "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", - "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "test_example = dataset[\"test\"][0]\n", "test_image = test_example[\"image\"]\n", @@ -772,16 +703,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "User: Extract JSON.\n", - "Assistant:\n" - ] - } - ], + "outputs": [], "source": [ "# prepare image and prompt for the model\n", "messages = [\n", @@ -801,22 +723,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The `seen_tokens` attribute is deprecated and will be removed in v4.41. Use the `cache_position` model input instead.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['User: Extract JSON. \\nAssistant:60.00060.0002.0060.00077305.45560.00060.00060.000- TICKET CP2']\n" - ] - } - ], + "outputs": [], "source": [ "inputs = processor(text=prompt, images=[test_image], return_tensors=\"pt\")\n", "inputs = {k: v.to(DEVICE) for k, v in inputs.items()}\n", @@ -896,15 +803,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'total': {'total_price': '60.000', 'total_etc': '60.000', 'menuqty_cnt': '2.00', 'changeprice': '60.000', 'cashprice': '7730'}, 'sub_total': {'tax_price': '5.455', 'subtotal_price': '60.000'}, 'menu': {'unitprice': '60.000', 'price': '60.000', 'nm': '- TICKET CP', 'cnt': '2'}}\n" - ] - } - ], + "outputs": [], "source": [ "generated_json = token2json(generated_texts[0])\n", "print(generated_json)" @@ -914,17 +813,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total {'total_price': '60.000', 'total_etc': '60.000', 'menuqty_cnt': '2.00', 'changeprice': '60.000', 'cashprice': '7730'}\n", - "sub_total {'tax_price': '5.455', 'subtotal_price': '60.000'}\n", - "menu {'unitprice': '60.000', 'price': '60.000', 'nm': '- TICKET CP', 'cnt': '2'}\n" - ] - } - ], + "outputs": [], "source": [ "for key, value in generated_json.items():\n", " print(key, value)" diff --git a/src/transformers/models/idefics2/fine_tune_idefics2.py b/src/transformers/models/idefics2/fine_tune_idefics2.py new file mode 100644 index 00000000000000..b674c0acd872b4 --- /dev/null +++ b/src/transformers/models/idefics2/fine_tune_idefics2.py @@ -0,0 +1,293 @@ +""" +Fine-tune Idefics2 using the `Seq2SeqTrainer` API. + +One can run the script using `CUDA_VISIBLE_DEVICES=3 python src/transformers/models/idefics2/fine_tune_idefics2.py`. +""" + +import json +import random +from typing import Any, List + +import Levenshtein +import numpy as np + +import torch +from torch.utils.data import Dataset +from peft import LoraConfig +from transformers import AutoProcessor, BitsAndBytesConfig, Idefics2ForConditionalGeneration +from transformers import Seq2SeqTrainingArguments, Seq2SeqTrainer + +from datasets import load_dataset + +DEVICE = "cuda:0" +USE_LORA = False +USE_QLORA = True + +## Load model + +# Three options for training, from the lowest precision training to the highest precision training: +# - QLora +# - Standard Lora +# - Full fine-tuning +if USE_QLORA or USE_LORA: + lora_config = LoraConfig( + r=8, + lora_alpha=8, + lora_dropout=0.1, + target_modules='.*(text_model|modality_projection|perceiver_resampler).*(down_proj|gate_proj|up_proj|k_proj|q_proj|v_proj|o_proj).*$', + use_dora=False if USE_QLORA else True, + init_lora_weights="gaussian" + ) + if USE_QLORA: + bnb_config = BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_quant_type="nf4", + bnb_4bit_compute_dtype=torch.float16 + ) + model = Idefics2ForConditionalGeneration.from_pretrained( + "HuggingFaceM4/idefics2-8b", + torch_dtype=torch.float16, + quantization_config=bnb_config if USE_QLORA else None, + ) + model.add_adapter(lora_config) + model.enable_adapters() +else: + model = Idefics2ForConditionalGeneration.from_pretrained( + "HuggingFaceM4/idefics2-8b", + torch_dtype=torch.float16, + _attn_implementation="flash_attention_2", # Only available on A100 or H100 + ).to(DEVICE) + + +## Load dataset +dataset = load_dataset("naver-clova-ix/cord-v2") + +## Create PyTorch dataset +added_tokens = [] +processor = AutoProcessor.from_pretrained("HuggingFaceM4/idefics2-8b", do_image_splitting=False) + +class CustomDataset(Dataset): + def __init__(self, hf_dataset, split, sort_json_key: bool = True,): + self.dataset = hf_dataset[split] + self.split = split + self.sort_json_key = sort_json_key + + ground_truth_token_sequences = [] + for sample in self.dataset: + ground_truth = json.loads(sample["ground_truth"]) + if "gt_parses" in ground_truth: # some datasets have multiple ground truths available, e.g. DocVQA + assert isinstance(ground_truth["gt_parses"], list) + ground_truth_jsons = ground_truth["gt_parses"] + else: + assert "gt_parse" in ground_truth and isinstance(ground_truth["gt_parse"], dict) + ground_truth_jsons = [ground_truth["gt_parse"]] + + ground_truth_token_sequences.append( + [ + self.json2token( + ground_truth_json, + update_special_tokens_for_json_key=self.split == "train", + sort_json_key=self.sort_json_key, + ) + for ground_truth_json in ground_truth_jsons # load json from list of json + ] + ) + + self.ground_truth_token_sequences = ground_truth_token_sequences + + def json2token(self, obj: Any, update_special_tokens_for_json_key: bool = True, sort_json_key: bool = True): + """ + Convert an ordered JSON object into a token sequence + """ + if type(obj) == dict: + if len(obj) == 1 and "text_sequence" in obj: + return obj["text_sequence"] + else: + output = "" + if sort_json_key: + keys = sorted(obj.keys(), reverse=True) + else: + keys = obj.keys() + for k in keys: + if update_special_tokens_for_json_key: + self.add_tokens([fr"", fr""]) + output += ( + fr"" + + self.json2token(obj[k], update_special_tokens_for_json_key, sort_json_key) + + fr"" + ) + return output + elif type(obj) == list: + return r"".join( + [self.json2token(item, update_special_tokens_for_json_key, sort_json_key) for item in obj] + ) + else: + obj = str(obj) + if f"<{obj}/>" in added_tokens: + obj = f"<{obj}/>" # for categorical special tokens + return obj + + def add_tokens(self, list_of_tokens: List[str]): + """ + Add special tokens to tokenizer and resize the token embeddings of the decoder + """ + newly_added_num = processor.tokenizer.add_tokens(list_of_tokens) + if newly_added_num > 0: + model.resize_token_embeddings(len(processor.tokenizer)) + added_tokens.extend(list_of_tokens) + + def __len__(self): + return len(self.dataset) + + def __getitem__(self, idx): + example = self.dataset[idx] + image = example["image"] + target_sequence = random.choice(self.ground_truth_token_sequences[idx]) # can be more than one, e.g., DocVQA + + return image, target_sequence + + +train_dataset = CustomDataset(hf_dataset=dataset, split="train") +eval_dataset = CustomDataset(hf_dataset=dataset, split="validation") + +## Define data collator +class MyDataCollator: + def __init__(self, processor): + self.processor = processor + self.image_token_id = processor.tokenizer.additional_special_tokens_ids[ + processor.tokenizer.additional_special_tokens.index("") + ] + + def __call__(self, examples): + texts = [] + images = [] + for example in examples: + image, ground_truth = example + messages = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "Extract JSON."}, + {"type": "image"}, + ] + }, + { + "role": "assistant", + "content": [ + {"type": "text", "text": ground_truth} + ] + } + ] + text = processor.apply_chat_template(messages, add_generation_prompt=False) + texts.append(text.strip()) + images.append([image]) + + batch = processor(text=texts, images=images, return_tensors="pt", padding=True) + + labels = batch["input_ids"].clone() + labels[labels == processor.tokenizer.pad_token_id] = self.image_token_id + batch["labels"] = labels + + return batch + +data_collator = MyDataCollator(processor) + + +## Define Training Arguments and Trainer + +def normalized_levenshtein(s1, s2): + len_s1, len_s2 = len(s1), len(s2) + distance = Levenshtein.distance(s1, s2) + return distance / max(len_s1, len_s2) + +def similarity_score(a_ij, o_q_i, tau=0.5): + nl = normalized_levenshtein(a_ij, o_q_i) + return 1 - nl if nl < tau else 0 + +def postprocess_text(preds, labels): + preds = [pred.strip() for pred in preds] + labels = [[label.strip()] for label in labels] + + return preds, labels + +def compute_metrics(eval_preds): + # Get the predicted and ground truth token sequences + # These are lists as they have different shapes for each batch + # We explicitly pass `eval_do_concat_batches=False` to the trainer + # TODO we could also just pad the input_ids/labels in the data collator + preds, labels = eval_preds + + final_preds = [] + final_labels = [] + for batch_pred, batch_label in zip(preds, labels): + if isinstance(batch_pred, tuple): + batch_pred = batch_pred[0] + + # Decode the generated ids and labels + decoded_preds = processor.batch_decode(batch_pred, skip_special_tokens=True) + decoded_labels = processor.batch_decode(batch_label, skip_special_tokens=True) + + print("Decoded predictions:", decoded_preds) + print("Decoded labels:", decoded_labels) + + # Some simple post-processing + decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels) + + final_preds.extend(decoded_preds) + final_labels.extend(decoded_labels) + + N = len(final_labels) + total_score = 0 + + for i in range(N): + a_i = final_labels[i] + o_q_i = final_preds[i] + if o_q_i == "": + print("Warning: Skipped an empty prediction.") + max_score = 0 + else: + max_score = max(similarity_score(a_ij, o_q_i) for a_ij in a_i) + + total_score += max_score + + return {"levenshtein": total_score / N} + + +training_args = Seq2SeqTrainingArguments( + num_train_epochs=2, + per_device_train_batch_size=2, + per_device_eval_batch_size=8, + gradient_accumulation_steps=8, + warmup_steps=50, + learning_rate=1e-4, + weight_decay=0.01, + logging_steps=25, + output_dir="idefics2_ft_tutorial", + eval_strategy="steps", + eval_steps=1, + save_strategy="steps", + save_steps=250, + save_total_limit=1, + # evaluation_strategy="epoch", + fp16=True, + # push_to_hub_model_id="idefics2-8b-docvqa-finetuned-tutorial", + remove_unused_columns=False, + report_to="none", + eval_do_concat_batches=False, + predict_with_generate=True, +) + +# important: we need to disable caching during training +# otherwise the model generates past_key_values which is of type DynamicCache +model.config.use_cache = False + +trainer = Seq2SeqTrainer( + model=model, + args=training_args, + data_collator=data_collator, + train_dataset=train_dataset, + eval_dataset=eval_dataset, # You can also evaluate (loss) on the eval set, note that it will incur some additional GPU memory, + compute_metrics=compute_metrics, +) + +trainer.train() \ No newline at end of file diff --git a/src/transformers/trainer_seq2seq.py b/src/transformers/trainer_seq2seq.py index b6bce1b57d5e2a..7aa258e90b8a77 100644 --- a/src/transformers/trainer_seq2seq.py +++ b/src/transformers/trainer_seq2seq.py @@ -307,6 +307,9 @@ def prediction_step( generation_inputs = { k: v for k, v in inputs.items() if k not in ("decoder_input_ids", "decoder_attention_mask") } + + # TODO fix this + gen_kwargs["max_new_tokens"] = 200 generated_tokens = self.model.generate(**generation_inputs, **gen_kwargs) # Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop From 8c8c7a53e1d05b713d076c857ef044b5c77dc54d Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Mon, 29 Apr 2024 15:59:27 +0200 Subject: [PATCH 05/12] Use generation_config --- src/transformers/models/idefics2/fine_tune_idefics2.py | 1 + src/transformers/trainer_seq2seq.py | 2 -- 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/src/transformers/models/idefics2/fine_tune_idefics2.py b/src/transformers/models/idefics2/fine_tune_idefics2.py index b674c0acd872b4..d6b1310b8d6ba9 100644 --- a/src/transformers/models/idefics2/fine_tune_idefics2.py +++ b/src/transformers/models/idefics2/fine_tune_idefics2.py @@ -275,6 +275,7 @@ def compute_metrics(eval_preds): report_to="none", eval_do_concat_batches=False, predict_with_generate=True, + generation_config={"max_new_tokens": 200}, ) # important: we need to disable caching during training diff --git a/src/transformers/trainer_seq2seq.py b/src/transformers/trainer_seq2seq.py index 7aa258e90b8a77..8ebd2a1d800afc 100644 --- a/src/transformers/trainer_seq2seq.py +++ b/src/transformers/trainer_seq2seq.py @@ -308,8 +308,6 @@ def prediction_step( k: v for k, v in inputs.items() if k not in ("decoder_input_ids", "decoder_attention_mask") } - # TODO fix this - gen_kwargs["max_new_tokens"] = 200 generated_tokens = self.model.generate(**generation_inputs, **gen_kwargs) # Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop From ab88b42b092b52d58bea5015aa50a1b92ae5b25a Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Mon, 29 Apr 2024 17:50:44 +0200 Subject: [PATCH 06/12] Overwrite prediction_step --- .../models/idefics2/fine_tune_idefics2.py | 250 +++++++++++++++--- src/transformers/models/idefics2/test_bis.py | 25 ++ src/transformers/trainer.py | 5 - src/transformers/trainer_seq2seq.py | 5 + 4 files changed, 241 insertions(+), 44 deletions(-) create mode 100644 src/transformers/models/idefics2/test_bis.py diff --git a/src/transformers/models/idefics2/fine_tune_idefics2.py b/src/transformers/models/idefics2/fine_tune_idefics2.py index d6b1310b8d6ba9..9632253ed3ba2e 100644 --- a/src/transformers/models/idefics2/fine_tune_idefics2.py +++ b/src/transformers/models/idefics2/fine_tune_idefics2.py @@ -6,15 +6,20 @@ import json import random -from typing import Any, List +from typing import Any, List, Union, Dict, Tuple, Optional import Levenshtein import numpy as np +import requests +from PIL import Image + +import evaluate import torch +from torch import nn from torch.utils.data import Dataset from peft import LoraConfig -from transformers import AutoProcessor, BitsAndBytesConfig, Idefics2ForConditionalGeneration +from transformers import AutoProcessor, BitsAndBytesConfig, Idefics2ForConditionalGeneration, GenerationConfig from transformers import Seq2SeqTrainingArguments, Seq2SeqTrainer from datasets import load_dataset @@ -182,7 +187,7 @@ def __call__(self, examples): texts.append(text.strip()) images.append([image]) - batch = processor(text=texts, images=images, return_tensors="pt", padding=True) + batch = processor(text=texts, images=images, return_tensors="pt", truncation=True, padding="max_length", max_length=200) labels = batch["input_ids"].clone() labels[labels == processor.tokenizer.pad_token_id] = self.image_token_id @@ -193,7 +198,7 @@ def __call__(self, examples): data_collator = MyDataCollator(processor) -## Define Training Arguments and Trainer +## Define metrics def normalized_levenshtein(s1, s2): len_s1, len_s2 = len(s1), len(s2) @@ -210,48 +215,74 @@ def postprocess_text(preds, labels): return preds, labels + +metric = evaluate.load("sacrebleu") + def compute_metrics(eval_preds): - # Get the predicted and ground truth token sequences - # These are lists as they have different shapes for each batch - # We explicitly pass `eval_do_concat_batches=False` to the trainer - # TODO we could also just pad the input_ids/labels in the data collator preds, labels = eval_preds - - final_preds = [] - final_labels = [] - for batch_pred, batch_label in zip(preds, labels): - if isinstance(batch_pred, tuple): - batch_pred = batch_pred[0] + if isinstance(preds, tuple): + preds = preds[0] + # Replace -100s used for padding as we can't decode them + preds = np.where(preds != -100, preds, processor.tokenizer.pad_token_id) + decoded_preds = processor.batch_decode(preds, skip_special_tokens=True) + labels = np.where(labels != -100, labels, processor.tokenizer.pad_token_id) + decoded_labels = processor.batch_decode(labels, skip_special_tokens=True) + + # Some simple post-processing + decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels) + + result = metric.compute(predictions=decoded_preds, references=decoded_labels) + result = {"bleu": result["score"]} + + prediction_lens = [np.count_nonzero(pred != processor.tokenizer.pad_token_id) for pred in preds] + result["gen_len"] = np.mean(prediction_lens) + result = {k: round(v, 4) for k, v in result.items()} + return result + + +# def compute_metrics(eval_preds): +# # Get the predicted and ground truth token sequences +# # These are lists as they have different shapes for each batch +# # We explicitly pass `eval_do_concat_batches=False` to the trainer +# # TODO we could also just pad the input_ids/labels in the data collator +# preds, labels = eval_preds + +# final_preds = [] +# final_labels = [] +# for batch_pred, batch_label in zip(preds, labels): +# if isinstance(batch_pred, tuple): +# batch_pred = batch_pred[0] - # Decode the generated ids and labels - decoded_preds = processor.batch_decode(batch_pred, skip_special_tokens=True) - decoded_labels = processor.batch_decode(batch_label, skip_special_tokens=True) +# # Decode the generated ids and labels +# decoded_preds = processor.batch_decode(batch_pred, skip_special_tokens=True) +# decoded_labels = processor.batch_decode(batch_label, skip_special_tokens=True) - print("Decoded predictions:", decoded_preds) - print("Decoded labels:", decoded_labels) +# # Some simple post-processing +# decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels) - # Some simple post-processing - decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels) +# final_preds.extend(decoded_preds) +# final_labels.extend(decoded_labels) - final_preds.extend(decoded_preds) - final_labels.extend(decoded_labels) +# N = len(final_labels) +# total_score = 0 - N = len(final_labels) - total_score = 0 +# for i in range(N): +# a_i = final_labels[i] +# o_q_i = final_preds[i] +# if o_q_i == "": +# print("Warning: Skipped an empty prediction.") +# max_score = 0 +# else: +# max_score = max(similarity_score(a_ij, o_q_i) for a_ij in a_i) - for i in range(N): - a_i = final_labels[i] - o_q_i = final_preds[i] - if o_q_i == "": - print("Warning: Skipped an empty prediction.") - max_score = 0 - else: - max_score = max(similarity_score(a_ij, o_q_i) for a_ij in a_i) +# total_score += max_score - total_score += max_score +# return {"levenshtein": total_score / N} - return {"levenshtein": total_score / N} +## Define Training Arguments and Trainer + +generation_config = GenerationConfig.from_pretrained("HuggingFaceM4/idefics2-8b", max_new_tokens=2) training_args = Seq2SeqTrainingArguments( num_train_epochs=2, @@ -273,21 +304,162 @@ def compute_metrics(eval_preds): # push_to_hub_model_id="idefics2-8b-docvqa-finetuned-tutorial", remove_unused_columns=False, report_to="none", - eval_do_concat_batches=False, + # eval_do_concat_batches=False, predict_with_generate=True, - generation_config={"max_new_tokens": 200}, + generation_config=generation_config, ) # important: we need to disable caching during training # otherwise the model generates past_key_values which is of type DynamicCache model.config.use_cache = False -trainer = Seq2SeqTrainer( + +class Idefics2Trainer(Seq2SeqTrainer): + def prediction_step( + self, + model: nn.Module, + inputs: Dict[str, Union[torch.Tensor, Any]], + prediction_loss_only: bool, + ignore_keys: Optional[List[str]] = None, + **gen_kwargs, + ) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]: + """ + Perform an evaluation step on `model` using `inputs`. + + Subclass and override to inject custom behavior. + + Args: + model (`nn.Module`): + The model to evaluate. + inputs (`Dict[str, Union[torch.Tensor, Any]]`): + The inputs and targets of the model. + + The dictionary will be unpacked before being fed to the model. Most models expect the targets under the + argument `labels`. Check your model's documentation for all accepted arguments. + prediction_loss_only (`bool`): + Whether or not to return the loss only. + gen_kwargs: + Additional `generate` specific kwargs. + + Return: + Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]: A tuple with the loss, logits and + labels (each being optional). + """ + if not self.args.predict_with_generate or prediction_loss_only: + return super().prediction_step( + model, inputs, prediction_loss_only=prediction_loss_only, ignore_keys=ignore_keys + ) + + has_labels = "labels" in inputs + inputs = self._prepare_inputs(inputs) + + # Priority (handled in generate): + # non-`None` gen_kwargs > model.generation_config > default GenerationConfig() + if len(gen_kwargs) == 0 and hasattr(self, "_gen_kwargs"): + gen_kwargs = self._gen_kwargs.copy() + if "num_beams" in gen_kwargs and gen_kwargs["num_beams"] is None: + gen_kwargs.pop("num_beams") + if "max_length" in gen_kwargs and gen_kwargs["max_length"] is None: + gen_kwargs.pop("max_length") + + default_synced_gpus = False + gen_kwargs["synced_gpus"] = ( + gen_kwargs["synced_gpus"] if gen_kwargs.get("synced_gpus") is not None else default_synced_gpus + ) + + generation_inputs = inputs.copy() + # If the `decoder_input_ids` was created from `labels`, evict the former, so that the model can freely generate + # (otherwise, it would continue generating from the padded `decoder_input_ids`) + if ( + "labels" in generation_inputs + and "decoder_input_ids" in generation_inputs + and generation_inputs["labels"].shape == generation_inputs["decoder_input_ids"].shape + ): + generation_inputs = { + k: v for k, v in inputs.items() if k not in ("decoder_input_ids", "decoder_attention_mask") + } + + # here we need to overwrite the input_ids to only include the prompt + processor = AutoProcessor.from_pretrained("HuggingFaceM4/idefics2-8b", do_image_splitting=False) + + # use dummy image + # we can do this since each image is always turned into 64 image tokens + url = "https://upload.wikimedia.org/wikipedia/commons/f/f3/Zinedine_Zidane_by_Tasnim_03.jpg" + test_image = Image.open(requests.get(url, stream=True).raw) + + # prepare prompt for the model + messages = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "Extract JSON."}, + {"type": "image"}, + ] + }, + ] + prompt = processor.apply_chat_template(messages, add_generation_prompt=True) + processor_inputs = processor(text=prompt, images=[test_image], return_tensors="pt") + custom_inputs = {} + batch_size = generation_inputs["pixel_values"].shape[0] + device = generation_inputs["pixel_values"].device + custom_inputs["input_ids"] = processor_inputs.input_ids.repeat(batch_size, 1).to(device) # repeat along batch dimension + custom_inputs["attention_mask"] = processor_inputs.attention_mask.repeat(batch_size, 1).to(device) # repeat along batch dimension + custom_inputs["pixel_values"] = generation_inputs["pixel_values"] + custom_inputs["pixel_attention_mask"] = generation_inputs["pixel_attention_mask"] + + print("Custom inputs:") + for k,v in custom_inputs.items(): + if isinstance(v, torch.Tensor): + print(k,v.shape) + + generated_tokens = self.model.generate(**custom_inputs, **gen_kwargs) + + # Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop + # TODO: remove this hack when the legacy code that initializes generation_config from a model config is + # removed in https://github.com/huggingface/transformers/blob/98d88b23f54e5a23e741833f1e973fdf600cc2c5/src/transformers/generation/utils.py#L1183 + if self.model.generation_config._from_model_config: + self.model.generation_config._from_model_config = False + + # Retrieves GenerationConfig from model.generation_config + gen_config = self.model.generation_config + # in case the batch is shorter than max length, the output should be padded + if generated_tokens.shape[-1] < gen_config.max_length: + generated_tokens = self._pad_tensors_to_max_len(generated_tokens, gen_config.max_length) + elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1: + generated_tokens = self._pad_tensors_to_max_len(generated_tokens, gen_config.max_new_tokens + 1) + + with torch.no_grad(): + if has_labels: + with self.compute_loss_context_manager(): + outputs = model(**inputs) + if self.label_smoother is not None: + loss = self.label_smoother(outputs, inputs["labels"]).mean().detach() + else: + loss = (outputs["loss"] if isinstance(outputs, dict) else outputs[0]).mean().detach() + else: + loss = None + + if self.args.prediction_loss_only: + return loss, None, None + + if has_labels: + labels = inputs["labels"] + if labels.shape[-1] < gen_config.max_length: + labels = self._pad_tensors_to_max_len(labels, gen_config.max_length) + elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1: + labels = self._pad_tensors_to_max_len(labels, gen_config.max_new_tokens + 1) + else: + labels = None + + return loss, generated_tokens, labels + + +trainer = Idefics2Trainer( model=model, args=training_args, data_collator=data_collator, train_dataset=train_dataset, - eval_dataset=eval_dataset, # You can also evaluate (loss) on the eval set, note that it will incur some additional GPU memory, + eval_dataset=eval_dataset, compute_metrics=compute_metrics, ) diff --git a/src/transformers/models/idefics2/test_bis.py b/src/transformers/models/idefics2/test_bis.py new file mode 100644 index 00000000000000..b6137725a1d848 --- /dev/null +++ b/src/transformers/models/idefics2/test_bis.py @@ -0,0 +1,25 @@ +from transformers import AutoProcessor +from PIL import Image +import requests + +processor = AutoProcessor.from_pretrained("HuggingFaceM4/idefics2-8b", do_image_splitting=False) + +url = "https://upload.wikimedia.org/wikipedia/commons/f/f3/Zinedine_Zidane_by_Tasnim_03.jpg" +test_image = Image.open(requests.get(url, stream=True).raw) + +# prepare image and prompt for the model +messages = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "Extract JSON."}, + {"type": "image"}, + ] + }, +] +prompt = processor.apply_chat_template(messages, add_generation_prompt=True) +inputs = processor(text=prompt, images=[test_image], return_tensors="pt") +for k,v in inputs.items(): + print(k, v.shape) + +print(processor.batch_decode(inputs.input_ids)) \ No newline at end of file diff --git a/src/transformers/trainer.py b/src/transformers/trainer.py index 0c20d0556e91e5..1d3c164984ea1c 100755 --- a/src/transformers/trainer.py +++ b/src/transformers/trainer.py @@ -3700,11 +3700,6 @@ def evaluation_loop( if is_torch_xla_available(): xm.mark_step() - for k,v in inputs.items(): - print(k,v.shape) - - print("Loss:", loss) - # Update containers if loss is not None: losses = self.gather_function((loss.repeat(batch_size))) diff --git a/src/transformers/trainer_seq2seq.py b/src/transformers/trainer_seq2seq.py index 8ebd2a1d800afc..3dbba108e95c7d 100644 --- a/src/transformers/trainer_seq2seq.py +++ b/src/transformers/trainer_seq2seq.py @@ -308,6 +308,11 @@ def prediction_step( k: v for k, v in inputs.items() if k not in ("decoder_input_ids", "decoder_attention_mask") } + print("Generation inputs:") + for k,v in generation_inputs.items(): + if isinstance(v, torch.Tensor): + print(k,v.shape) + generated_tokens = self.model.generate(**generation_inputs, **gen_kwargs) # Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop From bc142b309789c3a16c30d10a51bba841ffe80291 Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Mon, 29 Apr 2024 17:59:10 +0200 Subject: [PATCH 07/12] More improvements --- .../models/idefics2/fine_tune_idefics2.py | 34 ++++++++++++++----- 1 file changed, 25 insertions(+), 9 deletions(-) diff --git a/src/transformers/models/idefics2/fine_tune_idefics2.py b/src/transformers/models/idefics2/fine_tune_idefics2.py index 9632253ed3ba2e..70789119b4d6bb 100644 --- a/src/transformers/models/idefics2/fine_tune_idefics2.py +++ b/src/transformers/models/idefics2/fine_tune_idefics2.py @@ -209,15 +209,31 @@ def similarity_score(a_ij, o_q_i, tau=0.5): nl = normalized_levenshtein(a_ij, o_q_i) return 1 - nl if nl < tau else 0 +def average_normalized_levenshtein_similarity(ground_truth, predicted_answers): + assert len(ground_truth) == len(predicted_answers), "Length of ground_truth and predicted_answers must match." + + N = len(ground_truth) + total_score = 0 + + for i in range(N): + a_i = ground_truth[i] + o_q_i = predicted_answers[i] + if o_q_i == "": + print("Warning: Skipped an empty prediction.") + max_score = 0 + else: + max_score = max(similarity_score(a_ij, o_q_i) for a_ij in a_i) + + total_score += max_score + + return total_score / N + def postprocess_text(preds, labels): preds = [pred.strip() for pred in preds] labels = [[label.strip()] for label in labels] return preds, labels - -metric = evaluate.load("sacrebleu") - def compute_metrics(eval_preds): preds, labels = eval_preds if isinstance(preds, tuple): @@ -231,8 +247,8 @@ def compute_metrics(eval_preds): # Some simple post-processing decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels) - result = metric.compute(predictions=decoded_preds, references=decoded_labels) - result = {"bleu": result["score"]} + score = average_normalized_levenshtein_similarity(decoded_labels, decoded_preds) + result = {"levenshtein": score} prediction_lens = [np.count_nonzero(pred != processor.tokenizer.pad_token_id) for pred in preds] result["gen_len"] = np.mean(prediction_lens) @@ -407,10 +423,10 @@ def prediction_step( custom_inputs["pixel_values"] = generation_inputs["pixel_values"] custom_inputs["pixel_attention_mask"] = generation_inputs["pixel_attention_mask"] - print("Custom inputs:") - for k,v in custom_inputs.items(): - if isinstance(v, torch.Tensor): - print(k,v.shape) + # print("Custom inputs:") + # for k,v in custom_inputs.items(): + # if isinstance(v, torch.Tensor): + # print(k,v.shape) generated_tokens = self.model.generate(**custom_inputs, **gen_kwargs) From 8ec4117d4332b2b6fc06cf75a2f03b49a6b5322e Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Mon, 29 Apr 2024 18:53:56 +0200 Subject: [PATCH 08/12] More improvements --- .../models/idefics2/fine_tune_idefics2.py | 22 ++++++++++++++----- 1 file changed, 17 insertions(+), 5 deletions(-) diff --git a/src/transformers/models/idefics2/fine_tune_idefics2.py b/src/transformers/models/idefics2/fine_tune_idefics2.py index 70789119b4d6bb..a582912db3db71 100644 --- a/src/transformers/models/idefics2/fine_tune_idefics2.py +++ b/src/transformers/models/idefics2/fine_tune_idefics2.py @@ -13,8 +13,6 @@ import requests from PIL import Image -import evaluate - import torch from torch import nn from torch.utils.data import Dataset @@ -244,6 +242,9 @@ def compute_metrics(eval_preds): labels = np.where(labels != -100, labels, processor.tokenizer.pad_token_id) decoded_labels = processor.batch_decode(labels, skip_special_tokens=True) + print("Decoded predictions:", decoded_preds) + print("Decoded labels:", decoded_labels) + # Some simple post-processing decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels) @@ -298,7 +299,7 @@ def compute_metrics(eval_preds): ## Define Training Arguments and Trainer -generation_config = GenerationConfig.from_pretrained("HuggingFaceM4/idefics2-8b", max_new_tokens=2) +generation_config = GenerationConfig.from_pretrained("HuggingFaceM4/idefics2-8b", max_new_tokens=500) training_args = Seq2SeqTrainingArguments( num_train_epochs=2, @@ -310,8 +311,7 @@ def compute_metrics(eval_preds): weight_decay=0.01, logging_steps=25, output_dir="idefics2_ft_tutorial", - eval_strategy="steps", - eval_steps=1, + eval_strategy="epoch", save_strategy="steps", save_steps=250, save_total_limit=1, @@ -430,6 +430,9 @@ def prediction_step( generated_tokens = self.model.generate(**custom_inputs, **gen_kwargs) + # Strip the prompt from the generated_tokens + generated_tokens = generated_tokens[:, custom_inputs["input_ids"].size(1):] + # Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop # TODO: remove this hack when the legacy code that initializes generation_config from a model config is # removed in https://github.com/huggingface/transformers/blob/98d88b23f54e5a23e741833f1e973fdf600cc2c5/src/transformers/generation/utils.py#L1183 @@ -468,6 +471,15 @@ def prediction_step( labels = None return loss, generated_tokens, labels + + def _pad_tensors_to_max_len(self, tensor, max_length): + pad_token_id = processor.tokenizer.pad_token_id + + padded_tensor = pad_token_id * torch.ones( + (tensor.shape[0], max_length), dtype=tensor.dtype, device=tensor.device + ) + padded_tensor[:, : tensor.shape[-1]] = tensor + return padded_tensor trainer = Idefics2Trainer( From 33552c25560d3100e1b00830b9d86b026309597c Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Mon, 29 Apr 2024 20:01:48 +0200 Subject: [PATCH 09/12] Improve docstring --- src/transformers/models/idefics2/fine_tune_idefics2.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/transformers/models/idefics2/fine_tune_idefics2.py b/src/transformers/models/idefics2/fine_tune_idefics2.py index a582912db3db71..1727baa2525e41 100644 --- a/src/transformers/models/idefics2/fine_tune_idefics2.py +++ b/src/transformers/models/idefics2/fine_tune_idefics2.py @@ -1,5 +1,5 @@ """ -Fine-tune Idefics2 using the `Seq2SeqTrainer` API. +Fine-tune Idefics2 by tweaking the `Seq2SeqTrainer` class. One can run the script using `CUDA_VISIBLE_DEVICES=3 python src/transformers/models/idefics2/fine_tune_idefics2.py`. """ From b794532fef67ad2ece05cfb504983ef76cd24f08 Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Mon, 29 Apr 2024 22:11:16 +0200 Subject: [PATCH 10/12] Add pytorch lightning notebook --- .../idefics2/fine_tune_idefics2_pl.ipynb | 622 ++++++++++++++++++ 1 file changed, 622 insertions(+) create mode 100644 src/transformers/models/idefics2/fine_tune_idefics2_pl.ipynb diff --git a/src/transformers/models/idefics2/fine_tune_idefics2_pl.ipynb b/src/transformers/models/idefics2/fine_tune_idefics2_pl.ipynb new file mode 100644 index 00000000000000..0131fbb510ffb9 --- /dev/null +++ b/src/transformers/models/idefics2/fine_tune_idefics2_pl.ipynb @@ -0,0 +1,622 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from datasets import load_dataset\n", + "\n", + "dataset = load_dataset(\"naver-clova-ix/cord-v2\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DatasetDict({\n", + " train: Dataset({\n", + " features: ['image', 'ground_truth'],\n", + " num_rows: 800\n", + " })\n", + " validation: Dataset({\n", + " features: ['image', 'ground_truth'],\n", + " num_rows: 100\n", + " })\n", + " test: Dataset({\n", + " features: ['image', 'ground_truth'],\n", + " num_rows: 100\n", + " })\n", + "})" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at the first training example:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": "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", + "image/png": "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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "example = dataset['train'][0]\n", + "image = example['image']\n", + "# let's make the image a bit smaller when visualizing\n", + "width, height = image.size\n", + "display(image.resize((int(width*0.3), int(height*0.3))))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\"gt_parse\": {\"menu\": [{\"nm\": \"Nasi Campur Bali\", \"cnt\": \"1 x\", \"price\": \"75,000\"}, {\"nm\": \"Bbk Bengil Nasi\", \"cnt\": \"1 x\", \"price\": \"125,000\"}, {\"nm\": \"MilkShake Starwb\", \"cnt\": \"1 x\", \"price\": \"37,000\"}, {\"nm\": \"Ice Lemon Tea\", \"cnt\": \"1 x\", \"price\": \"24,000\"}, {\"nm\": \"Nasi Ayam Dewata\", \"cnt\": \"1 x\", \"price\": \"70,000\"}, {\"nm\": \"Free Ice Tea\", \"cnt\": \"3 x\", \"price\": \"0\"}, {\"nm\": \"Organic Green Sa\", \"cnt\": \"1 x\", \"price\": \"65,000\"}, {\"nm\": \"Ice Tea\", \"cnt\": \"1 x\", \"price\": \"18,000\"}, {\"nm\": \"Ice Orange\", \"cnt\": \"1 x\", \"price\": \"29,000\"}, {\"nm\": \"Ayam Suir Bali\", \"cnt\": \"1 x\", \"price\": \"85,000\"}, {\"nm\": \"Tahu Goreng\", \"cnt\": \"2 x\", \"price\": \"36,000\"}, {\"nm\": \"Tempe Goreng\", \"cnt\": \"2 x\", \"price\": \"36,000\"}, {\"nm\": \"Tahu Telor Asin\", \"cnt\": \"1 x\", \"price\": \"40,000.\"}, {\"nm\": \"Nasi Goreng Samb\", \"cnt\": \"1 x\", \"price\": \"70,000\"}, {\"nm\": \"Bbk Panggang Sam\", \"cnt\": \"3 x\", \"price\": \"366,000\"}, {\"nm\": \"Ayam Sambal Hija\", \"cnt\": \"1 x\", \"price\": \"92,000\"}, {\"nm\": \"Hot Tea\", \"cnt\": \"2 x\", \"price\": \"44,000\"}, {\"nm\": \"Ice Kopi\", \"cnt\": \"1 x\", \"price\": \"32,000\"}, {\"nm\": \"Tahu Telor Asin\", \"cnt\": \"1 x\", \"price\": \"40,000\"}, {\"nm\": \"Free Ice Tea\", \"cnt\": \"1 x\", \"price\": \"0\"}, {\"nm\": \"Bebek Street\", \"cnt\": \"1 x\", \"price\": \"44,000\"}, {\"nm\": \"Ice Tea Tawar\", \"cnt\": \"1 x\", \"price\": \"18,000\"}], \"sub_total\": {\"subtotal_price\": \"1,346,000\", \"service_price\": \"100,950\", \"tax_price\": \"144,695\", \"etc\": \"-45\"}, \"total\": {\"total_price\": \"1,591,600\"}}, \"meta\": {\"version\": \"2.0.0\", \"split\": \"train\", \"image_id\": 0, \"image_size\": {\"width\": 864, \"height\": 1296}}, \"valid_line\": [{\"words\": [{\"quad\": {\"x2\": 244, \"y3\": 390, \"x3\": 244, \"y4\": 390, \"x1\": 232, \"y1\": 372, \"x4\": 232, \"y2\": 372}, \"is_key\": 0, \"row_id\": 2179893, \"text\": \"1\"}, {\"quad\": {\"x2\": 270, \"y3\": 390, \"x3\": 270, \"y4\": 390, \"x1\": 256, \"y1\": 374, \"x4\": 256, \"y2\": 374}, \"is_key\": 0, \"row_id\": 2179893, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 3, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 354, \"y3\": 390, \"x3\": 354, \"y4\": 390, \"x1\": 302, \"y1\": 368, \"x4\": 302, \"y2\": 368}, \"is_key\": 0, \"row_id\": 2179893, \"text\": \"Nasi\"}, {\"quad\": {\"x2\": 440, \"y3\": 391, \"x3\": 439, \"y4\": 388, \"x1\": 364, \"y1\": 365, \"x4\": 363, \"y2\": 368}, \"is_key\": 0, \"row_id\": 2179893, \"text\": \"Campur\"}, {\"quad\": {\"x2\": 497, \"y3\": 385, \"x3\": 499, \"y4\": 388, \"x1\": 446, \"y1\": 365, \"x4\": 448, \"y2\": 362}, \"is_key\": 0, \"row_id\": 2179893, \"text\": \"Bali\"}], \"category\": \"menu.nm\", \"group_id\": 3, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 618, \"y3\": 388, \"x3\": 618, \"y4\": 388, \"x1\": 542, \"y1\": 362, \"x4\": 542, \"y2\": 362}, \"is_key\": 0, \"row_id\": 2179893, \"text\": \"75,000\"}], \"category\": \"menu.price\", \"group_id\": 3, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 246, \"y3\": 418, \"x3\": 246, \"y4\": 418, \"x1\": 234, \"y1\": 400, \"x4\": 234, \"y2\": 400}, \"is_key\": 0, \"row_id\": 2179894, \"text\": \"1\"}, {\"quad\": {\"x2\": 270, \"y3\": 418, \"x3\": 270, \"y4\": 418, \"x1\": 258, \"y1\": 402, \"x4\": 258, \"y2\": 402}, \"is_key\": 0, \"row_id\": 2179894, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 4, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 344, \"y3\": 418, \"x3\": 344, \"y4\": 418, \"x1\": 304, \"y1\": 394, \"x4\": 304, \"y2\": 394}, \"is_key\": 0, \"row_id\": 2179894, \"text\": \"Bbk\"}, {\"quad\": {\"x2\": 430, \"y3\": 418, \"x3\": 430, \"y4\": 418, \"x1\": 352, \"y1\": 394, \"x4\": 352, \"y2\": 394}, \"is_key\": 0, \"row_id\": 2179894, \"text\": \"Bengil\"}, {\"quad\": {\"x2\": 488, \"y3\": 414, \"x3\": 488, \"y4\": 414, \"x1\": 436, \"y1\": 392, \"x4\": 436, \"y2\": 392}, \"is_key\": 0, \"row_id\": 2179894, \"text\": \"Nasi\"}], \"category\": \"menu.nm\", \"group_id\": 4, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 618, \"y3\": 414, \"x3\": 618, \"y4\": 414, \"x1\": 534, \"y1\": 388, \"x4\": 534, \"y2\": 388}, \"is_key\": 0, \"row_id\": 2179894, \"text\": \"125,000\"}], \"category\": \"menu.price\", \"group_id\": 4, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 246, \"y3\": 444, \"x3\": 246, \"y4\": 444, \"x1\": 234, \"y1\": 426, \"x4\": 234, \"y2\": 426}, \"is_key\": 0, \"row_id\": 2179895, \"text\": \"1\"}, {\"quad\": {\"x2\": 272, \"y3\": 444, \"x3\": 272, \"y4\": 444, \"x1\": 258, \"y1\": 428, \"x4\": 258, \"y2\": 428}, \"is_key\": 0, \"row_id\": 2179895, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 5, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 417, \"y3\": 444, \"x3\": 417, \"y4\": 444, \"x1\": 303, \"y1\": 420, \"x4\": 303, \"y2\": 420}, \"is_key\": 0, \"row_id\": 2179895, \"text\": \"MilkShake\"}, {\"quad\": {\"x2\": 500, \"y3\": 440, \"x3\": 500, \"y4\": 440, \"x1\": 424, \"y1\": 418, \"x4\": 424, \"y2\": 418}, \"is_key\": 0, \"row_id\": 2179895, \"text\": \"Starwb\"}], \"category\": \"menu.nm\", \"group_id\": 5, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 620, \"y3\": 440, \"x3\": 620, \"y4\": 440, \"x1\": 544, \"y1\": 414, \"x4\": 544, \"y2\": 414}, \"is_key\": 0, \"row_id\": 2179895, \"text\": \"37,000\"}], \"category\": \"menu.price\", \"group_id\": 5, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 248, \"y3\": 470, \"x3\": 248, \"y4\": 470, \"x1\": 236, \"y1\": 452, \"x4\": 236, \"y2\": 452}, \"is_key\": 0, \"row_id\": 2179896, \"text\": \"1\"}, {\"quad\": {\"x2\": 274, \"y3\": 470, \"x3\": 274, \"y4\": 470, \"x1\": 260, \"y1\": 456, \"x4\": 260, \"y2\": 456}, \"is_key\": 0, \"row_id\": 2179896, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 6, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 348, \"y3\": 470, \"x3\": 348, \"y4\": 470, \"x1\": 306, \"y1\": 448, \"x4\": 306, \"y2\": 448}, \"is_key\": 0, \"row_id\": 2179896, \"text\": \"Ice\"}, {\"quad\": {\"x2\": 418, \"y3\": 468, \"x3\": 418, \"y4\": 468, \"x1\": 354, \"y1\": 446, \"x4\": 354, \"y2\": 446}, \"is_key\": 0, \"row_id\": 2179896, \"text\": \"Lemon\"}, {\"quad\": {\"x2\": 466, \"y3\": 466, \"x3\": 466, \"y4\": 466, \"x1\": 426, \"y1\": 446, \"x4\": 426, \"y2\": 446}, \"is_key\": 0, \"row_id\": 2179896, \"text\": \"Tea\"}], \"category\": \"menu.nm\", \"group_id\": 6, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 618, \"y3\": 466, \"x3\": 618, \"y4\": 466, \"x1\": 544, \"y1\": 440, \"x4\": 544, \"y2\": 440}, \"is_key\": 0, \"row_id\": 2179896, \"text\": \"24,000\"}], \"category\": \"menu.price\", \"group_id\": 6, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 250, \"y3\": 496, \"x3\": 250, \"y4\": 496, \"x1\": 238, \"y1\": 480, \"x4\": 238, \"y2\": 480}, \"is_key\": 0, \"row_id\": 2179897, \"text\": \"1\"}, {\"quad\": {\"x2\": 274, \"y3\": 496, \"x3\": 274, \"y4\": 496, \"x1\": 258, \"y1\": 480, \"x4\": 258, \"y2\": 480}, \"is_key\": 0, \"row_id\": 2179897, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 7, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 358, \"y3\": 496, \"x3\": 358, \"y4\": 496, \"x1\": 306, \"y1\": 474, \"x4\": 306, \"y2\": 474}, \"is_key\": 0, \"row_id\": 2179897, \"text\": \"Nasi\"}, {\"quad\": {\"x2\": 420, \"y3\": 496, \"x3\": 420, \"y4\": 496, \"x1\": 366, \"y1\": 474, \"x4\": 366, \"y2\": 474}, \"is_key\": 0, \"row_id\": 2179897, \"text\": \"Ayam\"}, {\"quad\": {\"x2\": 499, \"y3\": 491, \"x3\": 500, \"y4\": 494, \"x1\": 425, \"y1\": 471, \"x4\": 426, \"y2\": 469}, \"is_key\": 0, \"row_id\": 2179897, \"text\": \"Dewata\"}], \"category\": \"menu.nm\", \"group_id\": 7, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 618, \"y3\": 492, \"x3\": 618, \"y4\": 492, \"x1\": 544, \"y1\": 466, \"x4\": 544, \"y2\": 466}, \"is_key\": 0, \"row_id\": 2179897, \"text\": \"70,000\"}], \"category\": \"menu.price\", \"group_id\": 7, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 252, \"y3\": 522, \"x3\": 252, \"y4\": 522, \"x1\": 240, \"y1\": 504, \"x4\": 240, \"y2\": 504}, \"is_key\": 0, \"row_id\": 2179898, \"text\": \"3\"}, {\"quad\": {\"x2\": 276, \"y3\": 522, \"x3\": 276, \"y4\": 522, \"x1\": 260, \"y1\": 506, \"x4\": 260, \"y2\": 506}, \"is_key\": 0, \"row_id\": 2179898, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 8, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 360, \"y3\": 522, \"x3\": 360, \"y4\": 522, \"x1\": 306, \"y1\": 500, \"x4\": 306, \"y2\": 500}, \"is_key\": 0, \"row_id\": 2179898, \"text\": \"Free\"}, {\"quad\": {\"x2\": 408, \"y3\": 522, \"x3\": 408, \"y4\": 522, \"x1\": 368, \"y1\": 500, \"x4\": 368, \"y2\": 500}, \"is_key\": 0, \"row_id\": 2179898, \"text\": \"Ice\"}, {\"quad\": {\"x2\": 454, \"y3\": 520, \"x3\": 454, \"y4\": 520, \"x1\": 416, \"y1\": 498, \"x4\": 416, \"y2\": 498}, \"is_key\": 0, \"row_id\": 2179898, \"text\": \"Tea\"}], \"category\": \"menu.nm\", \"group_id\": 8, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 618, \"y3\": 514, \"x3\": 618, \"y4\": 514, \"x1\": 604, \"y1\": 494, \"x4\": 604, \"y2\": 494}, \"is_key\": 0, \"row_id\": 2179898, \"text\": \"0\"}], \"category\": \"menu.price\", \"group_id\": 8, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 254, \"y3\": 546, \"x3\": 254, \"y4\": 546, \"x1\": 242, \"y1\": 530, \"x4\": 242, \"y2\": 530}, \"is_key\": 0, \"row_id\": 2179899, \"text\": \"1\"}, {\"quad\": {\"x2\": 278, \"y3\": 548, \"x3\": 278, \"y4\": 548, \"x1\": 262, \"y1\": 532, \"x4\": 262, \"y2\": 532}, \"is_key\": 0, \"row_id\": 2179899, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 9, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 396, \"y3\": 548, \"x3\": 396, \"y4\": 548, \"x1\": 308, \"y1\": 526, \"x4\": 308, \"y2\": 526}, \"is_key\": 0, \"row_id\": 2179899, \"text\": \"Organic\"}, {\"quad\": {\"x2\": 466, \"y3\": 544, \"x3\": 466, \"y4\": 544, \"x1\": 404, \"y1\": 524, \"x4\": 404, \"y2\": 524}, \"is_key\": 0, \"row_id\": 2179899, \"text\": \"Green\"}, {\"quad\": {\"x2\": 502, \"y3\": 544, \"x3\": 502, \"y4\": 544, \"x1\": 474, \"y1\": 522, \"x4\": 474, \"y2\": 522}, \"is_key\": 0, \"row_id\": 2179899, \"text\": \"Sa\"}], \"category\": \"menu.nm\", \"group_id\": 9, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 620, \"y3\": 542, \"x3\": 620, \"y4\": 542, \"x1\": 544, \"y1\": 518, \"x4\": 544, \"y2\": 518}, \"is_key\": 0, \"row_id\": 2179899, \"text\": \"65,000\"}], \"category\": \"menu.price\", \"group_id\": 9, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 254, \"y3\": 572, \"x3\": 254, \"y4\": 572, \"x1\": 242, \"y1\": 556, \"x4\": 242, \"y2\": 556}, \"is_key\": 0, \"row_id\": 2179900, \"text\": \"1\"}, {\"quad\": {\"x2\": 280, \"y3\": 572, \"x3\": 280, \"y4\": 572, \"x1\": 266, \"y1\": 558, \"x4\": 266, \"y2\": 558}, \"is_key\": 0, \"row_id\": 2179900, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 10, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 352, \"y3\": 572, \"x3\": 352, \"y4\": 572, \"x1\": 312, \"y1\": 552, \"x4\": 312, \"y2\": 552}, \"is_key\": 0, \"row_id\": 2179900, \"text\": \"Ice\"}, {\"quad\": {\"x2\": 398, \"y3\": 572, \"x3\": 398, \"y4\": 572, \"x1\": 358, \"y1\": 550, \"x4\": 358, \"y2\": 550}, \"is_key\": 0, \"row_id\": 2179900, \"text\": \"Tea\"}], \"category\": \"menu.nm\", \"group_id\": 10, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 620, \"y3\": 568, \"x3\": 620, \"y4\": 568, \"x1\": 546, \"y1\": 544, \"x4\": 546, \"y2\": 544}, \"is_key\": 0, \"row_id\": 2179900, \"text\": \"18,000\"}], \"category\": \"menu.price\", \"group_id\": 10, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 256, \"y3\": 598, \"x3\": 256, \"y4\": 598, \"x1\": 244, \"y1\": 582, \"x4\": 244, \"y2\": 582}, \"is_key\": 0, \"row_id\": 2179901, \"text\": \"1\"}, {\"quad\": {\"x2\": 280, \"y3\": 598, \"x3\": 280, \"y4\": 598, \"x1\": 264, \"y1\": 582, \"x4\": 264, \"y2\": 582}, \"is_key\": 0, \"row_id\": 2179901, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 11, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 352, \"y3\": 596, \"x3\": 352, \"y4\": 596, \"x1\": 312, \"y1\": 576, \"x4\": 312, \"y2\": 576}, \"is_key\": 0, \"row_id\": 2179901, \"text\": \"Ice\"}, {\"quad\": {\"x2\": 434, \"y3\": 596, \"x3\": 434, \"y4\": 596, \"x1\": 358, \"y1\": 576, \"x4\": 358, \"y2\": 576}, \"is_key\": 0, \"row_id\": 2179901, \"text\": \"Orange\"}], \"category\": \"menu.nm\", \"group_id\": 11, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 620, \"y3\": 594, \"x3\": 620, \"y4\": 594, \"x1\": 544, \"y1\": 570, \"x4\": 544, \"y2\": 570}, \"is_key\": 0, \"row_id\": 2179901, \"text\": \"29,000\"}], \"category\": \"menu.price\", \"group_id\": 11, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 258, \"y3\": 622, \"x3\": 258, \"y4\": 622, \"x1\": 246, \"y1\": 606, \"x4\": 246, \"y2\": 606}, \"is_key\": 0, \"row_id\": 2179902, \"text\": \"1\"}, {\"quad\": {\"x2\": 282, \"y3\": 622, \"x3\": 282, \"y4\": 622, \"x1\": 268, \"y1\": 608, \"x4\": 268, \"y2\": 608}, \"is_key\": 0, \"row_id\": 2179902, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 12, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 364, \"y3\": 624, \"x3\": 364, \"y4\": 624, \"x1\": 312, \"y1\": 602, \"x4\": 312, \"y2\": 602}, \"is_key\": 0, \"row_id\": 2179902, \"text\": \"Ayam\"}, {\"quad\": {\"x2\": 422, \"y3\": 620, \"x3\": 422, \"y4\": 620, \"x1\": 370, \"y1\": 600, \"x4\": 370, \"y2\": 600}, \"is_key\": 0, \"row_id\": 2179902, \"text\": \"Suir\"}, {\"quad\": {\"x2\": 478, \"y3\": 620, \"x3\": 478, \"y4\": 620, \"x1\": 428, \"y1\": 598, \"x4\": 428, \"y2\": 598}, \"is_key\": 0, \"row_id\": 2179902, \"text\": \"Bali\"}], \"category\": \"menu.nm\", \"group_id\": 12, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 618, \"y3\": 620, \"x3\": 618, \"y4\": 620, \"x1\": 544, \"y1\": 596, \"x4\": 544, \"y2\": 596}, \"is_key\": 0, \"row_id\": 2179902, \"text\": \"85,000\"}], \"category\": \"menu.price\", \"group_id\": 12, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 260, \"y3\": 650, \"x3\": 260, \"y4\": 650, \"x1\": 242, \"y1\": 628, \"x4\": 242, \"y2\": 628}, \"is_key\": 0, \"row_id\": 2179903, \"text\": \"2\"}, {\"quad\": {\"x2\": 282, \"y3\": 648, \"x3\": 282, \"y4\": 648, \"x1\": 266, \"y1\": 632, \"x4\": 266, \"y2\": 632}, \"is_key\": 0, \"row_id\": 2179903, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 13, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 364, \"y3\": 648, \"x3\": 364, \"y4\": 648, \"x1\": 312, \"y1\": 626, \"x4\": 312, \"y2\": 626}, \"is_key\": 0, \"row_id\": 2179903, \"text\": \"Tahu\"}, {\"quad\": {\"x2\": 444, \"y3\": 648, \"x3\": 443, \"y4\": 645, \"x1\": 370, \"y1\": 625, \"x4\": 369, \"y2\": 628}, \"is_key\": 0, \"row_id\": 2179903, \"text\": \"Goreng\"}], \"category\": \"menu.nm\", \"group_id\": 13, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 619, \"y3\": 644, \"x3\": 620, \"y4\": 647, \"x1\": 543, \"y1\": 622, \"x4\": 544, \"y2\": 619}, \"is_key\": 0, \"row_id\": 2179903, \"text\": \"36,000\"}], \"category\": \"menu.price\", \"group_id\": 13, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 260, \"y3\": 674, \"x3\": 260, \"y4\": 674, \"x1\": 242, \"y1\": 654, \"x4\": 242, \"y2\": 654}, \"is_key\": 0, \"row_id\": 2179904, \"text\": \"2\"}, {\"quad\": {\"x2\": 284, \"y3\": 674, \"x3\": 284, \"y4\": 674, \"x1\": 268, \"y1\": 660, \"x4\": 268, \"y2\": 660}, \"is_key\": 0, \"row_id\": 2179904, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 14, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 376, \"y3\": 674, \"x3\": 376, \"y4\": 674, \"x1\": 314, \"y1\": 652, \"x4\": 314, \"y2\": 652}, \"is_key\": 0, \"row_id\": 2179904, \"text\": \"Tempe\"}, {\"quad\": {\"x2\": 458, \"y3\": 672, \"x3\": 458, \"y4\": 672, \"x1\": 384, \"y1\": 650, \"x4\": 384, \"y2\": 650}, \"is_key\": 0, \"row_id\": 2179904, \"text\": \"Goreng\"}], \"category\": \"menu.nm\", \"group_id\": 14, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 620, \"y3\": 668, \"x3\": 620, \"y4\": 668, \"x1\": 546, \"y1\": 644, \"x4\": 546, \"y2\": 644}, \"is_key\": 0, \"row_id\": 2179904, \"text\": \"36,000\"}], \"category\": \"menu.price\", \"group_id\": 14, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 258, \"y3\": 700, \"x3\": 258, \"y4\": 700, \"x1\": 244, \"y1\": 682, \"x4\": 244, \"y2\": 682}, \"is_key\": 0, \"row_id\": 2179905, \"text\": \"1\"}, {\"quad\": {\"x2\": 282, \"y3\": 700, \"x3\": 282, \"y4\": 700, \"x1\": 266, \"y1\": 686, \"x4\": 266, \"y2\": 686}, \"is_key\": 0, \"row_id\": 2179905, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 15, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 364, \"y3\": 698, \"x3\": 364, \"y4\": 698, \"x1\": 312, \"y1\": 676, \"x4\": 312, \"y2\": 676}, \"is_key\": 0, \"row_id\": 2179905, \"text\": \"Tahu\"}, {\"quad\": {\"x2\": 434, \"y3\": 696, \"x3\": 434, \"y4\": 696, \"x1\": 370, \"y1\": 674, \"x4\": 370, \"y2\": 674}, \"is_key\": 0, \"row_id\": 2179905, \"text\": \"Telor\"}, {\"quad\": {\"x2\": 494, \"y3\": 694, \"x3\": 494, \"y4\": 694, \"x1\": 440, \"y1\": 672, \"x4\": 440, \"y2\": 672}, \"is_key\": 0, \"row_id\": 2179905, \"text\": \"Asin\"}], \"category\": \"menu.nm\", \"group_id\": 15, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 630, \"y3\": 690, \"x3\": 630, \"y4\": 690, \"x1\": 548, \"y1\": 668, \"x4\": 548, \"y2\": 668}, \"is_key\": 0, \"row_id\": 2179905, \"text\": \"40,000.\"}], \"category\": \"menu.price\", \"group_id\": 15, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 256, \"y3\": 726, \"x3\": 256, \"y4\": 726, \"x1\": 244, \"y1\": 708, \"x4\": 244, \"y2\": 708}, \"is_key\": 0, \"row_id\": 2179906, \"text\": \"1\"}, {\"quad\": {\"x2\": 282, \"y3\": 726, \"x3\": 282, \"y4\": 726, \"x1\": 268, \"y1\": 710, \"x4\": 268, \"y2\": 710}, \"is_key\": 0, \"row_id\": 2179906, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 16, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 364, \"y3\": 724, \"x3\": 364, \"y4\": 724, \"x1\": 312, \"y1\": 702, \"x4\": 312, \"y2\": 702}, \"is_key\": 0, \"row_id\": 2179906, \"text\": \"Nasi\"}, {\"quad\": {\"x2\": 446, \"y3\": 722, \"x3\": 446, \"y4\": 722, \"x1\": 372, \"y1\": 700, \"x4\": 372, \"y2\": 700}, \"is_key\": 0, \"row_id\": 2179906, \"text\": \"Goreng\"}, {\"quad\": {\"x2\": 505, \"y3\": 715, \"x3\": 506, \"y4\": 718, \"x1\": 453, \"y1\": 696, \"x4\": 454, \"y2\": 693}, \"is_key\": 0, \"row_id\": 2179906, \"text\": \"Samb\"}], \"category\": \"menu.nm\", \"group_id\": 16, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 626, \"y3\": 716, \"x3\": 626, \"y4\": 716, \"x1\": 550, \"y1\": 692, \"x4\": 550, \"y2\": 692}, \"is_key\": 0, \"row_id\": 2179906, \"text\": \"70,000\"}], \"category\": \"menu.price\", \"group_id\": 16, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 256, \"y3\": 752, \"x3\": 256, \"y4\": 752, \"x1\": 242, \"y1\": 734, \"x4\": 242, \"y2\": 734}, \"is_key\": 0, \"row_id\": 2179907, \"text\": \"3\"}, {\"quad\": {\"x2\": 280, \"y3\": 752, \"x3\": 280, \"y4\": 752, \"x1\": 266, \"y1\": 738, \"x4\": 266, \"y2\": 738}, \"is_key\": 0, \"row_id\": 2179907, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 17, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 352, \"y3\": 750, \"x3\": 352, \"y4\": 750, \"x1\": 312, \"y1\": 728, \"x4\": 312, \"y2\": 728}, \"is_key\": 0, \"row_id\": 2179907, \"text\": \"Bbk\"}, {\"quad\": {\"x2\": 457, \"y3\": 747, \"x3\": 458, \"y4\": 749, \"x1\": 357, \"y1\": 726, \"x4\": 358, \"y2\": 723}, \"is_key\": 0, \"row_id\": 2179907, \"text\": \"Panggang\"}, {\"quad\": {\"x2\": 508, \"y3\": 742, \"x3\": 508, \"y4\": 742, \"x1\": 466, \"y1\": 722, \"x4\": 466, \"y2\": 722}, \"is_key\": 0, \"row_id\": 2179907, \"text\": \"Sam\"}], \"category\": \"menu.nm\", \"group_id\": 17, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 630, \"y3\": 742, \"x3\": 630, \"y4\": 742, \"x1\": 538, \"y1\": 716, \"x4\": 538, \"y2\": 716}, \"is_key\": 0, \"row_id\": 2179907, \"text\": \"366,000\"}], \"category\": \"menu.price\", \"group_id\": 17, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 254, \"y3\": 778, \"x3\": 254, \"y4\": 778, \"x1\": 242, \"y1\": 762, \"x4\": 242, \"y2\": 762}, \"is_key\": 0, \"row_id\": 2179908, \"text\": \"1\"}, {\"quad\": {\"x2\": 280, \"y3\": 778, \"x3\": 280, \"y4\": 778, \"x1\": 266, \"y1\": 764, \"x4\": 266, \"y2\": 764}, \"is_key\": 0, \"row_id\": 2179908, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 18, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 364, \"y3\": 778, \"x3\": 364, \"y4\": 778, \"x1\": 312, \"y1\": 754, \"x4\": 312, \"y2\": 754}, \"is_key\": 0, \"row_id\": 2179908, \"text\": \"Ayam\"}, {\"quad\": {\"x2\": 447, \"y3\": 771, \"x3\": 448, \"y4\": 774, \"x1\": 371, \"y1\": 750, \"x4\": 372, \"y2\": 747}, \"is_key\": 0, \"row_id\": 2179908, \"text\": \"Sambal\"}, {\"quad\": {\"x2\": 508, \"y3\": 772, \"x3\": 508, \"y4\": 772, \"x1\": 454, \"y1\": 746, \"x4\": 454, \"y2\": 746}, \"is_key\": 0, \"row_id\": 2179908, \"text\": \"Hija\"}], \"category\": \"menu.nm\", \"group_id\": 18, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 632, \"y3\": 768, \"x3\": 632, \"y4\": 768, \"x1\": 554, \"y1\": 742, \"x4\": 554, \"y2\": 742}, \"is_key\": 0, \"row_id\": 2179908, \"text\": \"92,000\"}], \"category\": \"menu.price\", \"group_id\": 18, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 254, \"y3\": 806, \"x3\": 254, \"y4\": 806, \"x1\": 236, \"y1\": 784, \"x4\": 236, \"y2\": 784}, \"is_key\": 0, \"row_id\": 2179909, \"text\": \"2\"}, {\"quad\": {\"x2\": 278, \"y3\": 804, \"x3\": 278, \"y4\": 804, \"x1\": 262, \"y1\": 788, \"x4\": 262, \"y2\": 788}, \"is_key\": 0, \"row_id\": 2179909, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 19, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 352, \"y3\": 802, \"x3\": 352, \"y4\": 802, \"x1\": 310, \"y1\": 780, \"x4\": 310, \"y2\": 780}, \"is_key\": 0, \"row_id\": 2179909, \"text\": \"Hot\"}, {\"quad\": {\"x2\": 400, \"y3\": 800, \"x3\": 400, \"y4\": 800, \"x1\": 358, \"y1\": 778, \"x4\": 358, \"y2\": 778}, \"is_key\": 0, \"row_id\": 2179909, \"text\": \"Tea\"}], \"category\": \"menu.nm\", \"group_id\": 19, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 634, \"y3\": 796, \"x3\": 634, \"y4\": 796, \"x1\": 554, \"y1\": 770, \"x4\": 554, \"y2\": 770}, \"is_key\": 0, \"row_id\": 2179909, \"text\": \"44,000\"}], \"category\": \"menu.price\", \"group_id\": 19, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 252, \"y3\": 834, \"x3\": 252, \"y4\": 834, \"x1\": 240, \"y1\": 816, \"x4\": 240, \"y2\": 816}, \"is_key\": 0, \"row_id\": 2179910, \"text\": \"1\"}, {\"quad\": {\"x2\": 278, \"y3\": 832, \"x3\": 278, \"y4\": 832, \"x1\": 262, \"y1\": 816, \"x4\": 262, \"y2\": 816}, \"is_key\": 0, \"row_id\": 2179910, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 20, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 352, \"y3\": 830, \"x3\": 352, \"y4\": 830, \"x1\": 312, \"y1\": 808, \"x4\": 312, \"y2\": 808}, \"is_key\": 0, \"row_id\": 2179910, \"text\": \"Ice\"}, {\"quad\": {\"x2\": 412, \"y3\": 830, \"x3\": 412, \"y4\": 830, \"x1\": 360, \"y1\": 804, \"x4\": 360, \"y2\": 804}, \"is_key\": 0, \"row_id\": 2179910, \"text\": \"Kopi\"}], \"category\": \"menu.nm\", \"group_id\": 20, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 636, \"y3\": 826, \"x3\": 636, \"y4\": 826, \"x1\": 556, \"y1\": 798, \"x4\": 556, \"y2\": 798}, \"is_key\": 0, \"row_id\": 2179910, \"text\": \"32,000\"}], \"category\": \"menu.price\", \"group_id\": 20, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 250, \"y3\": 862, \"x3\": 250, \"y4\": 862, \"x1\": 238, \"y1\": 844, \"x4\": 238, \"y2\": 844}, \"is_key\": 0, \"row_id\": 2179911, \"text\": \"1\"}, {\"quad\": {\"x2\": 276, \"y3\": 862, \"x3\": 276, \"y4\": 862, \"x1\": 260, \"y1\": 844, \"x4\": 260, \"y2\": 844}, \"is_key\": 0, \"row_id\": 2179911, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 21, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 364, \"y3\": 860, \"x3\": 364, \"y4\": 860, \"x1\": 310, \"y1\": 836, \"x4\": 310, \"y2\": 836}, \"is_key\": 0, \"row_id\": 2179911, \"text\": \"Tahu\"}, {\"quad\": {\"x2\": 438, \"y3\": 858, \"x3\": 438, \"y4\": 858, \"x1\": 372, \"y1\": 834, \"x4\": 372, \"y2\": 834}, \"is_key\": 0, \"row_id\": 2179911, \"text\": \"Telor\"}, {\"quad\": {\"x2\": 500, \"y3\": 854, \"x3\": 500, \"y4\": 854, \"x1\": 444, \"y1\": 832, \"x4\": 444, \"y2\": 832}, \"is_key\": 0, \"row_id\": 2179911, \"text\": \"Asin\"}], \"category\": \"menu.nm\", \"group_id\": 21, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 638, \"y3\": 854, \"x3\": 638, \"y4\": 854, \"x1\": 558, \"y1\": 826, \"x4\": 558, \"y2\": 826}, \"is_key\": 0, \"row_id\": 2179911, \"text\": \"40,000\"}], \"category\": \"menu.price\", \"group_id\": 21, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 250, \"y3\": 892, \"x3\": 250, \"y4\": 892, \"x1\": 238, \"y1\": 872, \"x4\": 238, \"y2\": 872}, \"is_key\": 0, \"row_id\": 2179912, \"text\": \"1\"}, {\"quad\": {\"x2\": 276, \"y3\": 890, \"x3\": 276, \"y4\": 890, \"x1\": 260, \"y1\": 872, \"x4\": 260, \"y2\": 872}, \"is_key\": 0, \"row_id\": 2179912, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 22, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 364, \"y3\": 888, \"x3\": 364, \"y4\": 888, \"x1\": 310, \"y1\": 866, \"x4\": 310, \"y2\": 866}, \"is_key\": 0, \"row_id\": 2179912, \"text\": \"Free\"}, {\"quad\": {\"x2\": 414, \"y3\": 886, \"x3\": 414, \"y4\": 886, \"x1\": 374, \"y1\": 864, \"x4\": 374, \"y2\": 864}, \"is_key\": 0, \"row_id\": 2179912, \"text\": \"Ice\"}, {\"quad\": {\"x2\": 464, \"y3\": 884, \"x3\": 464, \"y4\": 884, \"x1\": 422, \"y1\": 862, \"x4\": 422, \"y2\": 862}, \"is_key\": 0, \"row_id\": 2179912, \"text\": \"Tea\"}], \"category\": \"menu.nm\", \"group_id\": 22, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 640, \"y3\": 878, \"x3\": 640, \"y4\": 878, \"x1\": 622, \"y1\": 856, \"x4\": 622, \"y2\": 856}, \"is_key\": 0, \"row_id\": 2179912, \"text\": \"0\"}], \"category\": \"menu.price\", \"group_id\": 22, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 250, \"y3\": 920, \"x3\": 250, \"y4\": 920, \"x1\": 236, \"y1\": 900, \"x4\": 236, \"y2\": 900}, \"is_key\": 0, \"row_id\": 2179913, \"text\": \"1\"}, {\"quad\": {\"x2\": 276, \"y3\": 920, \"x3\": 276, \"y4\": 920, \"x1\": 260, \"y1\": 902, \"x4\": 260, \"y2\": 902}, \"is_key\": 0, \"row_id\": 2179913, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 23, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 376, \"y3\": 916, \"x3\": 376, \"y4\": 916, \"x1\": 308, \"y1\": 892, \"x4\": 308, \"y2\": 892}, \"is_key\": 0, \"row_id\": 2179913, \"text\": \"Bebek\"}, {\"quad\": {\"x2\": 464, \"y3\": 914, \"x3\": 464, \"y4\": 914, \"x1\": 384, \"y1\": 890, \"x4\": 384, \"y2\": 890}, \"is_key\": 0, \"row_id\": 2179913, \"text\": \"Street\"}], \"category\": \"menu.nm\", \"group_id\": 23, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 641, \"y3\": 908, \"x3\": 642, \"y4\": 911, \"x1\": 559, \"y1\": 884, \"x4\": 560, \"y2\": 881}, \"is_key\": 0, \"row_id\": 2179913, \"text\": \"44,000\"}], \"category\": \"menu.price\", \"group_id\": 23, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 250, \"y3\": 948, \"x3\": 250, \"y4\": 948, \"x1\": 238, \"y1\": 930, \"x4\": 238, \"y2\": 930}, \"is_key\": 0, \"row_id\": 2179914, \"text\": \"1\"}, {\"quad\": {\"x2\": 276, \"y3\": 946, \"x3\": 276, \"y4\": 946, \"x1\": 260, \"y1\": 930, \"x4\": 260, \"y2\": 930}, \"is_key\": 0, \"row_id\": 2179914, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 24, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 352, \"y3\": 946, \"x3\": 352, \"y4\": 946, \"x1\": 312, \"y1\": 924, \"x4\": 312, \"y2\": 924}, \"is_key\": 0, \"row_id\": 2179914, \"text\": \"Ice\"}, {\"quad\": {\"x2\": 402, \"y3\": 944, \"x3\": 402, \"y4\": 944, \"x1\": 360, \"y1\": 922, \"x4\": 360, \"y2\": 922}, \"is_key\": 0, \"row_id\": 2179914, \"text\": \"Tea\"}, {\"quad\": {\"x2\": 480, \"y3\": 942, \"x3\": 480, \"y4\": 942, \"x1\": 412, \"y1\": 920, \"x4\": 412, \"y2\": 920}, \"is_key\": 0, \"row_id\": 2179914, \"text\": \"Tawar\"}], \"category\": \"menu.nm\", \"group_id\": 24, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 642, \"y3\": 938, \"x3\": 642, \"y4\": 938, \"x1\": 564, \"y1\": 912, \"x4\": 564, \"y2\": 912}, \"is_key\": 0, \"row_id\": 2179914, \"text\": \"18,000\"}], \"category\": \"menu.price\", \"group_id\": 24, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 479, \"y3\": 998, \"x3\": 481, \"y4\": 1005, \"x1\": 360, \"y1\": 979, \"x4\": 362, \"y2\": 973}, \"is_key\": 1, \"row_id\": 2179915, \"text\": \"Sub-Total\"}, {\"quad\": {\"x2\": 645, \"y3\": 995, \"x3\": 646, \"y4\": 998, \"x1\": 527, \"y1\": 970, \"x4\": 528, \"y2\": 967}, \"is_key\": 0, \"row_id\": 2179915, \"text\": \"1,346,000\"}], \"category\": \"sub_total.subtotal_price\", \"group_id\": 25, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 481, \"y3\": 1027, \"x3\": 482, \"y4\": 1030, \"x1\": 387, \"y1\": 1007, \"x4\": 388, \"y2\": 1004}, \"is_key\": 1, \"row_id\": 2179916, \"text\": \"Service\"}, {\"quad\": {\"x2\": 646, \"y3\": 1026, \"x3\": 646, \"y4\": 1026, \"x1\": 554, \"y1\": 998, \"x4\": 554, \"y2\": 998}, \"is_key\": 0, \"row_id\": 2179916, \"text\": \"100,950\"}], \"category\": \"sub_total.service_price\", \"group_id\": 25, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 482, \"y3\": 1056, \"x3\": 482, \"y4\": 1056, \"x1\": 438, \"y1\": 1032, \"x4\": 438, \"y2\": 1032}, \"is_key\": 1, \"row_id\": 2179917, \"text\": \"PB1\"}, {\"quad\": {\"x2\": 648, \"y3\": 1052, \"x3\": 648, \"y4\": 1052, \"x1\": 556, \"y1\": 1026, \"x4\": 556, \"y2\": 1026}, \"is_key\": 0, \"row_id\": 2179917, \"text\": \"144,695\"}], \"category\": \"sub_total.tax_price\", \"group_id\": 25, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 481, \"y3\": 1085, \"x3\": 482, \"y4\": 1088, \"x1\": 375, \"y1\": 1063, \"x4\": 376, \"y2\": 1061}, \"is_key\": 1, \"row_id\": 2179918, \"text\": \"Rounding\"}, {\"quad\": {\"x2\": 648, \"y3\": 1078, \"x3\": 648, \"y4\": 1078, \"x1\": 606, \"y1\": 1054, \"x4\": 606, \"y2\": 1054}, \"is_key\": 0, \"row_id\": 2179918, \"text\": \"-45\"}], \"category\": \"sub_total.etc\", \"group_id\": 25, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 334, \"y3\": 1162, \"x3\": 334, \"y4\": 1162, \"x1\": 266, \"y1\": 1142, \"x4\": 266, \"y2\": 1142}, \"is_key\": 1, \"row_id\": 2179919, \"text\": \"Grand\"}, {\"quad\": {\"x2\": 408, \"y3\": 1160, \"x3\": 408, \"y4\": 1160, \"x1\": 340, \"y1\": 1138, \"x4\": 340, \"y2\": 1138}, \"is_key\": 1, \"row_id\": 2179919, \"text\": \"Total\"}, {\"quad\": {\"x2\": 647, \"y3\": 1153, \"x3\": 649, \"y4\": 1161, \"x1\": 418, \"y1\": 1117, \"x4\": 420, \"y2\": 1108}, \"is_key\": 0, \"row_id\": 2179919, \"text\": \"1,591,600\"}], \"category\": \"total.total_price\", \"group_id\": 26, \"sub_group_id\": 0}], \"roi\": {}, \"repeating_symbol\": [], \"dontcare\": []}\n" + ] + } + ], + "source": [ + "# let's load the corresponding JSON dictionary (as string representation)\n", + "ground_truth = example['ground_truth']\n", + "print(ground_truth)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load model" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "`low_cpu_mem_usage` was None, now set to True since model is quantized.\n", + "Downloading shards: 100%|██████████| 7/7 [00:00<00:00, 9.41it/s]\n", + "Loading checkpoint shards: 100%|██████████| 7/7 [00:06<00:00, 1.04it/s]\n" + ] + } + ], + "source": [ + "from peft import LoraConfig\n", + "from transformers import BitsAndBytesConfig, Idefics2ForConditionalGeneration\n", + "import torch\n", + "\n", + "lora_config = LoraConfig(\n", + " r=8,\n", + " lora_alpha=8,\n", + " lora_dropout=0.1,\n", + " target_modules='.*(text_model|modality_projection|perceiver_resampler).*(down_proj|gate_proj|up_proj|k_proj|q_proj|v_proj|o_proj).*$',\n", + " use_dora=False,\n", + " init_lora_weights=\"gaussian\"\n", + " )\n", + "bnb_config = BitsAndBytesConfig(\n", + " load_in_4bit=True,\n", + " bnb_4bit_quant_type=\"nf4\",\n", + " bnb_4bit_compute_dtype=torch.float16\n", + ")\n", + "\n", + "model = Idefics2ForConditionalGeneration.from_pretrained(\n", + " \"HuggingFaceM4/idefics2-8b\",\n", + " torch_dtype=torch.float16,\n", + " quantization_config=bnb_config,\n", + " )\n", + "model.add_adapter(lora_config)\n", + "model.enable_adapters()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create PyTorch dataset\n", + "\n", + "Here we create a regular PyTorch dataset.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n" + ] + } + ], + "source": [ + "import json\n", + "import random\n", + "from typing import Any, List, Dict\n", + "\n", + "import torch\n", + "from torch.utils.data import Dataset\n", + "\n", + "from transformers import AutoProcessor\n", + "\n", + "processor = AutoProcessor.from_pretrained(\"HuggingFaceM4/idefics2-8b\", do_image_splitting=False)\n", + "\n", + "added_tokens = []\n", + "\n", + "\n", + "class Idefics2Dataset(Dataset):\n", + " \"\"\"\n", + " PyTorch Dataset for Idefics2. This class takes a HuggingFace Dataset as input.\n", + " \n", + " Each row, consists of image path(png/jpg/jpeg) and gt data (json/jsonl/txt).\n", + " \"\"\"\n", + "\n", + " def __init__(\n", + " self,\n", + " dataset_name_or_path: str,\n", + " split: str = \"train\",\n", + " sort_json_key: bool = True,\n", + " ):\n", + " super().__init__()\n", + "\n", + " self.split = split\n", + " self.sort_json_key = sort_json_key\n", + "\n", + " self.dataset = load_dataset(dataset_name_or_path, split=self.split)\n", + " self.dataset_length = len(self.dataset)\n", + "\n", + " self.gt_token_sequences = []\n", + " for sample in self.dataset:\n", + " ground_truth = json.loads(sample[\"ground_truth\"])\n", + " if \"gt_parses\" in ground_truth: # when multiple ground truths are available, e.g., docvqa\n", + " assert isinstance(ground_truth[\"gt_parses\"], list)\n", + " gt_jsons = ground_truth[\"gt_parses\"]\n", + " else:\n", + " assert \"gt_parse\" in ground_truth and isinstance(ground_truth[\"gt_parse\"], dict)\n", + " gt_jsons = [ground_truth[\"gt_parse\"]]\n", + "\n", + " self.gt_token_sequences.append(\n", + " [\n", + " self.json2token(\n", + " gt_json,\n", + " update_special_tokens_for_json_key=self.split == \"train\",\n", + " sort_json_key=self.sort_json_key,\n", + " )\n", + " for gt_json in gt_jsons # load json from list of json\n", + " ]\n", + " )\n", + "\n", + " def json2token(self, obj: Any, update_special_tokens_for_json_key: bool = True, sort_json_key: bool = True):\n", + " \"\"\"\n", + " Convert an ordered JSON object into a token sequence\n", + " \"\"\"\n", + " if type(obj) == dict:\n", + " if len(obj) == 1 and \"text_sequence\" in obj:\n", + " return obj[\"text_sequence\"]\n", + " else:\n", + " output = \"\"\n", + " if sort_json_key:\n", + " keys = sorted(obj.keys(), reverse=True)\n", + " else:\n", + " keys = obj.keys()\n", + " for k in keys:\n", + " if update_special_tokens_for_json_key:\n", + " self.add_tokens([fr\"\", fr\"\"])\n", + " output += (\n", + " fr\"\"\n", + " + self.json2token(obj[k], update_special_tokens_for_json_key, sort_json_key)\n", + " + fr\"\"\n", + " )\n", + " return output\n", + " elif type(obj) == list:\n", + " return r\"\".join(\n", + " [self.json2token(item, update_special_tokens_for_json_key, sort_json_key) for item in obj]\n", + " )\n", + " else:\n", + " obj = str(obj)\n", + " if f\"<{obj}/>\" in added_tokens:\n", + " obj = f\"<{obj}/>\" # for categorical special tokens\n", + " return obj\n", + " \n", + " def add_tokens(self, list_of_tokens: List[str]):\n", + " \"\"\"\n", + " Add special tokens to tokenizer and resize the token embeddings of the decoder\n", + " \"\"\"\n", + " newly_added_num = processor.tokenizer.add_tokens(list_of_tokens)\n", + " if newly_added_num > 0:\n", + " model.resize_token_embeddings(len(processor.tokenizer))\n", + " added_tokens.extend(list_of_tokens)\n", + " \n", + " def __len__(self) -> int:\n", + " return self.dataset_length\n", + "\n", + " def __getitem__(self, idx: int) -> Dict:\n", + " \"\"\"\n", + " Load image from image_path of given dataset_path and convert into input_tensor and labels\n", + " Convert gt data into input_ids (tokenized string)\n", + " Returns:\n", + " input_tensor : preprocessed image\n", + " input_ids : tokenized gt_data\n", + " labels : masked labels (model doesn't need to predict prompt and pad token)\n", + " \"\"\"\n", + " sample = self.dataset[idx]\n", + "\n", + " # inputs\n", + " image = sample[\"image\"]\n", + " target_sequence = random.choice(self.gt_token_sequences[idx]) # can be more than one, e.g., DocVQA Task 1\n", + " \n", + " return image, target_sequence" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "train_dataset = Idefics2Dataset(\"naver-clova-ix/cord-v2\", split=\"train\", sort_json_key=False)\n", + "val_dataset = Idefics2Dataset(\"naver-clova-ix/cord-v2\", split=\"validation\", sort_json_key=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can verify that a token like `` was added to the vocabulary of the tokenizer (and the model):" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "''" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "processor.decode([57560])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As always, it's very important to verify whether our data is prepared correctly. Let's check the first training example:" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(,\n", + " 'Nasi Campur Bali1 x75,000Bbk Bengil Nasi1 x125,000MilkShake Starwb1 x37,000Ice Lemon Tea1 x24,000Nasi Ayam Dewata1 x70,000Free Ice Tea3 x0Organic Green Sa1 x65,000Ice Tea1 x18,000Ice Orange1 x29,000Ayam Suir Bali1 x85,000Tahu Goreng2 x36,000Tempe Goreng2 x36,000Tahu Telor Asin1 x40,000.Nasi Goreng Samb1 x70,000Bbk Panggang Sam3 x366,000Ayam Sambal Hija1 x92,000Hot Tea2 x44,000Ice Kopi1 x32,000Tahu Telor Asin1 x40,000Free Ice Tea1 x0Bebek Street1 x44,000Ice Tea Tawar1 x18,0001,346,000100,950144,695-451,591,600')" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_dataset[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create PyTorch DataLoaders\n", + "\n", + "Next, we create corresponding PyTorch DataLoaders, which allow us to loop over the dataset in batches:" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "from torch.utils.data import DataLoader\n", + "\n", + "image_token_id = processor.tokenizer.additional_special_tokens_ids[processor.tokenizer.additional_special_tokens.index(\"\")]\n", + "\n", + "\n", + "def collate_fn(examples):\n", + " texts = []\n", + " images = []\n", + " answers = []\n", + " for example in examples:\n", + " image, ground_truth = example\n", + " messages = [\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": \"Extract JSON.\"},\n", + " {\"type\": \"image\"},\n", + " ]\n", + " },\n", + " {\n", + " \"role\": \"assistant\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": ground_truth}\n", + " ]\n", + " }\n", + " ]\n", + " text = processor.apply_chat_template(messages, add_generation_prompt=False)\n", + " texts.append(text.strip())\n", + " images.append([image])\n", + " answers.append(ground_truth)\n", + "\n", + " batch = processor(text=texts, images=images, return_tensors=\"pt\", padding=True)\n", + "\n", + " labels = batch[\"input_ids\"].clone()\n", + " labels[labels == processor.tokenizer.pad_token_id] = image_token_id\n", + " batch[\"labels\"] = labels\n", + "\n", + " batch[\"images\"] = images\n", + " batch[\"answers\"] = answers\n", + " \n", + " return batch\n", + "\n", + "# feel free to increase the batch size if you have a lot of memory\n", + "# I'm fine-tuning on Colab and given the large image size, batch size > 1 is not feasible\n", + "train_dataloader = DataLoader(train_dataset, collate_fn=collate_fn, batch_size=2, shuffle=True, num_workers=4)\n", + "val_dataloader = DataLoader(val_dataset, collate_fn=collate_fn, batch_size=2, shuffle=False, num_workers=4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Let's verify a batch" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "input_ids torch.Size([2, 349])\n", + "attention_mask torch.Size([2, 349])\n", + "pixel_values torch.Size([2, 1, 3, 980, 653])\n", + "pixel_attention_mask torch.Size([2, 1, 980, 653])\n", + "labels torch.Size([2, 349])\n" + ] + } + ], + "source": [ + "batch = next(iter(train_dataloader))\n", + "for key, value in batch.items():\n", + " if isinstance(value, torch.Tensor):\n", + " print(key, value.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define LightningModule\n", + "\n", + "Next, we define a [LightningModule](https://pytorch-lightning.readthedocs.io/en/stable/common/lightning_module.html), which is the standard way to train a model in PyTorch Lightning. A LightningModule is an `nn.Module` with some additional functionality. \n", + "\n", + "Basically, PyTorch Lightning will take care of all device placements (`.to(device)`) for us, as well as the backward pass, putting the model in training mode, etc." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "from nltk import edit_distance\n", + "import numpy as np\n", + "\n", + "import pytorch_lightning as pl\n", + "\n", + "\n", + "class Idefics2ModelPLModule(pl.LightningModule):\n", + " def __init__(self, config, processor, model):\n", + " super().__init__()\n", + " self.config = config\n", + " self.processor = processor\n", + " self.model = model\n", + "\n", + " def training_step(self, batch, batch_idx):\n", + "\n", + " del batch[\"images\"]\n", + " del batch[\"answers\"]\n", + "\n", + " outputs = self.model(**batch)\n", + " loss = outputs.loss\n", + " \n", + " self.log(\"train_loss\", loss)\n", + " \n", + " return loss\n", + "\n", + " def validation_step(self, batch, batch_idx, dataset_idx=0):\n", + " # we feed the prompt to the model\n", + " batch_size = batch[\"pixel_values\"].shape[0]\n", + " images = batch[\"images\"]\n", + " answers = batch[\"answers\"]\n", + " texts = []\n", + " \n", + " for _ in range(batch_size):\n", + " messages = [\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": \"Extract JSON.\"},\n", + " {\"type\": \"image\"},\n", + " ]\n", + " }\n", + " ]\n", + " text = processor.apply_chat_template(messages, add_generation_prompt=True)\n", + " texts.append(text.strip())\n", + " inputs = processor(text=texts, images=images, padding=True, return_tensors=\"pt\")\n", + " generated_ids = model.generate(**inputs, max_new_tokens=64)\n", + " generated_texts = processor.batch_decode(generated_ids[:, inputs[\"input_ids\"].size(1):], skip_special_tokens=True)\n", + " \n", + " predictions = []\n", + " for seq in generated_texts:\n", + " seq = seq.replace(self.processor.tokenizer.eos_token, \"\").replace(self.processor.tokenizer.pad_token, \"\")\n", + " seq = re.sub(r\"<.*?>\", \"\", seq, count=1).strip() # remove first task start token\n", + " predictions.append(seq)\n", + "\n", + " scores = []\n", + " for pred, answer in zip(predictions, answers):\n", + " pred = re.sub(r\"(?:(?<=>) | (?= Date: Tue, 30 Apr 2024 09:39:13 +0200 Subject: [PATCH 11/12] More improvements --- .../models/idefics2/fine_tune_idefics2.py | 174 +++-- .../idefics2/fine_tune_idefics2_pl.ipynb | 322 ++++++-- .../models/idefics2/processing_idefics2.py | 2 +- src/transformers/models/idefics2/test_bis.py | 12 +- .../models/idefics2/wandb/latest-run | 1 + .../files/config.yaml | 58 ++ .../files/requirements.txt | 306 ++++++++ .../files/wandb-metadata.json | 703 ++++++++++++++++++ .../run-0w9d9xlt.wandb | Bin 0 -> 104832 bytes .../files/config.yaml | 38 + .../files/requirements.txt | 307 ++++++++ .../files/wandb-metadata.json | 703 ++++++++++++++++++ .../run-prlaj2s9.wandb | Bin 0 -> 1956 bytes src/transformers/trainer_seq2seq.py | 4 +- 14 files changed, 2498 insertions(+), 132 deletions(-) create mode 120000 src/transformers/models/idefics2/wandb/latest-run create mode 100644 src/transformers/models/idefics2/wandb/run-20240430_091855-0w9d9xlt/files/config.yaml create mode 100644 src/transformers/models/idefics2/wandb/run-20240430_091855-0w9d9xlt/files/requirements.txt create mode 100644 src/transformers/models/idefics2/wandb/run-20240430_091855-0w9d9xlt/files/wandb-metadata.json create mode 100644 src/transformers/models/idefics2/wandb/run-20240430_091855-0w9d9xlt/run-0w9d9xlt.wandb create mode 100644 src/transformers/models/idefics2/wandb/run-20240430_093846-prlaj2s9/files/config.yaml create mode 100644 src/transformers/models/idefics2/wandb/run-20240430_093846-prlaj2s9/files/requirements.txt create mode 100644 src/transformers/models/idefics2/wandb/run-20240430_093846-prlaj2s9/files/wandb-metadata.json create mode 100644 src/transformers/models/idefics2/wandb/run-20240430_093846-prlaj2s9/run-prlaj2s9.wandb diff --git a/src/transformers/models/idefics2/fine_tune_idefics2.py b/src/transformers/models/idefics2/fine_tune_idefics2.py index 1727baa2525e41..dedfda82e72563 100644 --- a/src/transformers/models/idefics2/fine_tune_idefics2.py +++ b/src/transformers/models/idefics2/fine_tune_idefics2.py @@ -6,21 +6,27 @@ import json import random -from typing import Any, List, Union, Dict, Tuple, Optional +from typing import Any, Dict, List, Optional, Tuple, Union import Levenshtein import numpy as np import requests -from PIL import Image - import torch +from datasets import load_dataset +from peft import LoraConfig +from PIL import Image from torch import nn from torch.utils.data import Dataset -from peft import LoraConfig -from transformers import AutoProcessor, BitsAndBytesConfig, Idefics2ForConditionalGeneration, GenerationConfig -from transformers import Seq2SeqTrainingArguments, Seq2SeqTrainer -from datasets import load_dataset +from transformers import ( + AutoProcessor, + BitsAndBytesConfig, + GenerationConfig, + Idefics2ForConditionalGeneration, + Seq2SeqTrainer, + Seq2SeqTrainingArguments, +) + DEVICE = "cuda:0" USE_LORA = False @@ -37,15 +43,13 @@ r=8, lora_alpha=8, lora_dropout=0.1, - target_modules='.*(text_model|modality_projection|perceiver_resampler).*(down_proj|gate_proj|up_proj|k_proj|q_proj|v_proj|o_proj).*$', + target_modules=".*(text_model|modality_projection|perceiver_resampler).*(down_proj|gate_proj|up_proj|k_proj|q_proj|v_proj|o_proj).*$", use_dora=False if USE_QLORA else True, - init_lora_weights="gaussian" + init_lora_weights="gaussian", ) if USE_QLORA: bnb_config = BitsAndBytesConfig( - load_in_4bit=True, - bnb_4bit_quant_type="nf4", - bnb_4bit_compute_dtype=torch.float16 + load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16 ) model = Idefics2ForConditionalGeneration.from_pretrained( "HuggingFaceM4/idefics2-8b", @@ -58,7 +62,7 @@ model = Idefics2ForConditionalGeneration.from_pretrained( "HuggingFaceM4/idefics2-8b", torch_dtype=torch.float16, - _attn_implementation="flash_attention_2", # Only available on A100 or H100 + _attn_implementation="flash_attention_2", # Only available on A100 or H100 ).to(DEVICE) @@ -69,36 +73,42 @@ added_tokens = [] processor = AutoProcessor.from_pretrained("HuggingFaceM4/idefics2-8b", do_image_splitting=False) + class CustomDataset(Dataset): - def __init__(self, hf_dataset, split, sort_json_key: bool = True,): - self.dataset = hf_dataset[split] - self.split = split - self.sort_json_key = sort_json_key - - ground_truth_token_sequences = [] - for sample in self.dataset: - ground_truth = json.loads(sample["ground_truth"]) - if "gt_parses" in ground_truth: # some datasets have multiple ground truths available, e.g. DocVQA - assert isinstance(ground_truth["gt_parses"], list) - ground_truth_jsons = ground_truth["gt_parses"] - else: - assert "gt_parse" in ground_truth and isinstance(ground_truth["gt_parse"], dict) - ground_truth_jsons = [ground_truth["gt_parse"]] - - ground_truth_token_sequences.append( - [ - self.json2token( - ground_truth_json, - update_special_tokens_for_json_key=self.split == "train", - sort_json_key=self.sort_json_key, - ) - for ground_truth_json in ground_truth_jsons # load json from list of json - ] - ) + def __init__( + self, + hf_dataset, + split, + sort_json_key: bool = True, + ): + self.dataset = hf_dataset[split] + self.split = split + self.sort_json_key = sort_json_key + + ground_truth_token_sequences = [] + for sample in self.dataset: + ground_truth = json.loads(sample["ground_truth"]) + if "gt_parses" in ground_truth: # some datasets have multiple ground truths available, e.g. DocVQA + assert isinstance(ground_truth["gt_parses"], list) + ground_truth_jsons = ground_truth["gt_parses"] + else: + assert "gt_parse" in ground_truth and isinstance(ground_truth["gt_parse"], dict) + ground_truth_jsons = [ground_truth["gt_parse"]] + + ground_truth_token_sequences.append( + [ + self.json2token( + ground_truth_json, + update_special_tokens_for_json_key=self.split == "train", + sort_json_key=self.sort_json_key, + ) + for ground_truth_json in ground_truth_jsons # load json from list of json + ] + ) - self.ground_truth_token_sequences = ground_truth_token_sequences + self.ground_truth_token_sequences = ground_truth_token_sequences - def json2token(self, obj: Any, update_special_tokens_for_json_key: bool = True, sort_json_key: bool = True): + def json2token(self, obj: Any, update_special_tokens_for_json_key: bool = True, sort_json_key: bool = True): """ Convert an ordered JSON object into a token sequence """ @@ -113,11 +123,11 @@ def json2token(self, obj: Any, update_special_tokens_for_json_key: bool = True, keys = obj.keys() for k in keys: if update_special_tokens_for_json_key: - self.add_tokens([fr"", fr""]) + self.add_tokens([rf"", rf""]) output += ( - fr"" + rf"" + self.json2token(obj[k], update_special_tokens_for_json_key, sort_json_key) - + fr"" + + rf"" ) return output elif type(obj) == list: @@ -129,30 +139,31 @@ def json2token(self, obj: Any, update_special_tokens_for_json_key: bool = True, if f"<{obj}/>" in added_tokens: obj = f"<{obj}/>" # for categorical special tokens return obj - - def add_tokens(self, list_of_tokens: List[str]): - """ - Add special tokens to tokenizer and resize the token embeddings of the decoder - """ - newly_added_num = processor.tokenizer.add_tokens(list_of_tokens) - if newly_added_num > 0: - model.resize_token_embeddings(len(processor.tokenizer)) - added_tokens.extend(list_of_tokens) - - def __len__(self): - return len(self.dataset) - - def __getitem__(self, idx): - example = self.dataset[idx] - image = example["image"] - target_sequence = random.choice(self.ground_truth_token_sequences[idx]) # can be more than one, e.g., DocVQA - - return image, target_sequence - + + def add_tokens(self, list_of_tokens: List[str]): + """ + Add special tokens to tokenizer and resize the token embeddings of the decoder + """ + newly_added_num = processor.tokenizer.add_tokens(list_of_tokens) + if newly_added_num > 0: + model.resize_token_embeddings(len(processor.tokenizer)) + added_tokens.extend(list_of_tokens) + + def __len__(self): + return len(self.dataset) + + def __getitem__(self, idx): + example = self.dataset[idx] + image = example["image"] + target_sequence = random.choice(self.ground_truth_token_sequences[idx]) # can be more than one, e.g., DocVQA + + return image, target_sequence + train_dataset = CustomDataset(hf_dataset=dataset, split="train") eval_dataset = CustomDataset(hf_dataset=dataset, split="validation") + ## Define data collator class MyDataCollator: def __init__(self, processor): @@ -172,20 +183,17 @@ def __call__(self, examples): "content": [ {"type": "text", "text": "Extract JSON."}, {"type": "image"}, - ] + ], }, - { - "role": "assistant", - "content": [ - {"type": "text", "text": ground_truth} - ] - } + {"role": "assistant", "content": [{"type": "text", "text": ground_truth}]}, ] text = processor.apply_chat_template(messages, add_generation_prompt=False) texts.append(text.strip()) images.append([image]) - batch = processor(text=texts, images=images, return_tensors="pt", truncation=True, padding="max_length", max_length=200) + batch = processor( + text=texts, images=images, return_tensors="pt", truncation=True, padding="max_length", max_length=200 + ) labels = batch["input_ids"].clone() labels[labels == processor.tokenizer.pad_token_id] = self.image_token_id @@ -193,20 +201,24 @@ def __call__(self, examples): return batch + data_collator = MyDataCollator(processor) ## Define metrics + def normalized_levenshtein(s1, s2): len_s1, len_s2 = len(s1), len(s2) distance = Levenshtein.distance(s1, s2) return distance / max(len_s1, len_s2) + def similarity_score(a_ij, o_q_i, tau=0.5): nl = normalized_levenshtein(a_ij, o_q_i) return 1 - nl if nl < tau else 0 + def average_normalized_levenshtein_similarity(ground_truth, predicted_answers): assert len(ground_truth) == len(predicted_answers), "Length of ground_truth and predicted_answers must match." @@ -226,12 +238,14 @@ def average_normalized_levenshtein_similarity(ground_truth, predicted_answers): return total_score / N + def postprocess_text(preds, labels): preds = [pred.strip() for pred in preds] labels = [[label.strip()] for label in labels] return preds, labels + def compute_metrics(eval_preds): preds, labels = eval_preds if isinstance(preds, tuple): @@ -269,7 +283,7 @@ def compute_metrics(eval_preds): # for batch_pred, batch_label in zip(preds, labels): # if isinstance(batch_pred, tuple): # batch_pred = batch_pred[0] - + # # Decode the generated ids and labels # decoded_preds = processor.batch_decode(batch_pred, skip_special_tokens=True) # decoded_labels = processor.batch_decode(batch_label, skip_special_tokens=True) @@ -410,7 +424,7 @@ def prediction_step( "content": [ {"type": "text", "text": "Extract JSON."}, {"type": "image"}, - ] + ], }, ] prompt = processor.apply_chat_template(messages, add_generation_prompt=True) @@ -418,8 +432,12 @@ def prediction_step( custom_inputs = {} batch_size = generation_inputs["pixel_values"].shape[0] device = generation_inputs["pixel_values"].device - custom_inputs["input_ids"] = processor_inputs.input_ids.repeat(batch_size, 1).to(device) # repeat along batch dimension - custom_inputs["attention_mask"] = processor_inputs.attention_mask.repeat(batch_size, 1).to(device) # repeat along batch dimension + custom_inputs["input_ids"] = processor_inputs.input_ids.repeat(batch_size, 1).to( + device + ) # repeat along batch dimension + custom_inputs["attention_mask"] = processor_inputs.attention_mask.repeat(batch_size, 1).to( + device + ) # repeat along batch dimension custom_inputs["pixel_values"] = generation_inputs["pixel_values"] custom_inputs["pixel_attention_mask"] = generation_inputs["pixel_attention_mask"] @@ -431,7 +449,7 @@ def prediction_step( generated_tokens = self.model.generate(**custom_inputs, **gen_kwargs) # Strip the prompt from the generated_tokens - generated_tokens = generated_tokens[:, custom_inputs["input_ids"].size(1):] + generated_tokens = generated_tokens[:, custom_inputs["input_ids"].size(1) :] # Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop # TODO: remove this hack when the legacy code that initializes generation_config from a model config is @@ -471,7 +489,7 @@ def prediction_step( labels = None return loss, generated_tokens, labels - + def _pad_tensors_to_max_len(self, tensor, max_length): pad_token_id = processor.tokenizer.pad_token_id @@ -491,4 +509,4 @@ def _pad_tensors_to_max_len(self, tensor, max_length): compute_metrics=compute_metrics, ) -trainer.train() \ No newline at end of file +trainer.train() diff --git a/src/transformers/models/idefics2/fine_tune_idefics2_pl.ipynb b/src/transformers/models/idefics2/fine_tune_idefics2_pl.ipynb index 0131fbb510ffb9..bfbd8efd493c5a 100644 --- a/src/transformers/models/idefics2/fine_tune_idefics2_pl.ipynb +++ b/src/transformers/models/idefics2/fine_tune_idefics2_pl.ipynb @@ -9,9 +9,18 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/niels/python_projects/transformers/env/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], "source": [ "from datasets import load_dataset\n", "\n", @@ -20,7 +29,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -42,7 +51,7 @@ "})" ] }, - "execution_count": 6, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -60,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -85,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -111,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -119,8 +128,7 @@ "output_type": "stream", "text": [ "`low_cpu_mem_usage` was None, now set to True since model is quantized.\n", - "Downloading shards: 100%|██████████| 7/7 [00:00<00:00, 9.41it/s]\n", - "Loading checkpoint shards: 100%|██████████| 7/7 [00:06<00:00, 1.04it/s]\n" + "Loading checkpoint shards: 100%|██████████| 7/7 [00:06<00:00, 1.12it/s]\n" ] } ], @@ -163,13 +171,16 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ + "2024-04-30 09:34:36.912138: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2024-04-30 09:34:37.555749: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n" ] } @@ -295,7 +306,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -312,7 +323,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -321,7 +332,7 @@ "''" ] }, - "execution_count": 31, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -339,7 +350,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -349,7 +360,7 @@ " 'Nasi Campur Bali1 x75,000Bbk Bengil Nasi1 x125,000MilkShake Starwb1 x37,000Ice Lemon Tea1 x24,000Nasi Ayam Dewata1 x70,000Free Ice Tea3 x0Organic Green Sa1 x65,000Ice Tea1 x18,000Ice Orange1 x29,000Ayam Suir Bali1 x85,000Tahu Goreng2 x36,000Tempe Goreng2 x36,000Tahu Telor Asin1 x40,000.Nasi Goreng Samb1 x70,000Bbk Panggang Sam3 x366,000Ayam Sambal Hija1 x92,000Hot Tea2 x44,000Ice Kopi1 x32,000Tahu Telor Asin1 x40,000Free Ice Tea1 x0Bebek Street1 x44,000Ice Tea Tawar1 x18,0001,346,000100,950144,695-451,591,600')" ] }, - "execution_count": 39, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -369,7 +380,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -430,46 +441,30 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", - "To disable this warning, you can either:\n", - "\t- Avoid using `tokenizers` before the fork if possible\n", - "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", - "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", - "To disable this warning, you can either:\n", - "\t- Avoid using `tokenizers` before the fork if possible\n", - "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", - "To disable this warning, you can either:\n", - "\t- Avoid using `tokenizers` before the fork if possible\n", - "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", - "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", - "To disable this warning, you can either:\n", - "\t- Avoid using `tokenizers` before the fork if possible\n", - "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" + "No chat template is set for this tokenizer, falling back to a default class-level template. This is very error-prone, because models are often trained with templates different from the class default! Default chat templates are a legacy feature and will be removed in Transformers v4.43, at which point any code depending on them will stop working. We recommend setting a valid chat template before then to ensure that this model continues working without issues.\n", + "No chat template is set for this tokenizer, falling back to a default class-level template. This is very error-prone, because models are often trained with templates different from the class default! Default chat templates are a legacy feature and will be removed in Transformers v4.43, at which point any code depending on them will stop working. We recommend setting a valid chat template before then to ensure that this model continues working without issues.\n", + "No chat template is set for this tokenizer, falling back to a default class-level template. This is very error-prone, because models are often trained with templates different from the class default! Default chat templates are a legacy feature and will be removed in Transformers v4.43, at which point any code depending on them will stop working. We recommend setting a valid chat template before then to ensure that this model continues working without issues.\n", + "No chat template is set for this tokenizer, falling back to a default class-level template. This is very error-prone, because models are often trained with templates different from the class default! Default chat templates are a legacy feature and will be removed in Transformers v4.43, at which point any code depending on them will stop working. We recommend setting a valid chat template before then to ensure that this model continues working without issues.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "input_ids torch.Size([2, 349])\n", - "attention_mask torch.Size([2, 349])\n", + "input_ids torch.Size([2, 377])\n", + "attention_mask torch.Size([2, 377])\n", "pixel_values torch.Size([2, 1, 3, 980, 653])\n", "pixel_attention_mask torch.Size([2, 1, 980, 653])\n", - "labels torch.Size([2, 349])\n" + "labels torch.Size([2, 377])\n", + "images 2\n", + "answers 2\n" ] } ], @@ -477,7 +472,28 @@ "batch = next(iter(train_dataloader))\n", "for key, value in batch.items():\n", " if isinstance(value, torch.Tensor):\n", - " print(key, value.shape)" + " print(key, value.shape)\n", + " else:\n", + " print(key, len(value))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TT20,000120,00020,00020,0000\n", + "KP BRANDING S11-11HYDROCOCO 250ML6,9002-3,90013,80013,8013,901( 1,236)9,90009,9002\n" + ] + } + ], + "source": [ + "print(batch[\"answers\"][0])\n", + "print(batch[\"answers\"][1])" ] }, { @@ -493,7 +509,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -501,10 +517,10 @@ "from nltk import edit_distance\n", "import numpy as np\n", "\n", - "import pytorch_lightning as pl\n", + "import lightning as L\n", "\n", "\n", - "class Idefics2ModelPLModule(pl.LightningModule):\n", + "class Idefics2ModelPLModule(L.LightningModule):\n", " def __init__(self, config, processor, model):\n", " super().__init__()\n", " self.config = config\n", @@ -592,9 +608,223 @@ "What's great is that we can automatically train on the hardware we have (in our case, a single GPU), enable mixed precision (`fp16=True`, which makes sure we don't consume as much memory), add Weights and Biases logging, and so on. " ] }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "config = {\"max_epochs\": 10,\n", + " \"val_check_interval\": 0.2, # how many times we want to validate during an epoch\n", + " \"check_val_every_n_epoch\": 1,\n", + " \"gradient_clip_val\": 1.0,\n", + " \"num_training_samples_per_epoch\": 800,\n", + " \"lr\": 1e-4,\n", + " \"train_batch_sizes\": [8],\n", + " \"val_batch_sizes\": [2],\n", + " # \"seed\":2022,\n", + " \"num_nodes\": 1,\n", + " \"warmup_steps\": 50,\n", + " \"result_path\": \"./result\",\n", + " \"verbose\": True,\n", + " }\n", + "\n", + "model_module = Idefics2ModelPLModule(config, processor, model)" + ] + }, { "cell_type": "markdown", "metadata": {}, + "source": [ + "Let's start training!" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using 16bit Automatic Mixed Precision (AMP)\n", + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "You are using a CUDA device ('NVIDIA A100-SXM4-80GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n", + "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mnielsrogge\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n" + ] + }, + { + "data": { + "text/html": [ + "Tracking run with wandb version 0.16.6" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Run data is saved locally in ./wandb/run-20240430_093846-prlaj2s9" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Syncing run demo-run-cord to Weights & Biases (docs)
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " View project at https://wandb.ai/nielsrogge/Idefics2" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " View run at https://wandb.ai/nielsrogge/Idefics2/runs/prlaj2s9" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4]\n", + "\n", + " | Name | Type | Params\n", + "-----------------------------------------------------------\n", + "0 | model | Idefics2ForConditionalGeneration | 4.4 B \n", + "-----------------------------------------------------------\n", + "23.3 M Trainable params\n", + "4.3 B Non-trainable params\n", + "4.4 B Total params\n", + "17,439.532Total estimated model params size (MB)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0: 0%| | 0/400 [00:00 35\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_module\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/pytorch/trainer/trainer.py:544\u001b[0m, in \u001b[0;36mTrainer.fit\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m 542\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mstatus \u001b[38;5;241m=\u001b[39m TrainerStatus\u001b[38;5;241m.\u001b[39mRUNNING\n\u001b[1;32m 543\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 544\u001b[0m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_and_handle_interrupt\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 545\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_impl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_dataloaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_dataloaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdatamodule\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\n\u001b[1;32m 546\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/pytorch/trainer/call.py:44\u001b[0m, in \u001b[0;36m_call_and_handle_interrupt\u001b[0;34m(trainer, trainer_fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39mlauncher \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 43\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39mlauncher\u001b[38;5;241m.\u001b[39mlaunch(trainer_fn, \u001b[38;5;241m*\u001b[39margs, trainer\u001b[38;5;241m=\u001b[39mtrainer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 44\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtrainer_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m _TunerExitException:\n\u001b[1;32m 47\u001b[0m _call_teardown_hook(trainer)\n", + "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/pytorch/trainer/trainer.py:580\u001b[0m, in \u001b[0;36mTrainer._fit_impl\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m 573\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mfn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 574\u001b[0m ckpt_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_checkpoint_connector\u001b[38;5;241m.\u001b[39m_select_ckpt_path(\n\u001b[1;32m 575\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mfn,\n\u001b[1;32m 576\u001b[0m ckpt_path,\n\u001b[1;32m 577\u001b[0m model_provided\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 578\u001b[0m model_connected\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlightning_module \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 579\u001b[0m )\n\u001b[0;32m--> 580\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mckpt_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 582\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mstopped\n\u001b[1;32m 583\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", + "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/pytorch/trainer/trainer.py:987\u001b[0m, in \u001b[0;36mTrainer._run\u001b[0;34m(self, model, ckpt_path)\u001b[0m\n\u001b[1;32m 982\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_signal_connector\u001b[38;5;241m.\u001b[39mregister_signal_handlers()\n\u001b[1;32m 984\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m 985\u001b[0m \u001b[38;5;66;03m# RUN THE TRAINER\u001b[39;00m\n\u001b[1;32m 986\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[0;32m--> 987\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_stage\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 989\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m 990\u001b[0m \u001b[38;5;66;03m# POST-Training CLEAN UP\u001b[39;00m\n\u001b[1;32m 991\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m 992\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: trainer tearing down\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/pytorch/trainer/trainer.py:1033\u001b[0m, in \u001b[0;36mTrainer._run_stage\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1031\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_run_sanity_check()\n\u001b[1;32m 1032\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mset_detect_anomaly(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_detect_anomaly):\n\u001b[0;32m-> 1033\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_loop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1034\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1035\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnexpected state \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/pytorch/loops/fit_loop.py:205\u001b[0m, in \u001b[0;36m_FitLoop.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 203\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 204\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_start()\n\u001b[0;32m--> 205\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madvance\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 206\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_end()\n\u001b[1;32m 207\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_restarting \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", + "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/pytorch/loops/fit_loop.py:363\u001b[0m, in \u001b[0;36m_FitLoop.advance\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 361\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_training_epoch\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 362\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data_fetcher \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 363\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mepoch_loop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_data_fetcher\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/pytorch/loops/training_epoch_loop.py:140\u001b[0m, in \u001b[0;36m_TrainingEpochLoop.run\u001b[0;34m(self, data_fetcher)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdone:\n\u001b[1;32m 139\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 140\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madvance\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_fetcher\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_end(data_fetcher)\n\u001b[1;32m 142\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_restarting \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", + "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/pytorch/loops/training_epoch_loop.py:223\u001b[0m, in \u001b[0;36m_TrainingEpochLoop.advance\u001b[0;34m(self, data_fetcher)\u001b[0m\n\u001b[1;32m 221\u001b[0m batch \u001b[38;5;241m=\u001b[39m trainer\u001b[38;5;241m.\u001b[39mprecision_plugin\u001b[38;5;241m.\u001b[39mconvert_input(batch)\n\u001b[1;32m 222\u001b[0m batch \u001b[38;5;241m=\u001b[39m trainer\u001b[38;5;241m.\u001b[39mlightning_module\u001b[38;5;241m.\u001b[39m_on_before_batch_transfer(batch, dataloader_idx\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m--> 223\u001b[0m batch \u001b[38;5;241m=\u001b[39m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_strategy_hook\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrainer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbatch_to_device\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdataloader_idx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 225\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbatch_progress\u001b[38;5;241m.\u001b[39mincrement_ready()\n\u001b[1;32m 226\u001b[0m trainer\u001b[38;5;241m.\u001b[39m_logger_connector\u001b[38;5;241m.\u001b[39mon_batch_start(batch)\n", + "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/pytorch/trainer/call.py:309\u001b[0m, in \u001b[0;36m_call_strategy_hook\u001b[0;34m(trainer, hook_name, *args, **kwargs)\u001b[0m\n\u001b[1;32m 306\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 308\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[Strategy]\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtrainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhook_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 309\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 311\u001b[0m \u001b[38;5;66;03m# restore current_fx when nested context\u001b[39;00m\n\u001b[1;32m 312\u001b[0m pl_module\u001b[38;5;241m.\u001b[39m_current_fx_name \u001b[38;5;241m=\u001b[39m prev_fx_name\n", + "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/pytorch/strategies/strategy.py:278\u001b[0m, in \u001b[0;36mStrategy.batch_to_device\u001b[0;34m(self, batch, device, dataloader_idx)\u001b[0m\n\u001b[1;32m 276\u001b[0m device \u001b[38;5;241m=\u001b[39m device \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mroot_device\n\u001b[1;32m 277\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m model \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 278\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_apply_batch_transfer_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdataloader_idx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdataloader_idx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 279\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m move_data_to_device(batch, device)\n", + "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/pytorch/core/module.py:347\u001b[0m, in \u001b[0;36mLightningModule._apply_batch_transfer_handler\u001b[0;34m(self, batch, device, dataloader_idx)\u001b[0m\n\u001b[1;32m 343\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_apply_batch_transfer_handler\u001b[39m(\n\u001b[1;32m 344\u001b[0m \u001b[38;5;28mself\u001b[39m, batch: Any, device: Optional[torch\u001b[38;5;241m.\u001b[39mdevice] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m, dataloader_idx: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m 345\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m 346\u001b[0m device \u001b[38;5;241m=\u001b[39m device \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice\n\u001b[0;32m--> 347\u001b[0m batch \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_batch_hook\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtransfer_batch_to_device\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdataloader_idx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 348\u001b[0m batch \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_batch_hook(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mon_after_batch_transfer\u001b[39m\u001b[38;5;124m\"\u001b[39m, batch, dataloader_idx)\n\u001b[1;32m 349\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m batch\n", + "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/pytorch/core/module.py:336\u001b[0m, in \u001b[0;36mLightningModule._call_batch_hook\u001b[0;34m(self, hook_name, *args)\u001b[0m\n\u001b[1;32m 333\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 334\u001b[0m trainer_method \u001b[38;5;241m=\u001b[39m call\u001b[38;5;241m.\u001b[39m_call_lightning_datamodule_hook\n\u001b[0;32m--> 336\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtrainer_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrainer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhook_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 337\u001b[0m hook \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, hook_name)\n\u001b[1;32m 338\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m hook(\u001b[38;5;241m*\u001b[39margs)\n", + "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/pytorch/trainer/call.py:157\u001b[0m, in \u001b[0;36m_call_lightning_module_hook\u001b[0;34m(trainer, hook_name, pl_module, *args, **kwargs)\u001b[0m\n\u001b[1;32m 154\u001b[0m pl_module\u001b[38;5;241m.\u001b[39m_current_fx_name \u001b[38;5;241m=\u001b[39m hook_name\n\u001b[1;32m 156\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[LightningModule]\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpl_module\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhook_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 157\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 159\u001b[0m \u001b[38;5;66;03m# restore current_fx when nested context\u001b[39;00m\n\u001b[1;32m 160\u001b[0m pl_module\u001b[38;5;241m.\u001b[39m_current_fx_name \u001b[38;5;241m=\u001b[39m prev_fx_name\n", + "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/pytorch/core/hooks.py:613\u001b[0m, in \u001b[0;36mDataHooks.transfer_batch_to_device\u001b[0;34m(self, batch, device, dataloader_idx)\u001b[0m\n\u001b[1;32m 562\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtransfer_batch_to_device\u001b[39m(\u001b[38;5;28mself\u001b[39m, batch: Any, device: torch\u001b[38;5;241m.\u001b[39mdevice, dataloader_idx: \u001b[38;5;28mint\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m 563\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Override this hook if your :class:`~torch.utils.data.DataLoader` returns tensors wrapped in a custom data\u001b[39;00m\n\u001b[1;32m 564\u001b[0m \u001b[38;5;124;03m structure.\u001b[39;00m\n\u001b[1;32m 565\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 611\u001b[0m \n\u001b[1;32m 612\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 613\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmove_data_to_device\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/fabric/utilities/apply_func.py:103\u001b[0m, in \u001b[0;36mmove_data_to_device\u001b[0;34m(batch, device)\u001b[0m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;66;03m# user wrongly implemented the `_TransferableDataType` and forgot to return `self`.\u001b[39;00m\n\u001b[1;32m 101\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m data\n\u001b[0;32m--> 103\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mapply_to_collection\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_TransferableDataType\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunction\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_to\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning_utilities/core/apply_func.py:64\u001b[0m, in \u001b[0;36mapply_to_collection\u001b[0;34m(data, dtype, function, wrong_dtype, include_none, allow_frozen, *args, **kwargs)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[38;5;66;03m# fast path for the most common cases:\u001b[39;00m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, dtype): \u001b[38;5;66;03m# single element\u001b[39;00m\n\u001b[0;32m---> 64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 65\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28mlist\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mall\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, dtype) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m data): \u001b[38;5;66;03m# 1d homogeneous list\u001b[39;00m\n\u001b[1;32m 66\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [function(x, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m data]\n", + "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/fabric/utilities/apply_func.py:97\u001b[0m, in \u001b[0;36mmove_data_to_device..batch_to\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m 95\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, Tensor) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(device, torch\u001b[38;5;241m.\u001b[39mdevice) \u001b[38;5;129;01mand\u001b[39;00m device\u001b[38;5;241m.\u001b[39mtype \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m _BLOCKING_DEVICE_TYPES:\n\u001b[1;32m 96\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnon_blocking\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m---> 97\u001b[0m data_output \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data_output \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 99\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m data_output\n", + "File \u001b[0;32m~/python_projects/transformers/src/transformers/feature_extraction_utils.py:229\u001b[0m, in \u001b[0;36mBatchFeature.to\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 226\u001b[0m \u001b[38;5;66;03m# We cast only floating point tensors to avoid issues with tokenizers casting `LongTensor` to `FloatTensor`\u001b[39;00m\n\u001b[1;32m 227\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m 228\u001b[0m \u001b[38;5;66;03m# check if v is a floating point\u001b[39;00m\n\u001b[0;32m--> 229\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mis_floating_point\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m 230\u001b[0m \u001b[38;5;66;03m# cast and send to device\u001b[39;00m\n\u001b[1;32m 231\u001b[0m new_data[k] \u001b[38;5;241m=\u001b[39m v\u001b[38;5;241m.\u001b[39mto(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 232\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m device \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[0;31mTypeError\u001b[0m: is_floating_point(): argument 'input' (position 1) must be Tensor, not list" + ] + } + ], + "source": [ + "from lightning.pytorch.callbacks import Callback\n", + "from lightning.pytorch.loggers import WandbLogger\n", + "from lightning.pytorch.callbacks.early_stopping import EarlyStopping\n", + "\n", + "wandb_logger = WandbLogger(project=\"Idefics2\", name=\"demo-run-cord\")\n", + "\n", + "class PushToHubCallback(Callback):\n", + " def on_train_epoch_end(self, trainer, pl_module):\n", + " print(f\"Pushing model to the hub, epoch {trainer.current_epoch}\")\n", + " pl_module.model.push_to_hub(\"nielsr/idefics2-cord-demo\",\n", + " commit_message=f\"Training in progress, epoch {trainer.current_epoch}\")\n", + "\n", + " def on_train_end(self, trainer, pl_module):\n", + " print(f\"Pushing model to the hub after training\")\n", + " pl_module.processor.push_to_hub(\"nielsr/idefics2-cord)demo\",\n", + " commit_message=f\"Training done\")\n", + " pl_module.model.push_to_hub(\"nielsr/donut-demo\",\n", + " commit_message=f\"Training done\")\n", + "\n", + "early_stop_callback = EarlyStopping(monitor=\"val_edit_distance\", patience=3, verbose=False, mode=\"min\")\n", + "\n", + "trainer = L.Trainer(\n", + " accelerator=\"gpu\",\n", + " devices=1,\n", + " max_epochs=config.get(\"max_epochs\"),\n", + " val_check_interval=config.get(\"val_check_interval\"),\n", + " check_val_every_n_epoch=config.get(\"check_val_every_n_epoch\"),\n", + " gradient_clip_val=config.get(\"gradient_clip_val\"),\n", + " precision=16, # we'll use mixed precision\n", + " num_sanity_val_steps=0,\n", + " logger=wandb_logger,\n", + " callbacks=[PushToHubCallback(), early_stop_callback],\n", + ")\n", + "\n", + "trainer.fit(model_module)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [] } ], diff --git a/src/transformers/models/idefics2/processing_idefics2.py b/src/transformers/models/idefics2/processing_idefics2.py index ea8a706983724f..bb08ee3ff0825e 100644 --- a/src/transformers/models/idefics2/processing_idefics2.py +++ b/src/transformers/models/idefics2/processing_idefics2.py @@ -119,7 +119,7 @@ def __call__( >>> from transformers.image_utils import load_image >>> # We specify `do_image_splitting=False` to reduce memory usage - >>> processor = Idefics2Processor.from_pretrained("HuggingFaceM4/idefics2-8b", image_seq_len=2, do_image_splitting=False) + >>> processor = Idefics2Processor.from_pretrained("HuggingFaceM4/idefics2-8b", image_seq_len=2, do_image_splitting=False) >>> url1 = "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" >>> url2 = "https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg" diff --git a/src/transformers/models/idefics2/test_bis.py b/src/transformers/models/idefics2/test_bis.py index b6137725a1d848..1928922a0ec8d1 100644 --- a/src/transformers/models/idefics2/test_bis.py +++ b/src/transformers/models/idefics2/test_bis.py @@ -1,6 +1,8 @@ -from transformers import AutoProcessor -from PIL import Image import requests +from PIL import Image + +from transformers import AutoProcessor + processor = AutoProcessor.from_pretrained("HuggingFaceM4/idefics2-8b", do_image_splitting=False) @@ -14,12 +16,12 @@ "content": [ {"type": "text", "text": "Extract JSON."}, {"type": "image"}, - ] + ], }, ] prompt = processor.apply_chat_template(messages, add_generation_prompt=True) inputs = processor(text=prompt, images=[test_image], return_tensors="pt") -for k,v in inputs.items(): +for k, v in inputs.items(): print(k, v.shape) -print(processor.batch_decode(inputs.input_ids)) \ No newline at end of file +print(processor.batch_decode(inputs.input_ids)) diff --git a/src/transformers/models/idefics2/wandb/latest-run b/src/transformers/models/idefics2/wandb/latest-run new file mode 120000 index 00000000000000..9632092f974408 --- /dev/null +++ b/src/transformers/models/idefics2/wandb/latest-run @@ -0,0 +1 @@ +run-20240430_093846-prlaj2s9 \ No newline at end of file diff --git a/src/transformers/models/idefics2/wandb/run-20240430_091855-0w9d9xlt/files/config.yaml b/src/transformers/models/idefics2/wandb/run-20240430_091855-0w9d9xlt/files/config.yaml new file mode 100644 index 00000000000000..f388adc0a6b2e1 --- /dev/null +++ b/src/transformers/models/idefics2/wandb/run-20240430_091855-0w9d9xlt/files/config.yaml @@ -0,0 +1,58 @@ +wandb_version: 1 + +_wandb: + desc: null + value: + python_version: 3.8.10 + cli_version: 0.16.6 + framework: huggingface + huggingface_version: 4.41.0.dev0 + is_jupyter_run: true + is_kaggle_kernel: false + start_time: 1714461535.0 + t: + 1: + - 1 + - 2 + - 3 + - 5 + - 9 + - 11 + - 12 + - 49 + - 51 + - 53 + - 55 + - 71 + - 98 + - 103 + 2: + - 1 + - 2 + - 3 + - 5 + - 9 + - 11 + - 12 + - 49 + - 51 + - 53 + - 55 + - 71 + - 98 + - 103 + 3: + - 7 + - 13 + - 23 + 4: 3.8.10 + 5: 0.16.6 + 6: 4.41.0.dev0 + 8: + - 1 + - 5 + 13: linux-x86_64 + m: + - 1: trainer/global_step + 6: + - 3 diff --git a/src/transformers/models/idefics2/wandb/run-20240430_091855-0w9d9xlt/files/requirements.txt b/src/transformers/models/idefics2/wandb/run-20240430_091855-0w9d9xlt/files/requirements.txt new file mode 100644 index 00000000000000..9c0500b508e809 --- /dev/null +++ b/src/transformers/models/idefics2/wandb/run-20240430_091855-0w9d9xlt/files/requirements.txt @@ -0,0 +1,306 @@ +APScheduler==3.10.4 +Babel==2.14.0 +Flask==3.0.2 +GitPython==3.1.18 +Jinja2==3.1.3 +Levenshtein==0.25.1 +Mako==1.3.2 +Markdown==3.6 +MarkupSafe==2.1.5 +PyYAML==6.0.1 +Pygments==2.17.2 +SQLAlchemy==2.0.28 +SudachiDict-core==20240109 +SudachiPy==0.6.8 +Werkzeug==3.0.1 +absl-py==2.1.0 +accelerate==0.28.0 +aiohttp==3.9.3 +aiosignal==1.3.1 +alembic==1.13.1 +annotated-types==0.6.0 +appdirs==1.4.4 +arrow==1.3.0 +asttokens==2.4.1 +astunparse==1.6.3 +async-timeout==4.0.3 +attrs==23.2.0 +audioread==3.0.1 +av==9.2.0 +backcall==0.2.0 +backoff==1.11.1 +backports.zoneinfo==0.2.1 +beautifulsoup4==4.12.3 +bibtexparser==2.0.0b7 +binaryornot==0.4.4 +bitsandbytes==0.42.0 +black==24.3.0 +blinker==1.7.0 +cached-property==1.5.2 +cachetools==5.3.3 +certifi==2024.2.2 +cffi==1.16.0 +chardet==5.2.0 +charset-normalizer==3.3.2 +chex==0.1.7 +click==8.1.7 +clldutils==3.22.2 +cmake==3.28.3 +codecarbon==1.2.0 +colorama==0.4.6 +coloredlogs==15.0.1 +colorlog==6.8.2 +comm==0.2.2 +cookiecutter==1.7.3 +csvw==3.3.0 +dash-bootstrap-components==1.5.0 +dash-core-components==2.0.0 +dash-html-components==2.0.0 +dash-table==5.0.0 +dash==2.16.1 +datasets==2.18.0 +debugpy==1.8.1 +decorator==5.1.1 +decord==0.6.0 +dill==0.3.4 +dlinfo==1.2.1 +dm-tree==0.1.8 +docker-pycreds==0.4.0 +einops==0.7.0 +etils==1.3.0 +evaluate==0.4.1 +exceptiongroup==1.2.0 +execnet==2.0.2 +executing==2.0.1 +faiss-cpu==1.8.0 +fastjsonschema==2.19.1 +filelock==3.13.1 +fire==0.6.0 +flatbuffers==24.3.7 +flax==0.7.0 +frozenlist==1.4.1 +fsspec==2024.3.0 +fugashi==1.3.1 +gast==0.4.0 +gitdb==4.0.11 +google-auth-oauthlib==1.0.0 +google-auth==2.28.2 +google-pasta==0.2.0 +greenlet==3.0.3 +grpcio==1.62.1 +h5py==3.11.0 +hf-doc-builder==0.5.0 +huggingface-hub==0.21.4 +humanfriendly==10.0 +hypothesis==6.99.8 +idna==3.6 +importlib_metadata==7.0.2 +importlib_resources==6.3.1 +iniconfig==2.0.0 +ipadic==1.0.0 +ipykernel==6.29.4 +ipython==8.12.3 +isodate==0.6.1 +isort==5.13.2 +itsdangerous==2.1.2 +jax==0.4.13 +jaxlib==0.4.13 +jedi==0.19.1 +jinja2-time==0.2.0 +joblib==1.3.2 +jsonschema-specifications==2023.12.1 +jsonschema==4.21.1 +jupyter_client==8.6.1 +jupyter_core==5.7.2 +kenlm==0.2.0 +keras-core==0.1.5 +keras-nlp==0.6.1 +keras==2.13.1 +language-tags==1.2.0 +lazy_loader==0.3 +libclang==18.1.1 +librosa==0.10.1 +lightning-utilities==0.11.2 +lit==18.1.1 +llvmlite==0.41.1 +lxml==5.1.0 +markdown-it-py==3.0.0 +matplotlib-inline==0.1.7 +mdurl==0.1.2 +ml-dtypes==0.2.0 +mpmath==1.3.0 +msgpack==1.0.8 +multidict==6.0.5 +multiprocess==0.70.16 +mypy-extensions==1.0.0 +namex==0.0.8 +nbformat==5.10.3 +nest-asyncio==1.6.0 +networkx==3.1 +nltk==3.8.1 +numba==0.58.1 +numpy==1.24.4 +nvidia-cublas-cu11==11.10.3.66 +nvidia-cublas-cu12==12.1.3.1 +nvidia-cuda-cupti-cu11==11.7.101 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cuda-nvrtc-cu11==11.7.99 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-runtime-cu11==11.7.99 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cudnn-cu11==8.5.0.96 +nvidia-cudnn-cu12==8.9.2.26 +nvidia-cufft-cu11==10.9.0.58 +nvidia-cufft-cu12==11.0.2.54 +nvidia-curand-cu11==10.2.10.91 +nvidia-curand-cu12==10.3.2.106 +nvidia-cusolver-cu11==11.4.0.1 +nvidia-cusolver-cu12==11.4.5.107 +nvidia-cusparse-cu11==11.7.4.91 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-nccl-cu11==2.14.3 +nvidia-nccl-cu12==2.20.5 +nvidia-nvjitlink-cu12==12.4.99 +nvidia-nvtx-cu11==11.7.91 +nvidia-nvtx-cu12==12.1.105 +oauthlib==3.2.2 +onnx==1.15.0 +onnxconverter-common==1.14.0 +onnxruntime-tools==1.7.0 +onnxruntime==1.16.3 +opt-einsum==3.3.0 +optax==0.1.4 +optuna==3.6.0 +orbax-checkpoint==0.2.3 +packaging==24.0 +pandas==2.0.3 +parameterized==0.9.0 +parso==0.8.4 +pathspec==0.12.1 +peft==0.10.0 +pexpect==4.9.0 +phonemizer==3.2.1 +pickleshare==0.7.5 +pillow==10.2.0 +pip==20.0.2 +pkg_resources==0.0.0 +pkgutil_resolve_name==1.3.10 +plac==1.4.3 +platformdirs==4.2.0 +plotly==5.20.0 +pluggy==1.4.0 +pooch==1.8.1 +portalocker==2.0.0 +poyo==0.5.0 +prompt-toolkit==3.0.43 +protobuf==4.25.3 +psutil==5.9.8 +ptyprocess==0.7.0 +pure-eval==0.2.2 +py-cpuinfo==9.0.0 +py3nvml==0.2.7 +pyarrow-hotfix==0.6 +pyarrow==15.0.1 +pyasn1-modules==0.3.0 +pyasn1==0.5.1 +pycparser==2.21 +pyctcdecode==0.5.0 +pydantic==2.6.4 +pydantic_core==2.16.3 +pygtrie==2.5.0 +pylatexenc==2.10 +pynvml==11.5.0 +pyparsing==3.1.2 +pypng==0.20220715.0 +pytest-timeout==2.3.1 +pytest-xdist==3.5.0 +pytest==7.4.4 +python-dateutil==2.9.0.post0 +python-slugify==8.0.4 +pytorch-lightning==2.2.3 +pytz==2024.1 +pyzmq==26.0.2 +rapidfuzz==3.8.1 +ray==2.9.3 +rdflib==7.0.0 +referencing==0.34.0 +regex==2023.12.25 +requests-oauthlib==1.4.0 +requests==2.31.0 +responses==0.18.0 +retrying==1.3.4 +rfc3986==1.5.0 +rhoknp==1.3.0 +rich==13.7.1 +rjieba==0.1.11 +rouge-score==0.1.2 +rpds-py==0.18.0 +rsa==4.9 +ruff==0.1.5 +sacrebleu==1.5.1 +sacremoses==0.1.1 +safetensors==0.4.2 +scikit-learn==1.3.2 +scipy==1.10.1 +segments==2.2.1 +sentencepiece==0.1.99 +sentry-sdk==2.0.1 +setproctitle==1.3.3 +setuptools==44.0.0 +sigopt==8.8.2 +six==1.16.0 +smmap==5.0.1 +sortedcontainers==2.4.0 +soundfile==0.12.1 +soupsieve==2.5 +soxr==0.3.7 +stack-data==0.6.3 +sympy==1.12 +tabulate==0.9.0 +tenacity==8.2.3 +tensorboard-data-server==0.7.2 +tensorboard==2.14.0 +tensorboardX==2.6.2.2 +tensorflow-estimator==2.13.0 +tensorflow-hub==0.16.1 +tensorflow-io-gcs-filesystem==0.34.0 +tensorflow-text==2.13.0 +tensorflow==2.13.1 +tensorstore==0.1.45 +termcolor==2.4.0 +text-unidecode==1.3 +tf2onnx==1.16.1 +tf_keras==2.15.1 +threadpoolctl==3.3.0 +timeout-decorator==0.5.0 +timm==0.9.16 +tokenizers==0.19.1 +tomli==2.0.1 +toolz==0.12.1 +torch==2.3.0 +torchaudio==2.1.2 +torchmetrics==1.3.2 +torchvision==0.18.0 +tornado==6.4 +tqdm==4.66.2 +traitlets==5.14.2 +transformers==4.41.0.dev0 +triton==2.3.0 +types-python-dateutil==2.9.0.20240316 +typing_extensions==4.10.0 +tzdata==2024.1 +tzlocal==5.2 +unidic-lite==1.0.8 +unidic==1.1.0 +uritemplate==4.1.1 +urllib3==1.26.18 +wandb==0.16.6 +wasabi==0.10.1 +wcwidth==0.2.13 +wheel==0.43.0 +wrapt==1.16.0 +xformers==0.0.22.post7 +xmltodict==0.13.0 +xxhash==3.4.1 +yarl==1.9.4 +zipp==3.18.1 \ No newline at end of file diff --git a/src/transformers/models/idefics2/wandb/run-20240430_091855-0w9d9xlt/files/wandb-metadata.json b/src/transformers/models/idefics2/wandb/run-20240430_091855-0w9d9xlt/files/wandb-metadata.json new file mode 100644 index 00000000000000..e28b4875f16d91 --- /dev/null +++ b/src/transformers/models/idefics2/wandb/run-20240430_091855-0w9d9xlt/files/wandb-metadata.json @@ -0,0 +1,703 @@ +{ + "os": "Linux-5.4.0-166-generic-x86_64-with-glibc2.29", + "python": "3.8.10", + "heartbeatAt": "2024-04-30T07:18:55.849340", + "startedAt": "2024-04-30T07:18:55.163285", + "docker": null, + "cuda": null, + "args": [], + "state": "running", + "program": "", + "codePathLocal": null, + "git": { + "remote": "git@github.com:NielsRogge/transformers.git", + "commit": "b794532fef67ad2ece05cfb504983ef76cd24f08" + }, + "email": "niels.rogge1@gmail.com", + "root": "/home/niels/python_projects/transformers", + "host": "hf-dgx-01", + "username": "niels", + "executable": "/home/niels/python_projects/transformers/env/bin/python", + "cpu_count": 64, + "cpu_count_logical": 128, + "cpu_freq": { + "current": 2486.0019218750003, + "min": 1500.0, + "max": 2250.0 + }, + "cpu_freq_per_core": [ + { + "current": 3267.516, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3270.823, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3265.242, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3281.016, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3264.948, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3273.113, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1634.62, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1689.157, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2399.842, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2354.78, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3237.882, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3291.941, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1636.381, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3266.2, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1635.641, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1638.544, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3279.859, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1889.79, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1636.975, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3264.014, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3265.078, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3275.907, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3126.734, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3264.993, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3272.368, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.656, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1635.79, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1638.488, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3280.23, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2428.724, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2364.214, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1635.998, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3267.796, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2281.893, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1989.929, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2305.238, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1640.678, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2183.398, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2241.726, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3278.783, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2142.262, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1638.711, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1638.456, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3264.444, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3270.976, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.525, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1636.91, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1636.756, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3262.078, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1676.115, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1639.357, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1622.436, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3266.68, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2245.016, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3266.47, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3268.124, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1546.657, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1553.958, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1634.219, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3263.907, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1632.134, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3266.913, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1549.011, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1556.62, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3275.794, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3274.676, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3270.156, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3275.157, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3266.754, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3275.375, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2123.271, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2186.738, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1631.842, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1991.91, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1629.332, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3263.79, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1631.686, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3255.912, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1633.448, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1633.242, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3262.619, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2201.728, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1633.348, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3257.616, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3258.482, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3254.205, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3257.062, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3262.794, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3261.239, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1633.854, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1631.718, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1632.122, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3263.092, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2218.45, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1994.986, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1630.486, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3271.66, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1631.976, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1633.762, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3255.157, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1688.142, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.897, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1752.091, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3262.871, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2194.616, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1631.369, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1629.919, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3255.245, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3256.658, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1632.372, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1632.91, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1632.473, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3267.894, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1836.147, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1633.211, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1631.449, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3286.487, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1664.18, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3265.422, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3124.3, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1632.847, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1632.459, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1634.187, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3258.696, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1633.723, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3265.47, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1673.331, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1573.322, + "min": 1500.0, + "max": 2250.0 + } + ], + "disk": { + "/": { + "total": 1757.8785285949707, + "used": 1643.9329261779785 + } + }, + "gpu": "NVIDIA A100-SXM4-80GB", + "gpu_count": 5, + "gpu_devices": [ + { + "name": "NVIDIA A100-SXM4-80GB", + "memory_total": 85899345920 + }, + { + "name": "NVIDIA A100-SXM4-80GB", + "memory_total": 85899345920 + }, + { + "name": "NVIDIA A100-SXM4-80GB", + "memory_total": 85899345920 + }, + { + "name": "NVIDIA DGX Display", + "memory_total": 4294967296 + }, + { + "name": "NVIDIA A100-SXM4-80GB", + "memory_total": 85899345920 + } + ], + "memory": { + "total": 503.5396919250488 + } +} diff --git a/src/transformers/models/idefics2/wandb/run-20240430_091855-0w9d9xlt/run-0w9d9xlt.wandb b/src/transformers/models/idefics2/wandb/run-20240430_091855-0w9d9xlt/run-0w9d9xlt.wandb new file mode 100644 index 0000000000000000000000000000000000000000..efa39717d90d2389dea1c91dc39e553dbdce5b4d GIT binary patch literal 104832 zcmeHw4V+~~b!XnpWoUW^fdQhl19S)|l9}ed@AogF^eCG6C*N-(GZQg=iFO$>eM;s z-I@F7=}eO6ZWOv+)vfy1sXFKU>(r_7S3K{WFMjfeerMfX&u;%vWvH^da>~ePTHD;I zZ|+W1tLyhWaxs)sjbsn7HX#)HGJgos!i8U%uR0I zU0Lz-|Mi?BC#Sa0Z`iqOZo`)Oos%<1T)lO}d=j}BFit~{r5dgc7ePgh=Dxp`$}&&tZ(D=QDc|4%BnuBtq+sxore)s+V-l~Y#L z$2w!x^o&*MShY3Qs;zE}HL7Fj*yPk}({qv`Xk=#1?DX8O>o#21X>DpX?x|cce8#rv z#h--#yL#8=u`To4$1k3qnq9bbe%rRGGZ%MG%q?u4-?@Ei=fW8LWbNsj+ucU9UfVjg zwbh=OtW9m1N}F4@Zf>THZl^xAwcXk>S!--fI}xN;&3}66xiBFsYtJ4&e)!l7h7H@N z7AGbr7AMAL7Ut*v^n#OK^fCBF^2*(n0~+w!gNe!k$xSrE*WLc<*IxqtKWy^}!^Z+@ z({och&)haUzj5=YGZ^9uU>6Tfxe`6FLlF+4i@y3va#wol>zUVi-!_}_y6yl(Vm6FVoi zFAQ(!|3AYid};gqW$lVDI?aonKlpvSxVYmL0oxSCVwBUL84M_!#&% zP+IWcn|4g?+%h${xVy5lI@a+;RZ&zMYx<&^D5{2{x+v<5wMItrZ#Ckgq?wMTzQtx- zwlWPZwt5$}MNu;}*bzmbpZ&MGq6okB+~KtdM^)efSk;=)|GZElg8lbp5)$61z$B?R z$LjP&iArs(9lmJ6;=gF&;)fxC$%o+!1vGJ?04JZMbvXbvfzDx#E_T+nTNCg^*wrLH zbpYKrEASD`N`SH{@!u2x!8ZeXHHi?)fJ3h)5CVYp)%*p_4UEq_{ThLkmO%onBN)R-;ylJ|ik*F+zZ+59w0Z)#r|D?bt9;N_@ zgV)gbgi(S89ruUNb3*`>f711*A@RvJ7GEgwsj(H^koe%Lb=WloKCm^0G64?^lBBsI z&~ei2Th;K;j6k5kPyvl5S{}D>SHxOS#5z$G|O!PPSzU-Tw7q1WJ}8z z3TSdg;tK^f@h2^Cd_@6HTv4M#+hj+h6BI>9qQjJT$3(|S0z%_T#EA^ zOItc>*fyp8c%&_z1Lb=RltE-gMDkV>IK>#(Bv8(ba6J}4#XS}{&DfP=yA~$4O})(ftO+b^)X2}`r7t-LYF1ld8g}a=M-3mIl}*oqhl?BL(ZfU8 zALe(V&x?OKZTQ()PqzbMyz~08iPug{&mx_25wvqeqgw5PmC)!yORMrH^#bM6hU50em06=^$YhO{mq?gD?`s3fAUmdx8C$oa?bUOQy>gSR)Nw^;s3SM224X^_>{tN z*|pp^)FAz@Ywdcq-At=pu#;2YBQ<3I>sCFjcWdzbv;)6BBk#W&zx(y-TH0*Z(hg`P z`mMsk|F#Q1u69~rIj1AX4X@$2I5Au|e4I6YSL*8camIi3x!) zV~bPUcfhIwKPNM2;;-{USEEWba8lhLYxGAPg!^ON{}Ei?rvGEZ|1tX@ZON#$u$TSO(k@Q#8mjYxY_r>d#q`qi&CG{1xS%6Ec z0GC$5-m7>ohFMA8i^d>%FFpoEd$I3H+AFG8u#zO}71b-irCoqayFgyr1v^Z;Kwc!{ z#rtUxUWBJ4yrOyqxG1`dVXf#cURHpM$#%INty6%DNp`tnf6&4|68o<70wFn`gH?>-$nnegzix7gtnu2DF5Hffzbh{jzR)mnGwIVSw4PoD` zWVPa&MF^R;Fg06*P^}0d(;TK|Eg7;2txkJC{88Ge!WLg`N&cwQi$Cf%!LS6Lr+`1| zAm=4mv?IE$v{?h&8RwG9avp8Cns!^QCK%7@o>lKU=}aJ=jvBuQiKm-#;_0UE`~-fF zvuGtawxb!x2IOC-3;sVN*^&;DpGCY@dZ4^k_QQX3UhBt-d95|T2(_y1RvXNkbfkV@ zv{qbcz-j?6v~;W%s`4JJ78d(eY>FiHaq5CpRTlq3eo%=FNSNSqanfp-)YbPFssmxugrY`qo0vkShQ z2K*Kaqetm>`?Cz^;(LP0Z@HO}{8l^@hXljrdMX*NxTi9hi;sW6ai!LZ@)SfWwwnw( zDztVRDimreu4!p0Uvt25fgd#BxKz+qr}KO3AN~5x=nPXYI``&T$U6qka9~U!kdhFiGE1|t(XP(E} zd*c3Ig7%JEoZq?S>aok<((UQ5s;wVwH0oEqpnR9tpYWwSvCGFrU4G)ew_=z3xXAk# zPfz@xE1y3Q-uLx)_KELw{d@llukD{Oa}vY%?l-&( z+FP?@`nsvvO|PAp-GyPYWu^D_E6)8Ec6B7`>eFwztxt4s|A)VO@iu7hxieMP)jjv@ zf%Z-o(pz0WT3=Mzp{d;q(AQq<*rpa0c90l#wB;wvYiy_09g7~pq*{XNj$It}!VTEZ~ce&_KEg`U#UEJF|_w1GtXgN-}}M$L3^tps2=j3lo#j^|KS-wkDWXv z>f{3-zN1fkf8{&xekF>;k47SK^C#aA?Jd!E{D$V_-(#0gjk>(=;ScnQ>rcP%`Zpu4 zr$xBl{rO)*Tp@|WwthB>Yp&mX3U=~&Q751N@(26G^>cT=>>6zE`B8gM?EQ6YuU3BE z?>}ie6VB74_CEFGoqdA%#BaQ$fhlBOFmnPE&OP6`3)(vz((qghIe5zV^0B$sUV@`O zBkJW1Ke)S36xV;@x$nmIek^Kl?;G#w6UD!K+xLDE+gl&Ecf-AXqIlczul&iGkY!@S z_&q0qE126{`<}k#LzR)$F3W^t90q~(KC=d+1Wk^`(S>FhkuNJaSUcp89Q_z9^2wE% z2ab+pln0J6$@m{OklrD)q(_dvO>yMNiP=N`z|l#C45N>w;*bcW_dOL{uIQ;n=K7j~ z%M~>X*3;m~Q5`ut*_TI-T=ox%14rLaAvIAyEt&E4gv>?EMjZl(n@dzco z#vR51xn?^!gB%q$E;3mwmn9j^AC-wMCMvUb7*CLiEyhW0UBwt-~3mJo)M{B*<)Z+8Lvy>85&`wi|Vji~VIuO}ia1?>bdU%3%Uv zD@*FCEX&3I!u$t0BkUBf1Bz=vapk#F`^kjbE1drHr$6`K*B!lLC>{U5rvU|ZV@5$e zy7!THRIF{I)5MLVYqHe!YNOt$0_%{DoLZ1vd8m>$_vH-!u%xepdrD%GP(u%Q2afH1!im7)!V* zQ=c~~vB5^&@QpL{FhT)M0{Lz5?Y#^eBTF-tso+wSQCCmpvtg63`0pAZ>gGb`XwH|5 zZ&XVSA)}Hs<683dBr;6A>q2TW(f6H^$UqKWnqMPJGa)0xOQBJE`^g9{r6pr9;3TDz z0nc|WH$s)p23`a7qc_so&>)O7&Gf#LOw*je!#udrWOAYSZ`{>2{j{6 zbIZ$x1h$dMhSo$&OAIYu6EeL7*9lnrY%yqUJiQCwE^y1}!l3?v_kslR$P2+PI87HZ&1Nre>YKX$@ zz;kIb(O*Ep$l1&aHAD;r4N>T=w2WawLrudnW;L}e1#p#Zj9ZXLNE*l4{sfdwhP*V6 zbM;D9HAJjOmK61fG3~tRmmaB)LAX_Q{6pFMTN&4N-#0#g>C(k{0eKXsmH~TukOp}W zwaFiC8*oSL<*#S(xK}SqJK~N|_Z|Y=5k9}$BXkna*?NRdq9NKNbP^kMBNK`Mxg+|R z)&pdyn3~f05r(29^G@kW@Z!kYQh}4m$oNL3mc&BdUQJac8s%JK8HWwCmL=vTLCNqgLxFeD%BNcKOgpe8#400yq z17Aqvh0X}6fr?bNgxnFaCuO;#&>6X&n5R*SI|6?#G%D8 zcSH`T;*QvAGM@Mf1C+qzm;W;1F2@})Hg1`?Bbwb(+!5RJj5odr&}HI|Xd(>mh$PhD zj!36z?g*}Ic+p-K?ucgD0EaKLxVS-`J&lfO6~|Xns>d8=*d7`q>>S7 zWJ=U=G}6{X{X@-au}TD9@* z_-1>?8Qt);-@JTDIU^Bp1oU-l^#k|))3K0pt3LkLljy^D-}a8V_-bHp?-qB5%<~ny zUjkCu@ouFg^iZ!mk5ex1{OmPrQ7mqRp1vm5b^`N6(H2^6sYowu-1)MviDjLC&2>_* zYT6WyMNPJjTGMEEd6!aPl7G(^3RF-((Y9}_XxhGw1ziUb%5;!)aN`?GYC*HZjjF&V zb7Z_rt+7FFKl`HP;F1xg9Nc{7^NIo-{z2v7GAmf}zg}eWMup5kqoFfWW*9MGIJjYi z0-5-O8XB4?<>12GcHRF0pGIs1eb0!EDD-d17L>6O3B)dtn=XSR5?Ej~4AaAP%R)v+ zL@vA#8Ym0HkTSy}HI#!}4KiZ{N6;Xw;0O{n6&!)lHf|7xgG(J%Z&YRj8sPSVV&IaCw_rWI{is$gqu6 zMMp4EQ_&H;Ol&h;M3slj#87&;P>=d)IJo3gmI{vGZAx6aVK-C`E~}>;TwW$D89sKy z!6lh6f+J|`MsNfzPZ=D6woE7#iYWnYMEkw*&QJKFM25|-;o;IKjOYkzQ$Y zQBw{s>x&T_5&EJvb|@3TMBkHU9$RI~!DW_};ovgYPF_I9<>?(X6&#V-s@}I#4leiJ zl!J@QiQ1JmFc^Qq2{T^#LBVmpu?C10N`!DmsHPO1{LI2Nt9P4F(=djavnkgV4z%C< zfq6`*^Ns~bAPOv^Ldp=@Hh!eiB+i703MEGQNs9ZX#3(;#aD@^hw55Hs(kO;+vOiCjcX%4;v)9@n02=WJ9gK&=69lb z&F}E+njc&Xgtf8*j{aR*qA_|`H39iTHiI6l_O}wdX`@?%ogBfVLid_=z1nGZI$)ir z3wPb}`Y*1m3^m68>TrC`I&;bQeE4nW;|G`GHEZh*5W3`pJ3xawK!ZC#xTN!N7XN+^ zZoda1j^n0JeCC3k&#DYH$DdqB>-?c7KXySB;DKt)3h;0wisOmo?%f1s?TVy9&4Mlj z+2fRuMS&4zlNAchmH^#E5gA(f_l)t#Rlmi7#e>sIjf^ov?}su83L0FVk`k!65B6fg z!1y8V!C=9{Cj~MYqW+Tt8G46*)F>i_qW5YL?FdDvO!s6j8DvY-(eGD`o z-2^KtjMtMw!^;YU#$>|WB+6Kicod2XV_!6>Ft^*bgxDmosngz9iVNc(lv2aXBs3lN zy#i>AXb+kS&Y#cl^I+Z_0tIU2xUTG zxFRvQuuxAfCG3k6Ld}KYWm8`$1O|gCDMV9pVF|1Q+VbUHp{T0`&=_PG4Z|SALRkSc zrN}TgO)A=hmkDIJYJ&SEal8VrXKlc4S5stIK215HVB8c+iOZ2>80ltL*8<10BL*9W zySJ+;Bqsq&Cy+Uc4Z}4>?Mj5A(O3{sO^H#&z*)lMYbvWaryTOJsIy_*{rY9Y_()B| zn7SnQ9Sq{8!Xk{+`EWECiBWEHxRDBs5{PCAA63>dAB!@Nqe3Z1Xjl2Ml%vB?7(DjU zJ{Hnzn#lB3OOSf;VL$tqCx3kw9M9JHSA*ku^WXgDOAj>kqV0W4X$;~aNDsFY`9xPF zTnN|`$;lP#j0}ukZYL#?&=N$Sv!LlGD>FM4Qwxm6NvrjR7yg|ACBvy`igO=O0E2$@ za=V=@9MK5A2mvNyX>kKuE-c(E>AX!XEFKFX*A*`l*sR3Mi{1(BB{k)vFWB%6ikYkh z$V3sTHjKWY!!hG}0pVtNouN#wrd(5GgOFZl0yY-~5-6t=S5%#6R zFPIE3i_?ihQ|xp)t4=zd=&KW^I{t!2*3xRR9KPEsB*;0T?LY-jx+AD%(&Fj@#+JDBVtoSz3Cplf|;B2n;;W@_msy zsup(4l3ph%jAimV!}r84oT+s;J|hAn)YGb)q(Y^qU=m|^ouN%jJ-NOZUMJOCLa#Hl zY2o4>OJNlw*F?AUIs%vkVt;~5DkLTWZA}Yp3B67l#v$!>GDD%1*NJE6PAm+sGei(5 zuM^iNIfp`T=f;4y%vA^m?(0-2ts1l=sa+vB3Ah&Fcw9N1hyk@LM8}!f&Y2aW<7_uq zDEHz1olbrwLK($Puu#0?)6*(;Zkz-O^^_l}ays!SiVDT@0(K&V)szjv2(c2%Fy>Jf zu!1Aga3yBC^Bt0=F+k4>g&aX)_@)tp!3rfpx#gI{Cl6&ZS@1F@ea={R%etKPo*@_y z-~8RX=D_7_kN@2|NW+@PG~9Rd9S^OD4m)vWWUihFd2)VIkHbzJZBMV0ifoLsuZe{g ze@)5|o(8zD=~YHwlWQ!OynfC)$&y!2C$sX^`ogker_=3w>JCpk?1^e|G4*Q%{L^WN zTw3JD=Wecrjqi=ze)t#V%3@{4!XhKWa5+Pnb~!_tB{BWFoS{`qXx!+P%gJU@x|}hl z%I9RVVfdWXn?6oDU0LB0m&N5|-%~Cpn?l3oq@`cd<>al3U6>2gN( zETQrBtgU8wJx(_IWjW{M-zz|-q{m51beTNP@Vx?wF+9#tuV4!<>2ZcO3zU=LaZBPR_XkFOgv+%<-cv$OMCCKjhA zcURyZL)sqsvEdOcPi49JUQd1a6#Q|Bq0XOdrbf+5>+>)8S0?0-kA&Bps_kZ4?SgTc zj<{d@cc0a-TlKWwt-Vtb|r5=8#$;PQp?N?rTzQ z=4pNU;gINH%g~t=+bUHiu%!)e8un0_&XqDaB;o=}b4bpommCr@lXL`W=*sXxtt!J1 zg{I^-tf42Vo~b6?;1x5A#)2=C0@De!oaAL=Yg-bt(9_TxOJ@8RrO;qKPg)6N#1}O- zbUwLE6cW2Tzho2=yX0RgY=PbQPz(~j(cvv%tTnRW>7Y#2IR~2A7{g>Hxp!2?Zl&AtVzt5+ky~ z2+_#~L(R(FWO4ts<0E0;cRUp^(^Tg|h&mX9YnEJZN27#Ssq5D(06I zRM0~H(3}ggkb=}=vt}B`9wccQCvXxOXe(tKN5qsgj#0AxoJ#9Bl3r;XJ42T0icKg2 zNu9CcS9?UtcYf)Hqu%<8q%zbUe{2XCq?`G&nP>6rqLn92ZiO z&|){|my6IM$Ij5)L@=pn!(4~v7hi+{n1Jy>yoJ=;nU3@vl05feCZ~Dqgb0a`N-S$OP9C0U|=^o@$Q07 zX;BJhNZDMty9~eY%eRcKSTXby5g9#rIXvibMUt1xy-B?p%L1ZkIhy(-2`HV0s$uh!om3Z;_NYjOf8|q0-`KC znyV;J)e`VR=!?<_fhi&5+Bj3Am>Xq*Ey-ZW0wYm*)}4CQ)W)O+Zr6uFnhQf`VU%FX{zFV^r*1HLNGa&f)IhJ}7T2<~K!r zp^%yckfhmANpjepqu4qA)UFg8US{nz4pYU>v00R2)b%G zBvBz}Y=n}ai#C3Pp(BpfVCbl$>egbOb}*Yn8FQAvzD!C%QFMG1VnxAjL-VViDr$x{ z6-CG7!nn3b0wtd+N?_BbHDlywr4_8+D&n2dIy}^q>nRN@U)@?{>(`*@NRz6UiWs0O zIV&@6j9ZH|yT(gJBo#(-RvMoXaz-l12sy*^;!I-TQcG;qfQlfAF*rJs3UzIfZTiNw z#c=!0xVFfi6cVOzl2NIiA5nR7P)a=$B$KlypqB#KV_aJd*WV0|j`dd{1WO}hEC9B# zs(g$$0a;>I<*MSGmm=xVZ1y8nws92TB|vhQFdTy?tz-9Mpeq%+3aX-Mv@T~no22OQqrSV!I^IvBoPdI|k5Ma8vl1dy zOH?Q!%7b2Wa19GNmmbhi5gX=iufZ?9cdS$x!x}`CW^< zE34{Yy40G35U)Xq*Witxa6k1Rzwz^DpLyvgUJi`dSu@WaLQ3qz83(oJ{f|7jV&sVF zxgEO}H%(72?5>St@Y~G&O~S8;WfjZCZ z%hh9-P5<1~>94A-AFVagt6ot46F>a=f4LF6yfNzX4WIs7?DBB=_8z?Sw%e|N_Rg6Z zWnJF;M|+{YXH6_FPR%V&&(CezKCy63`NqHfWb-QQ?72~ApLp-r`o#B3H=dXxzAuXK zeSF{FFCD%=a@kcC?DBbWm*4qhpZIQg^INxF4(+{o<|KyiqYr%p+FP?@`nsvvO|PAp z-8I#R^q%(J>NIxsB~e#z{@hc2qI>cSX2-Wdd*{zoSyylUmwnLQ$wGRo>qqPBN4uSL z{b;LM??;eV-+b@6*!v5j-hb*X|JWzcU;F;Y4@24bFOh8Q{m?g|z2kDApQ$*1VZ*Pj zz%IWu>hc|*{HHzv-*eY#_e?^27tV|^!0-Ojx1has8t`X=cK6I*{3v$)qNwXneEZvd zqWueJ4_$gOw0H5$b6D4(-upDPw`z7`^VDn~0{zI#zL$x_B~d4Dc*}SC#P?smb^9qO z5-*EHV&4Pbh4z+cJAV3~+9~4t@~F$Ve(qoU#Px-hRqsMvFO6`$;SJwMT&K5BZ0l#E z_?b8CU5A~#Eb8Q=Z}~x=xc=?LtzB&I@~FLiAG!hZP*!T?)x<|m*mXR%_ft`O58eL; zY_AWU_SidrqR&W-PRUJHBqil~>L`urQQmwgxu zui0_r2)6grQG0v7a$}z;rayDdUtxPc6ScQ@-<$eG@!~%?{;vOeRAuOu<4>PNHrLI! zfA9BUbL!i@I)=Gdhk<)lt4se1DBpJdfX)YjtfeD6>F=X=*w6VOGD-INARdCa7Nrx5 zq*g!YgGiJ0Iv+%u*FGO)e5K+*7=>2ud=QhoIDTBEphJD4k=d@FXO=9BZc!2HB-+MSUQ5R^z)zh)i`I%MR*Am+KEb0d8+$lmDECxb}kIownx(}Qs` z$euu1%9BBwi{&~QB)qMYK_ZY#o($4yygnJsDc4L+f$S7?HcL6Hp3h+CMoW1zNC*G& z&Blb2>&tX9NK1bSPX_aMxiY}5lR*-=k|%?Nn@mN4C(1Mq<7AL_l%+cvWEYQ&lR-LK zF-`_)&nR~?$j*0{=VXv6z9l;uWILvDGDxVEI~l}_?`|6|bu!31qm3FmIg=Spv-2JQ zJ*}a_DW%wIpkC;wKDQvTSi+OR(55`rVW*o*cQVM1MN6Ix5->}7GMGQUnV+s0Cxh%z zQJ|ARI7>JwI#f;u*|DLN9&j4ykV#p^Xf*i~D8npf=Wyy|kf&i#hVe5w8DyC%OxjqX{Xw(!(lysj|zRrY6CJ=Rqw&9qydWR z(={m7BW}k%Xf>d3$OKac1ZTN4AQ;|~d3rJ%Mgo__$pAI12Qriut;V<$lU8%ax1`m; zzz%InTri}_*VDjI%&8>RFkcsW0+LnZA5>NYi>sBSS_0vl6v%2A4b)S~s;y*1r7{^9 zYKFx?oQpIVkce|J)lOMvEtM~f(bRn zsj>G;RkS^O*idq6@K6(xIFeG!I^(8ZF>3i9l4hL8yff>fM)?z415@EeazM21%15)KDs%Y^zXv*jtFfRH61jcGDC}LJji%QEwEV z#-_Jaq&>um6Ql;87RtnG0=S94Rgw02h-%IXip7*b2r|uu!KV?Xice#Siw!=F_8=K) z4;@8sDC>-*(-MfFr%{xSw0FWLBkkdXVGvq5g))ImR?l}-Y%|eC62+%6ipSv7XunXA z_OQ>>ZYlXR)afLqGSohPK2|O$CK(2!M%IUlwa;0cY@%eWJ>D{+RcZv=lSC;>jXlyW zgY6U8BWd=HXnXpg5p7SnNlq;RAVZ@X8XCR~w}*|EG`Hl`@U8JsQOgajQiD_rbcYdc zpSk6E_`0z7Fp)vvZVXb5wJWr@6J$rnNjsQFUmMZ(jE$2!PX54vYgQ~Qe}T!BN{BdL zq@wNdSlcr&$|UB=Gz?CSF<4Srxhqw9kCauMZ;`H0Vw78yu29NQ#KuXCP~3ZRYG8!0 zk!p-WaJw{)-BESkBf~fjv!<+L-XmomXMyJ)d&52sOsF!D`B;>NoFC@8?n;a}@2G5K znB!V6RxN|J&v^SNAOuLbR#gUszz)=^59qD|-8G=QmW}SZ>9OlSxd=H`uNr@9-P+Yd zPiOqmeP8^CGh_P5aVdg6!dw58$5g0KxDih=TS76wUP`Q!)SUU%Rx>aQ^f5gbtyKcC zMAl~KSR$b^vwrjl%z|AU-4HV{E3+D%?W@!!ySr&qHQ5S&pVX2Gc*yicr6pNtl2Vc|^JGyf9m(8f zsUi~yF(k{-P?6+J8YB|AOj<#*3-&5BizlTJE)4g>G>OEnN=p(6fg^KUvPhU>geF;r zX5l4k@(T?biKfG#kt~rG-#wyruP{V zR7E4@r-0c2HI2jq`V@`Cq6-Zgi6~-8BcaDgkV+mY0U--)n3Nl~!VMyc1Y8kGEM`$9 zZGcH7sgXod0_%vDhQTC-FY5g!lnJTH3~0|z>5Z5s%3zWx)Kf7@5aUG7y<(C;jA*-+ zOcMH&B>aj>;t^JID`x9dg=WExQ5wHNC6S;Sp;@7x#D=A$QArzscM4>g78@F&qLOm$ zmPx-sCDCRhsU#F)nhnJz!Dd54EX^f(-baW{0;eM6_#3fVgd02`5$#g2+tON;SsM~i zTZEe;lUT9~gG?fd!XlGMX(%!YmK3diO(wBZ0+n2q859PYM0!z?NsMKbWD+nfgsH(L z(N3heBo^qcxFl{C7+ey`qQNE6GF4m>=p~w{QmGq2+lMd8CB-OamAZk&!5UmraPti= zDTEiv7pL*yDO}b!xFnWEL2NjA+7izYlnu~g{S^xf)(6WMArMMn0s|KU55vYwD}SRZ zCl?f%lqW87)QxZ$ATL*7%?$H6AgJtP*up}O6q%G;kM5(IOky>)%0fNpYD$cd3h7BE zftecSR9eXqU!<&LBw+kUrI`#hLwA*#%x6v8$$0!l?FvDH3CDdsAE~sI;iG{VQ zKr#(Trv6DL^ncxZ*8ZLBI8&;B5!?OsnBgPx7s27b-JgJvh#$TVMef(tvGAv3^9E}9 zpWHJc?ss+eJNKss`_r+*!}y_ej3;gYTPyxLFLX7kR09oA_s1Ij5zoflAM5^);0w|p z8~%^rl^j?SP}t3Sa!GaA*BplT1AT2djfB5SL2wS>Y1wu^cw zwIvIrk|sKPh(HlZ%t%q1GPfpjdPV?}p96VzNs6{ppcu#mmn>2OXV^3fr5u@gu5=@cw;R1; zk@6H*+32NajHXGkNPP4KG9y_eQ1R3mNg_cRO`+tFvNF&12$6|8YEVewgL)x_GAT3Y zoH^7=GDxAG28NtL#USAtbl)==BoY~w_mhF97$hunpeY6^JNon=l)y0JRWV46>yZo+ zcx^O;l0f1!Zv-MyJ;fif0V@6orO6FY@kijP(Rz{m5f?Q@A2AVCIX`)smY6UIxy!IM zXwXN2%F+@;161r0pFznUW!AErU4uNLj;c5$Hb6-p0ca$?iaX+E7BFs>WEc`|vFPMw zJCG!g;@8GhAQJ1U!5&dp6???Ouq1l~-XEy)nok6OoBLImeCp8k@|Q9MnOI?8mKlSIWFwZo>Tm?IWPsF)*0 z4l3q|k5Dfs8lhs2m{>^Wh@))~M>K;5aYPKCB96E?8N^ZOsI{CNpfU(4>Zlf((53_i zr`P+>25%G|Q&GH8HVmpNOg-pNUnU5ydr zqa;ZD9-=aa`AC&Dj50|qjld`li&DH1R`uOg260rVL1HyENUTt5tEgEC5f>F@6!WMk zWfkYA9gg0Shw(@v`?t)cT`Xozn#S4Y{oc?? zVYh2F;aDt&T(f}&r z1o8CH4?^O??jtDchXJFEAAE+&wP{d-mTbD*p#I*fqqy$MU3`&rkD-~46SY#Dc#x9K*lpxKnK?#z`9NLs1zqcsY5?e3J zLkVIE6}JdWQi3$QOGXJY#>}7uX|s?KWzK8P2{RGA5Uo#x5~RT!lpu*(NlK758AS=Q z2qqOymS0@T2O*SFe#yIEN|5Hspaesi)J$x{SCk;`m996E5{#KIgA=5q0D}{x87{>M zvL$20lZ7^=&Sp`z1}8|!l;Q+)7AEHzg%uM?qM`&_Aqr1Xf=s^}lpq0WP=a(+rzk-T z9dXOv2qq)Y3`&qz#gb8iIen6Wc4$+AoEkAG!R)0@`�`qXgM)N6mH>Qi3%im&#Lu zfj=r2O2%(z8kAr_(6)v>qovGY+ykgk`v?If!yslgr9~VyD>h}&I;^JF;~}WaFp61F zmrz*HEQ{NcyE00kXHBJD9J8j*>v=WI6u!k}3eRzQ`fc|DF8$*8g%1@7hQ7ki$>2q|B`br89n#X^EO@t|9swgmz{@v zBb39hcg;;N=A>k;l%;xznN6Q>KiHajvCs>8o zSNhS^;+C_*7xJIr-*6ZQi++8odiFk?;Hh|m|L_m`oZ$IyO}_WUp%p`0#~(it*3qrM zm#w3(f8tM99@u?Aj4yJ$hTLzEX6V7!patQIgrx%+Kd6@6Xz(_ft0mPH9+bEuxew=& zAlbb>vBe~Cdx~0=n=q>;j6XbNCo^3!HY}W6DJgcdL+L1RNkgL&6c#4sh+8&;_$cH>7;HsF?#+hC`HDvisE7hyb;Ak%!CoeMHhF}ML@9H z32q~bD|AN7OlVZBr3AQ`P*JQVFcLyfxC8y%JV8TiC)lEP~I;uD>=JTpJE+(W#92d!qB5lyQ@%)NdPCO1o#c>tg0|eKZ zB;O!yXz)fHSEwgthV3OrLMfVFBcW7iQ;Q7AuOe;OgXb!a3y2!}pdxM9EE%K?t!zcw zXn0oQxaH4S^t|xiAv}p>9w&w8CXL7hZ#> zRb_BDGz!JtFsebubLH20odS~qT}c)Z@5ZRe8@8h=@`lZ&B5#;#SL6-51h2@OHY1Cq z?6A2|#MuqIhmX(jVHRVSOc%Xk=+og7^glo&-d z1xBF3+P(Oj&i*30+9|j>O_6gMb#P%sR|5s1cSP4$Uh&pn_y~j=Z5#g$ynguQ%++ zof=s9a4Yzz;Z=S@FhNLBPIrhH-Ov7VV1yawH*idq)Pk63#uZ630VxSrB=`2*r7;s| zfwH7(+A*PMD!r{EGh{Pn`K`IEOsGA~v4I3YRwhh?1k3?4td(iNu0ScuEZ5PQF{caz z;6uoFP<8>cj}6LGGmW1Dawp_6$WJ%s5XHN`0?aa$snf$cpIvy%z<1p z+msn((I75H9pvZw<~Ylu2aR zsxUY+nqPx63vG%m800AoUT!qq%*@ZGP;q8}A}vFMGb2J+a%QMBsiy{Mh6ls>7o}$g zB1To!A^2Y#B+SX3Lngm1N6k6V_}QvxN+{j7F%~ zvZ8klyFq9SvW(DPO0ulO2_|U;=YpEwcr(bdz!H@u%TWGZvvO6TG%)Ba1G1-I6q~~3k{PvusMcJ90GZiQ4IPb&^gK~MswPaMw-P5*t@K%)>i=~Wf|kl zP`kp)$sGfagEEfuut$f31}R*4mF6+*TWC7xHI;dcg}z4?TNY+U8OU7Bl!c7)Kp)i@ V1sODKWL8sPlqKlvasRRN{{hv45O@Fp literal 0 HcmV?d00001 diff --git a/src/transformers/models/idefics2/wandb/run-20240430_093846-prlaj2s9/files/config.yaml b/src/transformers/models/idefics2/wandb/run-20240430_093846-prlaj2s9/files/config.yaml new file mode 100644 index 00000000000000..ea95a051da444b --- /dev/null +++ b/src/transformers/models/idefics2/wandb/run-20240430_093846-prlaj2s9/files/config.yaml @@ -0,0 +1,38 @@ +wandb_version: 1 + +_wandb: + desc: null + value: + python_version: 3.8.10 + cli_version: 0.16.6 + framework: huggingface + huggingface_version: 4.41.0.dev0 + is_jupyter_run: true + is_kaggle_kernel: false + start_time: 1714462726.0 + t: + 1: + - 1 + - 2 + - 3 + - 5 + - 9 + - 11 + - 12 + - 49 + - 51 + - 53 + - 55 + - 71 + - 98 + - 103 + 3: + - 13 + - 23 + 4: 3.8.10 + 5: 0.16.6 + 6: 4.41.0.dev0 + 8: + - 1 + - 5 + 13: linux-x86_64 diff --git a/src/transformers/models/idefics2/wandb/run-20240430_093846-prlaj2s9/files/requirements.txt b/src/transformers/models/idefics2/wandb/run-20240430_093846-prlaj2s9/files/requirements.txt new file mode 100644 index 00000000000000..f79e27fbf99175 --- /dev/null +++ b/src/transformers/models/idefics2/wandb/run-20240430_093846-prlaj2s9/files/requirements.txt @@ -0,0 +1,307 @@ +APScheduler==3.10.4 +Babel==2.14.0 +Flask==3.0.2 +GitPython==3.1.18 +Jinja2==3.1.3 +Levenshtein==0.25.1 +Mako==1.3.2 +Markdown==3.6 +MarkupSafe==2.1.5 +PyYAML==6.0.1 +Pygments==2.17.2 +SQLAlchemy==2.0.28 +SudachiDict-core==20240109 +SudachiPy==0.6.8 +Werkzeug==3.0.1 +absl-py==2.1.0 +accelerate==0.28.0 +aiohttp==3.9.3 +aiosignal==1.3.1 +alembic==1.13.1 +annotated-types==0.6.0 +appdirs==1.4.4 +arrow==1.3.0 +asttokens==2.4.1 +astunparse==1.6.3 +async-timeout==4.0.3 +attrs==23.2.0 +audioread==3.0.1 +av==9.2.0 +backcall==0.2.0 +backoff==1.11.1 +backports.zoneinfo==0.2.1 +beautifulsoup4==4.12.3 +bibtexparser==2.0.0b7 +binaryornot==0.4.4 +bitsandbytes==0.42.0 +black==24.3.0 +blinker==1.7.0 +cached-property==1.5.2 +cachetools==5.3.3 +certifi==2024.2.2 +cffi==1.16.0 +chardet==5.2.0 +charset-normalizer==3.3.2 +chex==0.1.7 +click==8.1.7 +clldutils==3.22.2 +cmake==3.28.3 +codecarbon==1.2.0 +colorama==0.4.6 +coloredlogs==15.0.1 +colorlog==6.8.2 +comm==0.2.2 +cookiecutter==1.7.3 +csvw==3.3.0 +dash-bootstrap-components==1.5.0 +dash-core-components==2.0.0 +dash-html-components==2.0.0 +dash-table==5.0.0 +dash==2.16.1 +datasets==2.18.0 +debugpy==1.8.1 +decorator==5.1.1 +decord==0.6.0 +dill==0.3.4 +dlinfo==1.2.1 +dm-tree==0.1.8 +docker-pycreds==0.4.0 +einops==0.7.0 +etils==1.3.0 +evaluate==0.4.1 +exceptiongroup==1.2.0 +execnet==2.0.2 +executing==2.0.1 +faiss-cpu==1.8.0 +fastjsonschema==2.19.1 +filelock==3.13.1 +fire==0.6.0 +flatbuffers==24.3.7 +flax==0.7.0 +frozenlist==1.4.1 +fsspec==2024.3.0 +fugashi==1.3.1 +gast==0.4.0 +gitdb==4.0.11 +google-auth-oauthlib==1.0.0 +google-auth==2.28.2 +google-pasta==0.2.0 +greenlet==3.0.3 +grpcio==1.62.1 +h5py==3.11.0 +hf-doc-builder==0.5.0 +huggingface-hub==0.21.4 +humanfriendly==10.0 +hypothesis==6.99.8 +idna==3.6 +importlib_metadata==7.0.2 +importlib_resources==6.3.1 +iniconfig==2.0.0 +ipadic==1.0.0 +ipykernel==6.29.4 +ipython==8.12.3 +isodate==0.6.1 +isort==5.13.2 +itsdangerous==2.1.2 +jax==0.4.13 +jaxlib==0.4.13 +jedi==0.19.1 +jinja2-time==0.2.0 +joblib==1.3.2 +jsonschema-specifications==2023.12.1 +jsonschema==4.21.1 +jupyter_client==8.6.1 +jupyter_core==5.7.2 +kenlm==0.2.0 +keras-core==0.1.5 +keras-nlp==0.6.1 +keras==2.13.1 +language-tags==1.2.0 +lazy_loader==0.3 +libclang==18.1.1 +librosa==0.10.1 +lightning-utilities==0.11.2 +lightning==2.2.3 +lit==18.1.1 +llvmlite==0.41.1 +lxml==5.1.0 +markdown-it-py==3.0.0 +matplotlib-inline==0.1.7 +mdurl==0.1.2 +ml-dtypes==0.2.0 +mpmath==1.3.0 +msgpack==1.0.8 +multidict==6.0.5 +multiprocess==0.70.16 +mypy-extensions==1.0.0 +namex==0.0.8 +nbformat==5.10.3 +nest-asyncio==1.6.0 +networkx==3.1 +nltk==3.8.1 +numba==0.58.1 +numpy==1.24.4 +nvidia-cublas-cu11==11.10.3.66 +nvidia-cublas-cu12==12.1.3.1 +nvidia-cuda-cupti-cu11==11.7.101 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cuda-nvrtc-cu11==11.7.99 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-runtime-cu11==11.7.99 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cudnn-cu11==8.5.0.96 +nvidia-cudnn-cu12==8.9.2.26 +nvidia-cufft-cu11==10.9.0.58 +nvidia-cufft-cu12==11.0.2.54 +nvidia-curand-cu11==10.2.10.91 +nvidia-curand-cu12==10.3.2.106 +nvidia-cusolver-cu11==11.4.0.1 +nvidia-cusolver-cu12==11.4.5.107 +nvidia-cusparse-cu11==11.7.4.91 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-nccl-cu11==2.14.3 +nvidia-nccl-cu12==2.20.5 +nvidia-nvjitlink-cu12==12.4.99 +nvidia-nvtx-cu11==11.7.91 +nvidia-nvtx-cu12==12.1.105 +oauthlib==3.2.2 +onnx==1.15.0 +onnxconverter-common==1.14.0 +onnxruntime-tools==1.7.0 +onnxruntime==1.16.3 +opt-einsum==3.3.0 +optax==0.1.4 +optuna==3.6.0 +orbax-checkpoint==0.2.3 +packaging==24.0 +pandas==2.0.3 +parameterized==0.9.0 +parso==0.8.4 +pathspec==0.12.1 +peft==0.10.0 +pexpect==4.9.0 +phonemizer==3.2.1 +pickleshare==0.7.5 +pillow==10.2.0 +pip==20.0.2 +pkg_resources==0.0.0 +pkgutil_resolve_name==1.3.10 +plac==1.4.3 +platformdirs==4.2.0 +plotly==5.20.0 +pluggy==1.4.0 +pooch==1.8.1 +portalocker==2.0.0 +poyo==0.5.0 +prompt-toolkit==3.0.43 +protobuf==4.25.3 +psutil==5.9.8 +ptyprocess==0.7.0 +pure-eval==0.2.2 +py-cpuinfo==9.0.0 +py3nvml==0.2.7 +pyarrow-hotfix==0.6 +pyarrow==15.0.1 +pyasn1-modules==0.3.0 +pyasn1==0.5.1 +pycparser==2.21 +pyctcdecode==0.5.0 +pydantic==2.6.4 +pydantic_core==2.16.3 +pygtrie==2.5.0 +pylatexenc==2.10 +pynvml==11.5.0 +pyparsing==3.1.2 +pypng==0.20220715.0 +pytest-timeout==2.3.1 +pytest-xdist==3.5.0 +pytest==7.4.4 +python-dateutil==2.9.0.post0 +python-slugify==8.0.4 +pytorch-lightning==2.2.3 +pytz==2024.1 +pyzmq==26.0.2 +rapidfuzz==3.8.1 +ray==2.9.3 +rdflib==7.0.0 +referencing==0.34.0 +regex==2023.12.25 +requests-oauthlib==1.4.0 +requests==2.31.0 +responses==0.18.0 +retrying==1.3.4 +rfc3986==1.5.0 +rhoknp==1.3.0 +rich==13.7.1 +rjieba==0.1.11 +rouge-score==0.1.2 +rpds-py==0.18.0 +rsa==4.9 +ruff==0.1.5 +sacrebleu==1.5.1 +sacremoses==0.1.1 +safetensors==0.4.2 +scikit-learn==1.3.2 +scipy==1.10.1 +segments==2.2.1 +sentencepiece==0.1.99 +sentry-sdk==2.0.1 +setproctitle==1.3.3 +setuptools==44.0.0 +sigopt==8.8.2 +six==1.16.0 +smmap==5.0.1 +sortedcontainers==2.4.0 +soundfile==0.12.1 +soupsieve==2.5 +soxr==0.3.7 +stack-data==0.6.3 +sympy==1.12 +tabulate==0.9.0 +tenacity==8.2.3 +tensorboard-data-server==0.7.2 +tensorboard==2.14.0 +tensorboardX==2.6.2.2 +tensorflow-estimator==2.13.0 +tensorflow-hub==0.16.1 +tensorflow-io-gcs-filesystem==0.34.0 +tensorflow-text==2.13.0 +tensorflow==2.13.1 +tensorstore==0.1.45 +termcolor==2.4.0 +text-unidecode==1.3 +tf2onnx==1.16.1 +tf_keras==2.15.1 +threadpoolctl==3.3.0 +timeout-decorator==0.5.0 +timm==0.9.16 +tokenizers==0.19.1 +tomli==2.0.1 +toolz==0.12.1 +torch==2.3.0 +torchaudio==2.1.2 +torchmetrics==1.3.2 +torchvision==0.18.0 +tornado==6.4 +tqdm==4.66.2 +traitlets==5.14.2 +transformers==4.41.0.dev0 +triton==2.3.0 +types-python-dateutil==2.9.0.20240316 +typing_extensions==4.10.0 +tzdata==2024.1 +tzlocal==5.2 +unidic-lite==1.0.8 +unidic==1.1.0 +uritemplate==4.1.1 +urllib3==1.26.18 +wandb==0.16.6 +wasabi==0.10.1 +wcwidth==0.2.13 +wheel==0.43.0 +wrapt==1.16.0 +xformers==0.0.22.post7 +xmltodict==0.13.0 +xxhash==3.4.1 +yarl==1.9.4 +zipp==3.18.1 \ No newline at end of file diff --git a/src/transformers/models/idefics2/wandb/run-20240430_093846-prlaj2s9/files/wandb-metadata.json b/src/transformers/models/idefics2/wandb/run-20240430_093846-prlaj2s9/files/wandb-metadata.json new file mode 100644 index 00000000000000..b68095fcbac751 --- /dev/null +++ b/src/transformers/models/idefics2/wandb/run-20240430_093846-prlaj2s9/files/wandb-metadata.json @@ -0,0 +1,703 @@ +{ + "os": "Linux-5.4.0-166-generic-x86_64-with-glibc2.29", + "python": "3.8.10", + "heartbeatAt": "2024-04-30T07:38:46.874230", + "startedAt": "2024-04-30T07:38:46.128256", + "docker": null, + "cuda": null, + "args": [], + "state": "running", + "program": "", + "codePathLocal": null, + "git": { + "remote": "git@github.com:NielsRogge/transformers.git", + "commit": "b794532fef67ad2ece05cfb504983ef76cd24f08" + }, + "email": "niels.rogge1@gmail.com", + "root": "/home/niels/python_projects/transformers", + "host": "hf-dgx-01", + "username": "niels", + "executable": "/home/niels/python_projects/transformers/env/bin/python", + "cpu_count": 64, + "cpu_count_logical": 128, + "cpu_freq": { + "current": 2260.5603593749997, + "min": 1500.0, + "max": 2250.0 + }, + "cpu_freq_per_core": [ + { + "current": 3293.257, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1643.619, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1642.696, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1644.381, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3281.737, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1641.368, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1644.824, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1646.25, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3280.069, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.083, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3276.561, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1644.977, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1646.131, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3293.061, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3283.703, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1643.933, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3285.291, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1644.851, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3199.14, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.366, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1644.891, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3291.193, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1646.212, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1644.806, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1631.507, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1643.98, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1643.962, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3282.619, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1643.745, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1644.243, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3284.222, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.968, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.193, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3283.865, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2156.243, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1646.067, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1647.016, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3279.841, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3292.037, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2176.502, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1644.441, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.717, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1646.098, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3285.099, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1703.344, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.387, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1643.687, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3290.489, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3281.652, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3288.547, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.362, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2159.969, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3284.02, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1929.8, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.039, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1642.621, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2301.431, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1731.327, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1774.1, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2640.429, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3297.246, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1643.784, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1646.272, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1643.799, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3283.651, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.61, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1647.573, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.665, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3299.517, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.785, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1643.848, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1757.714, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3289.268, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1646.474, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2779.172, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1646.714, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1647.329, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3283.964, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3296.812, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1644.877, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3293.616, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.179, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2233.211, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.247, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.402, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3284.078, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.068, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.267, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2163.916, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.032, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.853, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3296.469, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1644.044, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1642.211, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3286.49, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1646.061, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1642.78, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3298.218, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1665.988, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1644.226, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1643.321, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3285.514, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3297.003, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1713.823, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.321, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1695.646, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1644.141, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3295.811, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1646.42, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1643.313, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1644.436, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2196.683, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3279.45, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2053.204, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1984.948, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1718.551, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3289.541, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3155.855, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2161.777, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2027.994, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1693.131, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2280.921, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2285.889, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2739.695, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3280.425, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2144.284, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1661.353, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1720.485, + "min": 1500.0, + "max": 2250.0 + } + ], + "disk": { + "/": { + "total": 1757.8785285949707, + "used": 1643.9480934143066 + } + }, + "gpu": "NVIDIA A100-SXM4-80GB", + "gpu_count": 5, + "gpu_devices": [ + { + "name": "NVIDIA A100-SXM4-80GB", + "memory_total": 85899345920 + }, + { + "name": "NVIDIA A100-SXM4-80GB", + "memory_total": 85899345920 + }, + { + "name": "NVIDIA A100-SXM4-80GB", + "memory_total": 85899345920 + }, + { + "name": "NVIDIA DGX Display", + "memory_total": 4294967296 + }, + { + "name": "NVIDIA A100-SXM4-80GB", + "memory_total": 85899345920 + } + ], + "memory": { + "total": 503.5396919250488 + } +} diff --git a/src/transformers/models/idefics2/wandb/run-20240430_093846-prlaj2s9/run-prlaj2s9.wandb b/src/transformers/models/idefics2/wandb/run-20240430_093846-prlaj2s9/run-prlaj2s9.wandb new file mode 100644 index 0000000000000000000000000000000000000000..6c359a2aa953cfdea0118c2ebea3c95fa6f4e7dd GIT binary patch literal 1956 zcmcIkO>Epm6wceETgTZb(H2Bm1RZS!glO%|j=kO=RSnrBKt!5C8d6jgZN~O^NU2cuX{YqCaqR9Q{S zST@;fxY#A|B$b$ByNh+Hp3_R2dJ$&D!Ll7b&EJJZT{gUGVajGsFzuDgY$)`p8(5xS zWqu&@CGi10n^!Xmv6!W0sY#f@aK^Co3|8|wg;`n6Fo|m6T$`kG-c{idejuIXA@Mwc=TJqKHJ%jR|2^XM0=Gn~P8{joR< zCjV9%Zp*ER^IdR!{s>PkR7T=G!imC2rZ#0 zT4l{R^LmZPe;7;X1?tmkAWB;wBIZa{&twjdcbd*KoTRd#H zT;KxT9ZLug4Rf#H0>*oWQ)t)@hl07y`{nmfZyrC&p?fN0_dtL9-uE1u+yjjee*7=+ vD#rJrfET|x-`y-A)>go8*G_!UEMUCm84Cy(IPVSNqs=SO_vKIPXZzp}8|M7y literal 0 HcmV?d00001 diff --git a/src/transformers/trainer_seq2seq.py b/src/transformers/trainer_seq2seq.py index 3dbba108e95c7d..20f6a6e4740762 100644 --- a/src/transformers/trainer_seq2seq.py +++ b/src/transformers/trainer_seq2seq.py @@ -309,9 +309,9 @@ def prediction_step( } print("Generation inputs:") - for k,v in generation_inputs.items(): + for k, v in generation_inputs.items(): if isinstance(v, torch.Tensor): - print(k,v.shape) + print(k, v.shape) generated_tokens = self.model.generate(**generation_inputs, **gen_kwargs) From 6df2813c5428c3987cee8ff4f34542b31ce0ac15 Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Tue, 30 Apr 2024 12:45:43 +0200 Subject: [PATCH 12/12] More improvements --- ...ataset_(CORD)_with_PyTorch_Lightning.ipynb | 22471 ++++++++++++++++ .../idefics2/fine_tune_idefics2_pl.ipynb | 250 +- .../models/idefics2/wandb/latest-run | 2 +- .../files/config.yaml | 20 + .../files/config.yaml | 62 + .../files/requirements.txt | 307 + .../files/wandb-metadata.json | 703 + .../files/wandb-summary.json | 1 + .../run-z6z7tlgg.wandb | Bin 0 -> 60520 bytes .../files/config.yaml | 62 + .../files/requirements.txt | 307 + .../files/wandb-metadata.json | 703 + .../files/wandb-summary.json | 1 + .../run-t3hdwi09.wandb | Bin 0 -> 115590 bytes 14 files changed, 24822 insertions(+), 67 deletions(-) create mode 100644 src/transformers/models/idefics2/Fine_tune_Donut_on_a_custom_dataset_(CORD)_with_PyTorch_Lightning.ipynb create mode 100644 src/transformers/models/idefics2/wandb/run-20240430_120740-z6z7tlgg/files/config.yaml create mode 100644 src/transformers/models/idefics2/wandb/run-20240430_120740-z6z7tlgg/files/requirements.txt create mode 100644 src/transformers/models/idefics2/wandb/run-20240430_120740-z6z7tlgg/files/wandb-metadata.json create mode 100644 src/transformers/models/idefics2/wandb/run-20240430_120740-z6z7tlgg/files/wandb-summary.json create mode 100644 src/transformers/models/idefics2/wandb/run-20240430_120740-z6z7tlgg/run-z6z7tlgg.wandb create mode 100644 src/transformers/models/idefics2/wandb/run-20240430_124040-t3hdwi09/files/config.yaml create mode 100644 src/transformers/models/idefics2/wandb/run-20240430_124040-t3hdwi09/files/requirements.txt create mode 100644 src/transformers/models/idefics2/wandb/run-20240430_124040-t3hdwi09/files/wandb-metadata.json create mode 100644 src/transformers/models/idefics2/wandb/run-20240430_124040-t3hdwi09/files/wandb-summary.json create mode 100644 src/transformers/models/idefics2/wandb/run-20240430_124040-t3hdwi09/run-t3hdwi09.wandb diff --git a/src/transformers/models/idefics2/Fine_tune_Donut_on_a_custom_dataset_(CORD)_with_PyTorch_Lightning.ipynb b/src/transformers/models/idefics2/Fine_tune_Donut_on_a_custom_dataset_(CORD)_with_PyTorch_Lightning.ipynb new file mode 100644 index 00000000000000..439ced746b70f1 --- /dev/null +++ b/src/transformers/models/idefics2/Fine_tune_Donut_on_a_custom_dataset_(CORD)_with_PyTorch_Lightning.ipynb @@ -0,0 +1,22471 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "view-in-github" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DNMqJ821yNVo" + }, + "source": [ + "## Set-up environment\n", + "\n", + "First, let's install the relevant libraries:\n", + "* 🤗 Transformers, for the model\n", + "* 🤗 Datasets, for loading + processing the data\n", + "* PyTorch Lightning, for training the model \n", + "* Weights and Biases, for logging metrics during training\n", + "* Sentencepiece, used for tokenization.\n", + "\n", + "We'll use PyTorch Lightning for training here, but note that this is optional, you can of course also just train in native PyTorch or use 🤗 Accelerate, or the 🤗 Trainer." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kWYic8VNyDNU" + }, + "source": [ + "## Load dataset\n", + "\n", + "Next, let's load the dataset from the [hub](https://huggingface.co/datasets/naver-clova-ix/cord-v2). The dataset consists of (image, JSON) pairs. Note that it doesn't have to be JSON, it could also be JSON lines, plain text, etc. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 524, + "referenced_widgets": [ + "9b0fe2691d7041fe8841d87beed2bf7b", + "f0d43f6407674c12b85a6292284b4ba4", + "99bcc899c1d54ea6b6a95fdda3d4e54f", + "9c138abac1894ebebb334b8044ee6ca9", + "26bb592b09c94ba081cd1f913c318f73", + "6412ab45ef6c45fd88462f76731675f9", + "f09e42ab2e9f4560aa68de42fbc4d4e2", + "cbf0235e60724033a400c2cb38b3971a", + "90d6691af3fd49899b196ff7337d71b3", + "9aaff87ef6bf4f71b67dc13553aa73b2", + "4ebee50caff240aea9377fa4a2d0d7cf", + "5e592f3705ce411dbfccc726d2348933", + "749592c5ae004de4a5f31bab3c2d31f3", + "548572561a0045b485ef9dde63326446", + "674e652801844a33ba01395ebdb40263", + "d06409a05ec640f9a6a87a8925dd23c1", + "fe66b6883afa471eb687430534bd0820", + "2b8a201c62c241a89625630f550f6c02", + "e30df53a96fc474da072e8692a0d1d58", + "8c3fc7e7b9354d0fb59602100bc5ab1f", + "835bd092bfb64a47b104d20092d61a14", + "91d8224cea5940bb9858f43b42869729", + "0459618abd954a9a95c3eb137954523c", + "5947ad0e06a54e38b8d09d160bffb0d2", + "5cc9c7f2db1846b6905452d7a9568919", + "dd0b6870c8b6435691009ca66fa71b09", + "c663b10ffce1432c89a3948e324571b0", + "ebeb73a097574a8597d3592329674e44", + "81255865450c47ea8a9de6b7996514a3", + "507d4fec2b8847d79a884853b4d81dad", + "ef6284ce29684d8b9b16875076cd579f", + "eee6b3f385774227b35fc4d83eb0171f", + "f20da578968f43c3ac44efa032dbd6e2", + "ad251dd399bf4671978561773be7da7a", + "1d3f96ad71b74c31b18b50d0dafefb6e", + "6ff5e86e93914a9ab2ac20d2a32a206e", + "986ff3190ef14c5c907d83383571f267", + "4c06e9c680b6471c8305042a7e3757e3", + "fe170e9c276f49b6927ffcbf3809de3e", + "07acf858ebad4351bfd3cfdb339dddcb", + "330b85404fe84761a72a8cbfb696eae1", + "8030efae633f4b018e7863f46267e575", + "f59a9d0b14c348d6b9c221744c256afc", + "4fefc71218894bd4a6b2e95ff3a882e5", + "565a3479b2f140c4bdba43ba99d71c2c", + "fa5080966e724cf2901654c57dbe8abd", + "6394dfa7856b4bf49a9325f22dfe1cc1", + "baa432ed76ae437e8946ebe41301453c", + "6ce260c95cfa4736a22d77f5faba3888", + "cf862b504d86425f886ed6d19b55b05a", + "12636a29bb09482693911ac3c6ab429a", + "95594c0d3f7d4aafb3f61b21b823b32f", + "effb0f0cff3c4cc6a8321d0c411a86f6", + "1df30bb3175541a7b7a6541b38d51b89", + "e563fc2ca1ef4b668604bea1c50674e2", + "90761ecf88ad4a1c8dc190928b48fdba", + "f51518a3bf3b4a7582dbe26fca21ca6e", + "09c44c23e97c4293bc1d30f234634c42", + "561abfbe2ed9405d8bc9c67f3a4c4c8a", + "c44ea4249c324b7cbbb69798e034cb85", + "54e45826ac5f44d3b315e59391fbc347", + "f0b290d10e694b4c86355bdba583261a", + "00d41781e51e4adb823c44a68727693a", + "d56998a0695e434bb7fe3857f5075083", + "259d56ea4f394fe591b26941164dcae9", + "31d2bbdfa41b451eb71eba642b3db4e4", + "848f3079a55c475f91f8efd0f02d72e3", + "1b07db1c1be94ffe98ca2e23ca97e187", + "9f4c3f2e2b17443b9f104bf7e1bc80ea", + "67446ba01e9c4922b041e4ef36ff8774", + "5253bf28cfe948fd8520b5aefd5cdeff", + "ed76219299d54f908eff9c9678e1e00f", + "76839f93896d43dabcb10f14b031c037", + "d75985c018114da8943c9bad0c61869b", + "fc88c486f35747cd8a3e0eeaf67ebfb6", + "33a65dc99580418cb494e29ae5a0fb9a", + "c419e4cb99994a5bb0cbccca9c0bc5c6", + "d643c6ffb4964990899a47cc02a63b9d", + "04fa08f418e74bfe8d274620cd07e448", + "ae8b189059e94a7ab73fb256432c18df", + "5915cae6f7ba4d5caa52ff5c8f854b38", + "3db9e9823c2d4f418cd6707058ff07ba", + "d1837ece0226413489f3df0dfd7f3279", + "b0095f1cb2364fc991f05f312656947f", + "0098775e311344f681335445edadd72f", + "632cbce9ea2f4cc5a87028463a877a5c", + "487685e439f647efbc2350f87ae5d275", + "ca6fcf9c01fe4f2db382b6b0f8cb169d", + "15f7ce33a6304293939d23084881a33e", + "414f4fac280c49db98080b31e3facc69", + "3752aef8ee5147e2b603a691f233cc24", + "45ce4745da5840a090620d59ce4eb1e5", + "c3f54d242da14b689f6e213867b81cec", + "ba402e291fd94f8ab8f1a74c5c1ac551", + "fe92ba127a624524bfe051a6040b4c45", + "618716c2a7dc4557873d08a9ea2b0b22", + "663d883b5cbf4a6998db3319c55ee3d8", + "337672b1b8894951810dc1c645f66dbd", + "2b1ecdea6e49453588fdca19fc19eaaf", + "f2d79a145b3c45389a473ae2fb94e0fc", + "e347c9815ff64f93884e65d882c8dc64", + "df11ed2a5b2f4264b82cfd95724e7a32", + "35560ff2bef140aea29689440aa8f741", + "36c149d0545d43d7ac1cb34f4e3c33bd", + "293e17ab3b4a462ba7d4adc2de35aa05", + "36c215478b2f415e904a24de4b91d9d7", + "dcaa8093d0194d76871523cd07347bd6", + "8544e6360d83437cb03e028051e96be4", + "f794d85eed554a6ea4dabf8420da0160", + "f6679d2887ae4c759e6fe58b01a9d546", + "24d1c38ed9e241c2b13abd30ead9a8de", + "eb5f6ef011b44f7d8a62a35ee66899db", + "0bb2e5d6e3544a44940c79b7a5543407", + "5517cbf80d8b448d9a511070dfb4dc1d", + "107117ee9e3947058cf7586eba69cf6e", + "61223d9e086a42ab94927964a4455894", + "aeccbc9b9c3e4038874656c34f12d9f4", + "8eda879a6d224fb091f538c2806d7890", + "c34474a8f453410f8f3dc39c835e3a2d", + "1a457a54c55f4aab95a9c3cfaa2a23b5", + "fe52a9c056f843068d5588e488f490db", + "fee13e5677e84373a18a0f68feb13ab5", + "7982ba1cd95e4347ad3fca29f38d0638", + "f75aeb7b8cc74ef488ac24f02b208535", + "008e77e9b3cb4cc4b37e875cab3f3d6c", + "e0da7e4a45a846c0ac14269e5bbb77f8", + "3c7e904af22f42dca56b6421d35412ff", + "efb2989e68d844bb875fbdbd5c79d780", + "d5667dae1b4d479db3745879a34b9ab5", + "0fb032afb0904c358aaa984c8556d0c8", + "7d77a9f92e984ac7b9350addc57cb577", + "2d476a17141343508bcac54393555e9b", + "012b26d0aed048628e59dbb0c02f76e0", + "b132e3a6f07e452382eadbedff054b94", + "3920ef1a0817495696b874cc61516d7b", + "24d17f7c48fb4d41b02ec4731d629758", + "d91e67e21bfa49888bf6316c154bcf77", + "bd0e4a5ac7b5440a8b403a36fdaae181", + "fd3d2e91c7a64c2782727d2c29020fbc", + "be37899db94d4c15b8e194c03b057a45", + "4ec3f7acd47c476fbf7e030426c51a7a", + "3335761da6f84b2bb970a32f27f5abc4", + "69f55ea05abe4ca9b21135169edc7a71", + "d494ec40fa104f16ace482fa5738e771", + "ba77336ed8a64ce1b7b3a561844b53ef", + "02700c32e4d64a66818ac92209384b14", + "9e5bbac1661d4385b06a3caa40cfce63", + "bf2d692c9fd24236a6a760a293cb5a13", + "ee9f3bdcf3814bbbac4e053b4c169704", + "cebe7c4bb7e447949431cf58deedef30", + "2d4f64a23cc846a48f0906c74d1502b9", + "c6e2d6abd785494ca143b5978811c326", + "a867c537beb34cdf905e80246d31dd97", + "ab41966250404933aea9736461583520", + "f5449d2c5013488ab7396dc95604be0b", + "1105d8a1da0146e0963254b901f6fd80", + "c61132d2ccbb404ea969d7701fbc3ed7", + "45684f2d86954453b3f7059f847028c4", + "053e5fb5ff954b1bb8099c07ad77c35d", + "439674cf49124161b3783a681b407ed2", + "10153cc99ab24f089bc2c964eb5f4806", + "1355cef1e865410182443c3b8ba73fd2", + "ef4bc97031ee4e798ec0ccb3c1b76e2c", + "cc2651a0102d49c7b6a9ac6e82db8c9a", + "3a78f6a7e4814c23b19458c6f2857754" + ] + }, + "id": "5hU27XC2yEot", + "outputId": "c544eb62-6501-4733-a939-3df24824cdf7" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/niels/python_projects/transformers/env/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "from datasets import load_dataset\n", + "\n", + "dataset = load_dataset(\"naver-clova-ix/cord-v2\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1DYk7tDBy-ys", + "outputId": "154174a2-1bf1-4dcc-efc8-7f27f9e72874" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "DatasetDict({\n", + " train: Dataset({\n", + " features: ['image', 'ground_truth'],\n", + " num_rows: 800\n", + " })\n", + " validation: Dataset({\n", + " features: ['image', 'ground_truth'],\n", + " num_rows: 100\n", + " })\n", + " test: Dataset({\n", + " features: ['image', 'ground_truth'],\n", + " num_rows: 100\n", + " })\n", + "})" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-kFsrrh3jObj" + }, + "source": [ + "Let's take a look at the first training example:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 405 + }, + "id": "nHVKQMQvy_uU", + "outputId": "8364f84e-e909-4015-cc28-6445eb97dfbb" + }, + "outputs": [ + { + "data": { + "image/jpeg": "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", + "image/png": "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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "example = dataset['train'][0]\n", + "image = example['image']\n", + "# let's make the image a bit smaller when visualizing\n", + "width, height = image.size\n", + "display(image.resize((int(width*0.3), int(height*0.3))))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "826SLZx-ziCs", + "outputId": "dc7af551-fcb0-48bb-857a-2ebec1bface0" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\"gt_parse\": {\"menu\": [{\"nm\": \"Nasi Campur Bali\", \"cnt\": \"1 x\", \"price\": \"75,000\"}, {\"nm\": \"Bbk Bengil Nasi\", \"cnt\": \"1 x\", \"price\": \"125,000\"}, {\"nm\": \"MilkShake Starwb\", \"cnt\": \"1 x\", \"price\": \"37,000\"}, {\"nm\": \"Ice Lemon Tea\", \"cnt\": \"1 x\", \"price\": \"24,000\"}, {\"nm\": \"Nasi Ayam Dewata\", \"cnt\": \"1 x\", \"price\": \"70,000\"}, {\"nm\": \"Free Ice Tea\", \"cnt\": \"3 x\", \"price\": \"0\"}, {\"nm\": \"Organic Green Sa\", \"cnt\": \"1 x\", \"price\": \"65,000\"}, {\"nm\": \"Ice Tea\", \"cnt\": \"1 x\", \"price\": \"18,000\"}, {\"nm\": \"Ice Orange\", \"cnt\": \"1 x\", \"price\": \"29,000\"}, {\"nm\": \"Ayam Suir Bali\", \"cnt\": \"1 x\", \"price\": \"85,000\"}, {\"nm\": \"Tahu Goreng\", \"cnt\": \"2 x\", \"price\": \"36,000\"}, {\"nm\": \"Tempe Goreng\", \"cnt\": \"2 x\", \"price\": \"36,000\"}, {\"nm\": \"Tahu Telor Asin\", \"cnt\": \"1 x\", \"price\": \"40,000.\"}, {\"nm\": \"Nasi Goreng Samb\", \"cnt\": \"1 x\", \"price\": \"70,000\"}, {\"nm\": \"Bbk Panggang Sam\", \"cnt\": \"3 x\", \"price\": \"366,000\"}, {\"nm\": \"Ayam Sambal Hija\", \"cnt\": \"1 x\", \"price\": \"92,000\"}, {\"nm\": \"Hot Tea\", \"cnt\": \"2 x\", \"price\": \"44,000\"}, {\"nm\": \"Ice Kopi\", \"cnt\": \"1 x\", \"price\": \"32,000\"}, {\"nm\": \"Tahu Telor Asin\", \"cnt\": \"1 x\", \"price\": \"40,000\"}, {\"nm\": \"Free Ice Tea\", \"cnt\": \"1 x\", \"price\": \"0\"}, {\"nm\": \"Bebek Street\", \"cnt\": \"1 x\", \"price\": \"44,000\"}, {\"nm\": \"Ice Tea Tawar\", \"cnt\": \"1 x\", \"price\": \"18,000\"}], \"sub_total\": {\"subtotal_price\": \"1,346,000\", \"service_price\": \"100,950\", \"tax_price\": \"144,695\", \"etc\": \"-45\"}, \"total\": {\"total_price\": \"1,591,600\"}}, \"meta\": {\"version\": \"2.0.0\", \"split\": \"train\", \"image_id\": 0, \"image_size\": {\"width\": 864, \"height\": 1296}}, \"valid_line\": [{\"words\": [{\"quad\": {\"x2\": 244, \"y3\": 390, \"x3\": 244, \"y4\": 390, \"x1\": 232, \"y1\": 372, \"x4\": 232, \"y2\": 372}, \"is_key\": 0, \"row_id\": 2179893, \"text\": \"1\"}, {\"quad\": {\"x2\": 270, \"y3\": 390, \"x3\": 270, \"y4\": 390, \"x1\": 256, \"y1\": 374, \"x4\": 256, \"y2\": 374}, \"is_key\": 0, \"row_id\": 2179893, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 3, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 354, \"y3\": 390, \"x3\": 354, \"y4\": 390, \"x1\": 302, \"y1\": 368, \"x4\": 302, \"y2\": 368}, \"is_key\": 0, \"row_id\": 2179893, \"text\": \"Nasi\"}, {\"quad\": {\"x2\": 440, \"y3\": 391, \"x3\": 439, \"y4\": 388, \"x1\": 364, \"y1\": 365, \"x4\": 363, \"y2\": 368}, \"is_key\": 0, \"row_id\": 2179893, \"text\": \"Campur\"}, {\"quad\": {\"x2\": 497, \"y3\": 385, \"x3\": 499, \"y4\": 388, \"x1\": 446, \"y1\": 365, \"x4\": 448, \"y2\": 362}, \"is_key\": 0, \"row_id\": 2179893, \"text\": \"Bali\"}], \"category\": \"menu.nm\", \"group_id\": 3, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 618, \"y3\": 388, \"x3\": 618, \"y4\": 388, \"x1\": 542, \"y1\": 362, \"x4\": 542, \"y2\": 362}, \"is_key\": 0, \"row_id\": 2179893, \"text\": \"75,000\"}], \"category\": \"menu.price\", \"group_id\": 3, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 246, \"y3\": 418, \"x3\": 246, \"y4\": 418, \"x1\": 234, \"y1\": 400, \"x4\": 234, \"y2\": 400}, \"is_key\": 0, \"row_id\": 2179894, \"text\": \"1\"}, {\"quad\": {\"x2\": 270, \"y3\": 418, \"x3\": 270, \"y4\": 418, \"x1\": 258, \"y1\": 402, \"x4\": 258, \"y2\": 402}, \"is_key\": 0, \"row_id\": 2179894, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 4, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 344, \"y3\": 418, \"x3\": 344, \"y4\": 418, \"x1\": 304, \"y1\": 394, \"x4\": 304, \"y2\": 394}, \"is_key\": 0, \"row_id\": 2179894, \"text\": \"Bbk\"}, {\"quad\": {\"x2\": 430, \"y3\": 418, \"x3\": 430, \"y4\": 418, \"x1\": 352, \"y1\": 394, \"x4\": 352, \"y2\": 394}, \"is_key\": 0, \"row_id\": 2179894, \"text\": \"Bengil\"}, {\"quad\": {\"x2\": 488, \"y3\": 414, \"x3\": 488, \"y4\": 414, \"x1\": 436, \"y1\": 392, \"x4\": 436, \"y2\": 392}, \"is_key\": 0, \"row_id\": 2179894, \"text\": \"Nasi\"}], \"category\": \"menu.nm\", \"group_id\": 4, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 618, \"y3\": 414, \"x3\": 618, \"y4\": 414, \"x1\": 534, \"y1\": 388, \"x4\": 534, \"y2\": 388}, \"is_key\": 0, \"row_id\": 2179894, \"text\": \"125,000\"}], \"category\": \"menu.price\", \"group_id\": 4, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 246, \"y3\": 444, \"x3\": 246, \"y4\": 444, \"x1\": 234, \"y1\": 426, \"x4\": 234, \"y2\": 426}, \"is_key\": 0, \"row_id\": 2179895, \"text\": \"1\"}, {\"quad\": {\"x2\": 272, \"y3\": 444, \"x3\": 272, \"y4\": 444, \"x1\": 258, \"y1\": 428, \"x4\": 258, \"y2\": 428}, \"is_key\": 0, \"row_id\": 2179895, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 5, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 417, \"y3\": 444, \"x3\": 417, \"y4\": 444, \"x1\": 303, \"y1\": 420, \"x4\": 303, \"y2\": 420}, \"is_key\": 0, \"row_id\": 2179895, \"text\": \"MilkShake\"}, {\"quad\": {\"x2\": 500, \"y3\": 440, \"x3\": 500, \"y4\": 440, \"x1\": 424, \"y1\": 418, \"x4\": 424, \"y2\": 418}, \"is_key\": 0, \"row_id\": 2179895, \"text\": \"Starwb\"}], \"category\": \"menu.nm\", \"group_id\": 5, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 620, \"y3\": 440, \"x3\": 620, \"y4\": 440, \"x1\": 544, \"y1\": 414, \"x4\": 544, \"y2\": 414}, \"is_key\": 0, \"row_id\": 2179895, \"text\": \"37,000\"}], \"category\": \"menu.price\", \"group_id\": 5, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 248, \"y3\": 470, \"x3\": 248, \"y4\": 470, \"x1\": 236, \"y1\": 452, \"x4\": 236, \"y2\": 452}, \"is_key\": 0, \"row_id\": 2179896, \"text\": \"1\"}, {\"quad\": {\"x2\": 274, \"y3\": 470, \"x3\": 274, \"y4\": 470, \"x1\": 260, \"y1\": 456, \"x4\": 260, \"y2\": 456}, \"is_key\": 0, \"row_id\": 2179896, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 6, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 348, \"y3\": 470, \"x3\": 348, \"y4\": 470, \"x1\": 306, \"y1\": 448, \"x4\": 306, \"y2\": 448}, \"is_key\": 0, \"row_id\": 2179896, \"text\": \"Ice\"}, {\"quad\": {\"x2\": 418, \"y3\": 468, \"x3\": 418, \"y4\": 468, \"x1\": 354, \"y1\": 446, \"x4\": 354, \"y2\": 446}, \"is_key\": 0, \"row_id\": 2179896, \"text\": \"Lemon\"}, {\"quad\": {\"x2\": 466, \"y3\": 466, \"x3\": 466, \"y4\": 466, \"x1\": 426, \"y1\": 446, \"x4\": 426, \"y2\": 446}, \"is_key\": 0, \"row_id\": 2179896, \"text\": \"Tea\"}], \"category\": \"menu.nm\", \"group_id\": 6, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 618, \"y3\": 466, \"x3\": 618, \"y4\": 466, \"x1\": 544, \"y1\": 440, \"x4\": 544, \"y2\": 440}, \"is_key\": 0, \"row_id\": 2179896, \"text\": \"24,000\"}], \"category\": \"menu.price\", \"group_id\": 6, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 250, \"y3\": 496, \"x3\": 250, \"y4\": 496, \"x1\": 238, \"y1\": 480, \"x4\": 238, \"y2\": 480}, \"is_key\": 0, \"row_id\": 2179897, \"text\": \"1\"}, {\"quad\": {\"x2\": 274, \"y3\": 496, \"x3\": 274, \"y4\": 496, \"x1\": 258, \"y1\": 480, \"x4\": 258, \"y2\": 480}, \"is_key\": 0, \"row_id\": 2179897, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 7, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 358, \"y3\": 496, \"x3\": 358, \"y4\": 496, \"x1\": 306, \"y1\": 474, \"x4\": 306, \"y2\": 474}, \"is_key\": 0, \"row_id\": 2179897, \"text\": \"Nasi\"}, {\"quad\": {\"x2\": 420, \"y3\": 496, \"x3\": 420, \"y4\": 496, \"x1\": 366, \"y1\": 474, \"x4\": 366, \"y2\": 474}, \"is_key\": 0, \"row_id\": 2179897, \"text\": \"Ayam\"}, {\"quad\": {\"x2\": 499, \"y3\": 491, \"x3\": 500, \"y4\": 494, \"x1\": 425, \"y1\": 471, \"x4\": 426, \"y2\": 469}, \"is_key\": 0, \"row_id\": 2179897, \"text\": \"Dewata\"}], \"category\": \"menu.nm\", \"group_id\": 7, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 618, \"y3\": 492, \"x3\": 618, \"y4\": 492, \"x1\": 544, \"y1\": 466, \"x4\": 544, \"y2\": 466}, \"is_key\": 0, \"row_id\": 2179897, \"text\": \"70,000\"}], \"category\": \"menu.price\", \"group_id\": 7, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 252, \"y3\": 522, \"x3\": 252, \"y4\": 522, \"x1\": 240, \"y1\": 504, \"x4\": 240, \"y2\": 504}, \"is_key\": 0, \"row_id\": 2179898, \"text\": \"3\"}, {\"quad\": {\"x2\": 276, \"y3\": 522, \"x3\": 276, \"y4\": 522, \"x1\": 260, \"y1\": 506, \"x4\": 260, \"y2\": 506}, \"is_key\": 0, \"row_id\": 2179898, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 8, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 360, \"y3\": 522, \"x3\": 360, \"y4\": 522, \"x1\": 306, \"y1\": 500, \"x4\": 306, \"y2\": 500}, \"is_key\": 0, \"row_id\": 2179898, \"text\": \"Free\"}, {\"quad\": {\"x2\": 408, \"y3\": 522, \"x3\": 408, \"y4\": 522, \"x1\": 368, \"y1\": 500, \"x4\": 368, \"y2\": 500}, \"is_key\": 0, \"row_id\": 2179898, \"text\": \"Ice\"}, {\"quad\": {\"x2\": 454, \"y3\": 520, \"x3\": 454, \"y4\": 520, \"x1\": 416, \"y1\": 498, \"x4\": 416, \"y2\": 498}, \"is_key\": 0, \"row_id\": 2179898, \"text\": \"Tea\"}], \"category\": \"menu.nm\", \"group_id\": 8, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 618, \"y3\": 514, \"x3\": 618, \"y4\": 514, \"x1\": 604, \"y1\": 494, \"x4\": 604, \"y2\": 494}, \"is_key\": 0, \"row_id\": 2179898, \"text\": \"0\"}], \"category\": \"menu.price\", \"group_id\": 8, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 254, \"y3\": 546, \"x3\": 254, \"y4\": 546, \"x1\": 242, \"y1\": 530, \"x4\": 242, \"y2\": 530}, \"is_key\": 0, \"row_id\": 2179899, \"text\": \"1\"}, {\"quad\": {\"x2\": 278, \"y3\": 548, \"x3\": 278, \"y4\": 548, \"x1\": 262, \"y1\": 532, \"x4\": 262, \"y2\": 532}, \"is_key\": 0, \"row_id\": 2179899, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 9, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 396, \"y3\": 548, \"x3\": 396, \"y4\": 548, \"x1\": 308, \"y1\": 526, \"x4\": 308, \"y2\": 526}, \"is_key\": 0, \"row_id\": 2179899, \"text\": \"Organic\"}, {\"quad\": {\"x2\": 466, \"y3\": 544, \"x3\": 466, \"y4\": 544, \"x1\": 404, \"y1\": 524, \"x4\": 404, \"y2\": 524}, \"is_key\": 0, \"row_id\": 2179899, \"text\": \"Green\"}, {\"quad\": {\"x2\": 502, \"y3\": 544, \"x3\": 502, \"y4\": 544, \"x1\": 474, \"y1\": 522, \"x4\": 474, \"y2\": 522}, \"is_key\": 0, \"row_id\": 2179899, \"text\": \"Sa\"}], \"category\": \"menu.nm\", \"group_id\": 9, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 620, \"y3\": 542, \"x3\": 620, \"y4\": 542, \"x1\": 544, \"y1\": 518, \"x4\": 544, \"y2\": 518}, \"is_key\": 0, \"row_id\": 2179899, \"text\": \"65,000\"}], \"category\": \"menu.price\", \"group_id\": 9, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 254, \"y3\": 572, \"x3\": 254, \"y4\": 572, \"x1\": 242, \"y1\": 556, \"x4\": 242, \"y2\": 556}, \"is_key\": 0, \"row_id\": 2179900, \"text\": \"1\"}, {\"quad\": {\"x2\": 280, \"y3\": 572, \"x3\": 280, \"y4\": 572, \"x1\": 266, \"y1\": 558, \"x4\": 266, \"y2\": 558}, \"is_key\": 0, \"row_id\": 2179900, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 10, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 352, \"y3\": 572, \"x3\": 352, \"y4\": 572, \"x1\": 312, \"y1\": 552, \"x4\": 312, \"y2\": 552}, \"is_key\": 0, \"row_id\": 2179900, \"text\": \"Ice\"}, {\"quad\": {\"x2\": 398, \"y3\": 572, \"x3\": 398, \"y4\": 572, \"x1\": 358, \"y1\": 550, \"x4\": 358, \"y2\": 550}, \"is_key\": 0, \"row_id\": 2179900, \"text\": \"Tea\"}], \"category\": \"menu.nm\", \"group_id\": 10, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 620, \"y3\": 568, \"x3\": 620, \"y4\": 568, \"x1\": 546, \"y1\": 544, \"x4\": 546, \"y2\": 544}, \"is_key\": 0, \"row_id\": 2179900, \"text\": \"18,000\"}], \"category\": \"menu.price\", \"group_id\": 10, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 256, \"y3\": 598, \"x3\": 256, \"y4\": 598, \"x1\": 244, \"y1\": 582, \"x4\": 244, \"y2\": 582}, \"is_key\": 0, \"row_id\": 2179901, \"text\": \"1\"}, {\"quad\": {\"x2\": 280, \"y3\": 598, \"x3\": 280, \"y4\": 598, \"x1\": 264, \"y1\": 582, \"x4\": 264, \"y2\": 582}, \"is_key\": 0, \"row_id\": 2179901, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 11, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 352, \"y3\": 596, \"x3\": 352, \"y4\": 596, \"x1\": 312, \"y1\": 576, \"x4\": 312, \"y2\": 576}, \"is_key\": 0, \"row_id\": 2179901, \"text\": \"Ice\"}, {\"quad\": {\"x2\": 434, \"y3\": 596, \"x3\": 434, \"y4\": 596, \"x1\": 358, \"y1\": 576, \"x4\": 358, \"y2\": 576}, \"is_key\": 0, \"row_id\": 2179901, \"text\": \"Orange\"}], \"category\": \"menu.nm\", \"group_id\": 11, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 620, \"y3\": 594, \"x3\": 620, \"y4\": 594, \"x1\": 544, \"y1\": 570, \"x4\": 544, \"y2\": 570}, \"is_key\": 0, \"row_id\": 2179901, \"text\": \"29,000\"}], \"category\": \"menu.price\", \"group_id\": 11, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 258, \"y3\": 622, \"x3\": 258, \"y4\": 622, \"x1\": 246, \"y1\": 606, \"x4\": 246, \"y2\": 606}, \"is_key\": 0, \"row_id\": 2179902, \"text\": \"1\"}, {\"quad\": {\"x2\": 282, \"y3\": 622, \"x3\": 282, \"y4\": 622, \"x1\": 268, \"y1\": 608, \"x4\": 268, \"y2\": 608}, \"is_key\": 0, \"row_id\": 2179902, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 12, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 364, \"y3\": 624, \"x3\": 364, \"y4\": 624, \"x1\": 312, \"y1\": 602, \"x4\": 312, \"y2\": 602}, \"is_key\": 0, \"row_id\": 2179902, \"text\": \"Ayam\"}, {\"quad\": {\"x2\": 422, \"y3\": 620, \"x3\": 422, \"y4\": 620, \"x1\": 370, \"y1\": 600, \"x4\": 370, \"y2\": 600}, \"is_key\": 0, \"row_id\": 2179902, \"text\": \"Suir\"}, {\"quad\": {\"x2\": 478, \"y3\": 620, \"x3\": 478, \"y4\": 620, \"x1\": 428, \"y1\": 598, \"x4\": 428, \"y2\": 598}, \"is_key\": 0, \"row_id\": 2179902, \"text\": \"Bali\"}], \"category\": \"menu.nm\", \"group_id\": 12, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 618, \"y3\": 620, \"x3\": 618, \"y4\": 620, \"x1\": 544, \"y1\": 596, \"x4\": 544, \"y2\": 596}, \"is_key\": 0, \"row_id\": 2179902, \"text\": \"85,000\"}], \"category\": \"menu.price\", \"group_id\": 12, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 260, \"y3\": 650, \"x3\": 260, \"y4\": 650, \"x1\": 242, \"y1\": 628, \"x4\": 242, \"y2\": 628}, \"is_key\": 0, \"row_id\": 2179903, \"text\": \"2\"}, {\"quad\": {\"x2\": 282, \"y3\": 648, \"x3\": 282, \"y4\": 648, \"x1\": 266, \"y1\": 632, \"x4\": 266, \"y2\": 632}, \"is_key\": 0, \"row_id\": 2179903, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 13, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 364, \"y3\": 648, \"x3\": 364, \"y4\": 648, \"x1\": 312, \"y1\": 626, \"x4\": 312, \"y2\": 626}, \"is_key\": 0, \"row_id\": 2179903, \"text\": \"Tahu\"}, {\"quad\": {\"x2\": 444, \"y3\": 648, \"x3\": 443, \"y4\": 645, \"x1\": 370, \"y1\": 625, \"x4\": 369, \"y2\": 628}, \"is_key\": 0, \"row_id\": 2179903, \"text\": \"Goreng\"}], \"category\": \"menu.nm\", \"group_id\": 13, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 619, \"y3\": 644, \"x3\": 620, \"y4\": 647, \"x1\": 543, \"y1\": 622, \"x4\": 544, \"y2\": 619}, \"is_key\": 0, \"row_id\": 2179903, \"text\": \"36,000\"}], \"category\": \"menu.price\", \"group_id\": 13, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 260, \"y3\": 674, \"x3\": 260, \"y4\": 674, \"x1\": 242, \"y1\": 654, \"x4\": 242, \"y2\": 654}, \"is_key\": 0, \"row_id\": 2179904, \"text\": \"2\"}, {\"quad\": {\"x2\": 284, \"y3\": 674, \"x3\": 284, \"y4\": 674, \"x1\": 268, \"y1\": 660, \"x4\": 268, \"y2\": 660}, \"is_key\": 0, \"row_id\": 2179904, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 14, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 376, \"y3\": 674, \"x3\": 376, \"y4\": 674, \"x1\": 314, \"y1\": 652, \"x4\": 314, \"y2\": 652}, \"is_key\": 0, \"row_id\": 2179904, \"text\": \"Tempe\"}, {\"quad\": {\"x2\": 458, \"y3\": 672, \"x3\": 458, \"y4\": 672, \"x1\": 384, \"y1\": 650, \"x4\": 384, \"y2\": 650}, \"is_key\": 0, \"row_id\": 2179904, \"text\": \"Goreng\"}], \"category\": \"menu.nm\", \"group_id\": 14, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 620, \"y3\": 668, \"x3\": 620, \"y4\": 668, \"x1\": 546, \"y1\": 644, \"x4\": 546, \"y2\": 644}, \"is_key\": 0, \"row_id\": 2179904, \"text\": \"36,000\"}], \"category\": \"menu.price\", \"group_id\": 14, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 258, \"y3\": 700, \"x3\": 258, \"y4\": 700, \"x1\": 244, \"y1\": 682, \"x4\": 244, \"y2\": 682}, \"is_key\": 0, \"row_id\": 2179905, \"text\": \"1\"}, {\"quad\": {\"x2\": 282, \"y3\": 700, \"x3\": 282, \"y4\": 700, \"x1\": 266, \"y1\": 686, \"x4\": 266, \"y2\": 686}, \"is_key\": 0, \"row_id\": 2179905, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 15, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 364, \"y3\": 698, \"x3\": 364, \"y4\": 698, \"x1\": 312, \"y1\": 676, \"x4\": 312, \"y2\": 676}, \"is_key\": 0, \"row_id\": 2179905, \"text\": \"Tahu\"}, {\"quad\": {\"x2\": 434, \"y3\": 696, \"x3\": 434, \"y4\": 696, \"x1\": 370, \"y1\": 674, \"x4\": 370, \"y2\": 674}, \"is_key\": 0, \"row_id\": 2179905, \"text\": \"Telor\"}, {\"quad\": {\"x2\": 494, \"y3\": 694, \"x3\": 494, \"y4\": 694, \"x1\": 440, \"y1\": 672, \"x4\": 440, \"y2\": 672}, \"is_key\": 0, \"row_id\": 2179905, \"text\": \"Asin\"}], \"category\": \"menu.nm\", \"group_id\": 15, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 630, \"y3\": 690, \"x3\": 630, \"y4\": 690, \"x1\": 548, \"y1\": 668, \"x4\": 548, \"y2\": 668}, \"is_key\": 0, \"row_id\": 2179905, \"text\": \"40,000.\"}], \"category\": \"menu.price\", \"group_id\": 15, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 256, \"y3\": 726, \"x3\": 256, \"y4\": 726, \"x1\": 244, \"y1\": 708, \"x4\": 244, \"y2\": 708}, \"is_key\": 0, \"row_id\": 2179906, \"text\": \"1\"}, {\"quad\": {\"x2\": 282, \"y3\": 726, \"x3\": 282, \"y4\": 726, \"x1\": 268, \"y1\": 710, \"x4\": 268, \"y2\": 710}, \"is_key\": 0, \"row_id\": 2179906, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 16, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 364, \"y3\": 724, \"x3\": 364, \"y4\": 724, \"x1\": 312, \"y1\": 702, \"x4\": 312, \"y2\": 702}, \"is_key\": 0, \"row_id\": 2179906, \"text\": \"Nasi\"}, {\"quad\": {\"x2\": 446, \"y3\": 722, \"x3\": 446, \"y4\": 722, \"x1\": 372, \"y1\": 700, \"x4\": 372, \"y2\": 700}, \"is_key\": 0, \"row_id\": 2179906, \"text\": \"Goreng\"}, {\"quad\": {\"x2\": 505, \"y3\": 715, \"x3\": 506, \"y4\": 718, \"x1\": 453, \"y1\": 696, \"x4\": 454, \"y2\": 693}, \"is_key\": 0, \"row_id\": 2179906, \"text\": \"Samb\"}], \"category\": \"menu.nm\", \"group_id\": 16, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 626, \"y3\": 716, \"x3\": 626, \"y4\": 716, \"x1\": 550, \"y1\": 692, \"x4\": 550, \"y2\": 692}, \"is_key\": 0, \"row_id\": 2179906, \"text\": \"70,000\"}], \"category\": \"menu.price\", \"group_id\": 16, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 256, \"y3\": 752, \"x3\": 256, \"y4\": 752, \"x1\": 242, \"y1\": 734, \"x4\": 242, \"y2\": 734}, \"is_key\": 0, \"row_id\": 2179907, \"text\": \"3\"}, {\"quad\": {\"x2\": 280, \"y3\": 752, \"x3\": 280, \"y4\": 752, \"x1\": 266, \"y1\": 738, \"x4\": 266, \"y2\": 738}, \"is_key\": 0, \"row_id\": 2179907, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 17, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 352, \"y3\": 750, \"x3\": 352, \"y4\": 750, \"x1\": 312, \"y1\": 728, \"x4\": 312, \"y2\": 728}, \"is_key\": 0, \"row_id\": 2179907, \"text\": \"Bbk\"}, {\"quad\": {\"x2\": 457, \"y3\": 747, \"x3\": 458, \"y4\": 749, \"x1\": 357, \"y1\": 726, \"x4\": 358, \"y2\": 723}, \"is_key\": 0, \"row_id\": 2179907, \"text\": \"Panggang\"}, {\"quad\": {\"x2\": 508, \"y3\": 742, \"x3\": 508, \"y4\": 742, \"x1\": 466, \"y1\": 722, \"x4\": 466, \"y2\": 722}, \"is_key\": 0, \"row_id\": 2179907, \"text\": \"Sam\"}], \"category\": \"menu.nm\", \"group_id\": 17, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 630, \"y3\": 742, \"x3\": 630, \"y4\": 742, \"x1\": 538, \"y1\": 716, \"x4\": 538, \"y2\": 716}, \"is_key\": 0, \"row_id\": 2179907, \"text\": \"366,000\"}], \"category\": \"menu.price\", \"group_id\": 17, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 254, \"y3\": 778, \"x3\": 254, \"y4\": 778, \"x1\": 242, \"y1\": 762, \"x4\": 242, \"y2\": 762}, \"is_key\": 0, \"row_id\": 2179908, \"text\": \"1\"}, {\"quad\": {\"x2\": 280, \"y3\": 778, \"x3\": 280, \"y4\": 778, \"x1\": 266, \"y1\": 764, \"x4\": 266, \"y2\": 764}, \"is_key\": 0, \"row_id\": 2179908, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 18, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 364, \"y3\": 778, \"x3\": 364, \"y4\": 778, \"x1\": 312, \"y1\": 754, \"x4\": 312, \"y2\": 754}, \"is_key\": 0, \"row_id\": 2179908, \"text\": \"Ayam\"}, {\"quad\": {\"x2\": 447, \"y3\": 771, \"x3\": 448, \"y4\": 774, \"x1\": 371, \"y1\": 750, \"x4\": 372, \"y2\": 747}, \"is_key\": 0, \"row_id\": 2179908, \"text\": \"Sambal\"}, {\"quad\": {\"x2\": 508, \"y3\": 772, \"x3\": 508, \"y4\": 772, \"x1\": 454, \"y1\": 746, \"x4\": 454, \"y2\": 746}, \"is_key\": 0, \"row_id\": 2179908, \"text\": \"Hija\"}], \"category\": \"menu.nm\", \"group_id\": 18, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 632, \"y3\": 768, \"x3\": 632, \"y4\": 768, \"x1\": 554, \"y1\": 742, \"x4\": 554, \"y2\": 742}, \"is_key\": 0, \"row_id\": 2179908, \"text\": \"92,000\"}], \"category\": \"menu.price\", \"group_id\": 18, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 254, \"y3\": 806, \"x3\": 254, \"y4\": 806, \"x1\": 236, \"y1\": 784, \"x4\": 236, \"y2\": 784}, \"is_key\": 0, \"row_id\": 2179909, \"text\": \"2\"}, {\"quad\": {\"x2\": 278, \"y3\": 804, \"x3\": 278, \"y4\": 804, \"x1\": 262, \"y1\": 788, \"x4\": 262, \"y2\": 788}, \"is_key\": 0, \"row_id\": 2179909, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 19, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 352, \"y3\": 802, \"x3\": 352, \"y4\": 802, \"x1\": 310, \"y1\": 780, \"x4\": 310, \"y2\": 780}, \"is_key\": 0, \"row_id\": 2179909, \"text\": \"Hot\"}, {\"quad\": {\"x2\": 400, \"y3\": 800, \"x3\": 400, \"y4\": 800, \"x1\": 358, \"y1\": 778, \"x4\": 358, \"y2\": 778}, \"is_key\": 0, \"row_id\": 2179909, \"text\": \"Tea\"}], \"category\": \"menu.nm\", \"group_id\": 19, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 634, \"y3\": 796, \"x3\": 634, \"y4\": 796, \"x1\": 554, \"y1\": 770, \"x4\": 554, \"y2\": 770}, \"is_key\": 0, \"row_id\": 2179909, \"text\": \"44,000\"}], \"category\": \"menu.price\", \"group_id\": 19, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 252, \"y3\": 834, \"x3\": 252, \"y4\": 834, \"x1\": 240, \"y1\": 816, \"x4\": 240, \"y2\": 816}, \"is_key\": 0, \"row_id\": 2179910, \"text\": \"1\"}, {\"quad\": {\"x2\": 278, \"y3\": 832, \"x3\": 278, \"y4\": 832, \"x1\": 262, \"y1\": 816, \"x4\": 262, \"y2\": 816}, \"is_key\": 0, \"row_id\": 2179910, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 20, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 352, \"y3\": 830, \"x3\": 352, \"y4\": 830, \"x1\": 312, \"y1\": 808, \"x4\": 312, \"y2\": 808}, \"is_key\": 0, \"row_id\": 2179910, \"text\": \"Ice\"}, {\"quad\": {\"x2\": 412, \"y3\": 830, \"x3\": 412, \"y4\": 830, \"x1\": 360, \"y1\": 804, \"x4\": 360, \"y2\": 804}, \"is_key\": 0, \"row_id\": 2179910, \"text\": \"Kopi\"}], \"category\": \"menu.nm\", \"group_id\": 20, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 636, \"y3\": 826, \"x3\": 636, \"y4\": 826, \"x1\": 556, \"y1\": 798, \"x4\": 556, \"y2\": 798}, \"is_key\": 0, \"row_id\": 2179910, \"text\": \"32,000\"}], \"category\": \"menu.price\", \"group_id\": 20, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 250, \"y3\": 862, \"x3\": 250, \"y4\": 862, \"x1\": 238, \"y1\": 844, \"x4\": 238, \"y2\": 844}, \"is_key\": 0, \"row_id\": 2179911, \"text\": \"1\"}, {\"quad\": {\"x2\": 276, \"y3\": 862, \"x3\": 276, \"y4\": 862, \"x1\": 260, \"y1\": 844, \"x4\": 260, \"y2\": 844}, \"is_key\": 0, \"row_id\": 2179911, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 21, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 364, \"y3\": 860, \"x3\": 364, \"y4\": 860, \"x1\": 310, \"y1\": 836, \"x4\": 310, \"y2\": 836}, \"is_key\": 0, \"row_id\": 2179911, \"text\": \"Tahu\"}, {\"quad\": {\"x2\": 438, \"y3\": 858, \"x3\": 438, \"y4\": 858, \"x1\": 372, \"y1\": 834, \"x4\": 372, \"y2\": 834}, \"is_key\": 0, \"row_id\": 2179911, \"text\": \"Telor\"}, {\"quad\": {\"x2\": 500, \"y3\": 854, \"x3\": 500, \"y4\": 854, \"x1\": 444, \"y1\": 832, \"x4\": 444, \"y2\": 832}, \"is_key\": 0, \"row_id\": 2179911, \"text\": \"Asin\"}], \"category\": \"menu.nm\", \"group_id\": 21, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 638, \"y3\": 854, \"x3\": 638, \"y4\": 854, \"x1\": 558, \"y1\": 826, \"x4\": 558, \"y2\": 826}, \"is_key\": 0, \"row_id\": 2179911, \"text\": \"40,000\"}], \"category\": \"menu.price\", \"group_id\": 21, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 250, \"y3\": 892, \"x3\": 250, \"y4\": 892, \"x1\": 238, \"y1\": 872, \"x4\": 238, \"y2\": 872}, \"is_key\": 0, \"row_id\": 2179912, \"text\": \"1\"}, {\"quad\": {\"x2\": 276, \"y3\": 890, \"x3\": 276, \"y4\": 890, \"x1\": 260, \"y1\": 872, \"x4\": 260, \"y2\": 872}, \"is_key\": 0, \"row_id\": 2179912, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 22, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 364, \"y3\": 888, \"x3\": 364, \"y4\": 888, \"x1\": 310, \"y1\": 866, \"x4\": 310, \"y2\": 866}, \"is_key\": 0, \"row_id\": 2179912, \"text\": \"Free\"}, {\"quad\": {\"x2\": 414, \"y3\": 886, \"x3\": 414, \"y4\": 886, \"x1\": 374, \"y1\": 864, \"x4\": 374, \"y2\": 864}, \"is_key\": 0, \"row_id\": 2179912, \"text\": \"Ice\"}, {\"quad\": {\"x2\": 464, \"y3\": 884, \"x3\": 464, \"y4\": 884, \"x1\": 422, \"y1\": 862, \"x4\": 422, \"y2\": 862}, \"is_key\": 0, \"row_id\": 2179912, \"text\": \"Tea\"}], \"category\": \"menu.nm\", \"group_id\": 22, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 640, \"y3\": 878, \"x3\": 640, \"y4\": 878, \"x1\": 622, \"y1\": 856, \"x4\": 622, \"y2\": 856}, \"is_key\": 0, \"row_id\": 2179912, \"text\": \"0\"}], \"category\": \"menu.price\", \"group_id\": 22, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 250, \"y3\": 920, \"x3\": 250, \"y4\": 920, \"x1\": 236, \"y1\": 900, \"x4\": 236, \"y2\": 900}, \"is_key\": 0, \"row_id\": 2179913, \"text\": \"1\"}, {\"quad\": {\"x2\": 276, \"y3\": 920, \"x3\": 276, \"y4\": 920, \"x1\": 260, \"y1\": 902, \"x4\": 260, \"y2\": 902}, \"is_key\": 0, \"row_id\": 2179913, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 23, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 376, \"y3\": 916, \"x3\": 376, \"y4\": 916, \"x1\": 308, \"y1\": 892, \"x4\": 308, \"y2\": 892}, \"is_key\": 0, \"row_id\": 2179913, \"text\": \"Bebek\"}, {\"quad\": {\"x2\": 464, \"y3\": 914, \"x3\": 464, \"y4\": 914, \"x1\": 384, \"y1\": 890, \"x4\": 384, \"y2\": 890}, \"is_key\": 0, \"row_id\": 2179913, \"text\": \"Street\"}], \"category\": \"menu.nm\", \"group_id\": 23, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 641, \"y3\": 908, \"x3\": 642, \"y4\": 911, \"x1\": 559, \"y1\": 884, \"x4\": 560, \"y2\": 881}, \"is_key\": 0, \"row_id\": 2179913, \"text\": \"44,000\"}], \"category\": \"menu.price\", \"group_id\": 23, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 250, \"y3\": 948, \"x3\": 250, \"y4\": 948, \"x1\": 238, \"y1\": 930, \"x4\": 238, \"y2\": 930}, \"is_key\": 0, \"row_id\": 2179914, \"text\": \"1\"}, {\"quad\": {\"x2\": 276, \"y3\": 946, \"x3\": 276, \"y4\": 946, \"x1\": 260, \"y1\": 930, \"x4\": 260, \"y2\": 930}, \"is_key\": 0, \"row_id\": 2179914, \"text\": \"x\"}], \"category\": \"menu.cnt\", \"group_id\": 24, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 352, \"y3\": 946, \"x3\": 352, \"y4\": 946, \"x1\": 312, \"y1\": 924, \"x4\": 312, \"y2\": 924}, \"is_key\": 0, \"row_id\": 2179914, \"text\": \"Ice\"}, {\"quad\": {\"x2\": 402, \"y3\": 944, \"x3\": 402, \"y4\": 944, \"x1\": 360, \"y1\": 922, \"x4\": 360, \"y2\": 922}, \"is_key\": 0, \"row_id\": 2179914, \"text\": \"Tea\"}, {\"quad\": {\"x2\": 480, \"y3\": 942, \"x3\": 480, \"y4\": 942, \"x1\": 412, \"y1\": 920, \"x4\": 412, \"y2\": 920}, \"is_key\": 0, \"row_id\": 2179914, \"text\": \"Tawar\"}], \"category\": \"menu.nm\", \"group_id\": 24, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 642, \"y3\": 938, \"x3\": 642, \"y4\": 938, \"x1\": 564, \"y1\": 912, \"x4\": 564, \"y2\": 912}, \"is_key\": 0, \"row_id\": 2179914, \"text\": \"18,000\"}], \"category\": \"menu.price\", \"group_id\": 24, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 479, \"y3\": 998, \"x3\": 481, \"y4\": 1005, \"x1\": 360, \"y1\": 979, \"x4\": 362, \"y2\": 973}, \"is_key\": 1, \"row_id\": 2179915, \"text\": \"Sub-Total\"}, {\"quad\": {\"x2\": 645, \"y3\": 995, \"x3\": 646, \"y4\": 998, \"x1\": 527, \"y1\": 970, \"x4\": 528, \"y2\": 967}, \"is_key\": 0, \"row_id\": 2179915, \"text\": \"1,346,000\"}], \"category\": \"sub_total.subtotal_price\", \"group_id\": 25, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 481, \"y3\": 1027, \"x3\": 482, \"y4\": 1030, \"x1\": 387, \"y1\": 1007, \"x4\": 388, \"y2\": 1004}, \"is_key\": 1, \"row_id\": 2179916, \"text\": \"Service\"}, {\"quad\": {\"x2\": 646, \"y3\": 1026, \"x3\": 646, \"y4\": 1026, \"x1\": 554, \"y1\": 998, \"x4\": 554, \"y2\": 998}, \"is_key\": 0, \"row_id\": 2179916, \"text\": \"100,950\"}], \"category\": \"sub_total.service_price\", \"group_id\": 25, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 482, \"y3\": 1056, \"x3\": 482, \"y4\": 1056, \"x1\": 438, \"y1\": 1032, \"x4\": 438, \"y2\": 1032}, \"is_key\": 1, \"row_id\": 2179917, \"text\": \"PB1\"}, {\"quad\": {\"x2\": 648, \"y3\": 1052, \"x3\": 648, \"y4\": 1052, \"x1\": 556, \"y1\": 1026, \"x4\": 556, \"y2\": 1026}, \"is_key\": 0, \"row_id\": 2179917, \"text\": \"144,695\"}], \"category\": \"sub_total.tax_price\", \"group_id\": 25, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 481, \"y3\": 1085, \"x3\": 482, \"y4\": 1088, \"x1\": 375, \"y1\": 1063, \"x4\": 376, \"y2\": 1061}, \"is_key\": 1, \"row_id\": 2179918, \"text\": \"Rounding\"}, {\"quad\": {\"x2\": 648, \"y3\": 1078, \"x3\": 648, \"y4\": 1078, \"x1\": 606, \"y1\": 1054, \"x4\": 606, \"y2\": 1054}, \"is_key\": 0, \"row_id\": 2179918, \"text\": \"-45\"}], \"category\": \"sub_total.etc\", \"group_id\": 25, \"sub_group_id\": 0}, {\"words\": [{\"quad\": {\"x2\": 334, \"y3\": 1162, \"x3\": 334, \"y4\": 1162, \"x1\": 266, \"y1\": 1142, \"x4\": 266, \"y2\": 1142}, \"is_key\": 1, \"row_id\": 2179919, \"text\": \"Grand\"}, {\"quad\": {\"x2\": 408, \"y3\": 1160, \"x3\": 408, \"y4\": 1160, \"x1\": 340, \"y1\": 1138, \"x4\": 340, \"y2\": 1138}, \"is_key\": 1, \"row_id\": 2179919, \"text\": \"Total\"}, {\"quad\": {\"x2\": 647, \"y3\": 1153, \"x3\": 649, \"y4\": 1161, \"x1\": 418, \"y1\": 1117, \"x4\": 420, \"y2\": 1108}, \"is_key\": 0, \"row_id\": 2179919, \"text\": \"1,591,600\"}], \"category\": \"total.total_price\", \"group_id\": 26, \"sub_group_id\": 0}], \"roi\": {}, \"repeating_symbol\": [], \"dontcare\": []}\n" + ] + } + ], + "source": [ + "# let's load the corresponding JSON dictionary (as string representation)\n", + "ground_truth = example['ground_truth']\n", + "print(ground_truth)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8V8Rgq4jwoqL" + }, + "source": [ + "We can also parse the string as a Python dictionary using `ast.literal_eval`. Each training example has a single \"gt_parse\" key, which contains the ground truth parsing of the document:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0iODZViTwqVf", + "outputId": "9e269cf8-79d1-4660-9254-10a5a25c61bf" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'menu': [{'nm': 'Nasi Campur Bali', 'cnt': '1 x', 'price': '75,000'},\n", + " {'nm': 'Bbk Bengil Nasi', 'cnt': '1 x', 'price': '125,000'},\n", + " {'nm': 'MilkShake Starwb', 'cnt': '1 x', 'price': '37,000'},\n", + " {'nm': 'Ice Lemon Tea', 'cnt': '1 x', 'price': '24,000'},\n", + " {'nm': 'Nasi Ayam Dewata', 'cnt': '1 x', 'price': '70,000'},\n", + " {'nm': 'Free Ice Tea', 'cnt': '3 x', 'price': '0'},\n", + " {'nm': 'Organic Green Sa', 'cnt': '1 x', 'price': '65,000'},\n", + " {'nm': 'Ice Tea', 'cnt': '1 x', 'price': '18,000'},\n", + " {'nm': 'Ice Orange', 'cnt': '1 x', 'price': '29,000'},\n", + " {'nm': 'Ayam Suir Bali', 'cnt': '1 x', 'price': '85,000'},\n", + " {'nm': 'Tahu Goreng', 'cnt': '2 x', 'price': '36,000'},\n", + " {'nm': 'Tempe Goreng', 'cnt': '2 x', 'price': '36,000'},\n", + " {'nm': 'Tahu Telor Asin', 'cnt': '1 x', 'price': '40,000.'},\n", + " {'nm': 'Nasi Goreng Samb', 'cnt': '1 x', 'price': '70,000'},\n", + " {'nm': 'Bbk Panggang Sam', 'cnt': '3 x', 'price': '366,000'},\n", + " {'nm': 'Ayam Sambal Hija', 'cnt': '1 x', 'price': '92,000'},\n", + " {'nm': 'Hot Tea', 'cnt': '2 x', 'price': '44,000'},\n", + " {'nm': 'Ice Kopi', 'cnt': '1 x', 'price': '32,000'},\n", + " {'nm': 'Tahu Telor Asin', 'cnt': '1 x', 'price': '40,000'},\n", + " {'nm': 'Free Ice Tea', 'cnt': '1 x', 'price': '0'},\n", + " {'nm': 'Bebek Street', 'cnt': '1 x', 'price': '44,000'},\n", + " {'nm': 'Ice Tea Tawar', 'cnt': '1 x', 'price': '18,000'}],\n", + " 'sub_total': {'subtotal_price': '1,346,000',\n", + " 'service_price': '100,950',\n", + " 'tax_price': '144,695',\n", + " 'etc': '-45'},\n", + " 'total': {'total_price': '1,591,600'}}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from ast import literal_eval\n", + "\n", + "literal_eval(ground_truth)['gt_parse']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BCjMK93Cz3zf" + }, + "source": [ + "## Load model and processor\n", + "\n", + "Next, we load the model (Donut is an instance of [VisionEncoderDecoderModel](https://huggingface.co/docs/transformers/model_doc/vision-encoder-decoder)), and the processor, which is the object that can be used to prepare inputs for the model.\n", + "\n", + "We'll update some settings for fine-tuning, namely the image size and the max length of the decoder." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "ddd6a6f26e4a40de85db830c02834c4c", + "646592d608724342ad765c7e7457740d", + "c46ec088b99646f091f934536af46d5e", + "3ce6f898ce604f258efab30dd9f65b79", + "38fff333701c4678b5c69863aa8992ac", + "2472d601c42041e6acb51d19de547a0a", + "59585c4440a64e1fb76601df8aee255d", + "2a459b1c958240d89f753fe695b457e0", + "7939062da2ee4ebabadb0a5f06ee3276", + "ac08a565f2d5447a95d2e6af307f3f09", + "4ed83abdd375497f9edb35c828d70be7" + ] + }, + "id": "ahkkeo8_o69z", + "outputId": "ea22fae8-ca64-49d1-c886-046140d0d2a1" + }, + "outputs": [], + "source": [ + "from transformers import VisionEncoderDecoderConfig\n", + "\n", + "image_size = [1280, 960]\n", + "max_length = 768\n", + "\n", + "# update image_size of the encoder\n", + "# during pre-training, a larger image size was used\n", + "config = VisionEncoderDecoderConfig.from_pretrained(\"naver-clova-ix/donut-base\")\n", + "config.encoder.image_size = image_size # (height, width)\n", + "# update max_length of the decoder (for generation)\n", + "config.decoder.max_length = max_length\n", + "# TODO we should actually update max_position_embeddings and interpolate the pre-trained ones:\n", + "# https://github.com/clovaai/donut/blob/0acc65a85d140852b8d9928565f0f6b2d98dc088/donut/model.py#L602" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MUiQda9_mjAC" + }, + "source": [ + "Next, we instantiate the model with our custom config, as well as the processor. Make sure that all pre-trained weights are correctly loaded (a warning would tell you if that's not the case)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 279, + "referenced_widgets": [ + "f0e0d969d5f943c3ac158a6a443bceee", + "c21173370f63409b84b9f40abc202efd", + "cd63bf0c96b6466586c66240f67b35a5", + "ab6b00b5c38d40e4878e5de67bba6b95", + "0f2dc68b57614e79b95ddeb715f124af", + "2781fb98daf147638db891d565d2f194", + "da910c9d7bc94d6fabe99bfcd626d9a2", + "5b515a9e88074259b1067880ad7aa637", + "6dd394d212694b969b540123f515957a", + "262f11dfba44467c882893cec6523189", + "3e8e09b6655a4c8fb3995915a69c795f", + "0c565bd0bd89433286f309c0a728e020", + "9ab3a07b87854294b687ab1bd1ce2fe1", + "61c95b7243604620a9151ce8b6fcc910", + "f21af123c24f43498942dd4f3e91c54f", + "f6c554c9119f4152bd747905b3b2ade3", + "45c29055f8a74361b562fb2baaa3b71f", + "731ee8128e584ddcbe117dd03612467d", + "d68dcd34129d4922b23a3bc96739814f", + "2e682e41d92343b792991eaa2b72d7de", + "c6656f51d29747c7a831f8b4739b8cd7", + "ed98bdb5c8364a6782a1883411acd5f5", + "def152f1a9e84c7e90e6e24f001f29a2", + "140f7d5cb0fb4b4b8598d1552a429052", + "ad391511f2ec4e3ebb69865afee7ae6b", + "c5083eb93376434e9a53fc44d7d215b9", + "56fea1b142db47fbab0b118185587e76", + "a4eacb994f2147d29d2d68e9b3c456aa", + "205926dbd19445c08bd1b8a02e33634f", + "9479170c55a34e5a9ccce3115f01b68e", + "0fedf481a76a4d319ddc6afe51345a36", + "394b5ca6361e4d38ba6cca638de5cdcb", + "7b2f94dc358c4e5cb6662463e771bb2d", + "efbbbb45e1554253bbf331525f219e4c", + "ab1c3ba6663a40c383a57333f5b13ddb", + "4664a4a1ab034098a12b6ad238c0391b", + "257fa5076dd74486b7751bffa39c2ef8", + "b02d03f2ac8d47559d03eb83a01d467e", + "3612afa317c5414bbffdbc1f241eb133", + "ca6747f101dc44fa8d3b85f668fff5ed", + "f8c2d38923c948cf8f2cf8c49ffb2c8d", + "37c969be0a7946c6a7d45940fcde5cf9", + "b3a98298b4ea434da0d8815ba0f2c4a4", + "e6523a394c4442bd949cae3f7666b6d4", + "9a45e993bffc469a8a156cba70774d25", + "82f65ddff78c4c528169704a78d6d9f5", + "7bd828ddfe814d05a62736a9d91a8272", + "9da3a58c74e64d4d8e39921e472fb6ea", + "c253f1450fdc49939ad663b6f98b6929", + "4f2be832c1f04902af8bfc4f5d971057", + "dda5061d7d3d4a2cb166bb9473dc3fbb", + "d3ff6bc3da3b4754af245dcbacd0914c", + "87407d9ae1f54c3c8383e2934aef15b4", + "617aea856f93495d9f56dfc83a33d4bb", + "23e4cf09102a4753844ceed08a211d53", + "9bce140411404cad931ec4022a76989e", + "50ce7fc092624343a610286377ecd39a", + "b28110567ad04fdeafea8f405f71e7ef", + "f578278adfb146e8bdf89810a39d43cb", + "fbed03f8b8ad47638a418b9f423e4c26", + "840a81ea307a4633bfd9846f89121257", + "7a2a38deff05410b92c7f0aca5e2f906", + "690c1c1fdb98452ea766955c7a4966be", + "c08e405d7915496089723b6d5e78d163", + "8f12f7c6ea604de7ab155971ffea8275", + "165a9c0135cb45869feb49beacb8101a", + "2ca98f8a45fb4335ac197e9b7c019e41", + "6d146fd4003e47978a253192463dd514", + "33596291f0c344ad9219c9a7eb6c5c90", + "30374f50d4af4b9598cb13823ad7780c", + "6c9cca8ae41441b29f2dd7ac81cd35d4", + "9525813936a14ac49a7ca525a411c2f9", + "6a6be6466e6a4814870c38d55279e1c1", + "fe5fe724aaa14f2bb80e84fc589103fc", + "c65a6cc99b06467cbc7f4efe8d80aae0", + "6a3d2f0a94ea4f72bc81400880f8c430", + "c3735a62d26743cc88f0bb71712edaa2" + ] + }, + "id": "84TkZP5zz4hE", + "outputId": "9d49492d-5b3f-41b1-88a6-4c8a85e3cdd3" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-04-30 11:46:46.237889: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2024-04-30 11:46:46.861365: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", + "Could not find image processor class in the image processor config or the model config. Loading based on pattern matching with the model's feature extractor configuration. Please open a PR/issue to update `preprocessor_config.json` to use `image_processor_type` instead of `feature_extractor_type`. This warning will be removed in v4.40.\n" + ] + } + ], + "source": [ + "from transformers import DonutProcessor, VisionEncoderDecoderModel\n", + "\n", + "processor = DonutProcessor.from_pretrained(\"naver-clova-ix/donut-base\")\n", + "model = VisionEncoderDecoderModel.from_pretrained(\"naver-clova-ix/donut-base\", config=config)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b46s3KR-x8Iv" + }, + "source": [ + "## Create PyTorch dataset\n", + "\n", + "Here we create a regular PyTorch dataset.\n", + "\n", + "The model doesn't directly take the (image, JSON) pairs as input and labels. Rather, we create `pixel_values` and `labels`. Both are PyTorch tensors. The `pixel_values` are the input images (resized, padded and normalized), and the `labels` are the `input_ids` of the target sequence (which is a flattened version of the JSON), with padding tokens replaced by -100 (to make sure these are ignored by the loss function). Both are created using `DonutProcessor` (which internally combines an image processor, for the image modality, and a tokenizer, for the text modality).\n", + "\n", + "Note that we're also adding tokens to the vocabulary of the decoder (and corresponding tokenizer) for all keys of the dictionaries in our dataset, like \"\\\". This makes sure the model learns an embedding vector for them. Without doing this, some keys might get split up into multiple subword tokens, in which case the model just learns an embedding for the subword tokens, rather than a direct embedding for these keys." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "7tWX_qJDvw_S" + }, + "outputs": [], + "source": [ + "import json\n", + "import random\n", + "from typing import Any, List, Tuple\n", + "\n", + "import torch\n", + "from torch.utils.data import Dataset\n", + "\n", + "added_tokens = []\n", + "\n", + "class DonutDataset(Dataset):\n", + " \"\"\"\n", + " PyTorch Dataset for Donut. This class takes a HuggingFace Dataset as input.\n", + " \n", + " Each row, consists of image path(png/jpg/jpeg) and gt data (json/jsonl/txt),\n", + " and it will be converted into pixel_values (vectorized image) and labels (input_ids of the tokenized string).\n", + " \n", + " Args:\n", + " dataset_name_or_path: name of dataset (available at huggingface.co/datasets) or the path containing image files and metadata.jsonl\n", + " max_length: the max number of tokens for the target sequences\n", + " split: whether to load \"train\", \"validation\" or \"test\" split\n", + " ignore_id: ignore_index for torch.nn.CrossEntropyLoss\n", + " task_start_token: the special token to be fed to the decoder to conduct the target task\n", + " prompt_end_token: the special token at the end of the sequences\n", + " sort_json_key: whether or not to sort the JSON keys\n", + " \"\"\"\n", + "\n", + " def __init__(\n", + " self,\n", + " dataset_name_or_path: str,\n", + " max_length: int,\n", + " split: str = \"train\",\n", + " ignore_id: int = -100,\n", + " task_start_token: str = \"\",\n", + " prompt_end_token: str = None,\n", + " sort_json_key: bool = True,\n", + " ):\n", + " super().__init__()\n", + "\n", + " self.max_length = max_length\n", + " self.split = split\n", + " self.ignore_id = ignore_id\n", + " self.task_start_token = task_start_token\n", + " self.prompt_end_token = prompt_end_token if prompt_end_token else task_start_token\n", + " self.sort_json_key = sort_json_key\n", + "\n", + " self.dataset = load_dataset(dataset_name_or_path, split=self.split)\n", + " self.dataset_length = len(self.dataset)\n", + "\n", + " self.gt_token_sequences = []\n", + " for sample in self.dataset:\n", + " ground_truth = json.loads(sample[\"ground_truth\"])\n", + " if \"gt_parses\" in ground_truth: # when multiple ground truths are available, e.g., docvqa\n", + " assert isinstance(ground_truth[\"gt_parses\"], list)\n", + " gt_jsons = ground_truth[\"gt_parses\"]\n", + " else:\n", + " assert \"gt_parse\" in ground_truth and isinstance(ground_truth[\"gt_parse\"], dict)\n", + " gt_jsons = [ground_truth[\"gt_parse\"]]\n", + "\n", + " self.gt_token_sequences.append(\n", + " [\n", + " self.json2token(\n", + " gt_json,\n", + " update_special_tokens_for_json_key=self.split == \"train\",\n", + " sort_json_key=self.sort_json_key,\n", + " )\n", + " + processor.tokenizer.eos_token\n", + " for gt_json in gt_jsons # load json from list of json\n", + " ]\n", + " )\n", + "\n", + " self.add_tokens([self.task_start_token, self.prompt_end_token])\n", + " self.prompt_end_token_id = processor.tokenizer.convert_tokens_to_ids(self.prompt_end_token)\n", + "\n", + " def json2token(self, obj: Any, update_special_tokens_for_json_key: bool = True, sort_json_key: bool = True):\n", + " \"\"\"\n", + " Convert an ordered JSON object into a token sequence\n", + " \"\"\"\n", + " if type(obj) == dict:\n", + " if len(obj) == 1 and \"text_sequence\" in obj:\n", + " return obj[\"text_sequence\"]\n", + " else:\n", + " output = \"\"\n", + " if sort_json_key:\n", + " keys = sorted(obj.keys(), reverse=True)\n", + " else:\n", + " keys = obj.keys()\n", + " for k in keys:\n", + " if update_special_tokens_for_json_key:\n", + " self.add_tokens([fr\"\", fr\"\"])\n", + " output += (\n", + " fr\"\"\n", + " + self.json2token(obj[k], update_special_tokens_for_json_key, sort_json_key)\n", + " + fr\"\"\n", + " )\n", + " return output\n", + " elif type(obj) == list:\n", + " return r\"\".join(\n", + " [self.json2token(item, update_special_tokens_for_json_key, sort_json_key) for item in obj]\n", + " )\n", + " else:\n", + " obj = str(obj)\n", + " if f\"<{obj}/>\" in added_tokens:\n", + " obj = f\"<{obj}/>\" # for categorical special tokens\n", + " return obj\n", + " \n", + " def add_tokens(self, list_of_tokens: List[str]):\n", + " \"\"\"\n", + " Add special tokens to tokenizer and resize the token embeddings of the decoder\n", + " \"\"\"\n", + " newly_added_num = processor.tokenizer.add_tokens(list_of_tokens)\n", + " if newly_added_num > 0:\n", + " model.decoder.resize_token_embeddings(len(processor.tokenizer))\n", + " added_tokens.extend(list_of_tokens)\n", + " \n", + " def __len__(self) -> int:\n", + " return self.dataset_length\n", + "\n", + " def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n", + " \"\"\"\n", + " Load image from image_path of given dataset_path and convert into input_tensor and labels\n", + " Convert gt data into input_ids (tokenized string)\n", + " Returns:\n", + " input_tensor : preprocessed image\n", + " input_ids : tokenized gt_data\n", + " labels : masked labels (model doesn't need to predict prompt and pad token)\n", + " \"\"\"\n", + " sample = self.dataset[idx]\n", + "\n", + " # inputs\n", + " pixel_values = processor(sample[\"image\"], random_padding=self.split == \"train\", return_tensors=\"pt\").pixel_values\n", + " pixel_values = pixel_values.squeeze()\n", + "\n", + " # targets\n", + " target_sequence = random.choice(self.gt_token_sequences[idx]) # can be more than one, e.g., DocVQA Task 1\n", + " input_ids = processor.tokenizer(\n", + " target_sequence,\n", + " add_special_tokens=False,\n", + " max_length=self.max_length,\n", + " padding=\"max_length\",\n", + " truncation=True,\n", + " return_tensors=\"pt\",\n", + " )[\"input_ids\"].squeeze(0)\n", + "\n", + " labels = input_ids.clone()\n", + " labels[labels == processor.tokenizer.pad_token_id] = self.ignore_id # model doesn't need to predict pad token\n", + " # labels[: torch.nonzero(labels == self.prompt_end_token_id).sum() + 1] = self.ignore_id # model doesn't need to predict prompt (for VQA)\n", + " return pixel_values, labels, target_sequence" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KBseZac0m_W8" + }, + "source": [ + "Next, we instantiate the datasets:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3JpazNkf8CnA", + "outputId": "2532ee02-08cb-4e10-cf61-87e9c28ba46d" + }, + "outputs": [], + "source": [ + "# we update some settings which differ from pretraining; namely the size of the images + no rotation required\n", + "# source: https://github.com/clovaai/donut/blob/master/config/train_cord.yaml\n", + "processor.image_processor.size = image_size[::-1] # should be (width, height)\n", + "processor.image_processor.do_align_long_axis = False\n", + "\n", + "train_dataset = DonutDataset(\"naver-clova-ix/cord-v2\", max_length=max_length,\n", + " split=\"train\", task_start_token=\"\", prompt_end_token=\"\",\n", + " sort_json_key=False, # cord dataset is preprocessed, so no need for this\n", + " )\n", + "\n", + "val_dataset = DonutDataset(\"naver-clova-ix/cord-v2\", max_length=max_length,\n", + " split=\"validation\", task_start_token=\"\", prompt_end_token=\"\",\n", + " sort_json_key=False, # cord dataset is preprocessed, so no need for this\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QWYmtYxIoHnX" + }, + "source": [ + "Let's check which tokens are added:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IfvtQVenFIy1", + "outputId": "d3abb09b-2baa-49c2-8332-43849a78745f" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "56" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(added_tokens)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "E2hTrsrCB1rp", + "outputId": "e3d6601f-2d2d-4a11-8399-e4cf4ea41c3b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '']\n" + ] + } + ], + "source": [ + "print(added_tokens)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "W0Z9JhK3E7WR", + "outputId": "2873d2fb-bf4f-4f94-8175-1a1dddaba4d1" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original number of tokens: 57522\n", + "Number of tokens after adding special tokens: 57580\n" + ] + } + ], + "source": [ + "# the vocab size attribute stays constants (might be a bit unintuitive - but doesn't include special tokens)\n", + "print(\"Original number of tokens:\", processor.tokenizer.vocab_size)\n", + "print(\"Number of tokens after adding special tokens:\", len(processor.tokenizer))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tPnYfnwYoMHB" + }, + "source": [ + "You can verify that a token like `` was added to the vocabulary of the tokenizer (and the model):" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "id": "OLySqOmgE_QC", + "outputId": "ab70eb94-9414-43fa-e3e1-c454b45f11e4" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "''" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "processor.decode([57560])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bd5mNnuPqUAN" + }, + "source": [ + "As always, it's very important to verify whether our data is prepared correctly. Let's check the first training example:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "mkHzamYl90we" + }, + "outputs": [], + "source": [ + "pixel_values, labels, target_sequence = train_dataset[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "07bHWGlFtpIg" + }, + "source": [ + "This returns the `pixel_values` (the image, but prepared for the model as a PyTorch tensor), the `labels` (which are the encoded `input_ids` of the target sequence, which we want Donut to learn to generate) and the original `target_sequence`. The reason we also return the latter is because this will allow us to compute metrics between the generated sequences and the ground truth target sequences." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kNyN_Af0-QMA", + "outputId": "6e255ca3-6e8d-47a0-9599-2cd5faf53231" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([3, 1280, 960])\n" + ] + } + ], + "source": [ + "print(pixel_values.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "uvrqeUmwcNY0", + "outputId": "2e049f5b-60d3-48b8-8656-247290172a10" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Nasi\n", + "Camp\n", + "ur\n", + "Bali\n", + "\n", + "\n", + "1\n", + "x\n", + "\n", + "\n", + "7\n", + "5,000\n", + "\n", + "\n", + "\n", + "B\n", + "b\n", + "k\n", + "Ben\n", + "gil\n", + "Nasi\n", + "\n", + "\n", + "1\n", + "x\n", + "\n", + "\n", + "12\n" + ] + } + ], + "source": [ + "# let's print the labels (the first 30 token ID's)\n", + "for id in labels.tolist()[:30]:\n", + " if id != -100:\n", + " print(processor.decode([id]))\n", + " else:\n", + " print(id)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cwCgvma4z_kp", + "outputId": "a94a538c-199a-4e4e-cc85-5935292d9964" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Nasi Campur Bali1 x75,000Bbk Bengil Nasi1 x125,000MilkShake Starwb1 x37,000Ice Lemon Tea1 x24,000Nasi Ayam Dewata1 x70,000Free Ice Tea3 x0Organic Green Sa1 x65,000Ice Tea1 x18,000Ice Orange1 x29,000Ayam Suir Bali1 x85,000Tahu Goreng2 x36,000Tempe Goreng2 x36,000Tahu Telor Asin1 x40,000.Nasi Goreng Samb1 x70,000Bbk Panggang Sam3 x366,000Ayam Sambal Hija1 x92,000Hot Tea2 x44,000Ice Kopi1 x32,000Tahu Telor Asin1 x40,000Free Ice Tea1 x0Bebek Street1 x44,000Ice Tea Tawar1 x18,0001,346,000100,950144,695-451,591,600\n" + ] + } + ], + "source": [ + "# let's check the corresponding target sequence, as a string\n", + "print(target_sequence)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "poYwvFdAdikM" + }, + "source": [ + "Another important thing is that we need to set 2 additional attributes in the configuration of the model. This is not required, but will allow us to train the model by only providing the decoder targets, without having to provide any decoder inputs.\n", + "\n", + "The model will automatically create the `decoder_input_ids` (the decoder inputs) based on the `labels`, by shifting them one position to the right and prepending the decoder_start_token_id. I recommend checking [this video](https://www.youtube.com/watch?v=IGu7ivuy1Ag&t=888s&ab_channel=NielsRogge) if you want to understand how models like Donut automatically create decoder_input_ids - and more broadly how Donut works." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "VgRLEVMdc1lx" + }, + "outputs": [], + "source": [ + "model.config.pad_token_id = processor.tokenizer.pad_token_id\n", + "model.config.decoder_start_token_id = processor.tokenizer.convert_tokens_to_ids([''])[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bTn0nmssdRWE", + "outputId": "9532a846-7b7f-4214-860a-c2d85ca739a3" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pad token ID: \n", + "Decoder start token ID: \n" + ] + } + ], + "source": [ + "# sanity check\n", + "print(\"Pad token ID:\", processor.decode([model.config.pad_token_id]))\n", + "print(\"Decoder start token ID:\", processor.decode([model.config.decoder_start_token_id]))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ygTIylugfasG" + }, + "source": [ + "## Create PyTorch DataLoaders\n", + "\n", + "Next, we create corresponding PyTorch DataLoaders, which allow us to loop over the dataset in batches:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "nLQ_Vl5MLugu" + }, + "outputs": [], + "source": [ + "from torch.utils.data import DataLoader\n", + "\n", + "# feel free to increase the batch size if you have a lot of memory\n", + "# I'm fine-tuning on Colab and given the large image size, batch size > 1 is not feasible\n", + "train_dataloader = DataLoader(train_dataset, batch_size=1, shuffle=True, num_workers=4)\n", + "val_dataloader = DataLoader(val_dataset, batch_size=1, shuffle=False, num_workers=4)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AxtTVgNnfdkD" + }, + "source": [ + "Let's verify a batch:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WHurHlLnL8Xm", + "outputId": "3489d45e-8761-40dc-d6c9-1b60af7d8edb" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([1, 3, 1280, 960])\n" + ] + } + ], + "source": [ + "batch = next(iter(train_dataloader))\n", + "pixel_values, labels, target_sequences = batch\n", + "print(pixel_values.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "f8ehAwgPZrcc", + "outputId": "786ddcaa-4cf7-4613-d3e4-325df6738c7a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "HA\n", + "ZE\n", + "LN\n", + "UT\n", + "C\n", + "HO\n", + "CO\n", + "III\n", + "(\n", + "R\n", + ")\n", + "\n", + "\n", + "1\n", + "x\n", + "\n", + "\n", + "1\n", + ",\n", + "500\n", + "\n", + "\n", + "2\n", + "4,000\n", + "\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "for id in labels.squeeze().tolist()[:30]:\n", + " if id != -100:\n", + " print(processor.decode([id]))\n", + " else:\n", + " print(id)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IoXL0MnTzmdZ", + "outputId": "e9500613-d241-420f-feac-e94ac78ee387" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "800\n", + "100\n" + ] + } + ], + "source": [ + "print(len(train_dataset))\n", + "print(len(val_dataset))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SYdUWe1YzDE8", + "outputId": "c38fc7bd-8287-4008-fb69-a8159fa13c9f" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([1, 3, 1280, 960])\n" + ] + } + ], + "source": [ + "# let's check the first validation batch\n", + "batch = next(iter(val_dataloader))\n", + "pixel_values, labels, target_sequences = batch\n", + "print(pixel_values.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "xbCw5mYH0Mvu", + "outputId": "d321a64e-a911-446f-ea2e-a3ce221133de" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "REAL GANACHE116,500EGG TART113,000PIZZA TOAST116,00045,50050,0004,500\n" + ] + } + ], + "source": [ + "print(target_sequences[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mnmD7rRy2WLI" + }, + "source": [ + "## Define LightningModule\n", + "\n", + "Next, we define a [LightningModule](https://pytorch-lightning.readthedocs.io/en/stable/common/lightning_module.html), which is the standard way to train a model in PyTorch Lightning. A LightningModule is an `nn.Module` with some additional functionality. \n", + "\n", + "Basically, PyTorch Lightning will take care of all device placements (`.to(device)`) for us, as well as the backward pass, putting the model in training mode, etc." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "id": "oRm5i4gWG-sb" + }, + "outputs": [], + "source": [ + "import re\n", + "from nltk import edit_distance\n", + "import numpy as np\n", + "\n", + "import pytorch_lightning as pl\n", + "\n", + "\n", + "class DonutModelPLModule(pl.LightningModule):\n", + " def __init__(self, config, processor, model):\n", + " super().__init__()\n", + " self.config = config\n", + " self.processor = processor\n", + " self.model = model\n", + "\n", + " def training_step(self, batch, batch_idx):\n", + " pixel_values, labels, _ = batch\n", + " \n", + " outputs = self.model(pixel_values, labels=labels)\n", + " loss = outputs.loss\n", + " self.log(\"train_loss\", loss)\n", + " return loss\n", + "\n", + " def validation_step(self, batch, batch_idx, dataset_idx=0):\n", + " pixel_values, labels, answers = batch\n", + " batch_size = pixel_values.shape[0]\n", + " # we feed the prompt to the model\n", + " decoder_input_ids = torch.full((batch_size, 1), self.model.config.decoder_start_token_id, device=self.device)\n", + " \n", + " outputs = self.model.generate(pixel_values,\n", + " decoder_input_ids=decoder_input_ids,\n", + " max_length=max_length,\n", + " early_stopping=True,\n", + " pad_token_id=self.processor.tokenizer.pad_token_id,\n", + " eos_token_id=self.processor.tokenizer.eos_token_id,\n", + " use_cache=True,\n", + " num_beams=1,\n", + " bad_words_ids=[[self.processor.tokenizer.unk_token_id]],\n", + " return_dict_in_generate=True,)\n", + " \n", + " predictions = []\n", + " for seq in self.processor.tokenizer.batch_decode(outputs.sequences):\n", + " seq = seq.replace(self.processor.tokenizer.eos_token, \"\").replace(self.processor.tokenizer.pad_token, \"\")\n", + " seq = re.sub(r\"<.*?>\", \"\", seq, count=1).strip() # remove first task start token\n", + " predictions.append(seq)\n", + "\n", + " scores = []\n", + " for pred, answer in zip(predictions, answers):\n", + " pred = re.sub(r\"(?:(?<=>) | (?=\", \"\", answer, count=1)\n", + " answer = answer.replace(self.processor.tokenizer.eos_token, \"\")\n", + " scores.append(edit_distance(pred, answer) / max(len(pred), len(answer)))\n", + "\n", + " if self.config.get(\"verbose\", False) and len(scores) == 1:\n", + " print(f\"Prediction: {pred}\")\n", + " print(f\" Answer: {answer}\")\n", + " print(f\" Normed ED: {scores[0]}\")\n", + "\n", + " self.log(\"val_edit_distance\", np.mean(scores))\n", + " \n", + " return scores\n", + "\n", + " def configure_optimizers(self):\n", + " # you could also add a learning rate scheduler if you want\n", + " optimizer = torch.optim.Adam(self.parameters(), lr=self.config.get(\"lr\"))\n", + " \n", + " return optimizer\n", + "\n", + " def train_dataloader(self):\n", + " return train_dataloader\n", + "\n", + " def val_dataloader(self):\n", + " return val_dataloader" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0ZoPiDOPKg0o" + }, + "source": [ + "## Train!\n", + "\n", + "Next, let's train! This happens instantiating a PyTorch Lightning `Trainer`, and then calling `trainer.fit`.\n", + "\n", + "What's great is that we can automatically train on the hardware we have (in our case, a single GPU), enable mixed precision (`fp16=True`, which makes sure we don't consume as much memory), add Weights and Biases logging, and so on. " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "id": "pxNJhCGjKhtR" + }, + "outputs": [], + "source": [ + "config = {\"max_epochs\":30,\n", + " \"val_check_interval\":0.2, # how many times we want to validate during an epoch\n", + " \"check_val_every_n_epoch\":1,\n", + " \"gradient_clip_val\":1.0,\n", + " \"num_training_samples_per_epoch\": 800,\n", + " \"lr\":3e-5,\n", + " \"train_batch_sizes\": [8],\n", + " \"val_batch_sizes\": [1],\n", + " # \"seed\":2022,\n", + " \"num_nodes\": 1,\n", + " \"warmup_steps\": 300, # 800/8*30/10, 10%\n", + " \"result_path\": \"./result\",\n", + " \"verbose\": True,\n", + " }\n", + "\n", + "model_module = DonutModelPLModule(config, processor, model)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6l4byTwPRBZx" + }, + "source": [ + "We'll use a custom callback to push our model to the hub during training (after each epoch + end of training). For that we'll log into our HuggingFace account." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "d135d783486d456494d15ef2a339ebff", + "74478f6d2baf47999e56952b01ba938f", + "24e315cb82a747d6b9566b461db6aee9", + "0d2f68be02e7495fab83926f99b93586", + "65277dbddbb34381b788a0ee526a0245", + "fe0bc6d3aa0b4d88a63aa611af3bf65d", + "73094daa6d4c4bbf93bd29b8c0253e09", + "f98e3325c6b64df7b028a71eb6c1aa5e", + "c5f481bd341a48668518912efbd6a35a", + "bfc5138baadf4385a0ed6b87107fabec", + "b760aaf9ca3047378404f446549355ef", + "5536aeeebaad4d158a11a706940a0220", + "3427bc46e6f84ac5a403b42afcde9b08", + "b294221a318349228f631ca89407740b", + "45df3e9080c7437680d57cb5936dc1de", + "4a86c267cbe34ba3bbe0ec2e0035bbbb", + "3bcce8d28d894837824f52bae420976a", + "ae1f8706e72943989d91f9a44a40eeeb", + "bc7bb44709434398a99c395a01d2b3b6", + "0e2de7dbc3f3450bb17060ce2687c3ed", + "44a17877ab654e0481b0fe827a991e5a", + "083ca4f1dea5421bb07bf9cfc802871d", + "8249f31d416e4ed388233cd633c212a7", + "de3f7930ae454905be655bf33f045280", + "5628c489cf31430c8b7efe8118c64815", + "b04b0c322c624afa8bb5a128b2af08b6", + "b71da8215fd74394aeef7b2835ebdce2", + "aad2e43eeafa4cccb5e476d3fce4dacb", + "18db9e56c0354600851d762c8de0f774", + "a524d30afbdc4574be77a664888d32e6", + "30f78b0e910549a6a5e79e8a49c4c67d", + "b12a725c43db4fa4ad13d82c75738994", + "144e18e547844b1789fa6759bad4b1a2", + "cdf0d854eba142779798e0ac495c02af", + "bfc6976f3a4b4de3bd42aa1dc9ab73e5", + "74cbba5d476540188be2501bf934b5cd", + "c08394b19b98474798fc290bc540ddd5", + "6fcd1c300caa489dadeaa50c6c814a14", + "f60b9e69e69e40189644c2cde41fae18", + "297ba8af5a704c5ca34d0f406de3e918", + "95ec17f083be45d1a48d643e62f4dbf8", + "8a3af3818f69463f845e95c933ac00cd", + "b968d28f243543b29cf0733e2d9fc516", + "e0f0d44a4e834e9291b2c021fcb1dad9", + "e0732c157cbf446f80cd1b4d52f7f6b8", + "259d3cba0a5b4943b4b025f921edf06b", + "4d6704d874404c9996e32b16b7773169", + "0c44b50f550a42d5a89d35c0233fa0bb", + "bf3538bb8a33408088307bb3dba05a14", + "24c7acb091ab4037a6b4a2eb8a1085e3", + "fad67a0d8f834d6eb9609fc0ecec94c8", + "9c081ba4b4b7409caef98b8c7754c9b6", + "a535138c7cfc454e8a096f31b4625331", + "745bf50f671a44bc9abddcc6efb736ee", + "07b75ae72a6a4d16a3ff73a1debe2ee5", + "082d0b472b90409f8ab9b02391df63a4", + "1c2bd9e72e334917b3ce39810bf5ea0b", + "2aa84a0d65254ef49ee100f66e393ae9", + "87a1b92e248549a5b9b9833d52e85c76", + "1cda98448c074471ae879ab9410e49d5", + "9a7cb4d7e9514575a93e2f86fc32fb4b", + "1c7a17fb7dd041c2949e5df85ee78570", + "c6e7bfb4e3144b16bcfd3fd12673d853", + "1eecabada33e46da975375f9fce5517e", + "1674e26429bc4c80991bb8942b56a7d7", + "72e608fe672b40c99de89ad5ce1e33c0", + "a35f8906c1ae46828120319694065815", + "6340e0aa27c04db0b48448513e9875c4", + "c97025e7d015445fafa406c4c1a89af7", + "1dc7760bdd2944ad9c4991c82dc86e25", + "144c2d74865e41c19a4a6efb4b120ca9", + "e8dba2b5fdc94b5598e6ab9f75671cbb", + "1c1855ade6204625bfaab54ca2159af5", + "210766df14774c1e89d0df73f294daa6", + "7171afc8b8dd4e64966065b7f39f2dd6", + "6fe01f5548f947d2aebc95273a592581", + "c2f3547e877a4f2dac7a1130e6f9acab", + "67b22ef1c41541dd99fc2b8d445513d2", + "9bbc7693c651492f843088817a65497f", + "20eb3303eb2b429891247566f52218c0", + "25721e39542d43029d26ac34b4af6d89", + "aef15ac1c7214177b7a890917b2a350d", + "75e8cbe174904f91b3a9ee3f4572625b", + "484fac29339449d58309c92dbbbd2af2", + "d216a5de8270482b90d297cf31a09d35", + "fc4c72e25d9a4b648c14d03560bba0a2", + "58a4b18d859e4c47bc97f2e6ce8c6c73", + "6a63b2bbe3f34bc6b9a4258a04ca9104", + "9812cfa5ccd14b7aa756a383c793c2c5", + "4a2f2fa2885347aebb436467a49b8857", + "a7fb70b9171c4958bc5f5c37e51f8cd6", + "31d5cb042096428fa4e8a0cd682ddc09", + "9f546de07edf4386ae8ef9394eb6c692", + "3350308cf34f4c02bd89f6e2a345ed12", + "7ec029d2493f40649921347adbb03ae7", + "c922e749c8464d2f9080b3bd2333d04f", + "1646a7c133a94af7bcfda5fb6ca800bc", + "80cc4498465e4fecba040320c9bf1af6", + "55e69f30eb7f44dfb9fa64f938cd41dd", + "ea7c0bd905074e5d8105bb82036bea1c", + "e529a78e1aba43d980b2953aef3cdfba", + "25296141a79c4477aac6635dd30a80e5", + "2ee2d4a8635f45dba573b749244f9e2a", + "404fed29990348b7ac2fba4a5b61aa89", + "9b045fecb5c24541b6611ba71237f6f7", + "3528f8245a2044c4b2037481bdaf39a7", + "047bdaa0ff9d4a4f8d6f46fca23bd0d2", + "91f16cb20b2d4590ab22a4b964290569", + "82e30cf225894092a6be203ae026b742", + "4a987dc4a0e54e5da442be8d6a1c7f09", + "cd21d646227b4d478abb70ba04588215", + "01b01e7329334686ba39c11ec18e9df5", + "2e8a6bd2ef104b32868443ac711c88b6", + "0e590d1bb05d4bfaa6c0c41cb41e4794", + "279ce49322904b87b2ec18b4f497465c", + "15799ffca3624428926e2ddb16728b68", + "ac96d471b96049c4aace63a7d88ec166", + "86a4453fe0d7495aafe95e8f68f7a44c", + "fcb2db503d9a49c3b45282bebec92322", + "1384b04401ee4d5e9a7ab9574e660ee6", + "86b758931e9b4f6c81b2b5d61bb0ed86", + "ca8a046ed39e45f79234a10f85ed09d4", + "9d0bb9e6cae5452a8f7909340d0ab21c", + "9db7c2d529ea46d48e22271d8432f137", + "6d6241a0e8544b03bd406739bd2e3914", + "3475f0d8415b4f3ba6b5ad701c365e61", + "bb1ee26e35f04a429d3df1f7273b4603", + "5b72a50beccd472399740c67d5b8a775", + "488139fa453f40e6895a0bac7d5cf840", + "d6e90d985e8b4f7fa5d8258c72a8ba9e", + "5053663ab6874c378d764dc8584b6aca", + "873c26ebd5a0403885933700f87ae39f", + "4058a3b2505f441f84f78fe4f7188aa8", + "d525b6ed32414b8583d8726d83dd747b", + "932854c5974142b3b906ac1db17725fc", + "baad5e47bfc940229c7c19cb8372b59c", + "3ca24d66c54b42868c814abb11d4214e", + "0fe66bd8c6314fdb86ea236d9638f538", + "10e6ba61808f471d82b431c607b65d99", + "5c9eb0211674488bbe8ec74c68a6e9a3", + "f71dd43430aa4d4cb5168644a972df59", + "92f74f940eb74ceb89139e25dc1fe869", + "b6168e1cb7354844a2ac17dee7a31168", + "324b10e8d5ea4f1598111ce9321f5809", + "b3d5b8a11f8948d0bb7273e036841869", + "caf81cab2e1c45c38007dc88bc9a9e0e", + "b30a4626320c4e78b3a033cf257946fe", + "d22010093bc54928b96389c8cc48d409", + "148b60b8a9a44244874d2db13de48a00", + "311acaa0e5844400b54281fe18b50b34", + "6016947c02af4021851e950c72479029", + "3a6dce72d0ed40c48bbd91b3f07aa67a", + "36777dc617bf4589a8ab9802f363ecf4", + "f2e58d8539584eebae048ed68d50a641", + "9dde5575c8e7438fa2d86e4b9a4fb33f", + "18e8af9f9ff247c88797e03d48b675f9", + "8cef8071dc984acc8a003134e8d64a11", + "cd80b89157374e759c3a24091fced46b", + "3563de5d5e394c578b7667bdae8644e6", + "2040593b2df341c68670f38512b423d0", + "16ea181fc8694d29b499b46678fbdaf3", + "e6b5096cac864d27ad88010c2180db29", + "e32e4cee40f14064b9cd93b7688c3ff5", + "e82ab023dc0e4a2dbe8da18f1b346ca0", + "542c34c7d55c400a8f623232403fe250", + "0759cda7bd084b02921689bc5e319ba5", + "543c3f05eb0547298950b7e7d8c64467", + "f414756091e34756a98d812bcf299f69", + "d510866a711a4f5bb98748690dc89fbe", + "56959726851f49a9b05b1b92cfdd6e74", + "3f10740717d4479ba2acf18f7957fc44", + "31081f8d85954a33bcc789b1f5f93c56", + "ec2fc9e744d9460f9063f9c0aac4c3d5", + "8206f8f3de1a4907ae54d474d422633f", + "3880476d3f0e4962be29180763486deb", + "ce88d1d6c0494245b812ae65ea2654ad", + "18f20450f84d48d7839bb3c676cb53d4", + "4619abf6125240f7862c7583a6f68af1", + "f24b9f61a1fe4c3f86e5086676a5e5d5", + "8299c1ef69414603a1604699c777e1f4", + "648f377c77da4818b1817ab95677d5bc", + "ccd7cb252ac34be1afa95313cd576555", + "75469c205b314779aef9d1e7ff2fd612", + "2dd3e0f914f6471587bbe40beeacd7f3", + "e6f8227c5e914a279428bfc620591ef0", + "79c3843d7f6045799cfc76b2bc4f0837", + "33170adc2cbe4b3daa9073cee501d854", + "8f6e8799b4e1454ea04bb9c94b70fdb8", + "9ea85c5be7104fdbb67f52414ba1a1bd", + "81ae284f0d064843be6279936fba8a13", + "1e25f97059cf482587bc1dcb055d4f7c", + "583a3d03b1f344d6b7424589c4db1634", + "56628597cea5425684265b293010eefe", + "a999d2c30b9644a7967b7109c3aae629", + "08c07275cbbe4fd0822c354d99c303ea", + "ee9eb1bc7197470487cf0dea02a3d8f9", + "a4ddea7815394732a0125128fcd9e1f2", + "3da1fd9a348f4c7bb07889c017cc0c2a", + "62bad7effcf24059b293404071b83263", + "c5d372a9962c49ada49b96a66c5ecd7e", + "c2a0bcc5ca8e42439ef2e153193714be", + "dd4114a8f79b4725951e78ab4f31d19b", + "0dfe3ae073634ba8a1747bc96b30e2bd", + "9dcbe88c6ba446ea9584331776d2748a", + "9b6856e1b63c46b78a50bee3336577e7", + "9cc8e3acf3d74f9796353f621fb6dfbf", + "ab1569f009a54b018f8e1d90a51172ed", + "d2d0f66f84874e679594bc856182c6c2", + "e5562397a34348be901b498203690a42", + "cfdf85f797074bc8af900f5623b63e67", + "ee26b20d24b94233b242a490bbb528bd", + "b64f8b95e23c4baf87fcec7fb16aedfa", + "f9bf6bbc52ea46bc8204f3756f2f981b", + "f3cb306bf10b448ab173861f392ad1d7", + "52cbbbf6bc6643e9b43d844586ab453e", + "a071ed8d726f45b4a07251b60793dc6a", + "5f71880b66d1458e923cc893a88f91bf", + "3e528e7f8cbf4ca597c63d1789a556a5", + "4184d862482d4589806f8d09b5b85737", + "c99fc92b5c87414faa6bafd1d91ce9c4", + "098aae301ab04e688652dd0c05f8de1c", + "aff4e5ac7e384d408fc23ca0a8d3658f", + "c0cf700f7872458385250ba075a8edb1", + "2e6a63d04fd747a280f813b1335a52b1", + "dd41ea80146343bcbee92bed78324879", + "04b01b03fcc143fa8633b52ec231f026", + "10d449f46d9c4502baaad1606f873c09", + "44b4f06f3c0841d2ac960337d18de7a3", + "dbe52dd9651f42238a11351f10b22b4a", + "3f76ca5381b64731af382a63b890292b", + "5af452039cad49ee92c938aaffa25765", + "05c1c99b4aff486a9c5eeadf9bd8230a", + "167cf75950f6434293f6809a84f998f3", + "d3995239fdf54fabb7fb42417dd2d1e2", + "32cb9bbc1e4547cfbe4df1e4ca70e254", + "3e9927167bb84cac9cf56f350bee5aae", + "ff830930e9e04bdaa73f57e4eafe1979", + "72254d54d49a4054b49882200eecc49b", + "cfa9b34f74b541fc9c54b905cffa05ad", + "ea0303a1646b454cb26f023c0d8b7804", + "a4dc6daaa87e4a4689ffc623b0a3b259", + "24b5e61aecd84402a6261b4b06146559", + "db3b31f6234f4c0781e02e27c0a78139", + "c1f64aaadd484bb2900aaebadeb305d5", + "97e80dbb785548e0ad4b7724e91a27f7", + "20e0bf2b09d841d6bc9e1f36ec4079bc", + "bf9d9715e7684c4e8ac29cbcabf1c600", + "e71b1fe193ba49afb42fe50f96663070", + "c0c9ce38c68446ddad343c8abc4aa3a9", + "a7f7c95743214d17a1cd103c2add1b3c", + "699755e8ae9840f0a87246bd89608f67", + "1e6e26bf5f3e4322b0f9ff4352976adb", + "b38da01ce29b4009bf824a779d4cafe7", + "5f3800af43c84d3596987e49afd4961d", + "d34ff744caf646e88cb0606d7a6de704", + "5c6c3811d9e04247abd52e0f835b6a5e", + "fdf8fb828c1d4ad6b864b9197a0aa486", + "bb888b3abe26402897177229c0a6ab01", + "009ae1d114cb48d0990e934e19ee961f", + "aaa6238192004463834b5ee1b3464cff", + "299e59499eda4a1db01880e4fb1d5cd2", + "b50f6068222b4b7d9c8a865bd41ce277", + "ca44a4c94aea499ea738f536eacf4b9b", + "582d8cd7787443f59398e2f79c328b51", + "821ebf47157741ae9045004304e1d66d", + "8f29c28a5f154fcfb6cb2f382eb1e1b1", + "c5f9fb53d093461282bc9ebd4a68ead4", + "7ddc094c928a4b87a7d5895dc558b15b", + "554913277fc34084990cd041ca766701", + "e8f2e16d8a9f4091a2d6438892c731b4", + "0ce302531a69423aa36a39e0502f7970", + "d80a188b254f4bd1be7b3c18442547ea" + ] + }, + "id": "NiK6-vQHKnBy", + "outputId": "7b5b64fc-9eae-43e0-8598-7bc5f3608cfe" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/niels/python_projects/transformers/env/lib/python3.8/site-packages/lightning_fabric/connector.py:563: `precision=16` is supported for historical reasons but its usage is discouraged. Please set your precision to 16-mixed instead!\n", + "Using 16bit Automatic Mixed Precision (AMP)\n", + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "You are using a CUDA device ('NVIDIA A100-SXM4-80GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4]\n", + "\n", + " | Name | Type | Params\n", + "----------------------------------------------------\n", + "0 | model | VisionEncoderDecoderModel | 201 M \n", + "----------------------------------------------------\n", + "201 M Trainable params\n", + "0 Non-trainable params\n", + "201 M Total params\n", + "807.633 Total estimated model params size (MB)\n", + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0: 20%|██ | 160/800 [00:25<01:40, 6.37it/s, v_num=1]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/niels/python_projects/transformers/src/transformers/generation/configuration_utils.py:543: UserWarning: `num_beams` is set to 1. However, `early_stopping` is set to `True` -- this flag is only used in beam-based generation modes. You should set `num_beams>1` or unset `early_stopping`.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Prediction: REAL GANACHE116,500113,000111111111111111114,5004,500\n", + " Answer: REAL GANACHE116,500EGG TART113,000PIZZA TOAST116,00045,50050,0004,500\n", + " Normed ED: 0.5036231884057971\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/niels/python_projects/transformers/env/lib/python3.8/site-packages/pytorch_lightning/utilities/data.py:77: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 1. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Prediction: Kopi Susu Kolonel123.00023.00050.00027.000\n", + " Answer: Kopi Susu Kolonel123.00023.00050.00027.000\n", + " Normed ED: 0.0\n", + "Prediction: S-Ovaltine120,00020,00018,18120,000100,00080,000\n", + " Answer: S-Ovaltine 50%20,000120,00010% Tax Included18,1811,81820,000100,00080,000\n", + " Normed ED: 0.32460732984293195\n", + "Prediction: M-Caramel Black Tea128,0001X28,0001X28,00028,00028,0000\n", + " Answer: M-Caramel Black Tea@28,0001X28,00070%Less Ice28,000028,00028,0000\n", + " Normed ED: 0.3009950248756219\n", + "Prediction: BBQ Chicken141,00041,000- Sedang1041,00050.0009000\n", + " Answer: BBQ Chicken141,000Sedang1041,00041,00050.000:9,0001\n", + " Normed ED: 0.3447368421052632\n", + "Prediction: LE MINERAL18,0007277278,0008,000\n", + " Answer: LE MINERAL1.008,0007,2737278,0008,000\n", + " Normed ED: 0.22304832713754646\n", + "Prediction: POTATO SAUSAGE BREAD119,0001111152,000123,000123,000123,000\n", + " Answer: POTATO SAUSAGE BREAD119,000OREO GREEN TEA SPREAD152,000WHITE CHOCO BANANA SPREAD152,000123,000123,000\n", + " Normed ED: 0.39893617021276595\n", + "Prediction: Choco Devil463.63663.636-9,545-9,5455,40959,500100,50040,500:\n", + " Answer: Choco Devil4-9,54563,63663,636-9,5455,40959,500100,00040,500\n", + " Normed ED: 0.321353065539112\n", + "Prediction: TALAM UNGU3X19,500TALAM UNGU119,500-40.000%-7,80004.00xITEMs11,70011,7008,300\n", + " Answer: TALAM UNGU@65003X-7,80019,500MIKA KECIL@01X011,70011,70020,0008,3004.00xITEMs\n", + " Normed ED: 0.46107784431137727\n", + "Prediction: Tahu Ikan Oma Gisk120.00020.00020.0000\n", + " Answer: Tahu Ikan Oma Giok120,00020,00020,0000\n", + " Normed ED: 0.01904761904761905\n", + "Prediction: Serbu140.000220.00060.00060.0000\n", + " Answer: Serbu 1240.000Choco Peanut Bread220.00060.00060.0000\n", + " Normed ED: 0.1371841155234657\n", + "Prediction: Se'I Sapi Sambal Matah ( R )120.00020.00035.00010.000210.00016.00010010010010010089.100\n", + " Answer: Se'I Sapi Sambal Matah ( R )120.000Se'I Sapi Lada Hitam (J)135.000Nasi Putih210.000Milk Shake Coklat116.00081.0008.10089.100089.100\n", + " Normed ED: 0.4283185840707965\n", + "Prediction: ES KOPI SUSU472.00072.00072.0000\n", + " Answer: ES KOPI SUSU472.00072.000072.000\n", + " Normed ED: 0.14903846153846154\n", + "Prediction: MINERAL 600 ML17,7277,72733,63641,3644,13645,50050,000-4,500\n", + " Answer: MINERAL 600 ML17,727BULGOGI RICE R133,63641,3644,13645,50050,000-4,500\n", + " Normed ED: 0.25176470588235295\n", + "Prediction: Arem Arem Arem Arem Arem Arem Arem2x24.0002x24.0001x12.0001x12.000Rp 36.000Rp 39.600Rp 39.600Rp 39.6000\n", + " Answer: Arem Arem@ 12.0002 x24.000Kroket@ 12.0001 x12.000Rp 36.000Rp 3.600Rp 39.600Rp 39.600\n", + " Normed ED: 0.38875878220140514\n", + "Prediction: Arem Arem Arem Arem Arem Arem Arem Arem Arem Arem Arem Arem Arem Arem Arem Arem Arem Arem Arem Arem Arem Arem AremployRp 54.000Rp 59.400Rp 100.000Rp 40.600\n", + " Answer: Arem Arem12.000224.000Pepenero Pastel15.000230.000Rp 54.000Rp 5.400Rp 59.400Rp 100.000Rp 40.600\n", + " Normed ED: 0.5032258064516129\n", + "Prediction: 20,000120,00020,000100,00080,000\n", + " Answer: TT20,000120,00020,000100,00080,000\n", + " Normed ED: 0.0944206008583691\n", + "Prediction: LEMONADE 16OZ120.000120.00020,00020.000100,00080,00080,000\n", + " Answer: LEMONADE 16OZ20,000120,00020,00020,000100,00080.000\n", + " Normed ED: 0.14681440443213298\n", + "Prediction: beef C roll 3pcs110,000110,00015,00015,000215,00025,00075,000\n", + " Answer: beef C roll 3pcs10,000110,000kaya bred15,000115,00025,000100,00075,0002\n", + " Normed ED: 0.31758530183727035\n", + "Prediction: FUTAMI GREEN TEA (CLAS112,500113,00017,00042,50050,0007,500\n", + " Answer: FUTAMI 17 GREEN TEA (CLAS112,500EGG TART113,000GRAIN CROQUE MONSIEUR117,00042,50050,0007,500\n", + " Normed ED: 0.2648648648648649\n", + "Prediction: JAMUR210,00015,00015,0001,50020,0003,500\n", + " Answer: JAMUR210,000TAHU15,00015,0001,50016,50020,0003,500\n", + " Normed ED: 0.16939890710382513\n", + "Prediction: Mango Lemon Tea1Rp29,090Rp113,636Rp86,363Rp34,363Rp9,736Rp20,446Rp224,908\n", + " Answer: Mango Lemon Tea1Rp 29,090Sliders Set1Rp 113,636Chicken Vege Rice Bowl1Rp 86,363Discount BCA 15%1-Rp 34,363Rp 194,726Rp 9,736Rp 20,446Rp 224,908Rp 224,908\n", + " Normed ED: 0.4500846023688663\n", + "Prediction: RedVelvet Nutella1280,000280,000280,00028,000308,000\n", + " Answer: RedVelvet Nutella1280,000Free Mini Candle.5Large Box1280,00028,000308,000308,000\n", + " Normed ED: 0.4339152119700748\n", + "Prediction: BUBBLE GUM118,18218,181818181811820.00020.00020.000\n", + " Answer: BUBBLE GUM118,18218,1821,81820.00020.0001\n", + " Normed ED: 0.5249110320284698\n", + "Prediction: PAIN AU CHOCOLATE111,00011111119,000113,50020,000100,50019,500\n", + " Answer: PAIN AU CHOCOLATE111,000CHOCO CUSTARD PASTRY112,000MILK PASTRY ROLL19,000REAL CHEESE INSIDE BREAD113,500SAUSAGE BREAD115,000HAM CHEESE FLAT BREAD120,00080,500100,00019,500\n", + " Normed ED: 0.3848684210526316\n", + "Prediction: Prs Sop Sui Jiao1Prs Ha Kaou Udng1Prs Sio May Kpting1Prs Siomay1Prs Siomay1Prs Licp Sniったり1Prs Mio1Prs Mio1Prs Mio1Prs Siomay1Prs Mio1Prs Mio1Prs1Prs1Prs1Prs1Prs1Prs7,000180,00018,900207,900777\n", + " Answer: Sop Sui Jiao1Prs33,000Ha Kaou Udng1Prs28,000Sio May Kpting1Prs23,500Siomay Kmbinasi1Prs23,000Leng Hong Kien1Prs30,000Mie Trsi Kgkung1Prs35,500Es Teh Tawar1Gls7,000180,0009,00018,900207,90077\n", + " Normed ED: 0.4827586206896552\n", + "Prediction: NASI MERAH/PUTIH1x5,0001x5,0005,0001x6.0002x68,0002.0002.0001x14.0001x6.0001x6.0001x6.000\n", + " Answer: NASI MERAH/PUTIH5.0001x5.000SAYUR4.0002x8.000KERUPUK/SAMBEL2.0001x2.000AYAM14.0001x14.000MINUMAN KEMASAN/REFILL6.0001x6.000Rp. 35.000\n", + " Normed ED: 0.47474747474747475\n", + "Prediction: THAI ICED TEA (L)116,36316,36116,99916,99916,917,99916,9\n", + " Answer: THAI ICED TEA (L)16,363116.36316,3631,63617,9991\n", + " Normed ED: 0.3487179487179487\n", + "Prediction: ELEPHANT READ BEAN112,000112,0001110,000110,000110,00022,00022,00022,00022,00022,00022,00020\n", + " Answer: ELEPHANT READ BEAN12,000112,000chapsal twister donnut10,000110,00022,00022,00002\n", + " Normed ED: 0.46557377049180326\n", + "Prediction: Sabun Beras13000030000130000333350000200002000020000\n", + " Answer: Sabun Beras3000013000030000Discount(0%)5000020000\n", + " Normed ED: 0.41371158392434987\n", + "Prediction: REDBEAN BREAD19,00012,00021,00050,00029,000\n", + " Answer: REDBEAN BRE/D19,000FRANKFRUT S/USAGE ROLL112,00021,00050,00029,000\n", + " Normed ED: 0.16838487972508592\n", + "Prediction: PREMIUM TOAST PAN BREAD124,00024,00024,00024,0000\n", + " Answer: PREMIUM TOAST PAN BREAD124,00024,00024,0000\n", + " Normed ED: 0.14\n", + "Prediction: Nasi (MLY)16.0006.0006.0006.0000\n", + " Answer: Nasi (MLY)16.0006.0006.0006.000\n", + " Normed ED: 0.23333333333333334\n", + "Prediction: GRAINS PAN BREAD120,50020,50020,50037,00057,50050,0007,500\n", + " Answer: GRAINS PAN BREAD120,500ICED HIBISCUS LYCHEE TEA137,00057,50050,0007,5000\n", + " Normed ED: 0.3152454780361757\n", + "Prediction: GRILLED BABY POTATO (R150,50076,00070,50046,000243,00014,58025,758283,338283,338283,3585962\n", + " Answer: GRILLED BABY POTATO (R150,500TRUFFLE CREAM176,000CARBONARA170,500ORIGINAL BREWED TEA246,000243,00014,58025,758283,338283,3385962\n", + " Normed ED: 0.4353146853146853\n", + "Prediction: BLACK PERPER MEATBALL176,50076,50082,00056,00046,000BREWED TEA2261,00015,66027,666304,326\n", + " Answer: BLACK PEPPER MEATBALL176,500QUARTO FORMANGGI PASTA182,500GREEN TEA WITH CRUMBLE156,000ORIGINAL BREWED TEA246,000261,00015,66027,666304,326\n", + " Normed ED: 0.4823091247672253\n", + "Prediction: Soft Ori 3 Top117,27217,27217,2717,2718,99918,99918,99918,99918,99918,99918,999\n", + " Answer: Soft Ori 3 Top117,272Top Oreo0Top Oreo0Top Banana017,2721,72718,99918,999\n", + " Normed ED: 0.4378698224852071\n", + "Prediction: Ice Lemon Tea113,650,00063,6366,36470,00070,00050,000\n", + " Answer: Ice Lemon Tea13,636-Gyro Platter Regular50,00063,6366,36470,00070,000\n", + " Normed ED: 0.34782608695652173\n", + "Prediction: TT120,00020,00020,000100,000-80.000\n", + " Answer: TT20,000120,00020,000100,00080,000\n", + " Normed ED: 0.2966101694915254\n", + "Prediction: PKT TEL OR VERKEDEL「26,00026,00025,00025,00020,0008,000100,0002,100\n", + " Answer: PKT TELOR/PERKEDEL26,000TERONG12,000PARU23,000SBL GR ATI/AMPLA20,000NESTLE 330 ML8,00089,0008,90097,900100,0002,1005.00\n", + " Normed ED: 0.37636363636363634\n", + "Prediction: Gojek Chicken195,00095,00095,0001180,00070,000345,00034,500379,500379,500379,500379,500379,500379,500379,500काे පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර පියවර\n", + " Answer: Gojek Chicken Chilli Sauce H195,000Gojek Chicken Soy Sauce F1180,000Gojek French Fries170,000345,0000034,500379,500\n", + " Normed ED: 0.9254208395482634\n", + "Prediction: 01150,00011230,000230,000230,000230,000230,000230,000390,0000390,0000390,0000390,0000\n", + " Answer: SOGOGI JAPCHAE1150,000GONG GIBAB7105,000CHULPAN CHEESE DAK GALBI1230,000CREAM TEOK PO KI195,000EL KEUN HWANGTAE1120,000SUNDUBU(TUKBEGI) CHAM KKOT SAL2620,000U SAM GYEOP2290,000U SEOL2390,000U SAM GYEOP10ICE COFFE MIX30ICE BLACK COFFE102,000,0000140,000212,4602,352,46002,352,460\n", + " Normed ED: 0.5826839826839827\n", + "Prediction: STIX CINNAMON119,00019,000119,000117,000117,00017,00053,00053,00018510\n", + " Answer: STIX CINNAMON19,000119,000CINNAMON SUGAR17,000117,000TRIPPLE CHEESE17,000117,00053,00053,000053,000\n", + " Normed ED: 0.3838951310861423\n", + "Prediction: ROTI UNYIL170272.000272.0006.000278.000300.00022.000171\n", + " Answer: ROTI UNYIL170272.000PAPER BAG BESAR16.000278.000300.00022.000171\n", + " Normed ED: 0.29444444444444445\n", + "Prediction: THAI ICED TEA (L)116,36316,36116,99916,99916,917,99916,9\n", + " Answer: THAI ICED TEA (L)16,363116.36316.3631,63617,9991\n", + " Normed ED: 0.35128205128205126\n", + "Prediction: AOP Seafood185,0000murice Katsu Curry195,00035,00025,000240,00021,60026,160287,7,760\n", + " Answer: AOP Seafood185,000Omurice Katsu Curry195,000Earl Gray Tea135,000Hot Tea125,000240,00021,60026,160287,760\n", + " Normed ED: 0.32803180914512925\n", + "Prediction: MARBLE CASTELA22,000122,000122,00022,00022,00022,0000\n", + " Answer: MARBLE CASTELA22,000122,00022,00022,00001\n", + " Normed ED: 0.29357798165137616\n", + "Prediction: Seaweed Chicken142,00042,00042,00042,00050,0008,000\n", + " Answer: Seaweed Chicken142,000- Sedang1042,00042,00050,0008,0001\n", + " Normed ED: 0.2051948051948052\n", + "Prediction: SB OR128,636Chokocha Flt113,63613,63655,9085,59261,50070,0008,500\n", + " Answer: SB 1 OR128,636Chokocha Flt113,636Bbq Bento113,63655,9085,59261,50070,0008,5003\n", + " Normed ED: 0.2698744769874477\n", + "Prediction: 52.0001x52.0001x52.0001x52.0001x22.0001x22.0001x22.00074.0007.40081-4000\n", + " Answer: Nasi CampurBali52.0001x52.000Lemon22.0001x22.00074.0007.40081.400\n", + " Normed ED: 0.38169642857142855\n", + "Prediction: CHOCOLATE ECLAIRIN26,00026,00025,0000\n", + " Answer: CHOCOLATE ECLAIR226,00026,00026,0000\n", + " Normed ED: 0.10096153846153846\n", + "Prediction: 118 Round Wagyu (1gr)147.20010.00057.20057.20057.200\n", + " Answer: Round Wagyu (1gr)11847.200Wagyu Rice Box110.00057.20057.20057.200\n", + " Normed ED: 0.31343283582089554\n", + "Prediction: KENTHIR242.0006.0008.00056.00056.0000\n", + " Answer: KENTHIR 2242.000KOL GORENG26.000TEH MANIS28.00056.00056.00056.0000\n", + " Normed ED: 0.24754901960784315\n", + "Prediction: Lar ge111.00011.0001020.000031.00040.0009.000\n", + " Answer: Large 1111.000*Rhum10Pastry Keju120.000*Plastik kcl1031.00040.0009.0004\n", + " Normed ED: 0.36585365853658536\n", + "Prediction: 4003-Blueberry Fuji4003-40.000x 140.0006001-Plastic Bag Small0x 102\n", + " Answer: 4003-Blueberry Fuji40.000x140.0006001-Plastic Bag Small0x1040.00050.00010.0002\n", + " Normed ED: 0.47164948453608246\n", + "Prediction: Cha Keaw160.00060.00060.00060.00060.00060.000\n", + " Answer: Cha Keaw L...x260.000Cha Keaw L...x260.000)60.00060.00060.000\n", + " Normed ED: 0.40397350993377484\n", + "Prediction: Thai Iced T.x」20.00020.00020.0000\n", + " Answer: Thai Iced T. .x120.00020.00020.00020.0000\n", + " Normed ED: 0.2862595419847328\n", + "Prediction: SEAFOOD MARINARA185,000185,500247,50014,850PB16\n", + " Answer: SEAFOOD MARINARA185,000CREAMY MARINARA SALMON185.500LYCHEE ICE TEA277,000247,50014,85026,235288,585\n", + " Normed ED: 0.43820224719101125\n", + "Prediction: Viet Milk Coffee125.000+Hot125.00025.000Rp 0\n", + " Answer: Viet Milk Coffee125.000+Hot+M25.00025.00025.0000\n", + " Normed ED: 0.3413897280966767\n", + "Prediction: XLB Org Pork 10x178,00065,00058,000DF Fish Fillet Garic1108,00088,00038,00038,00042,00060,394\n", + " Answer: XLB Org Pork 10x178,000Sicito Babi165,000Hotplate Tahu158,000DF Fish Fillet Garlc1108,000LM Pork Belly188,000Siew Mai138,800Kwan Yin Cup550,000Herbal Jelly138,000Onde-Onde138,000561,80042,13560,394664,32913\n", + " Normed ED: 0.5460599334073252\n", + "Prediction: VANILLA CHOCO HEART CAKE1180,000180,000180,000\n", + " Answer: VANILLA CHOCO HEART CAKE1180,000180,000180,000\n", + " Normed ED: 0.1407035175879397\n", + "Prediction: Copuluire Cho ulate14,5004,5004,5004,5003,0003,0007,500(10%)8,25010,000501,800\n", + " Answer: Populaire Chocolate4,50014,500Paddle Pop Choco Magma3,00013,0007,5007508,2505010,0001,800\n", + " Normed ED: 0.381651376146789\n", + "Prediction: French Fries110,9092,0000236,3649,0915,636100,00038,000\n", + " Answer: French Fries110,909Cheese Burger236,364Milo19.09156,3645,63662,000100,00038,000\n", + " Normed ED: 0.30580357142857145\n", + "Prediction: ISI CAMPUR143.63643.6368.0008.00051,6365,16456,800110.00053,200\n", + " Answer: ISI CAMPUR43,636143.636A.MINERAL BOTOL8.00018.00051,6365,16456,800110,00053.200\n", + " Normed ED: 0.3857758620689655\n", + "Prediction: WHOLE WHEAT PAN BREAD120,00020,000100,00080,000\n", + " Answer: WHOLE WHEAT PAN BREAD120,00020,000100,00080,000\n", + " Normed ED: 0.0410958904109589\n", + "Prediction: BASO TAHU143.643.63643.6366.000149.6364.9654.60060.1005.500Terima0\n", + " Answer: BASO TAHU43.636143.636NASI PUTIH6.00016.00049.6364.96454.60060.1005.500\n", + " Normed ED: 0.3927893738140417\n", + "Prediction: AIR MINERAL7,0007,0007,0007,000\n", + " Answer: AIR MINERAL7,0007,0007,000\n", + " Normed ED: 0.22043010752688172\n", + "Prediction: PAKET BER2169,091PAKET BER2169,09169,90976,00076,00076,00076,000\n", + " Answer: PAKET BER2169,091BREAD N CHEESE DEL1MAC N CHEESE DEL1PEPSI DEL1P/P THUOSAND ISL DEL1LIPTON ICE TEA DEL169,0916,90976,000\n", + " Normed ED: 0.5059523809523809\n", + "Prediction: Honey Mandarin2X13,00026,00026,00030,0004,000\n", + " Answer: Honey Mandarin13,0002 X26,00026,00030,0004,000\n", + " Normed ED: 0.15730337078651685\n", + "Prediction: SOTO MEDAN+NASI159.500GADO-GRDO134.50057.500151.5007.57515.908174.983\n", + " Answer: SOTO MEDAN+NASI159.500GADO-GADO134.500SOTO BETAWI + NASI157.500151.5007.57515.908174.983\n", + " Normed ED: 0.31105990783410137\n", + "Prediction: KaraageCurryTeishoku169,00019,00015,000103,0007,725\n", + " Answer: KaraageCurryTeishoku169,000Lemon Plate119,000Ocha Hot115,000103,0007,72511,073121,79833\n", + " Normed ED: 0.5734406438631791\n", + "Prediction: Dum Dum Thai Coffee119.0001.00020.00050.00030.000\n", + " Answer: Dum Dum Thai Coffee119.000Ice11.00020.00020.00050.00030.000\n", + " Normed ED: 0.3081232492997199\n", + "Prediction: STIX CINNAMON119,00019,000119,000117,000117,000117,00036,00046070\n", + " Answer: STIX CINNAMON19,0001 x19,000TRIPPLE CHEESE17,0001 x17,00036,00036,000036,000\n", + " Normed ED: 0.419953596287703\n", + "Prediction: ICED TT20,00020,00050,00050,000.30,000\n", + " Answer: ICED TT20,00020,00050,00030,000\n", + " Normed ED: 0.2072072072072072\n", + "Prediction: THAI ICED TEA1@20.000120.000120.00020.0000\n", + " Answer: THAI ICED TEA@20.000120.00020.00020.00020.0000\n", + " Normed ED: 0.28802588996763756\n", + "Prediction: KFC Winger HC240,00013,6365,90900960,4546,046100,00033,500\n", + " Answer: KFC Winger HC240,000Ori Bento113,636Mango Float15,909F.Wngr GES-HC10CHARGE TA190960,4546,04666,500100,00033,5006\n", + " Normed ED: 0.3656957928802589\n", + "Prediction: Enting2 Dua Bandeng Ori131.00031.000168.00099.00099.000\n", + " Answer: Enting2 Dua Bandeng Ori0106931.000131.000TAHU BAKSO Grg Ayam VC [biji071656.8001068.00099.00099.000211\n", + " Normed ED: 0.46710526315789475\n", + "Prediction: Cheese Tart*Rp58000.00Rp58000.00Rp5272.73Rp58000.00Rp58000.00Rp100Rp42000.00\n", + " Answer: Cheese Tart*2Rp 58000.00Bag-SPS CarrierRp 58000.00Rp 5272.73Rp 58000.00Rp 100000.00Rp 42000.00\n", + " Normed ED: 0.37681159420289856\n", + "Prediction: Mineral Water218000111.00011.0009000154,00010016 170177.870177.870177.870\n", + " Answer: Mineral Water218 000Teh Tawar Dingin111.000Soto Betawi2116.000NASI PUTIH19.000154 0007 70016 170177.8706\n", + " Normed ED: 0.4250936329588015\n", + "Prediction: Crab Stick19.00010.0007.0008.000224.0006.00072.00072.000\n", + " Answer: Crab Stick19.000Bakso Lobster110.000Sawi Putih17.000Jamur Enoki18.000Jamur Kuping18.000Maling12000224.000Nasi (MLY)16.00072.00072.00072.000\n", + " Normed ED: 0.5221112696148359\n", + "Prediction: Sate Padang125.00025.00025.000\n", + " Answer: Sate Padang125.00025.00025.0001\n", + " Normed ED: 0.1724137931034483\n", + "Prediction: SURIMI129,091CREAMY CHK CLS FTC142,727MIX 4FUN CHOCOLATE119,09160,90999,091250,909\n", + " Answer: SURIMI129,091CREAMY CHK CLS FTC142,727MIX 4FUN CHOCOLATE119,091GREEN ITSODA PITCHER160,909SC/R GRILLED STEAK199,091250,90925,091276,000\n", + " Normed ED: 0.4438405797101449\n", + "Prediction: PEARL CHOCO TEA117,000GREEN TEA LYCHEE118.00035.000100.000\n", + " Answer: PEARL CHOCO TEA117.000GREEN TEA LYCHEE118.000TUTUP SEAL20CUP 14 OZ2035.00035.000100.00065.0006\n", + " Normed ED: 0.5288461538461539\n", + "Prediction: S-Milk tea@17,0001X17,00017,00017,0000\n", + " Answer: S-Milk tea@17,0001X17,000100%17,000017,00017,0000\n", + " Normed ED: 0.37329700272479566\n", + "Prediction: HAZELNUT CHOCO MT ( R )124,00024,00024,00022222252,00052,00052,00052,00052,0000\n", + " Answer: HAZELNUT CHOCO MT (R)1 x24,000PEARL (R)4,000HAZELNUT CHOCO MT (R)1 x24,00052,00052,00052,0002\n", + " Normed ED: 0.4675090252707581\n", + "Prediction: Peanut & Cheese113,63613,6361,36415,00020,0005,000\n", + " Answer: Peanut & Cheese113,63613,6361,36415,00020,0005,000\n", + " Normed ED: 0.09063444108761329\n", + "Prediction: LEMONADE22OZ126,00026,00026,00026,0000\n", + " Answer: LEMONADE22OZ126,00026,00026,00026,000026,000\n", + " Normed ED: 0.2893081761006289\n", + "Prediction: M-Passion Fruit Mac1X29,0001X29,0001X29,00030,000\n", + " Answer: M-Passion Fruit Mac@29,0001X29,00025%Less Ice29,000029,00030,0001,000\n", + " Normed ED: 0.4975369458128079\n", + "Prediction: PAKET BER4 UP31154,545BREAD CHEESE DEL11111111111115DEL1111111111111111111111154,545170,000170,000170,000115,455\n", + " Answer: PAKET BER4 UP31154,545BREAD N CHEESE DEL1TUNA DEL1CRABSTICK FUSILLI DEL1LEMON TEA HANGAT DEL1BLACKCURRANT DEL1SC/P THOUSAND IS DEL1SCSC/P BBQ BEEF DEL1CR/P BEEF MAYO DEL1SUNRISE ITALIAN SODA1SUNRISE ITALIAN SODA1154,54515,455170,000\n", + " Normed ED: 0.5075834175935288\n", + "Prediction: 1023-Chocochip & Walnut1029,00029.0006001-Plastic Bag Small0029.000100.00071.000\n", + " Answer: Chocochip & Walnut1023-29.000x129.000Plastic Bag Small6001-0x1029.000100.00071.0002\n", + " Normed ED: 0.375886524822695\n", + "Prediction: CINEMAXX CINEMAXX CINEL CINE REGULAR235000.0070000.0070000.00CINEMAXX0.00CINEMAXX2\n", + " Answer: REGULAR35000.00270000.0070000.0070000.000.00\n", + " Normed ED: 0.4604105571847507\n", + "Prediction: K71217ADD1239,09190940,0004,000Rp.50,000Rp.50,000\n", + " Answer: K7 121739,091ADD CHICKEN BOX90940,0004,00044,000Rp. 50,000Rp. 6,000\n", + " Normed ED: 0.3453038674033149\n", + "Prediction: 120,00020,00020,00020,00025,0002,000\n", + " Answer: 120,000CHARGE TA190920,9092,09123,00025,0002,0002\n", + " Normed ED: 0.31784841075794623\n", + "Prediction: PAKET BER41138.182BREAD N CHEESE DEL1111111DEL111111111111111111111111111111111111115,6172,000172,000172,000172,000172,000172,000172,000172,000171111111111000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000\n", + " Answer: PAKET BER41138,182BREAD N CHEESE DEL1BREAD N CHEESE DEL1CRABSTICK FUSILLI DEL1LIME SQUASH DEL1LIME SQUASH DEL1P/P SPICY TUNA DEL1P/P THUOSAND ISL DEL1P/P BEEF CORN1SUNRISE ITALIAN SODA1LIPTON ICE TEA DEL150% CHICKEN ROYAL118,182156,36415,636172,000\n", + " Normed ED: 0.8715419257988419\n", + "Prediction: Emily's Shrimp Scampi169.00069.0000SERVICE7.280+2580.10080.1000\n", + " Answer: Emily's Shrimp Scampi Fettucine169.00069.00003.7957.280+2580.10080.1000\n", + " Normed ED: 0.3157894736842105\n", + "Prediction: SAYOR113.613.63627.272.70030,00050,00020,000\n", + " Answer: SAYAP113,636PAHA BAWAH113,63627,2722,70030,00050,00020,0002\n", + " Normed ED: 0.29310344827586204\n", + "Prediction: Choco Cheese113,636-2,04513,636-2,0451,15912,75012,75012,75012,750\n", + " Answer: Choco Cheese13,6361-2,04513,636-2,0451,15912,75012,750\n", + " Normed ED: 0.3868131868131868\n", + "Prediction: Lemon Tea (L)125.00025.00030.0005.000\n", + " Answer: Lemon Tea (L)125.00025.00030.0005.000\n", + " Normed ED: 0.0\n", + "Prediction: Hulk Topper Package1100.000100.000100.0000\n", + " Answer: Hulk Topper Package1100.000100.000100.0000\n", + " Normed ED: 0.0\n", + "Prediction: Giant Squidx」Rp. 39.000Rp. 0Rp. 0Rp. 39.000Rp. 39.000Rp. 50.000Rp. 50.000\n", + " Answer: Giant Squidx1Rp. 39.000C.Finishing - CutRp. 0B.Spicy Level - Extreme HotRp. 0A.Flavour- Salt & PepperRp. 0Rp. 39.000Rp. 39.000Rp. 50.000Rp. 11.000\n", + " Normed ED: 0.35445544554455444\n", + "Epoch 0: 40%|████ | 320/800 [01:47<02:40, 2.99it/s, v_num=1]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Prediction: REAL GANACHE116,500EGG TART113,000PIZZA TOAST116,000TOTAL145,5004,50050,0004,500\n", + " Answer: REAL GANACHE116,500EGG TART113,000PIZZA TOAST116,00045,50050,0004,500\n", + " Normed ED: 0.16058394160583941\n", + "Prediction: Kopi Susu Kolonel123.000Total23.000Tunai23.000Kembali23.000Tunai50.000Kembali 27.0001\n", + " Answer: Kopi Susu Kolonel123.00023.00050.00027.000\n", + " Normed ED: 0.5014245014245015\n", + "Prediction: S-Ovaltine120,00050%11,818PB1:18,181Subtotal:20,000Total:20,000Cash:100,00080,000100,00010%\n", + " Answer: S-Ovaltine 50%20,000120,00010% Tax Included18,1811,81820,000100,00080,000\n", + " Normed ED: 0.44017094017094016\n", + "Prediction: Black Tea1X28,000M-Caramel Black Tea1X28,00070%28,000Less Ice0-PB1:0-Subtotal:28,000Total:28,000Cash:28,000CHANGE:28,000\n", + " Answer: M-Caramel Black Tea@28,0001X28,00070%Less Ice28,000028,00028,0000\n", + " Normed ED: 0.4961089494163424\n", + "Prediction: BBQ Chicken141,000- Sedang10ITEMS:1141,000Total10-0-0ITEMS:1141,000Total141,000Cash 50.00019,000\n", + " Answer: BBQ Chicken141,000Sedang1041,00041,00050.000:9,0001\n", + " Normed ED: 0.5175438596491229\n", + "Prediction: LE MINERAL8,0001.00NETI ANT7,273TAX AMT727TOTAL8,000CASH8,0008,0008,0008,000\n", + " Answer: LE MINERAL1.008,0007,2737278,0008,000\n", + " Normed ED: 0.5085158150851582\n", + "Prediction: POTATO SAUSAGE BREAD119,000OREO GREEN TEA SPREAD1WHITE CHOCO BANANA SPREAD152,000OREO GREEN TEA SPREAD152,000WHITE152,000WHITE CHOCO BANANA SPREAD152,000TOTAL1123,000123,000123,000\n", + " Answer: POTATO SAUSAGE BREAD119,000OREO GREEN TEA SPREAD152,000WHITE CHOCO BANANA SPREAD152,000123,000123,000\n", + " Normed ED: 0.4555921052631579\n", + "Prediction: Choco Devil63,636CP 360 Club Card-9,545SubTotal:63,636Discount:-9,545PB1:5,40959,500100,00040,500\n", + " Answer: Choco Devil4-9,54563,63663,636-9,5455,40959,500100,00040,500\n", + " Normed ED: 0.3108433734939759\n", + "Prediction: TALAM UNGU19,500DISC ITEM-40.000嵗AMOUNT-7,800MIKA KECIL04.00xITEMs11,700SUBTOTAL1X11,700TOTAL11,70020,0008,300\n", + " Answer: TALAM UNGU@65003X-7,80019,500MIKA KECIL@01X011,70011,70020,0008,3004.00xITEMs\n", + " Normed ED: 0.4444444444444444\n", + "Prediction: Tahu Ikan Oma Glok120.000Tahu Ikan Oma Glok120.000TOTAL20.000CASH20.000CHANGED120.00020.00020.0000\n", + " Answer: Tahu Ikan Oma Giok120,00020,00020,0000\n", + " Normed ED: 0.5420353982300885\n", + "Prediction: Serbu240.000Choco Peanut Bread220.000TOTAL60.00060.00060.0000\n", + " Answer: Serbu 1240.000Choco Peanut Bread220.00060.00060.0000\n", + " Normed ED: 0.1574074074074074\n", + "Prediction: Se'I Sapi Sambal Matah ( R )120.000Se'I Sapi Lada Hitam (J)135.000Nasi Putih2Milk Shake Coklat210.000Milk1Milk1Milk1Subtotal1PB1Bi.00010%18.10089.10089.10089.10089.10089.10089.10089.10089.10089.10089.10089.10089.10089.10089.10089.10089.10089.10089.1000\n", + " Answer: Se'I Sapi Sambal Matah ( R )120.000Se'I Sapi Lada Hitam (J)135.000Nasi Putih210.000Milk Shake Coklat116.00081.0008.10089.100089.100\n", + " Normed ED: 0.49637305699481865\n", + "Prediction: ES KOPI SUSU72.000Total72.000GoPay72.000Kembali72.000GoPay72.000Kembali0Kembali0GoPay72.000Kembali0\n", + " Answer: ES KOPI SUSU472.00072.000072.000\n", + " Normed ED: 0.6114649681528662\n", + "Prediction: MINERAL 600 ML17,727BULGOGI RICE R133,636Subtotal141,364Tax14,136Total145,500CASH50,000-4,500-4,500\n", + " Answer: MINERAL 600 ML17,727BULGOGI RICE R133,63641,3644,13645,50050,000-4,500\n", + " Normed ED: 0.3088235294117647\n", + "Prediction: Arem Arem Arem@12.000Kroket24.000@12.000Rp 36.0002xRp 3.600Rp 39.600Rp 39.600Rp 39.600\n", + " Answer: Arem Arem@ 12.0002 x24.000Kroket@ 12.0001 x12.000Rp 36.000Rp 3.600Rp 39.600Rp 39.600\n", + " Normed ED: 0.3981264637002342\n", + "Prediction: Arem Arem Arem12.000224.000Pepenero Pastel15.000Rp 54.000Rp 5.4002Rp 5.400Rp 59.400Rp 100.000Rp Rp Rp 40.600Rp 100.000Rp Rp 40.600Rp 100.000\n", + " Answer: Arem Arem12.000224.000Pepenero Pastel15.000230.000Rp 54.000Rp 5.400Rp 59.400Rp 100.000Rp 40.600\n", + " Normed ED: 0.4043010752688172\n", + "Prediction: TT20,000TOTAL20,000CASH100,000CHANGE80,000CASH100,000CHANGE80,000CASH100,000CHANGE80,000\n", + " Answer: TT20,000120,00020,000100,00080,000\n", + " Normed ED: 0.569377990430622\n", + "Prediction: LEMONADE 16OZX20.000X20.000SUB TOTAL20,000GRAND TOTAL20,000CASHIDR100,00020,000100,00080,000\n", + " Answer: LEMONADE 16OZ20,000120,00020,00020,000100,00080.000\n", + " Normed ED: 0.3767772511848341\n", + "Prediction: beef C roll 3pcs10,000Kaya bred10,000Kaya bred10,000110,000115,000TOTAL225,000CASH1100,00075,000100,0007\n", + " Answer: beef C roll 3pcs10,000110,000kaya bred15,000115,00025,000100,00075,0002\n", + " Normed ED: 0.4036697247706422\n", + "Prediction: FUTAMI 17 GREEN TEA (CLAS112,500EGG TART113,000GRAIN CROQUE MONSIEUR117,000GRAIN CROQUE117,00042,50050,0007,500\n", + " Answer: FUTAMI 17 GREEN TEA (CLAS112,500EGG TART113,000GRAIN CROQUE MONSIEUR117,00042,50050,0007,500\n", + " Normed ED: 0.16289592760180996\n", + "Prediction: JAMUR210,000TAHU25,000SUBTOTAL115,000PB 11,500TOTAL20,000CASH3,500Change2\n", + " Answer: JAMUR210,000TAHU15,00015,0001,50016,50020,0003,500\n", + " Normed ED: 0.39900249376558605\n", + "Prediction: Mango Lemon Tea1Rp 29,090Sliders Set1Rp 113,636Chicken Vege Rice Bowl1Rp 86,363Discount BCA 15%1- Rp 34,363Net Total: Rp 194,726Rp 9,736Rp 224,908Rp 224,908Rp 224,908\n", + " Answer: Mango Lemon Tea1Rp 29,090Sliders Set1Rp 113,636Chicken Vege Rice Bowl1Rp 86,363Discount BCA 15%1-Rp 34,363Rp 194,726Rp 9,736Rp 20,446Rp 224,908Rp 224,908\n", + " Normed ED: 0.2825719120135364\n", + "Prediction: RedVelvet Nutella1280,000Free Mini Candle15Large Box1280,000SUBTOTAL1280,000PB 128,000308,000308,000308,000\n", + " Answer: RedVelvet Nutella1280,000Free Mini Candle.5Large Box1280,00028,000308,000308,000\n", + " Normed ED: 0.33052631578947367\n", + "Prediction: BUBBLE GUM118,182Subtotal118,182PAJAK 10%11,818Total120,00020,00020.00020.000\n", + " Answer: BUBBLE GUM118,18218,1821,81820.00020.0001\n", + " Normed ED: 0.40931372549019607\n", + "Prediction: PAIN AU CHOCOLATE111,000CHOCO CUSTARD PASTRY112,000MILK PASTRY ROLL1REAL CHEESE INSIDE BREAD1SAUSAGE BREAD1HAM CHEESE FLAT BREAD120,000TOTAL1100,000CASH1CHANGE119,500\n", + " Answer: PAIN AU CHOCOLATE111,000CHOCO CUSTARD PASTRY112,000MILK PASTRY ROLL19,000REAL CHEESE INSIDE BREAD113,500SAUSAGE BREAD115,000HAM CHEESE FLAT BREAD120,00080,500100,00019,500\n", + " Normed ED: 0.2878289473684211\n", + "Prediction: Prs Sop Sui Jiao1Prs Na Kaou Udng1Prs Sio May Kpting1Prs1Prs28,000Siomay Kmbinasi1Prs1Prs Lio Kong Kien1Prs1Prs1Prs1Prs1Prs1Prs1Prs1Prs1Prs1Prs1Prs123,000Mic Kokung Trsi1Teh Jawar1Teh1180,000Service19,000Pb118,900Total207,900iPodiPodiPodiPodiPodiPodiPodiPodiPodiPodiPodiPodiPodiPodiPodiPodiPodiPodiPodiPodiPodiPod1Prs Sop Sui丁戶戶戶戶戶戶戶戶戶 PIC Sui1Prs Sop Sui Xiao1Prs Sop Sui1Prs Sop Sui Xiao1Prs Sop Sui1Prs Sop Sui1Prs Sop Sui1Prs Sop Sui1Prs Sop Sui1Prs Sui1Prs Sui1Prs Sui1Prs Sui1Prs Sui1Prs Sui1Prs Sui1Prs Sui1Prs Sui1Prs Sui事 PIC Sui1Prs Sui1Prs Sui1Prs Sui事 PIC Sui1Prs Sui1Prs Sui事1Prs Sui事1Prs Sui事1Prs Sui事1Prs Sui事1Prs Sui事1Prs Sui事1Prs Sui事1Prs Sui事1Prs Sui事1Prs Sui事1Prs Sui事1Prs Sui事1Prs Sui事1Prs Sui 1Prs Sui 1Prs Sui 1Prs Sui 1Prs Sui 1Prs Sui\n", + " Answer: Sop Sui Jiao1Prs33,000Ha Kaou Udng1Prs28,000Sio May Kpting1Prs23,500Siomay Kmbinasi1Prs23,000Leng Hong Kien1Prs30,000Mie Trsi Kgkung1Prs35,500Es Teh Tawar1Gls7,000180,0009,00018,900207,90077\n", + " Normed ED: 0.826215505913272\n", + "Prediction: NASI MERAH/PUTIH5,0005,0005,0004.000KERUPUK SAMBEL8,000KERUPUK SAMBEL2.0001x2.000AYAM2.0001x14.000MINUMAN KEMASAN/REFILL014.000KEMASAN/REFILL6.000Total0\n", + " Answer: NASI MERAH/PUTIH5.0001x5.000SAYUR4.0002x8.000KERUPUK/SAMBEL2.0001x2.000AYAM14.0001x14.000MINUMAN KEMASAN/REFILL6.0001x6.000Rp. 35.000\n", + " Normed ED: 0.39603960396039606\n", + "Prediction: THAI ICED TEA (L)16,36316,363Jumlah Item16,363Sub Total16,363Pajak Resto1,636Grand Total17,999TH C InvestTHAI THAI ITE (THAI ITEATHAI ITEATHAI ITEA代表 ITEATHAI ICEDTHAI ICED ICED ICED ICED ICED ICED ICED TEA (L)THAI TEA (L)THAI TEA (L)THAI TEA (L)THAI ICED TEA (L) TEA (L)16,36316,36316,36316,36316,36316,36316,36316,36316,36316,36316,363 ICED ICED ICED TEA (L) ICED TEA (L) TEA (L) TEA16,363 ICED TEA16,363 TEA16,363 ICED TEA16,363 TEA16,363 ICED TEA16,363 TEA16,363 ICED ICED TEA16,363 ITE (L) ITE (L) ITE (L) ITE (L) ITE (L) ITE (L) ITE (L) ITE) ITE (L) ITE) ITE) ITE (L) ITE) ITE) ITE (L) ITE) ITE (L) ITE) 016,363 016,363 016,363 016,363 016,363 016,363 016,363 016,363 016,363 016,363 016,363 016,36316,36316,36316,36316,36316,36316,36316,36316,36316,363Jumlah ItemJumlah ItemJumlah ItemJumlah ItemJumlah ItemJumlah ItemJumlah ItemJumlah ItemJumlah ItemSumlah ItemSumlah ItemSumlah ItemSub\n", + " Answer: THAI ICED TEA (L)16,363116.36316,3631,63617,9991\n", + " Normed ED: 0.9081412103746398\n", + "Prediction: ELEPHANT READ BEAN112,000chapsal twister donnat112,000twilister110,000TOTAL22,000CASH222,0002\n", + " Answer: ELEPHANT READ BEAN12,000112,000chapsal twister donnut10,000110,00022,00022,00002\n", + " Normed ED: 0.4117647058823529\n", + "Prediction: Sabun Beras130000Sabun Beras13000030000130000Sub Total30000Discount (0 %)0Tunai500000\n", + " Answer: Sabun Beras3000013000030000Discount(0%)5000020000\n", + " Normed ED: 0.4801980198019802\n", + "Prediction: REDBEAN BREAD19,000FRANKFRUT SAUSAGE ROLL112,000TOTAL121,000CASH150,00021,00050,00029,000\n", + " Answer: REDBEAN BRE/D19,000FRANKFRUT S/USAGE ROLL112,00021,00050,00029,000\n", + " Normed ED: 0.3119047619047619\n", + "Prediction: PREMIUM TOAST PAN BREAD124,000TOTAL124,000CASH24,00024,00024,0000\n", + " Answer: PREMIUM TOAST PAN BREAD124,00024,00024,0000\n", + " Normed ED: 0.3445121951219512\n", + "Prediction: Nasi (MLY)16.000Subtotal6.000TOTAL6.000Go-Pay static QR6.000G.000G.CO6.0006.0006.000QR6.000\n", + " Answer: Nasi (MLY)16.0006.0006.0006.000\n", + " Normed ED: 0.5514223194748359\n", + "Prediction: GRAINS PAN BREAD120,500ICED HIBISCUS LYCHEE TEA137,000TOTAL157,500GIFT50150,0008119232703117,5005,0000\n", + " Answer: GRAINS PAN BREAD120,500ICED HIBISCUS LYCHEE TEA137,00057,50050,0007,5000\n", + " Normed ED: 0.34913793103448276\n", + "Prediction: GRILLED BABY POTATO (R150,500TRUFFLE CREAM176,000CARBONARA170,500ORIGINAL BREWED TEA2SUBTTL246,000SUC CHG 6%114,580PB110%225,758283,338283,3385962\n", + " Answer: GRILLED BABY POTATO (R150,500TRUFFLE CREAM176,000CARBONARA170,500ORIGINAL BREWED TEA246,000243,00014,58025,758283,338283,3385962\n", + " Normed ED: 0.268370607028754\n", + "Prediction: BLACK PEPPER MEATBALL176,500QUARTO FORMANGGI PASTA182,500GREEN TEA WITH CRUMBLE1ORIGINAL BREWED TEA2SUBTTL2SVC CHG 6%1SVC CHG 6%1ORIGINAL1261,000SVC CHG 6%1PB110%1SB 1SB7,666BLACK P BLACK PEPPER BLACK PEPPER BLACK PEPPER BLACK PEPPER MEA 1BLACK PEPPER MEAリスト BLACK PEPPER MEAリスト BLACK PEPPER MEATRE 1BRACK PEPPER MEATRE 11BRACK PEPPER MEATRE BRACK PEPPER MEATS ALLOCK PEPPER MEATS ALLOCK PEPPER MEATBALL1BRACK PEPPER MEATBALL1BRACK PEPPER MEATS ALLOPER MEATBALL1QUARTO BRACK PEPPER MEATBALL1BRACK PEPPER1QUARTO BRACK PEPPER MEATS PEPPER1BRACK PEPP ER BRACK PEPP ER WITHALL1QUARTO GREEN GREEN GREEN GREEN GREEN GREEN GREEN GREEN GREEN GREEN GREEN GREEN GREEN GREEN GREEN GREEN GREEN GREEN GREEN GREEN GREEN GREEN GREEN GREEN GREEN GREEN BRACK BRACK BRACK BRACK BRACK BRACK BRACK BRACK BRACK BRACK BRACK BRACK BRACK BRACK BRACK TEA WITH CRUNGGI PASTA WITH CRUNGGI PASTA WITH CRUNGGI PASTA WITH CRUMBLE1GREEN GREEN GREEN GREEN GREEN GREEN GREEN GREEN GREEN GREEN GREEN GREEN\n", + " Answer: BLACK PEPPER MEATBALL176,500QUARTO FORMANGGI PASTA182,500GREEN TEA WITH CRUMBLE156,000ORIGINAL BREWED TEA246,000261,00015,66027,666304,326\n", + " Normed ED: 0.8583038869257951\n", + "Prediction: Soft Ori 3 Top117,2722Top OreoTop Oreo10Top Oreo10Top Banana10SubTotal:117,272PB1:1,72718,99918,999\n", + " Answer: Soft Ori 3 Top117,272Top Oreo0Top Oreo0Top Banana017,2721,72718,99918,999\n", + " Normed ED: 0.28378378378378377\n", + "Prediction: Ice Lemon Tea13,636Gyro Platter - Regular150,000Subtotal63,636PB1-TAX Tax6,364Here Total70,00070,00070,00070,000\n", + " Answer: Ice Lemon Tea13,636-Gyro Platter Regular50,00063,6366,36470,00070,000\n", + " Normed ED: 0.38990825688073394\n", + "Prediction: TT20,000TOTAL20,000CASH100,000CHANGE-80,000CASH100,000CHANGE-80,000TOTAL20,000CASH100,000CHANGE-80,000\n", + " Answer: TT20,000120,00020,000100,00080,000\n", + " Normed ED: 0.6017316017316018\n", + "Prediction: PKT TEL OR PERK EDI C 26,000TERONG12,000P K P Kraft C P Kraft C P Kraft C P Kraft C P Kraft C P Kraft C P Kraft C P Kraft C PKT TER G PKT TER G PKT TER Gの方が TER Gの方が TER Gの方が TER G TER OKE TER OKE TER OKE TER OKE TERONG TER ONG TER ONG TER ONG TER ONGPKT TER ONGTER ONGTER ONGTER ONGTER ONGTER ONGTER ONGTER ONGTER ONGTER ONGTER ONGTER G TERONGTER G TERONGTER G PARU SBL GR TER G PARU SBL GR ARI PERK TER G PARU SBL GR ARI PERK ARI PERK ARIZ PERK ARIZ PERK ATE/AMPLA12,000ATE TER G PARU SBL GR ARI ATE ATE/AMPLAATE/AMPLA20,000 °C ATE/AMPLA 20,000 °C 20,000 °CAL 20,000 °CAL 20,000 °CAL 20,000 °CAL TER G PARU SBL GR 20,000 °CAL 20,000 °CAL ATE/AMPLA 20,000 °CAL 20,000 °CAL 20,000 °CAL 20,000 °CAL 20,000 °CAL 20,000 °CAL 20,000 °CAL 20,000 °CAL TER OPERKEDEL TERONGTER G TERONGTER G TERONGTER G PARU SBL GR 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限 限優惠優惠優惠優惠優惠優惠優惠優惠優惠優惠優惠優惠優惠優惠優惠優惠優惠優惠優惠優惠優惠優惠優惠優惠優惠優惠優惠優惠優惠優惠優惠優惠優惠優惠優惠優惠優惠優惠優惠優惠優惠\n", + " Answer: PKT TELOR/PERKEDEL26,000TERONG12,000PARU23,000SBL GR ATI/AMPLA20,000NESTLE 330 ML8,00089,0008,90097,900100,0002,1005.00\n", + " Normed ED: 0.8754578754578755\n", + "Prediction: Gojek Chicken195,000Chilli Sauce H1180,00Gojek Chicken1Soy Sauce F1Gojek French1Fries1Sub Total1Service1Tax1Discount1BiBiChickenChickenChickenGojek ChickenGojek ChickenGojek ChickenGojek ChickenGojek ChickenGojek ChickenGojek ChickenGojek ChickenGojek ChickenGojek ChickenGojek ChickenGojek ChickenGojek Chicken Quick ChickenGojek Chicken Quick ChickenGojek Chicken Quick ChickenGojek Chicken Quick ChickenGojek Chicken Quick ChickenGojek ChickenGojek ChickenGojek Chicken Quick ChickenGojek ChickenGojek Chilli Sauce H CHI Chilli Sauce H CHI Chilli Sauce H CHI Chilli Sauce H CHI Chilli Sauce H CHI Chilli Sauce H CHI Chilli Sauce H CHI Chilli Sauce H CHI Chilli Sauce H CHI Chilli Sauce H CHI Chilli Sauce H CHI Chilli Sauce H CHI Chilli Sauce H CHI Chilli Sauce H CHI Chilli Sauce H1Gojek Chilli Sauce H1Gojek Chilli Sauce H CHI Chilli Sauce H1Gojek Chilli Sauce H1Gojek Chilli Sauce H1Gojek Chilli Sauce H1Gojek Chilli Sauce H1Gojek Chilli Sauce HGojek Chilli Sauce H CHI Chilli Sauce H CHI Chilli Sauce HGojek Chilli Cajek Chilli Cajek Chilli Cajek Chilli Cajek Chilli Cajek Chilli Cajek Chilli Cajek Chilli Cajek Chilli Cajek Chilli Cajek Chilli Cajek Chilli Cajek Chicken CHI Gojek Chicken CHI Gojek Chicken CHI Gojek Chicken CHI Chicken CHI Chicken CHI SíochánGojek Chilli Sauce f CHI Gojek Chilli Sauce f CHI Gojek Chilli Sauce f CHI Suce f\n", + " Answer: Gojek Chicken Chilli Sauce H195,000Gojek Chicken Soy Sauce F1180,000Gojek French Fries170,000345,0000034,500379,500\n", + " Normed ED: 0.880388978930308\n", + "Prediction: SOGOGI JAPCHAE1150,000labelCREAM TEOK PO KI PO KI PO KI KI 1CREA丁 以來,丁 CHEESE PO KICHEM CHEESECREAM C風 CHEM C風 CHEM C風 CHEM CREAM TEOK PO KI C風 ONE C風 ONE C風 ONE C風 ONE C風 ONE C風 ONE C風 ONE ONEOK PO KI PO KIМЕНТ МЕНТ МЕНТ МЕНТ МЕНТ МЕНТ МЕНТ МЕНТ МЕНТ МЕНТ МЕНТ МЕНТ CHEAM TEOK PO KI00000嵗 CHEAM TEOK PO KI EK KEUN GI KEUN GI KEUN GI KEUN GI KEUN GI KEUN GI) KIP CREAM KIP CREAM KEUN GI KEUN GI) GYE CREAM CREAM KEUN GI) GYE CREAM KEUN GI) GYE CREAM KEUN GI) GYE CREAM KEUN GI) GYE CREAM KEUN G YE C E SUNDUB PO KI PO KI PO KI EK PO KI EK SUNDUB PO KI EK SUNDUB PO KI EK SUNDUB C E G E G E GUE PO K SUNDUB SUNDUB C ICE C ICE COFFE MIX ICE COFFE MIX ICE COFFE MIX ICE COFFE MIX ICE COFFE MIX ICE COFFE MIX BLACK COFFE MIX BLACK COFFE MIX BLACK COFFE MIX BLACK COFFE MIX BLACK COFFE MIX BLACK COFFE MIX BLACK COFFE MIX ICE BLACK COFFE MIX BLACK COFFE MIX ICE BLACK COFFE MIX ICE BLACK COFFE MIX ICE BLACK COFFE MIX ICE ICE BLACK COFFEE COFFEE COFFEE MIX BLACK COFFEE COFFEE MIX BLACK COFFEE COFFEE COFFEE MIX BLACK COFFEE COFFEE CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES CHEES\n", + " Answer: SOGOGI JAPCHAE1150,000GONG GIBAB7105,000CHULPAN CHEESE DAK GALBI1230,000CREAM TEOK PO KI195,000EL KEUN HWANGTAE1120,000SUNDUBU(TUKBEGI) CHAM KKOT SAL2620,000U SAM GYEOP2290,000U SEOL2390,000U SAM GYEOP10ICE COFFE MIX30ICE BLACK COFFE102,000,0000140,000212,4602,352,46002,352,460\n", + " Normed ED: 0.835204081632653\n", + "Prediction: STIX CINNAMON19,000CINNAMON SUGAR119,000CINNAMON SUGAR117,000TRIPPLE CHEESE1TRIPPLE117,00017,000CHEESE117,00017,000SUB TOTAL 5,0001STIX CINNAMON SUBIKSTIX CINNAMON SUBT ONL SUBT OTAL CINNAMON SUBT OTAL CINNAMON SUGARSTIX CINNAMONSTIX CINNAMON19,000STIX CINNAMON19,000STIX CINNAMON19,000CINNAMON19,000CINNAMON19,000CINNAMON19,000CINNAMON19,000CINNAMON19,000CINNAMON19,000CINNAMON19,000CINNAMONCINNAMON19,000CINNAMONCINNAMONCINNAMONCINNAMONCINNAMON19,000CINNAMONCINNAMONCINNAMONCINNAMONCINNAMONCINNAMONCINNAMONCINNAMONCINNAMONCINNAMONCINNAMONCINNAMONCINNAMONCINNAMONCINNAMONCINNAMONCINNAMONCINNAMONCINNAMONCINNAMONCINNAMONCINNAMONCINNAMONCINNAMONCINNAMONCINNAMONCINNAMONCINNAMONCINNAMONCINNAMON SUGARCINNAMONCINNAMON SUGARCINNAMON SUGARTRY TRY TRY CINNAMON SUGARCINNAMON SUGARTRY CINNA\n", + " Answer: STIX CINNAMON19,000119,000CINNAMON SUGAR17,000117,000TRIPPLE CHEESE17,000117,00053,00053,000053,000\n", + " Normed ED: 0.8720641061066593\n", + "Prediction: ROTI UNYIL1701272.000PAPER BAG BESAR16.000Total1278.000Cash1300.00022.000171Qty.122.000\n", + " Answer: ROTI UNYIL170272.000PAPER BAG BESAR16.000278.000300.00022.000171\n", + " Normed ED: 0.43907563025210083\n", + "Prediction: THAI ICED TEA (L)16,36316,363Jumlah Item16,363Sub Total16,363Pajak Resto1,636Grand Total17,999TH C InvestTHAI THAI ITE (THAI ITEATHAI ITEATHAI ITEA代表 ITEATHAI ICEDTHAI ICED ICED ICED ICED ICED ICED ICED TEA (L)THAI TEA (L)THAI TEA (L)THAI TEA (L)THAI TEA (L) ICED TEA (L)16,36316,36316,36316,36316,36316,36316,36316,36316,36316,36316,363 ICED ICED ICED TEA (L) ICED TEA (L) TEA (L) TEA16,363 ICED TEA16,363 TEA16,363 ICED TEA16,363 TEA16,363 ICED TEA16,363 TEA16,363 ICED ICED TEA16,363 ITE (L) ITE (L) ITE (L) ITE (L) ITE (L) ITE (L) ITE (L) ITE) ITE (L) ITE) ITE) ITE) ITE (L) ITE) ITE (L) ITE) ITE) ITE) 016,363 016,363 016,363 016,363 016,363 016,363 016,363 016,363 016,363 016,363 016,36316,36316,36316,36316,36316,36316,36316,36316,36316,363Jumlah ItemJumlah ItemJumlah ItemJumlah ItemJumlah ItemJumlah ItemJumlah ItemJumlah ItemJumlah ItemSumlah ItemSumlah ItemSumlah ItemSumlah Item\n", + " Answer: THAI ICED TEA (L)16,363116.36316.3631,63617,9991\n", + " Normed ED: 0.9094151212553495\n", + "Prediction: AOP Seafood185,000Omurice Katsu Curry195,000Earl Gray Tea1Hot Tea1Hot Tea135,000Hot Tea1SUBTOTAL1240,00Service121,60028,7,760AOP SeafoodAOP SeafoodAOP SeafoodAOP SeafoodAOP SeafoodAOP SeafoodAOP SeafoodAOP SeafoodAOP Seafood1AOP Seafood1AOP Seafood1AOP Seafood1AOP Seafood1AOP Seafood1AOP Seafood1AOP Seafood1AOP Seafood1AOP Seafood1AOP Seafood1AOP Seafood1AOP1AOP1AOP1AOP1AOP1AOP1AOP1AOP1AOP1AOP1AOP1AOP1AOP1AOP1AOP1AOP1AOP Seafood1AOP Seafood1AOP1AOP Seafood1AOP Seafood1AOP Seafood1AOP\n", + " Answer: AOP Seafood185,000Omurice Katsu Curry195,000Earl Gray Tea135,000Hot Tea125,000240,00021,60026,160287,760\n", + " Normed ED: 0.8896120150187735\n", + "Prediction: MARBLE CASTELA22,00022,000122,000TOTAL122,000CASH122,00022,00000\n", + " Answer: MARBLE CASTELA22,000122,00022,00022,00001\n", + " Normed ED: 0.44556962025316454\n", + "Prediction: Seaweed Chicken142,000Seaweed Chicken10- Sedang10ITEMS:1142,000Total142,000Cash 50,00018,000\n", + " Answer: Seaweed Chicken142,000- Sedang1042,00042,00050,0008,0001\n", + " Normed ED: 0.4485981308411215\n", + "Prediction: SB 1 OR128,636Chokocha Flt113,636Bbq Bento113,636Sub Total155,908P.Rest 1015,59261,50070,0008,500\n", + " Answer: SB 1 OR128,636Chokocha Flt113,636Bbq Bento113,63655,9085,59261,50070,0008,5003\n", + " Normed ED: 0.25614754098360654\n", + "Prediction: 52.000Nasi Campur Bali1x52.000Nasi Campur Bali1x 22.000Lemon22.000SubTTL74.000TAX7.40081-40081-40081.400\n", + " Answer: Nasi CampurBali52.0001x52.000Lemon22.0001x22.00074.0007.40081.400\n", + " Normed ED: 0.4592274678111588\n", + "Prediction: CHOCOLATE ECLAIRY226,000TOTAL26,000CASH26,000CHANGE0CASH26,000CHANGE0TOTAL26,000CASH26,000CHANGE26,000CHANGE CHANGE CHANGE GCHANGE CHANGE CHANGE CHANGE G caisCHANGE CHANGE CHANGE CHANGE G cais nacionaisCHANGECHANGECHANGE2,000,000CHANGE2,000,000CHANGE2,000,000 CHANGECHANGE ECLAIRCHANGE E CHANGE ECLAIR20,000CHANGE E CHANGE E CHANGE E CHANGE E CHANGE E CHANGE E CHANGE E CHANGECLASH OCOOLATE ECLATE ECLATE ECLATE ECLATE ECLATE ECLATE ECLATE ECLATE ECLATE ECLATE ECLATE ECLATE ECLATE ECLATE ECLATE ECLATE ECLATE ECLATE ECLATE ECL Across Citroen Citroen Citroen Citroen Citroen Citroen Citroen Citroen Citroen Citroen Citroen Citroen Citroen Citroen Citroen Citroen Citroen Citroen Citroen Citroen Citroen Citroen Citroen Citroen Citroen Citroen Citroen Citroen Citroen Citroen Citroen Citroen निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगम निगमCHANGE 6,000CHANGE BLUE E CHANGE 6,0006,0006,0006,0006,0006,0006,0006,0006,0006,0006,0006,0006,0006,0006,0006,0006,000\n", + " Answer: CHOCOLATE ECLAIR226,00026,00026,0000\n", + " Normed ED: 0.9509536784741145\n", + "Prediction: 118 Round Wigyu (1gr)147.200Wagyu Rice Box110,000Subtotal157.200TOTAL157.200BCA157.20057.20057.200BCA\n", + " Answer: Round Wagyu (1gr)11847.200Wagyu Rice Box110.00057.20057.20057.200\n", + " Normed ED: 0.41649484536082476\n", + "Prediction: KENTHIR 242.000KOL GORENG6.000TEH MANIS8.000Subtotal56.000Total56.000CASH56.000Kembalan56.000Kembalan0\n", + " Answer: KENTHIR 2242.000KOL GORENG26.000TEH MANIS28.00056.00056.00056.0000\n", + " Normed ED: 0.4652777777777778\n", + "Prediction: Large111.000*Rhum10Pastry Keju120.000*Plastik KCL10Total131.000Bayar140.0009.000\n", + " Answer: Large 1111.000*Rhum10Pastry Keju120.000*Plastik kcl1031.00040.0009.0004\n", + " Normed ED: 0.296137339055794\n", + "Prediction: 4003-Blueberry Fuji40.0006001-Plastic Bag Small06001-Plastic Bag Small0Total Item;0Cash0Cash0Tendered:50.000Change:\n", + " Answer: 4003-Blueberry Fuji40.000x140.0006001-Plastic Bag Small0x1040.00050.00010.0002\n", + " Normed ED: 0.3752808988764045\n", + "Prediction: Cha Keaw L... x260.000Cha Keaw L... x260.000BCA PAY... (100%)60.000Total60.000EDC60.000B CBCA(100%)60.000Total60.00060.00060.000\n", + " Answer: Cha Keaw L...x260.000Cha Keaw L...x260.000)60.00060.00060.000\n", + " Normed ED: 0.5323886639676113\n", + "Prediction: Thai Iced T..x]20.000Subtotal20.000Total20.000Cash20.000Change20.00020.00020.00020.000\n", + " Answer: Thai Iced T. .x120.00020.00020.00020.0000\n", + " Normed ED: 0.41012658227848103\n", + "Prediction: SEAFOOD MARINARA185,000CREAMY MARINARA SALMON185,500LYCHEE ICE TEA277,000SUBTTL1247,500SVC CHG 6%114,850PBl 10%126,235LYCHEE ICE TEA2SUBTTL1SVC CHG 6%1CHG1PBl 10%1PBl 10%1ICE TEA1LYCHEE ICE TEA2| SEAFOOD MARINARAFOOD MARINARA績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績績SEAFOOD MARINARASEAFOOD MARINARASEAFOOD MARINARASEAFOOD MARINARASEAFOOD MARINARASEAFOOD MARINARAPICNICHIY MARINARA1PICNICHINESSEAFOOD MARINARAPICNICHINESSEAFOOD MARINARA1SEAFOOD MARINARAPICNICHINESSEAFOOD MARINARAPICNICHINESSEAFOOD MARINARAPICNICHINESSEAFOOD MARINARA1\n", + " Answer: SEAFOOD MARINARA185,000CREAMY MARINARA SALMON185.500LYCHEE ICE TEA277,000247,50014,85026,235288,585\n", + " Normed ED: 0.8889568675435913\n", + "Prediction: Viet Milk Coffee125.000+Hot+M25.00025,00025.000Rp 0\n", + " Answer: Viet Milk Coffee125.000+Hot+M25.00025.00025.0000\n", + " Normed ED: 0.22054380664652568\n" + ] + } + ], + "source": [ + "from pytorch_lightning.loggers import WandbLogger\n", + "from pytorch_lightning.callbacks import Callback, EarlyStopping\n", + "\n", + "wandb_logger = WandbLogger(project=\"Donut\", name=\"demo-run-cord\")\n", + "\n", + "class PushToHubCallback(Callback):\n", + " def on_train_epoch_end(self, trainer, pl_module):\n", + " print(f\"Pushing model to the hub, epoch {trainer.current_epoch}\")\n", + " pl_module.model.push_to_hub(\"nielsr/donut-demo\",\n", + " commit_message=f\"Training in progress, epoch {trainer.current_epoch}\")\n", + "\n", + " def on_train_end(self, trainer, pl_module):\n", + " print(f\"Pushing model to the hub after training\")\n", + " pl_module.processor.push_to_hub(\"nielsr/donut-demo\",\n", + " commit_message=f\"Training done\")\n", + " pl_module.model.push_to_hub(\"nielsr/donut-demo\",\n", + " commit_message=f\"Training done\")\n", + "\n", + "early_stop_callback = EarlyStopping(monitor=\"val_edit_distance\", patience=3, verbose=False, mode=\"min\")\n", + "\n", + "trainer = pl.Trainer(\n", + " accelerator=\"gpu\",\n", + " devices=1,\n", + " max_epochs=config.get(\"max_epochs\"),\n", + " val_check_interval=config.get(\"val_check_interval\"),\n", + " check_val_every_n_epoch=config.get(\"check_val_every_n_epoch\"),\n", + " gradient_clip_val=config.get(\"gradient_clip_val\"),\n", + " precision=16, # we'll use mixed precision\n", + " num_sanity_val_steps=0,\n", + " # logger=wandb_logger,\n", + " callbacks=[early_stop_callback],\n", + ")\n", + "\n", + "trainer.fit(model_module)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LjDHXmhFprFw" + }, + "source": [ + "## Evaluate\n", + "\n", + "After training, we can evaluate the model on the test set.\n", + "\n", + "As we pushed the model to the hub, we can very easily load it back again using the `from_pretrained` method. You can see in the [repo](https://huggingface.co/nielsr/donut-demo/tree/main) that we have the following files:\n", + "\n", + "![Screenshot 2022-08-16 at 11.05.59.png](data:image/png;base64,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)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NpHF6EdN6WLr" + }, + "source": [ + "Note that you can also easily refer to a specific commit in the `from_pretrained` method using the [`revision`](https://huggingface.co/docs/transformers/v4.21.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained.revision) argument, or use the private hub in case you'd like to keep your models private and only shared with certain colleagues for instance.\n", + "\n", + "Here we're just loading from the main branch, which means the latest commit." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 396, + "referenced_widgets": [ + "da517c560a174ed3b3a0d342ab9531d4", + "0d29cb058287441397849858958eecc5", + "971fb37793b04b02916266e0606a8cd3", + "f397c359a4844fe0b43cbd55f7e45e2c", + "5be79748747e48dc9557e635185b09b7", + "5cf9cf4d06af477aaadf2c2eafb21a0f", + "acb18d163a98436abc6a34c80d0b9875", + "9e5391e58b654a54af53ca6b156d4027", + "a772a8f8485b4cbc954363c5d25a1355", + "c8d62278ed494aa1948d4ddac0b5e94c", + "9ef9d3b7c4e141c99299d21e270347af", + "c5810b986d064e93ac00ff911d3d09de", + "0d5904889d6246e4b0bd8223d9ce79ab", + "09e57cfef8d04198b07337e6adfd3cfd", + "9460488365d343e887fb88aafc4c434b", + "8ad84d92e3f2470084c072e64a46cfb3", + "8b4f59bf71c64c2293a9e089b963b914", + "7372f1fa693345ad8f26f0148b2f527b", + "3b5d54289850421e821f88fc5f0c0421", + "bcaced45f306473887ab7c07df871481", + "4cacd92cb1f04dcea344f93e816d6244", + "fd4d064de57d469c977318add3069303", + "5d99e7c78ae54a2fa6824cfe36498ab4", + "6eec510e0a3844dfb7c7bcb3a6b9d910", + "dcbf40b9f3d04a29abb5b82cc4f4f43a", + "03a5186fafe7424c896a5c8f4b356098", + "0c9bb2443ecf460a876558b547c3b97f", + "e797e849c5e0443e84af5c12061589d4", + "422523d56ec74035b0a55b9c97d1ed19", + "acfeebabb13647ddbbe4372e192b235a", + "b2f81adfc7624d0ebec30a6f49d3b0d3", + "f1a2bee7a7dc408087833912c951119d", + "8eef683c23a14e9ca9cc8e260a8d25aa", + "314069aa673f41f9abd2c6d2b634a64c", + "7df7c889e4794145ba5b20177df36189", + "f16c7f25616a478ab6ba69ce88a69724", + "e9b2a001c8dc4591b00a21893c56dceb", + "96b45a24defe461998123ec31aba1d97", + "f387bdb0ba094db2892ed92e786886f6", + "1510960e07b742c4b1d838b2d533439b", + "bcb3bc46a920467d8441c8fe2c0081f7", + "212fc0393d3a4ea9a76571c9b636ae20", + "a9ed7db85a364e6384f84f216080d24a", + "98c6ca051cde41cf806b1b8bd9caf6c9", + "183c1ce81c8544719ce85a79cf44f9e9", + "538e66a6277b478db47627b3f38b7da0", + "7de626274586484c966e3fcd322559af", + "07a8d73caf1741d78f637c5093884834", + "049b89f7db7b4b278b2b266771a22ef1", + "53f439b9ac714080972b77d4ce2e15ed", + "8d98ed226dd1489e83099782d51d7b55", + "202cb4369e4845a6a60408400f88b0ed", + "92c719b392564d339ff73ccc83c966d0", + "938da511fca04f77af54fe3b34287e6c", + "3f2d02e521e848398853c526866d3585", + "ce963a069b50408abc6cf6073857c5ee", + "21d179e89c8d47768585c9d9d3bd6e20", + "f88cf9ec7d8b4faa80017e24f99d5dc8", + "7a20309eef57435f9f35082d5285f444", + "6d669ab16e374f0faa089a183bd52532", + "7d207f93b4a24696abf379bcc01723dc", + "b0ab92d005974b959f7cb0f2cb9b11d6", + "7b93f73af3274920ab2b51418e95cff6", + "e4dd0f4da33c453e886144de5c93aeb3", + "6d34020216d74b588708e9c9f33f800b", + "b0ee851984d9431597446f79d656d64a", + "c75e6f8363fc4bd1ab7d1c6513f60dd5", + "56c8c883282e4f74ae1c353455136380", + "e13b3d868fe3450cac508a8799743696", + "3a1168d83bbd44c887b93d20f8eb59a6", + "64f05915a7b040f9a73dfba9e02d249d", + "5ec271f8377a43878a090aaecdde01e6", + "8473ba1cc0d44bba85462cdbf12e9514", + "2a058b66d21642f3ab38b917a57ead4c", + "0eccda6238eb4756863a6efeeadc9efb", + "4db3cc54264240ccac6402b67dfa1049", + "ec11d0d99c684e2c87c1bf1c05220a95", + "84a6704158ab4806a23226678b1be9a8", + "7b6f963d98df4d36b01e21a89fafd957", + "19026bb7f79446bc970e1210cccf206f", + "e45b9b8af30042d0a0f91ef4ec1aad76", + "3735eff345c9459a986b52ac7c469a33", + "7d99b26699f0438ea804db1871d2a172", + "791025ac27604718a24542401ab225e2", + "230aa7332e4e4114a923b317b4e71991", + "45b9943cd00546afad178c901cd94ade", + "11e6194ce58c4c05a4cf3c9c55b4acc4", + "5f177c38708d4492909da4b8ae0a4120", + "b9b02352a20e45ac8128f58b9387435d", + "07054014fd004b44993a392856f25f58", + "b4afe9af86d1487cbe0cb9f4956db2e2", + "abd398c4b93343a49f7882d3b141ccf4", + "3fda98dab7574ba489090143ce8a0e82", + "8b00742b502d493094ad605fc90b5b09", + "e05ca35fa13a42e0bfb8daef4706d291", + "a27fc920f8c94953a0c3e9b2120f1b86", + "722dd7a1bb984a22ad1b4da14741d7fd", + "7c8f64cb9eda44eca40a6ef5a203afc0", + "10aad821076a43b88b833f52d41df0ef" + ] + }, + "id": "HRyAujuuw0IG", + "outputId": "d806f452-f0c0-4571-ed6d-a568be3106fb" + }, + "outputs": [], + "source": [ + "from transformers import DonutProcessor, VisionEncoderDecoderModel\n", + "\n", + "processor = DonutProcessor.from_pretrained(\"nielsr/donut-demo\")\n", + "model = VisionEncoderDecoderModel.from_pretrained(\"nielsr/donut-demo\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "r5uhIMxCw-ZI" + }, + "source": [ + "As we don't have a test split here, let's evaluate on the validation split.\n", + "\n", + "We'll use the `token2json` method of the processor to turn the generated sequences into JSON, and the `JSONParseEvaluator` object available in the Donut package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EDdzbjIOuCfI", + "outputId": "702b39c7-5e75-4072-fb6e-965b899314b9" + }, + "outputs": [], + "source": [ + "!pip install -q donut-python" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 123, + "referenced_widgets": [ + "674feecda5844fc8b669e8153a982b3f", + "9ed0a46f8705464f81f2c8e1a4b8cc89", + "0f6e284513e94a3f8681d4b4e39fcb54", + "7208cb24790c447aae18746d11b7cc7e", + "c1fe650a41b14253b0c398f09293310c", + "a105133b82344a97b79250d8cde50f74", + "c474aabdf84b459aae6c9c0de4526b47", + "75a3f953de714650a03ad3b2319f8a82", + "b49ab085f6e04c7398e90e09eda48c5a", + "0755ff4ddf1c453f8f6ca501001fc486", + "43d49626f9284b23b3bca84aaf9f4616" + ] + }, + "id": "PZSG38-10YVL", + "outputId": "ca551149-e451-4233-ab31-5d9d7bcc818f" + }, + "outputs": [], + "source": [ + "import re\n", + "import json\n", + "import torch\n", + "from tqdm.auto import tqdm\n", + "import numpy as np\n", + "\n", + "from donut import JSONParseEvaluator\n", + "\n", + "from datasets import load_dataset\n", + "\n", + "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + "\n", + "model.eval()\n", + "model.to(device)\n", + "\n", + "output_list = []\n", + "accs = []\n", + "\n", + "dataset = load_dataset(\"naver-clova-ix/cord-v2\", split=\"validation\")\n", + "\n", + "for idx, sample in tqdm(enumerate(dataset), total=len(dataset)):\n", + " # prepare encoder inputs\n", + " pixel_values = processor(sample[\"image\"].convert(\"RGB\"), return_tensors=\"pt\").pixel_values\n", + " pixel_values = pixel_values.to(device)\n", + " # prepare decoder inputs\n", + " task_prompt = \"\"\n", + " decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors=\"pt\").input_ids\n", + " decoder_input_ids = decoder_input_ids.to(device)\n", + " \n", + " # autoregressively generate sequence\n", + " outputs = model.generate(\n", + " pixel_values,\n", + " decoder_input_ids=decoder_input_ids,\n", + " max_length=model.decoder.config.max_position_embeddings,\n", + " early_stopping=True,\n", + " pad_token_id=processor.tokenizer.pad_token_id,\n", + " eos_token_id=processor.tokenizer.eos_token_id,\n", + " use_cache=True,\n", + " num_beams=1,\n", + " bad_words_ids=[[processor.tokenizer.unk_token_id]],\n", + " return_dict_in_generate=True,\n", + " )\n", + "\n", + " # turn into JSON\n", + " seq = processor.batch_decode(outputs.sequences)[0]\n", + " seq = seq.replace(processor.tokenizer.eos_token, \"\").replace(processor.tokenizer.pad_token, \"\")\n", + " seq = re.sub(r\"<.*?>\", \"\", seq, count=1).strip() # remove first task start token\n", + " seq = processor.token2json(seq)\n", + "\n", + " ground_truth = json.loads(sample[\"ground_truth\"])\n", + " ground_truth = ground_truth[\"gt_parse\"]\n", + " evaluator = JSONParseEvaluator()\n", + " score = evaluator.cal_acc(seq, ground_truth)\n", + "\n", + " accs.append(score)\n", + " output_list.append(seq)\n", + "\n", + "scores = {\"accuracies\": accs, \"mean_accuracy\": np.mean(accs)}\n", + "print(scores, f\"length : {len(accs)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "hN-FiUAJy1YX", + "outputId": "e8943e8f-a936-4c00-8f96-a055924ec8ad" + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'np' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMean accuracy:\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[43mnp\u001b[49m\u001b[38;5;241m.\u001b[39mmean(accs))\n", + "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined" + ] + } + ], + "source": [ + "print(\"Mean accuracy:\", np.mean(accs))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "3R6rov0i0bkN" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "authorship_tag": "ABX9TyMs0jX0vkostGjO0/wWd8Do", + "include_colab_link": true, + "machine_shape": "hm", + "provenance": [] + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "008e77e9b3cb4cc4b37e875cab3f3d6c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_7d77a9f92e984ac7b9350addc57cb577", + "placeholder": "​", + "style": "IPY_MODEL_2d476a17141343508bcac54393555e9b", + "value": " 100/100 [00:01<00:00, 93.70 examples/s]" + } + }, + "0098775e311344f681335445edadd72f": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "009ae1d114cb48d0990e934e19ee961f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "00d41781e51e4adb823c44a68727693a": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "012b26d0aed048628e59dbb0c02f76e0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_b132e3a6f07e452382eadbedff054b94", + "IPY_MODEL_3920ef1a0817495696b874cc61516d7b", + "IPY_MODEL_24d17f7c48fb4d41b02ec4731d629758" + ], + "layout": "IPY_MODEL_d91e67e21bfa49888bf6316c154bcf77" + } + }, + "01b01e7329334686ba39c11ec18e9df5": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": "inline-flex", + "flex": null, + "flex_flow": "row wrap", + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": "hidden", + "width": "100%" + } + }, + "02700c32e4d64a66818ac92209384b14": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_2d4f64a23cc846a48f0906c74d1502b9", + "max": 100, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_c6e2d6abd785494ca143b5978811c326", + "value": 100 + } + }, + "03a5186fafe7424c896a5c8f4b356098": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_f1a2bee7a7dc408087833912c951119d", + "placeholder": "​", + "style": "IPY_MODEL_8eef683c23a14e9ca9cc8e260a8d25aa", + "value": " 489/489 [00:00<00:00, 8.52kB/s]" + } + }, + "0459618abd954a9a95c3eb137954523c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_5947ad0e06a54e38b8d09d160bffb0d2", + "IPY_MODEL_5cc9c7f2db1846b6905452d7a9568919", + "IPY_MODEL_dd0b6870c8b6435691009ca66fa71b09" + ], + "layout": "IPY_MODEL_c663b10ffce1432c89a3948e324571b0" + } + }, + "047bdaa0ff9d4a4f8d6f46fca23bd0d2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "049b89f7db7b4b278b2b266771a22ef1": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "04b01b03fcc143fa8633b52ec231f026": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "04fa08f418e74bfe8d274620cd07e448": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_d1837ece0226413489f3df0dfd7f3279", + "placeholder": "​", + "style": "IPY_MODEL_b0095f1cb2364fc991f05f312656947f", + "value": "Downloading data: 100%" + } + }, + "053e5fb5ff954b1bb8099c07ad77c35d": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "05c1c99b4aff486a9c5eeadf9bd8230a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_72254d54d49a4054b49882200eecc49b", + "placeholder": "​", + "style": "IPY_MODEL_cfa9b34f74b541fc9c54b905cffa05ad", + "value": " 1/1 [01:03<00:00, 63.26s/it]" + } + }, + "07054014fd004b44993a392856f25f58": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_8b00742b502d493094ad605fc90b5b09", + "placeholder": "​", + "style": "IPY_MODEL_e05ca35fa13a42e0bfb8daef4706d291", + "value": "Downloading: 100%" + } + }, + "0755ff4ddf1c453f8f6ca501001fc486": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "0759cda7bd084b02921689bc5e319ba5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_31081f8d85954a33bcc789b1f5f93c56", + "placeholder": "​", + "style": "IPY_MODEL_ec2fc9e744d9460f9063f9c0aac4c3d5", + "value": " 1/1 [01:03<00:00, 63.02s/it]" + } + }, + "07a8d73caf1741d78f637c5093884834": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_938da511fca04f77af54fe3b34287e6c", + "placeholder": "​", + "style": "IPY_MODEL_3f2d02e521e848398853c526866d3585", + "value": " 4.02M/4.02M [00:00<00:00, 14.3MB/s]" + } + }, + "07acf858ebad4351bfd3cfdb339dddcb": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "07b75ae72a6a4d16a3ff73a1debe2ee5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_1cda98448c074471ae879ab9410e49d5", + "max": 100, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_9a7cb4d7e9514575a93e2f86fc32fb4b", + "value": 100 + } + }, + "082d0b472b90409f8ab9b02391df63a4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_1c7a17fb7dd041c2949e5df85ee78570", + "placeholder": "​", + "style": "IPY_MODEL_c6e7bfb4e3144b16bcfd3fd12673d853", + "value": " 100/100 [01:21<00:00, 1.22it/s]" + } + }, + "083ca4f1dea5421bb07bf9cfc802871d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_b71da8215fd74394aeef7b2835ebdce2", + "max": 100, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_aad2e43eeafa4cccb5e476d3fce4dacb", + "value": 100 + } + }, + "08c07275cbbe4fd0822c354d99c303ea": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "098aae301ab04e688652dd0c05f8de1c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_10d449f46d9c4502baaad1606f873c09", + "placeholder": "​", + "style": "IPY_MODEL_44b4f06f3c0841d2ac960337d18de7a3", + "value": " 100/100 [01:25<00:00, 1.17it/s]" + } + }, + "09c44c23e97c4293bc1d30f234634c42": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_00d41781e51e4adb823c44a68727693a", + "max": 441418432, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_d56998a0695e434bb7fe3857f5075083", + "value": 441418432 + } + }, + "09e57cfef8d04198b07337e6adfd3cfd": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_3b5d54289850421e821f88fc5f0c0421", + "max": 362, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_bcaced45f306473887ab7c07df871481", + "value": 362 + } + }, + "0bb2e5d6e3544a44940c79b7a5543407": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_8eda879a6d224fb091f538c2806d7890", + "max": 3, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_c34474a8f453410f8f3dc39c835e3a2d", + "value": 3 + } + }, + "0c44b50f550a42d5a89d35c0233fa0bb": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "0c565bd0bd89433286f309c0a728e020": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_9ab3a07b87854294b687ab1bd1ce2fe1", + "IPY_MODEL_61c95b7243604620a9151ce8b6fcc910", + "IPY_MODEL_f21af123c24f43498942dd4f3e91c54f" + ], + "layout": "IPY_MODEL_f6c554c9119f4152bd747905b3b2ade3" + } + }, + "0c9bb2443ecf460a876558b547c3b97f": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "0ce302531a69423aa36a39e0502f7970": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "0d29cb058287441397849858958eecc5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_5cf9cf4d06af477aaadf2c2eafb21a0f", + "placeholder": "​", + "style": "IPY_MODEL_acb18d163a98436abc6a34c80d0b9875", + "value": "" + } + }, + "0d2f68be02e7495fab83926f99b93586": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_bfc5138baadf4385a0ed6b87107fabec", + "placeholder": "​", + "style": "IPY_MODEL_b760aaf9ca3047378404f446549355ef", + "value": " 477/1300 [05:37<09:42, 1.41it/s, loss=0.301, v_num=n3o8]" + } + }, + "0d5904889d6246e4b0bd8223d9ce79ab": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_8b4f59bf71c64c2293a9e089b963b914", + "placeholder": "​", + "style": "IPY_MODEL_7372f1fa693345ad8f26f0148b2f527b", + "value": "Downloading: 100%" + } + }, + "0dfe3ae073634ba8a1747bc96b30e2bd": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": "2", + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "0e2de7dbc3f3450bb17060ce2687c3ed": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_44a17877ab654e0481b0fe827a991e5a", + "IPY_MODEL_083ca4f1dea5421bb07bf9cfc802871d", + "IPY_MODEL_8249f31d416e4ed388233cd633c212a7" + ], + "layout": "IPY_MODEL_de3f7930ae454905be655bf33f045280" + } + }, + "0e590d1bb05d4bfaa6c0c41cb41e4794": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "0eccda6238eb4756863a6efeeadc9efb": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "0f2dc68b57614e79b95ddeb715f124af": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "0f6e284513e94a3f8681d4b4e39fcb54": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_75a3f953de714650a03ad3b2319f8a82", + "max": 100, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_b49ab085f6e04c7398e90e09eda48c5a", + "value": 100 + } + }, + "0fb032afb0904c358aaa984c8556d0c8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "0fe66bd8c6314fdb86ea236d9638f538": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "0fedf481a76a4d319ddc6afe51345a36": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "10153cc99ab24f089bc2c964eb5f4806": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "107117ee9e3947058cf7586eba69cf6e": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "10aad821076a43b88b833f52d41df0ef": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "10d449f46d9c4502baaad1606f873c09": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "10e6ba61808f471d82b431c607b65d99": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "1105d8a1da0146e0963254b901f6fd80": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_439674cf49124161b3783a681b407ed2", + "placeholder": "​", + "style": "IPY_MODEL_10153cc99ab24f089bc2c964eb5f4806", + "value": "100%" + } + }, + "11e6194ce58c4c05a4cf3c9c55b4acc4": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "12636a29bb09482693911ac3c6ab429a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "1355cef1e865410182443c3b8ba73fd2": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "1384b04401ee4d5e9a7ab9574e660ee6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_9db7c2d529ea46d48e22271d8432f137", + "placeholder": "​", + "style": "IPY_MODEL_6d6241a0e8544b03bd406739bd2e3914", + "value": "Validation DataLoader 0: 100%" + } + }, + "140f7d5cb0fb4b4b8598d1552a429052": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_a4eacb994f2147d29d2d68e9b3c456aa", + "placeholder": "​", + "style": "IPY_MODEL_205926dbd19445c08bd1b8a02e33634f", + "value": "Downloading (…)ncepiece.bpe.model";: 100%" + } + }, + "144c2d74865e41c19a4a6efb4b120ca9": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": "2", + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "144e18e547844b1789fa6759bad4b1a2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_6fcd1c300caa489dadeaa50c6c814a14", + "max": 100, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_f60b9e69e69e40189644c2cde41fae18", + "value": 100 + } + }, + "148b60b8a9a44244874d2db13de48a00": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "1510960e07b742c4b1d838b2d533439b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "15799ffca3624428926e2ddb16728b68": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "15f7ce33a6304293939d23084881a33e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_414f4fac280c49db98080b31e3facc69", + "IPY_MODEL_3752aef8ee5147e2b603a691f233cc24", + "IPY_MODEL_45ce4745da5840a090620d59ce4eb1e5" + ], + "layout": "IPY_MODEL_c3f54d242da14b689f6e213867b81cec" + } + }, + "1646a7c133a94af7bcfda5fb6ca800bc": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_80cc4498465e4fecba040320c9bf1af6", + "IPY_MODEL_55e69f30eb7f44dfb9fa64f938cd41dd", + "IPY_MODEL_ea7c0bd905074e5d8105bb82036bea1c" + ], + "layout": "IPY_MODEL_e529a78e1aba43d980b2953aef3cdfba" + } + }, + "165a9c0135cb45869feb49beacb8101a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "1674e26429bc4c80991bb8942b56a7d7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_c97025e7d015445fafa406c4c1a89af7", + "placeholder": "​", + "style": "IPY_MODEL_1dc7760bdd2944ad9c4991c82dc86e25", + "value": "Validation DataLoader 0: 100%" + } + }, + "167cf75950f6434293f6809a84f998f3": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "16ea181fc8694d29b499b46678fbdaf3": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "183c1ce81c8544719ce85a79cf44f9e9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_538e66a6277b478db47627b3f38b7da0", + "IPY_MODEL_7de626274586484c966e3fcd322559af", + "IPY_MODEL_07a8d73caf1741d78f637c5093884834" + ], + "layout": "IPY_MODEL_049b89f7db7b4b278b2b266771a22ef1" + } + }, + "18db9e56c0354600851d762c8de0f774": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "18e8af9f9ff247c88797e03d48b675f9": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "18f20450f84d48d7839bb3c676cb53d4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_75469c205b314779aef9d1e7ff2fd612", + "placeholder": "​", + "style": "IPY_MODEL_2dd3e0f914f6471587bbe40beeacd7f3", + "value": " 100/100 [01:23<00:00, 1.20it/s]" + } + }, + "19026bb7f79446bc970e1210cccf206f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_230aa7332e4e4114a923b317b4e71991", + "max": 4900, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_45b9943cd00546afad178c901cd94ade", + "value": 4900 + } + }, + "1a457a54c55f4aab95a9c3cfaa2a23b5": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "1b07db1c1be94ffe98ca2e23ca97e187": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_ed76219299d54f908eff9c9678e1e00f", + "placeholder": "​", + "style": "IPY_MODEL_76839f93896d43dabcb10f14b031c037", + "value": "Downloading data: 100%" + } + }, + "1c1855ade6204625bfaab54ca2159af5": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "1c2bd9e72e334917b3ce39810bf5ea0b": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": "inline-flex", + "flex": null, + "flex_flow": "row wrap", + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": "hidden", + "width": "100%" + } + }, + "1c7a17fb7dd041c2949e5df85ee78570": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "1cda98448c074471ae879ab9410e49d5": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": "2", + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "1d3f96ad71b74c31b18b50d0dafefb6e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_fe170e9c276f49b6927ffcbf3809de3e", + "placeholder": "​", + "style": "IPY_MODEL_07acf858ebad4351bfd3cfdb339dddcb", + "value": "Downloading data: 100%" + } + }, + "1dc7760bdd2944ad9c4991c82dc86e25": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "1df30bb3175541a7b7a6541b38d51b89": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "1e25f97059cf482587bc1dcb055d4f7c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "1e6e26bf5f3e4322b0f9ff4352976adb": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_5c6c3811d9e04247abd52e0f835b6a5e", + "placeholder": "​", + "style": "IPY_MODEL_fdf8fb828c1d4ad6b864b9197a0aa486", + "value": "Validation DataLoader 0: 100%" + } + }, + "1eecabada33e46da975375f9fce5517e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_1674e26429bc4c80991bb8942b56a7d7", + "IPY_MODEL_72e608fe672b40c99de89ad5ce1e33c0", + "IPY_MODEL_a35f8906c1ae46828120319694065815" + ], + "layout": "IPY_MODEL_6340e0aa27c04db0b48448513e9875c4" + } + }, + "202cb4369e4845a6a60408400f88b0ed": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "2040593b2df341c68670f38512b423d0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "205926dbd19445c08bd1b8a02e33634f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "20e0bf2b09d841d6bc9e1f36ec4079bc": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "20eb3303eb2b429891247566f52218c0": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "210766df14774c1e89d0df73f294daa6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "212fc0393d3a4ea9a76571c9b636ae20": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "21d179e89c8d47768585c9d9d3bd6e20": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_7d207f93b4a24696abf379bcc01723dc", + "placeholder": "​", + "style": "IPY_MODEL_b0ab92d005974b959f7cb0f2cb9b11d6", + "value": "Downloading: 100%" + } + }, + "230aa7332e4e4114a923b317b4e71991": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "23e4cf09102a4753844ceed08a211d53": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "2472d601c42041e6acb51d19de547a0a": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "24b5e61aecd84402a6261b4b06146559": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_bf9d9715e7684c4e8ac29cbcabf1c600", + "max": 809404185, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_e71b1fe193ba49afb42fe50f96663070", + "value": 809404185 + } + }, + "24c7acb091ab4037a6b4a2eb8a1085e3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "24d17f7c48fb4d41b02ec4731d629758": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_3335761da6f84b2bb970a32f27f5abc4", + "placeholder": "​", + "style": "IPY_MODEL_69f55ea05abe4ca9b21135169edc7a71", + "value": " 800/800 [00:08<00:00, 90.56 examples/s]" + } + }, + "24d1c38ed9e241c2b13abd30ead9a8de": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_eb5f6ef011b44f7d8a62a35ee66899db", + "IPY_MODEL_0bb2e5d6e3544a44940c79b7a5543407", + "IPY_MODEL_5517cbf80d8b448d9a511070dfb4dc1d" + ], + "layout": "IPY_MODEL_107117ee9e3947058cf7586eba69cf6e" + } + }, + "24e315cb82a747d6b9566b461db6aee9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_f98e3325c6b64df7b028a71eb6c1aa5e", + "max": 1300, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_c5f481bd341a48668518912efbd6a35a", + "value": 477 + } + }, + "25296141a79c4477aac6635dd30a80e5": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "25721e39542d43029d26ac34b4af6d89": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "257fa5076dd74486b7751bffa39c2ef8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_b3a98298b4ea434da0d8815ba0f2c4a4", + "placeholder": "​", + "style": "IPY_MODEL_e6523a394c4442bd949cae3f7666b6d4", + "value": " 4.01M/4.01M [00:01<00:00, 2.27MB/s]" + } + }, + "259d3cba0a5b4943b4b025f921edf06b": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": "inline-flex", + "flex": null, + "flex_flow": "row wrap", + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": "hidden", + "width": "100%" + } + }, + "259d56ea4f394fe591b26941164dcae9": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "262f11dfba44467c882893cec6523189": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "26bb592b09c94ba081cd1f913c318f73": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "2781fb98daf147638db891d565d2f194": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "279ce49322904b87b2ec18b4f497465c": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": "2", + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "293e17ab3b4a462ba7d4adc2de35aa05": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "297ba8af5a704c5ca34d0f406de3e918": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "299e59499eda4a1db01880e4fb1d5cd2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "2a058b66d21642f3ab38b917a57ead4c": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "2a459b1c958240d89f753fe695b457e0": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "2aa84a0d65254ef49ee100f66e393ae9": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "2b1ecdea6e49453588fdca19fc19eaaf": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "2b8a201c62c241a89625630f550f6c02": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "2ca98f8a45fb4335ac197e9b7c019e41": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_6d146fd4003e47978a253192463dd514", + "IPY_MODEL_33596291f0c344ad9219c9a7eb6c5c90", + "IPY_MODEL_30374f50d4af4b9598cb13823ad7780c" + ], + "layout": "IPY_MODEL_6c9cca8ae41441b29f2dd7ac81cd35d4" + } + }, + "2d476a17141343508bcac54393555e9b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "2d4f64a23cc846a48f0906c74d1502b9": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "2dd3e0f914f6471587bbe40beeacd7f3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "2e682e41d92343b792991eaa2b72d7de": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "2e6a63d04fd747a280f813b1335a52b1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "2e8a6bd2ef104b32868443ac711c88b6": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "2ee2d4a8635f45dba573b749244f9e2a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "30374f50d4af4b9598cb13823ad7780c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_6a3d2f0a94ea4f72bc81400880f8c430", + "placeholder": "​", + "style": "IPY_MODEL_c3735a62d26743cc88f0bb71712edaa2", + "value": " 809M/809M [00:06<00:00, 137MB/s]" + } + }, + "30f78b0e910549a6a5e79e8a49c4c67d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_b12a725c43db4fa4ad13d82c75738994", + "IPY_MODEL_144e18e547844b1789fa6759bad4b1a2", + "IPY_MODEL_cdf0d854eba142779798e0ac495c02af" + ], + "layout": "IPY_MODEL_bfc6976f3a4b4de3bd42aa1dc9ab73e5" + } + }, + "31081f8d85954a33bcc789b1f5f93c56": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "311acaa0e5844400b54281fe18b50b34": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "314069aa673f41f9abd2c6d2b634a64c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_7df7c889e4794145ba5b20177df36189", + "IPY_MODEL_f16c7f25616a478ab6ba69ce88a69724", + "IPY_MODEL_e9b2a001c8dc4591b00a21893c56dceb" + ], + "layout": "IPY_MODEL_96b45a24defe461998123ec31aba1d97" + } + }, + "31d2bbdfa41b451eb71eba642b3db4e4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "31d5cb042096428fa4e8a0cd682ddc09": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "324b10e8d5ea4f1598111ce9321f5809": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_311acaa0e5844400b54281fe18b50b34", + "placeholder": "​", + "style": "IPY_MODEL_6016947c02af4021851e950c72479029", + "value": " 100/100 [01:22<00:00, 1.21it/s]" + } + }, + "32cb9bbc1e4547cfbe4df1e4ca70e254": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "330b85404fe84761a72a8cbfb696eae1": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "33170adc2cbe4b3daa9073cee501d854": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_583a3d03b1f344d6b7424589c4db1634", + "max": 100, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_56628597cea5425684265b293010eefe", + "value": 100 + } + }, + "3335761da6f84b2bb970a32f27f5abc4": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "3350308cf34f4c02bd89f6e2a345ed12": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "33596291f0c344ad9219c9a7eb6c5c90": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_fe5fe724aaa14f2bb80e84fc589103fc", + "max": 809168699, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_c65a6cc99b06467cbc7f4efe8d80aae0", + "value": 809168699 + } + }, + "337672b1b8894951810dc1c645f66dbd": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "33a65dc99580418cb494e29ae5a0fb9a": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "3427bc46e6f84ac5a403b42afcde9b08": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "LabelModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "LabelModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "LabelView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_4a86c267cbe34ba3bbe0ec2e0035bbbb", + "placeholder": "​", + "style": "IPY_MODEL_3bcce8d28d894837824f52bae420976a", + "value": "Waiting for wandb.init()...\r" + } + }, + "3475f0d8415b4f3ba6b5ad701c365e61": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": "2", + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "3528f8245a2044c4b2037481bdaf39a7": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "35560ff2bef140aea29689440aa8f741": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_f794d85eed554a6ea4dabf8420da0160", + "placeholder": "​", + "style": "IPY_MODEL_f6679d2887ae4c759e6fe58b01a9d546", + "value": " 6/6 [00:06<00:00, 1.04s/it]" + } + }, + "3563de5d5e394c578b7667bdae8644e6": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "3612afa317c5414bbffdbc1f241eb133": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "36777dc617bf4589a8ab9802f363ecf4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_8cef8071dc984acc8a003134e8d64a11", + "placeholder": "​", + "style": "IPY_MODEL_cd80b89157374e759c3a24091fced46b", + "value": "pytorch_model.bin: 100%" + } + }, + "36c149d0545d43d7ac1cb34f4e3c33bd": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "36c215478b2f415e904a24de4b91d9d7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "3735eff345c9459a986b52ac7c469a33": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "3752aef8ee5147e2b603a691f233cc24": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_618716c2a7dc4557873d08a9ea2b0b22", + "max": 234202795, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_663d883b5cbf4a6998db3319c55ee3d8", + "value": 234202795 + } + }, + "37c969be0a7946c6a7d45940fcde5cf9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "3880476d3f0e4962be29180763486deb": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_f24b9f61a1fe4c3f86e5086676a5e5d5", + "placeholder": "​", + "style": "IPY_MODEL_8299c1ef69414603a1604699c777e1f4", + "value": "Validation DataLoader 0: 100%" + } + }, + "38fff333701c4678b5c69863aa8992ac": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "3920ef1a0817495696b874cc61516d7b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_be37899db94d4c15b8e194c03b057a45", + "max": 800, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_4ec3f7acd47c476fbf7e030426c51a7a", + "value": 800 + } + }, + "394b5ca6361e4d38ba6cca638de5cdcb": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "3a1168d83bbd44c887b93d20f8eb59a6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_4db3cc54264240ccac6402b67dfa1049", + "placeholder": "​", + "style": "IPY_MODEL_ec11d0d99c684e2c87c1bf1c05220a95", + "value": " 355/355 [00:00<00:00, 5.12kB/s]" + } + }, + "3a6dce72d0ed40c48bbd91b3f07aa67a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_36777dc617bf4589a8ab9802f363ecf4", + "IPY_MODEL_f2e58d8539584eebae048ed68d50a641", + "IPY_MODEL_9dde5575c8e7438fa2d86e4b9a4fb33f" + ], + "layout": "IPY_MODEL_18e8af9f9ff247c88797e03d48b675f9" + } + }, + "3a78f6a7e4814c23b19458c6f2857754": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "3b5d54289850421e821f88fc5f0c0421": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "3bcce8d28d894837824f52bae420976a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "3c7e904af22f42dca56b6421d35412ff": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "3ca24d66c54b42868c814abb11d4214e": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": "2", + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "3ce6f898ce604f258efab30dd9f65b79": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_ac08a565f2d5447a95d2e6af307f3f09", + "placeholder": "​", + "style": "IPY_MODEL_4ed83abdd375497f9edb35c828d70be7", + "value": " 4.74k/4.74k [00:00<00:00, 212kB/s]" + } + }, + "3da1fd9a348f4c7bb07889c017cc0c2a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_0dfe3ae073634ba8a1747bc96b30e2bd", + "max": 100, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_9dcbe88c6ba446ea9584331776d2748a", + "value": 100 + } + }, + "3db9e9823c2d4f418cd6707058ff07ba": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "3e528e7f8cbf4ca597c63d1789a556a5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_4184d862482d4589806f8d09b5b85737", + "IPY_MODEL_c99fc92b5c87414faa6bafd1d91ce9c4", + "IPY_MODEL_098aae301ab04e688652dd0c05f8de1c" + ], + "layout": "IPY_MODEL_aff4e5ac7e384d408fc23ca0a8d3658f" + } + }, + "3e8e09b6655a4c8fb3995915a69c795f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "3e9927167bb84cac9cf56f350bee5aae": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "3f10740717d4479ba2acf18f7957fc44": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "3f2d02e521e848398853c526866d3585": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "3f76ca5381b64731af382a63b890292b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_d3995239fdf54fabb7fb42417dd2d1e2", + "placeholder": "​", + "style": "IPY_MODEL_32cb9bbc1e4547cfbe4df1e4ca70e254", + "value": "Upload 1 LFS files: 100%" + } + }, + "3fda98dab7574ba489090143ce8a0e82": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "404fed29990348b7ac2fba4a5b61aa89": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": "2", + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "4058a3b2505f441f84f78fe4f7188aa8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_10e6ba61808f471d82b431c607b65d99", + "placeholder": "​", + "style": "IPY_MODEL_5c9eb0211674488bbe8ec74c68a6e9a3", + "value": " 100/100 [01:21<00:00, 1.23it/s]" + } + }, + "414f4fac280c49db98080b31e3facc69": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_ba402e291fd94f8ab8f1a74c5c1ac551", + "placeholder": "​", + "style": "IPY_MODEL_fe92ba127a624524bfe051a6040b4c45", + "value": "Downloading data: 100%" + } + }, + "4184d862482d4589806f8d09b5b85737": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_c0cf700f7872458385250ba075a8edb1", + "placeholder": "​", + "style": "IPY_MODEL_2e6a63d04fd747a280f813b1335a52b1", + "value": "Validation DataLoader 0: 100%" + } + }, + "422523d56ec74035b0a55b9c97d1ed19": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "439674cf49124161b3783a681b407ed2": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "43d49626f9284b23b3bca84aaf9f4616": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "44a17877ab654e0481b0fe827a991e5a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_5628c489cf31430c8b7efe8118c64815", + "placeholder": "​", + "style": "IPY_MODEL_b04b0c322c624afa8bb5a128b2af08b6", + "value": "Validation DataLoader 0: 100%" + } + }, + "44b4f06f3c0841d2ac960337d18de7a3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "45684f2d86954453b3f7059f847028c4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_cc2651a0102d49c7b6a9ac6e82db8c9a", + "placeholder": "​", + "style": "IPY_MODEL_3a78f6a7e4814c23b19458c6f2857754", + "value": " 3/3 [00:00<00:00, 76.29it/s]" + } + }, + "45b9943cd00546afad178c901cd94ade": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "45c29055f8a74361b562fb2baaa3b71f": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "45ce4745da5840a090620d59ce4eb1e5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_337672b1b8894951810dc1c645f66dbd", + "placeholder": "​", + "style": "IPY_MODEL_2b1ecdea6e49453588fdca19fc19eaaf", + "value": " 234M/234M [00:03<00:00, 72.9MB/s]" + } + }, + "45df3e9080c7437680d57cb5936dc1de": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "4619abf6125240f7862c7583a6f68af1": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": "inline-flex", + "flex": null, + "flex_flow": "row wrap", + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": "hidden", + "width": "100%" + } + }, + "4664a4a1ab034098a12b6ad238c0391b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_f8c2d38923c948cf8f2cf8c49ffb2c8d", + "max": 4011031, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_37c969be0a7946c6a7d45940fcde5cf9", + "value": 4011031 + } + }, + "484fac29339449d58309c92dbbbd2af2": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "487685e439f647efbc2350f87ae5d275": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "488139fa453f40e6895a0bac7d5cf840": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "4a2f2fa2885347aebb436467a49b8857": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "4a86c267cbe34ba3bbe0ec2e0035bbbb": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "4a987dc4a0e54e5da442be8d6a1c7f09": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_279ce49322904b87b2ec18b4f497465c", + "max": 100, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_15799ffca3624428926e2ddb16728b68", + "value": 100 + } + }, + "4c06e9c680b6471c8305042a7e3757e3": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "4cacd92cb1f04dcea344f93e816d6244": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "4d6704d874404c9996e32b16b7773169": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "4db3cc54264240ccac6402b67dfa1049": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "4ebee50caff240aea9377fa4a2d0d7cf": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "4ec3f7acd47c476fbf7e030426c51a7a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "4ed83abdd375497f9edb35c828d70be7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "4f2be832c1f04902af8bfc4f5d971057": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "4fefc71218894bd4a6b2e95ff3a882e5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "5053663ab6874c378d764dc8584b6aca": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_932854c5974142b3b906ac1db17725fc", + "placeholder": "​", + "style": "IPY_MODEL_baad5e47bfc940229c7c19cb8372b59c", + "value": "Validation DataLoader 0: 100%" + } + }, + "507d4fec2b8847d79a884853b4d81dad": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "50ce7fc092624343a610286377ecd39a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_840a81ea307a4633bfd9846f89121257", + "placeholder": "​", + "style": "IPY_MODEL_7a2a38deff05410b92c7f0aca5e2f906", + "value": "Downloading (…)cial_tokens_map.json: 100%" + } + }, + "5253bf28cfe948fd8520b5aefd5cdeff": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "52cbbbf6bc6643e9b43d844586ab453e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "538e66a6277b478db47627b3f38b7da0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_53f439b9ac714080972b77d4ce2e15ed", + "placeholder": "​", + "style": "IPY_MODEL_8d98ed226dd1489e83099782d51d7b55", + "value": "Downloading: 100%" + } + }, + "53f439b9ac714080972b77d4ce2e15ed": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "542c34c7d55c400a8f623232403fe250": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_56959726851f49a9b05b1b92cfdd6e74", + "max": 1, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_3f10740717d4479ba2acf18f7957fc44", + "value": 1 + } + }, + "543c3f05eb0547298950b7e7d8c64467": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "548572561a0045b485ef9dde63326446": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_e30df53a96fc474da072e8692a0d1d58", + "max": 27, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_8c3fc7e7b9354d0fb59602100bc5ab1f", + "value": 27 + } + }, + "54e45826ac5f44d3b315e59391fbc347": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "5517cbf80d8b448d9a511070dfb4dc1d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_1a457a54c55f4aab95a9c3cfaa2a23b5", + "placeholder": "​", + "style": "IPY_MODEL_fe52a9c056f843068d5588e488f490db", + "value": " 3/3 [00:00<00:00, 147.78it/s]" + } + }, + "5536aeeebaad4d158a11a706940a0220": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "VBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "VBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "VBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_3427bc46e6f84ac5a403b42afcde9b08", + "IPY_MODEL_b294221a318349228f631ca89407740b" + ], + "layout": "IPY_MODEL_45df3e9080c7437680d57cb5936dc1de" + } + }, + "554913277fc34084990cd041ca766701": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": "2", + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "55e69f30eb7f44dfb9fa64f938cd41dd": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_404fed29990348b7ac2fba4a5b61aa89", + "max": 100, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_9b045fecb5c24541b6611ba71237f6f7", + "value": 100 + } + }, + "561abfbe2ed9405d8bc9c67f3a4c4c8a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_259d56ea4f394fe591b26941164dcae9", + "placeholder": "​", + "style": "IPY_MODEL_31d2bbdfa41b451eb71eba642b3db4e4", + "value": " 441M/441M [00:06<00:00, 49.4MB/s]" + } + }, + "5628c489cf31430c8b7efe8118c64815": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "565a3479b2f140c4bdba43ba99d71c2c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_fa5080966e724cf2901654c57dbe8abd", + "IPY_MODEL_6394dfa7856b4bf49a9325f22dfe1cc1", + "IPY_MODEL_baa432ed76ae437e8946ebe41301453c" + ], + "layout": "IPY_MODEL_6ce260c95cfa4736a22d77f5faba3888" + } + }, + "56628597cea5425684265b293010eefe": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "56959726851f49a9b05b1b92cfdd6e74": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "56c8c883282e4f74ae1c353455136380": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_5ec271f8377a43878a090aaecdde01e6", + "placeholder": "​", + "style": "IPY_MODEL_8473ba1cc0d44bba85462cdbf12e9514", + "value": "Downloading: 100%" + } + }, + "56fea1b142db47fbab0b118185587e76": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "582d8cd7787443f59398e2f79c328b51": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_554913277fc34084990cd041ca766701", + "max": 100, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_e8f2e16d8a9f4091a2d6438892c731b4", + "value": 57 + } + }, + "583a3d03b1f344d6b7424589c4db1634": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": "2", + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "58a4b18d859e4c47bc97f2e6ce8c6c73": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_a7fb70b9171c4958bc5f5c37e51f8cd6", + "placeholder": "​", + "style": "IPY_MODEL_31d5cb042096428fa4e8a0cd682ddc09", + "value": "pytorch_model.bin: 100%" + } + }, + "5915cae6f7ba4d5caa52ff5c8f854b38": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_487685e439f647efbc2350f87ae5d275", + "placeholder": "​", + "style": "IPY_MODEL_ca6fcf9c01fe4f2db382b6b0f8cb169d", + "value": " 456M/456M [00:27<00:00, 17.4MB/s]" + } + }, + "5947ad0e06a54e38b8d09d160bffb0d2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_ebeb73a097574a8597d3592329674e44", + "placeholder": "​", + "style": "IPY_MODEL_81255865450c47ea8a9de6b7996514a3", + "value": "Downloading data files: 100%" + } + }, + "59585c4440a64e1fb76601df8aee255d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "5af452039cad49ee92c938aaffa25765": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_3e9927167bb84cac9cf56f350bee5aae", + "max": 1, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_ff830930e9e04bdaa73f57e4eafe1979", + "value": 1 + } + }, + "5b515a9e88074259b1067880ad7aa637": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "5b72a50beccd472399740c67d5b8a775": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "5be79748747e48dc9557e635185b09b7": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "5c6c3811d9e04247abd52e0f835b6a5e": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "5c9eb0211674488bbe8ec74c68a6e9a3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "5cc9c7f2db1846b6905452d7a9568919": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_507d4fec2b8847d79a884853b4d81dad", + "max": 3, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_ef6284ce29684d8b9b16875076cd579f", + "value": 3 + } + }, + "5cf9cf4d06af477aaadf2c2eafb21a0f": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "5d99e7c78ae54a2fa6824cfe36498ab4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_6eec510e0a3844dfb7c7bcb3a6b9d910", + "IPY_MODEL_dcbf40b9f3d04a29abb5b82cc4f4f43a", + "IPY_MODEL_03a5186fafe7424c896a5c8f4b356098" + ], + "layout": "IPY_MODEL_0c9bb2443ecf460a876558b547c3b97f" + } + }, + "5e592f3705ce411dbfccc726d2348933": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_749592c5ae004de4a5f31bab3c2d31f3", + "IPY_MODEL_548572561a0045b485ef9dde63326446", + "IPY_MODEL_674e652801844a33ba01395ebdb40263" + ], + "layout": "IPY_MODEL_d06409a05ec640f9a6a87a8925dd23c1" + } + }, + "5ec271f8377a43878a090aaecdde01e6": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "5f177c38708d4492909da4b8ae0a4120": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "5f3800af43c84d3596987e49afd4961d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_aaa6238192004463834b5ee1b3464cff", + "placeholder": "​", + "style": "IPY_MODEL_299e59499eda4a1db01880e4fb1d5cd2", + "value": " 100/100 [01:25<00:00, 1.18it/s]" + } + }, + "5f71880b66d1458e923cc893a88f91bf": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "6016947c02af4021851e950c72479029": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "61223d9e086a42ab94927964a4455894": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "617aea856f93495d9f56dfc83a33d4bb": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "618716c2a7dc4557873d08a9ea2b0b22": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "61c95b7243604620a9151ce8b6fcc910": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_d68dcd34129d4922b23a3bc96739814f", + "max": 518, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_2e682e41d92343b792991eaa2b72d7de", + "value": 518 + } + }, + "62bad7effcf24059b293404071b83263": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_9b6856e1b63c46b78a50bee3336577e7", + "placeholder": "​", + "style": "IPY_MODEL_9cc8e3acf3d74f9796353f621fb6dfbf", + "value": " 100/100 [01:21<00:00, 1.22it/s]" + } + }, + "632cbce9ea2f4cc5a87028463a877a5c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "6340e0aa27c04db0b48448513e9875c4": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": "inline-flex", + "flex": null, + "flex_flow": "row wrap", + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": "hidden", + "width": "100%" + } + }, + "6394dfa7856b4bf49a9325f22dfe1cc1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_95594c0d3f7d4aafb3f61b21b823b32f", + "max": 490224630, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_effb0f0cff3c4cc6a8321d0c411a86f6", + "value": 490224630 + } + }, + "6412ab45ef6c45fd88462f76731675f9": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "646592d608724342ad765c7e7457740d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_2472d601c42041e6acb51d19de547a0a", + "placeholder": "​", + "style": "IPY_MODEL_59585c4440a64e1fb76601df8aee255d", + "value": "Downloading (…)lve/main/config.json: 100%" + } + }, + "648f377c77da4818b1817ab95677d5bc": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": "2", + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "64f05915a7b040f9a73dfba9e02d249d": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "65277dbddbb34381b788a0ee526a0245": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": "inline-flex", + "flex": null, + "flex_flow": "row wrap", + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": "100%" + } + }, + "663d883b5cbf4a6998db3319c55ee3d8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "67446ba01e9c4922b041e4ef36ff8774": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_33a65dc99580418cb494e29ae5a0fb9a", + "placeholder": "​", + "style": "IPY_MODEL_c419e4cb99994a5bb0cbccca9c0bc5c6", + "value": " 444M/444M [00:06<00:00, 69.3MB/s]" + } + }, + "674e652801844a33ba01395ebdb40263": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_835bd092bfb64a47b104d20092d61a14", + "placeholder": "​", + "style": "IPY_MODEL_91d8224cea5940bb9858f43b42869729", + "value": " 27.0/27.0 [00:00<00:00, 1.73kB/s]" + } + }, + "674feecda5844fc8b669e8153a982b3f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_9ed0a46f8705464f81f2c8e1a4b8cc89", + "IPY_MODEL_0f6e284513e94a3f8681d4b4e39fcb54", + "IPY_MODEL_7208cb24790c447aae18746d11b7cc7e" + ], + "layout": "IPY_MODEL_c1fe650a41b14253b0c398f09293310c" + } + }, + "67b22ef1c41541dd99fc2b8d445513d2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_484fac29339449d58309c92dbbbd2af2", + "placeholder": "​", + "style": "IPY_MODEL_d216a5de8270482b90d297cf31a09d35", + "value": " 1/1 [01:03<00:00, 63.09s/it]" + } + }, + "690c1c1fdb98452ea766955c7a4966be": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "699755e8ae9840f0a87246bd89608f67": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_1e6e26bf5f3e4322b0f9ff4352976adb", + "IPY_MODEL_b38da01ce29b4009bf824a779d4cafe7", + "IPY_MODEL_5f3800af43c84d3596987e49afd4961d" + ], + "layout": "IPY_MODEL_d34ff744caf646e88cb0606d7a6de704" + } + }, + "69f55ea05abe4ca9b21135169edc7a71": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "6a3d2f0a94ea4f72bc81400880f8c430": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "6a63b2bbe3f34bc6b9a4258a04ca9104": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_9f546de07edf4386ae8ef9394eb6c692", + "max": 809404185, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_3350308cf34f4c02bd89f6e2a345ed12", + "value": 809404185 + } + }, + "6a6be6466e6a4814870c38d55279e1c1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "6c9cca8ae41441b29f2dd7ac81cd35d4": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "6ce260c95cfa4736a22d77f5faba3888": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "6d146fd4003e47978a253192463dd514": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_9525813936a14ac49a7ca525a411c2f9", + "placeholder": "​", + "style": "IPY_MODEL_6a6be6466e6a4814870c38d55279e1c1", + "value": "Downloading (…)"pytorch_model.bin";: 100%" + } + }, + "6d34020216d74b588708e9c9f33f800b": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "6d6241a0e8544b03bd406739bd2e3914": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "6d669ab16e374f0faa089a183bd52532": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "6dd394d212694b969b540123f515957a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "6eec510e0a3844dfb7c7bcb3a6b9d910": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_e797e849c5e0443e84af5c12061589d4", + "placeholder": "​", + "style": "IPY_MODEL_422523d56ec74035b0a55b9c97d1ed19", + "value": "Downloading: 100%" + } + }, + "6fcd1c300caa489dadeaa50c6c814a14": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": "2", + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "6fe01f5548f947d2aebc95273a592581": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_20eb3303eb2b429891247566f52218c0", + "placeholder": "​", + "style": "IPY_MODEL_25721e39542d43029d26ac34b4af6d89", + "value": "Upload 1 LFS files: 100%" + } + }, + "6ff5e86e93914a9ab2ac20d2a32a206e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_330b85404fe84761a72a8cbfb696eae1", + "max": 242080800, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_8030efae633f4b018e7863f46267e575", + "value": 242080800 + } + }, + "7171afc8b8dd4e64966065b7f39f2dd6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_6fe01f5548f947d2aebc95273a592581", + "IPY_MODEL_c2f3547e877a4f2dac7a1130e6f9acab", + "IPY_MODEL_67b22ef1c41541dd99fc2b8d445513d2" + ], + "layout": "IPY_MODEL_9bbc7693c651492f843088817a65497f" + } + }, + "7208cb24790c447aae18746d11b7cc7e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_0755ff4ddf1c453f8f6ca501001fc486", + "placeholder": "​", + "style": "IPY_MODEL_43d49626f9284b23b3bca84aaf9f4616", + "value": " 100/100 [02:47<00:00, 1.43s/it]" + } + }, + "72254d54d49a4054b49882200eecc49b": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "722dd7a1bb984a22ad1b4da14741d7fd": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "72e608fe672b40c99de89ad5ce1e33c0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_144c2d74865e41c19a4a6efb4b120ca9", + "max": 100, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_e8dba2b5fdc94b5598e6ab9f75671cbb", + "value": 100 + } + }, + "73094daa6d4c4bbf93bd29b8c0253e09": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "731ee8128e584ddcbe117dd03612467d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "7372f1fa693345ad8f26f0148b2f527b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "74478f6d2baf47999e56952b01ba938f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_fe0bc6d3aa0b4d88a63aa611af3bf65d", + "placeholder": "​", + "style": "IPY_MODEL_73094daa6d4c4bbf93bd29b8c0253e09", + "value": "Epoch 3: 37%" + } + }, + "745bf50f671a44bc9abddcc6efb736ee": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_2aa84a0d65254ef49ee100f66e393ae9", + "placeholder": "​", + "style": "IPY_MODEL_87a1b92e248549a5b9b9833d52e85c76", + "value": "Validation DataLoader 0: 100%" + } + }, + "749592c5ae004de4a5f31bab3c2d31f3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_fe66b6883afa471eb687430534bd0820", + "placeholder": "​", + "style": "IPY_MODEL_2b8a201c62c241a89625630f550f6c02", + "value": "Downloading readme: 100%" + } + }, + "74cbba5d476540188be2501bf934b5cd": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "75469c205b314779aef9d1e7ff2fd612": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "75a3f953de714650a03ad3b2319f8a82": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "75e8cbe174904f91b3a9ee3f4572625b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "76839f93896d43dabcb10f14b031c037": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "791025ac27604718a24542401ab225e2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "7939062da2ee4ebabadb0a5f06ee3276": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "7982ba1cd95e4347ad3fca29f38d0638": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_3c7e904af22f42dca56b6421d35412ff", + "placeholder": "​", + "style": "IPY_MODEL_efb2989e68d844bb875fbdbd5c79d780", + "value": "Generating validation split: 100%" + } + }, + "79c3843d7f6045799cfc76b2bc4f0837": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_81ae284f0d064843be6279936fba8a13", + "placeholder": "​", + "style": "IPY_MODEL_1e25f97059cf482587bc1dcb055d4f7c", + "value": "Validation DataLoader 0: 100%" + } + }, + "7a20309eef57435f9f35082d5285f444": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_6d34020216d74b588708e9c9f33f800b", + "placeholder": "​", + "style": "IPY_MODEL_b0ee851984d9431597446f79d656d64a", + "value": " 1.52k/1.52k [00:00<00:00, 22.3kB/s]" + } + }, + "7a2a38deff05410b92c7f0aca5e2f906": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "7b2f94dc358c4e5cb6662463e771bb2d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "7b6f963d98df4d36b01e21a89fafd957": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_7d99b26699f0438ea804db1871d2a172", + "placeholder": "​", + "style": "IPY_MODEL_791025ac27604718a24542401ab225e2", + "value": "Downloading: 100%" + } + }, + "7b93f73af3274920ab2b51418e95cff6": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "7bd828ddfe814d05a62736a9d91a8272": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_d3ff6bc3da3b4754af245dcbacd0914c", + "max": 71, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_87407d9ae1f54c3c8383e2934aef15b4", + "value": 71 + } + }, + "7c8f64cb9eda44eca40a6ef5a203afc0": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "7d207f93b4a24696abf379bcc01723dc": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "7d77a9f92e984ac7b9350addc57cb577": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "7d99b26699f0438ea804db1871d2a172": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "7ddc094c928a4b87a7d5895dc558b15b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "7de626274586484c966e3fcd322559af": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_202cb4369e4845a6a60408400f88b0ed", + "max": 4021706, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_92c719b392564d339ff73ccc83c966d0", + "value": 4021706 + } + }, + "7df7c889e4794145ba5b20177df36189": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_f387bdb0ba094db2892ed92e786886f6", + "placeholder": "​", + "style": "IPY_MODEL_1510960e07b742c4b1d838b2d533439b", + "value": "Downloading: 100%" + } + }, + "7ec029d2493f40649921347adbb03ae7": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "8030efae633f4b018e7863f46267e575": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "80cc4498465e4fecba040320c9bf1af6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_25296141a79c4477aac6635dd30a80e5", + "placeholder": "​", + "style": "IPY_MODEL_2ee2d4a8635f45dba573b749244f9e2a", + "value": "Validation DataLoader 0: 100%" + } + }, + "81255865450c47ea8a9de6b7996514a3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "81ae284f0d064843be6279936fba8a13": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "8206f8f3de1a4907ae54d474d422633f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_3880476d3f0e4962be29180763486deb", + "IPY_MODEL_ce88d1d6c0494245b812ae65ea2654ad", + "IPY_MODEL_18f20450f84d48d7839bb3c676cb53d4" + ], + "layout": "IPY_MODEL_4619abf6125240f7862c7583a6f68af1" + } + }, + "821ebf47157741ae9045004304e1d66d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_0ce302531a69423aa36a39e0502f7970", + "placeholder": "​", + "style": "IPY_MODEL_d80a188b254f4bd1be7b3c18442547ea", + "value": " 57/100 [00:46<00:34, 1.24it/s]" + } + }, + "8249f31d416e4ed388233cd633c212a7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_18db9e56c0354600851d762c8de0f774", + "placeholder": "​", + "style": "IPY_MODEL_a524d30afbdc4574be77a664888d32e6", + "value": " 100/100 [01:05<00:00, 1.53it/s]" + } + }, + "8299c1ef69414603a1604699c777e1f4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "82e30cf225894092a6be203ae026b742": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_2e8a6bd2ef104b32868443ac711c88b6", + "placeholder": "​", + "style": "IPY_MODEL_0e590d1bb05d4bfaa6c0c41cb41e4794", + "value": "Validation DataLoader 0: 100%" + } + }, + "82f65ddff78c4c528169704a78d6d9f5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_4f2be832c1f04902af8bfc4f5d971057", + "placeholder": "​", + "style": "IPY_MODEL_dda5061d7d3d4a2cb166bb9473dc3fbb", + "value": "Downloading (…)in/added_tokens.json: 100%" + } + }, + "835bd092bfb64a47b104d20092d61a14": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "840a81ea307a4633bfd9846f89121257": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "8473ba1cc0d44bba85462cdbf12e9514": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "848f3079a55c475f91f8efd0f02d72e3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_1b07db1c1be94ffe98ca2e23ca97e187", + "IPY_MODEL_9f4c3f2e2b17443b9f104bf7e1bc80ea", + "IPY_MODEL_67446ba01e9c4922b041e4ef36ff8774" + ], + "layout": "IPY_MODEL_5253bf28cfe948fd8520b5aefd5cdeff" + } + }, + "84a6704158ab4806a23226678b1be9a8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_7b6f963d98df4d36b01e21a89fafd957", + "IPY_MODEL_19026bb7f79446bc970e1210cccf206f", + "IPY_MODEL_e45b9b8af30042d0a0f91ef4ec1aad76" + ], + "layout": "IPY_MODEL_3735eff345c9459a986b52ac7c469a33" + } + }, + "8544e6360d83437cb03e028051e96be4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "86a4453fe0d7495aafe95e8f68f7a44c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "86b758931e9b4f6c81b2b5d61bb0ed86": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_3475f0d8415b4f3ba6b5ad701c365e61", + "max": 100, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_bb1ee26e35f04a429d3df1f7273b4603", + "value": 100 + } + }, + "873c26ebd5a0403885933700f87ae39f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_3ca24d66c54b42868c814abb11d4214e", + "max": 100, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_0fe66bd8c6314fdb86ea236d9638f538", + "value": 100 + } + }, + "87407d9ae1f54c3c8383e2934aef15b4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "87a1b92e248549a5b9b9833d52e85c76": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "8a3af3818f69463f845e95c933ac00cd": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_b968d28f243543b29cf0733e2d9fc516", + "IPY_MODEL_e0f0d44a4e834e9291b2c021fcb1dad9", + "IPY_MODEL_e0732c157cbf446f80cd1b4d52f7f6b8" + ], + "layout": "IPY_MODEL_259d3cba0a5b4943b4b025f921edf06b" + } + }, + "8ad84d92e3f2470084c072e64a46cfb3": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "8b00742b502d493094ad605fc90b5b09": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "8b4f59bf71c64c2293a9e089b963b914": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "8c3fc7e7b9354d0fb59602100bc5ab1f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "8cef8071dc984acc8a003134e8d64a11": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "8d98ed226dd1489e83099782d51d7b55": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "8eda879a6d224fb091f538c2806d7890": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "8eef683c23a14e9ca9cc8e260a8d25aa": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "8f12f7c6ea604de7ab155971ffea8275": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "8f29c28a5f154fcfb6cb2f382eb1e1b1": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": "inline-flex", + "flex": null, + "flex_flow": "row wrap", + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": "100%" + } + }, + "8f6e8799b4e1454ea04bb9c94b70fdb8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_a999d2c30b9644a7967b7109c3aae629", + "placeholder": "​", + "style": "IPY_MODEL_08c07275cbbe4fd0822c354d99c303ea", + "value": " 100/100 [01:20<00:00, 1.24it/s]" + } + }, + "90761ecf88ad4a1c8dc190928b48fdba": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_f51518a3bf3b4a7582dbe26fca21ca6e", + "IPY_MODEL_09c44c23e97c4293bc1d30f234634c42", + "IPY_MODEL_561abfbe2ed9405d8bc9c67f3a4c4c8a" + ], + "layout": "IPY_MODEL_c44ea4249c324b7cbbb69798e034cb85" + } + }, + "90d6691af3fd49899b196ff7337d71b3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "91d8224cea5940bb9858f43b42869729": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "91f16cb20b2d4590ab22a4b964290569": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_82e30cf225894092a6be203ae026b742", + "IPY_MODEL_4a987dc4a0e54e5da442be8d6a1c7f09", + "IPY_MODEL_cd21d646227b4d478abb70ba04588215" + ], + "layout": "IPY_MODEL_01b01e7329334686ba39c11ec18e9df5" + } + }, + "92c719b392564d339ff73ccc83c966d0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "92f74f940eb74ceb89139e25dc1fe869": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_caf81cab2e1c45c38007dc88bc9a9e0e", + "placeholder": "​", + "style": "IPY_MODEL_b30a4626320c4e78b3a033cf257946fe", + "value": "Validation DataLoader 0: 100%" + } + }, + "932854c5974142b3b906ac1db17725fc": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "938da511fca04f77af54fe3b34287e6c": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "9460488365d343e887fb88aafc4c434b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_4cacd92cb1f04dcea344f93e816d6244", + "placeholder": "​", + "style": "IPY_MODEL_fd4d064de57d469c977318add3069303", + "value": " 362/362 [00:00<00:00, 8.92kB/s]" + } + }, + "9479170c55a34e5a9ccce3115f01b68e": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "9525813936a14ac49a7ca525a411c2f9": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "95594c0d3f7d4aafb3f61b21b823b32f": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "95ec17f083be45d1a48d643e62f4dbf8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "96b45a24defe461998123ec31aba1d97": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "971fb37793b04b02916266e0606a8cd3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_9e5391e58b654a54af53ca6b156d4027", + "max": 1, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_a772a8f8485b4cbc954363c5d25a1355", + "value": 0 + } + }, + "97e80dbb785548e0ad4b7724e91a27f7": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "9812cfa5ccd14b7aa756a383c793c2c5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_7ec029d2493f40649921347adbb03ae7", + "placeholder": "​", + "style": "IPY_MODEL_c922e749c8464d2f9080b3bd2333d04f", + "value": " 809M/809M [01:03<00:00, 14.0MB/s]" + } + }, + "986ff3190ef14c5c907d83383571f267": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_f59a9d0b14c348d6b9c221744c256afc", + "placeholder": "​", + "style": "IPY_MODEL_4fefc71218894bd4a6b2e95ff3a882e5", + "value": " 242M/242M [00:03<00:00, 65.5MB/s]" + } + }, + "98c6ca051cde41cf806b1b8bd9caf6c9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "99bcc899c1d54ea6b6a95fdda3d4e54f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_cbf0235e60724033a400c2cb38b3971a", + "max": 1048, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_90d6691af3fd49899b196ff7337d71b3", + "value": 1048 + } + }, + "9a45e993bffc469a8a156cba70774d25": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_82f65ddff78c4c528169704a78d6d9f5", + "IPY_MODEL_7bd828ddfe814d05a62736a9d91a8272", + "IPY_MODEL_9da3a58c74e64d4d8e39921e472fb6ea" + ], + "layout": "IPY_MODEL_c253f1450fdc49939ad663b6f98b6929" + } + }, + "9a7cb4d7e9514575a93e2f86fc32fb4b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "9aaff87ef6bf4f71b67dc13553aa73b2": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "9ab3a07b87854294b687ab1bd1ce2fe1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_45c29055f8a74361b562fb2baaa3b71f", + "placeholder": "​", + "style": "IPY_MODEL_731ee8128e584ddcbe117dd03612467d", + "value": "Downloading (…)okenizer_config.json: 100%" + } + }, + "9b045fecb5c24541b6611ba71237f6f7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "9b0fe2691d7041fe8841d87beed2bf7b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_f0d43f6407674c12b85a6292284b4ba4", + "IPY_MODEL_99bcc899c1d54ea6b6a95fdda3d4e54f", + "IPY_MODEL_9c138abac1894ebebb334b8044ee6ca9" + ], + "layout": "IPY_MODEL_26bb592b09c94ba081cd1f913c318f73" + } + }, + "9b6856e1b63c46b78a50bee3336577e7": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "9bbc7693c651492f843088817a65497f": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "9bce140411404cad931ec4022a76989e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_50ce7fc092624343a610286377ecd39a", + "IPY_MODEL_b28110567ad04fdeafea8f405f71e7ef", + "IPY_MODEL_f578278adfb146e8bdf89810a39d43cb" + ], + "layout": "IPY_MODEL_fbed03f8b8ad47638a418b9f423e4c26" + } + }, + "9c081ba4b4b7409caef98b8c7754c9b6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "9c138abac1894ebebb334b8044ee6ca9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_9aaff87ef6bf4f71b67dc13553aa73b2", + "placeholder": "​", + "style": "IPY_MODEL_4ebee50caff240aea9377fa4a2d0d7cf", + "value": " 1.05k/1.05k [00:00<00:00, 41.0kB/s]" + } + }, + "9cc8e3acf3d74f9796353f621fb6dfbf": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "9d0bb9e6cae5452a8f7909340d0ab21c": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": "inline-flex", + "flex": null, + "flex_flow": "row wrap", + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": "hidden", + "width": "100%" + } + }, + "9da3a58c74e64d4d8e39921e472fb6ea": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_617aea856f93495d9f56dfc83a33d4bb", + "placeholder": "​", + "style": "IPY_MODEL_23e4cf09102a4753844ceed08a211d53", + "value": " 71.0/71.0 [00:00<00:00, 4.02kB/s]" + } + }, + "9db7c2d529ea46d48e22271d8432f137": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "9dcbe88c6ba446ea9584331776d2748a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "9dde5575c8e7438fa2d86e4b9a4fb33f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_16ea181fc8694d29b499b46678fbdaf3", + "placeholder": "​", + "style": "IPY_MODEL_e6b5096cac864d27ad88010c2180db29", + "value": " 809M/809M [01:03<00:00, 14.1MB/s]" + } + }, + "9e5391e58b654a54af53ca6b156d4027": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": "20px" + } + }, + "9e5bbac1661d4385b06a3caa40cfce63": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_a867c537beb34cdf905e80246d31dd97", + "placeholder": "​", + "style": "IPY_MODEL_ab41966250404933aea9736461583520", + "value": " 100/100 [00:00<00:00, 124.87 examples/s]" + } + }, + "9ea85c5be7104fdbb67f52414ba1a1bd": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": "inline-flex", + "flex": null, + "flex_flow": "row wrap", + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": "hidden", + "width": "100%" + } + }, + "9ed0a46f8705464f81f2c8e1a4b8cc89": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_a105133b82344a97b79250d8cde50f74", + "placeholder": "​", + "style": "IPY_MODEL_c474aabdf84b459aae6c9c0de4526b47", + "value": "100%" + } + }, + "9ef9d3b7c4e141c99299d21e270347af": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "9f4c3f2e2b17443b9f104bf7e1bc80ea": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_d75985c018114da8943c9bad0c61869b", + "max": 443802181, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_fc88c486f35747cd8a3e0eeaf67ebfb6", + "value": 443802181 + } + }, + "9f546de07edf4386ae8ef9394eb6c692": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "a071ed8d726f45b4a07251b60793dc6a": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "a105133b82344a97b79250d8cde50f74": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "a27fc920f8c94953a0c3e9b2120f1b86": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "a35f8906c1ae46828120319694065815": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_1c1855ade6204625bfaab54ca2159af5", + "placeholder": "​", + "style": "IPY_MODEL_210766df14774c1e89d0df73f294daa6", + "value": " 100/100 [01:21<00:00, 1.22it/s]" + } + }, + "a4dc6daaa87e4a4689ffc623b0a3b259": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_97e80dbb785548e0ad4b7724e91a27f7", + "placeholder": "​", + "style": "IPY_MODEL_20e0bf2b09d841d6bc9e1f36ec4079bc", + "value": "pytorch_model.bin: 100%" + } + }, + "a4ddea7815394732a0125128fcd9e1f2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_c2a0bcc5ca8e42439ef2e153193714be", + "placeholder": "​", + "style": "IPY_MODEL_dd4114a8f79b4725951e78ab4f31d19b", + "value": "Validation DataLoader 0: 100%" + } + }, + "a4eacb994f2147d29d2d68e9b3c456aa": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "a524d30afbdc4574be77a664888d32e6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "a535138c7cfc454e8a096f31b4625331": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_745bf50f671a44bc9abddcc6efb736ee", + "IPY_MODEL_07b75ae72a6a4d16a3ff73a1debe2ee5", + "IPY_MODEL_082d0b472b90409f8ab9b02391df63a4" + ], + "layout": "IPY_MODEL_1c2bd9e72e334917b3ce39810bf5ea0b" + } + }, + "a772a8f8485b4cbc954363c5d25a1355": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "a7f7c95743214d17a1cd103c2add1b3c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "a7fb70b9171c4958bc5f5c37e51f8cd6": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "a867c537beb34cdf905e80246d31dd97": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "a999d2c30b9644a7967b7109c3aae629": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "a9ed7db85a364e6384f84f216080d24a": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "aaa6238192004463834b5ee1b3464cff": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "aad2e43eeafa4cccb5e476d3fce4dacb": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "ab1569f009a54b018f8e1d90a51172ed": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_d2d0f66f84874e679594bc856182c6c2", + "IPY_MODEL_e5562397a34348be901b498203690a42", + "IPY_MODEL_cfdf85f797074bc8af900f5623b63e67" + ], + "layout": "IPY_MODEL_ee26b20d24b94233b242a490bbb528bd" + } + }, + "ab1c3ba6663a40c383a57333f5b13ddb": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_3612afa317c5414bbffdbc1f241eb133", + "placeholder": "​", + "style": "IPY_MODEL_ca6747f101dc44fa8d3b85f668fff5ed", + "value": "Downloading (…)/main/tokenizer.json: 100%" + } + }, + "ab41966250404933aea9736461583520": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "ab6b00b5c38d40e4878e5de67bba6b95": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_262f11dfba44467c882893cec6523189", + "placeholder": "​", + "style": "IPY_MODEL_3e8e09b6655a4c8fb3995915a69c795f", + "value": " 362/362 [00:00<00:00, 16.7kB/s]" + } + }, + "abd398c4b93343a49f7882d3b141ccf4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_7c8f64cb9eda44eca40a6ef5a203afc0", + "placeholder": "​", + "style": "IPY_MODEL_10aad821076a43b88b833f52d41df0ef", + "value": " 809M/809M [00:19<00:00, 43.4MB/s]" + } + }, + "ac08a565f2d5447a95d2e6af307f3f09": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "ac96d471b96049c4aace63a7d88ec166": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "acb18d163a98436abc6a34c80d0b9875": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "acfeebabb13647ddbbe4372e192b235a": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "ad251dd399bf4671978561773be7da7a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_1d3f96ad71b74c31b18b50d0dafefb6e", + "IPY_MODEL_6ff5e86e93914a9ab2ac20d2a32a206e", + "IPY_MODEL_986ff3190ef14c5c907d83383571f267" + ], + "layout": "IPY_MODEL_4c06e9c680b6471c8305042a7e3757e3" + } + }, + "ad391511f2ec4e3ebb69865afee7ae6b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_9479170c55a34e5a9ccce3115f01b68e", + "max": 1296245, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_0fedf481a76a4d319ddc6afe51345a36", + "value": 1296245 + } + }, + "ae1f8706e72943989d91f9a44a40eeeb": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "ae8b189059e94a7ab73fb256432c18df": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_0098775e311344f681335445edadd72f", + "max": 455555434, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_632cbce9ea2f4cc5a87028463a877a5c", + "value": 455555434 + } + }, + "aeccbc9b9c3e4038874656c34f12d9f4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "aef15ac1c7214177b7a890917b2a350d": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "aff4e5ac7e384d408fc23ca0a8d3658f": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": "inline-flex", + "flex": null, + "flex_flow": "row wrap", + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": "hidden", + "width": "100%" + } + }, + "b0095f1cb2364fc991f05f312656947f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "b02d03f2ac8d47559d03eb83a01d467e": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "b04b0c322c624afa8bb5a128b2af08b6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "b0ab92d005974b959f7cb0f2cb9b11d6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "b0ee851984d9431597446f79d656d64a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "b12a725c43db4fa4ad13d82c75738994": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_74cbba5d476540188be2501bf934b5cd", + "placeholder": "​", + "style": "IPY_MODEL_c08394b19b98474798fc290bc540ddd5", + "value": "Validation DataLoader 0: 100%" + } + }, + "b132e3a6f07e452382eadbedff054b94": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_bd0e4a5ac7b5440a8b403a36fdaae181", + "placeholder": "​", + "style": "IPY_MODEL_fd3d2e91c7a64c2782727d2c29020fbc", + "value": "Generating train split: 100%" + } + }, + "b28110567ad04fdeafea8f405f71e7ef": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_690c1c1fdb98452ea766955c7a4966be", + "max": 355, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_c08e405d7915496089723b6d5e78d163", + "value": 355 + } + }, + "b294221a318349228f631ca89407740b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_ae1f8706e72943989d91f9a44a40eeeb", + "max": 1, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_bc7bb44709434398a99c395a01d2b3b6", + "value": 1 + } + }, + "b2f81adfc7624d0ebec30a6f49d3b0d3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "b30a4626320c4e78b3a033cf257946fe": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "b38da01ce29b4009bf824a779d4cafe7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_bb888b3abe26402897177229c0a6ab01", + "max": 100, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_009ae1d114cb48d0990e934e19ee961f", + "value": 100 + } + }, + "b3a98298b4ea434da0d8815ba0f2c4a4": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "b3d5b8a11f8948d0bb7273e036841869": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": "inline-flex", + "flex": null, + "flex_flow": "row wrap", + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": "hidden", + "width": "100%" + } + }, + "b49ab085f6e04c7398e90e09eda48c5a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "b4afe9af86d1487cbe0cb9f4956db2e2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_a27fc920f8c94953a0c3e9b2120f1b86", + "max": 809400699, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_722dd7a1bb984a22ad1b4da14741d7fd", + "value": 809400699 + } + }, + "b50f6068222b4b7d9c8a865bd41ce277": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_ca44a4c94aea499ea738f536eacf4b9b", + "IPY_MODEL_582d8cd7787443f59398e2f79c328b51", + "IPY_MODEL_821ebf47157741ae9045004304e1d66d" + ], + "layout": "IPY_MODEL_8f29c28a5f154fcfb6cb2f382eb1e1b1" + } + }, + "b6168e1cb7354844a2ac17dee7a31168": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_d22010093bc54928b96389c8cc48d409", + "max": 100, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_148b60b8a9a44244874d2db13de48a00", + "value": 100 + } + }, + "b64f8b95e23c4baf87fcec7fb16aedfa": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "b71da8215fd74394aeef7b2835ebdce2": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": "2", + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "b760aaf9ca3047378404f446549355ef": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "b968d28f243543b29cf0733e2d9fc516": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_4d6704d874404c9996e32b16b7773169", + "placeholder": "​", + "style": "IPY_MODEL_0c44b50f550a42d5a89d35c0233fa0bb", + "value": "Validation DataLoader 0: 100%" + } + }, + "b9b02352a20e45ac8128f58b9387435d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_07054014fd004b44993a392856f25f58", + "IPY_MODEL_b4afe9af86d1487cbe0cb9f4956db2e2", + "IPY_MODEL_abd398c4b93343a49f7882d3b141ccf4" + ], + "layout": "IPY_MODEL_3fda98dab7574ba489090143ce8a0e82" + } + }, + "ba402e291fd94f8ab8f1a74c5c1ac551": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "ba77336ed8a64ce1b7b3a561844b53ef": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_ee9f3bdcf3814bbbac4e053b4c169704", + "placeholder": "​", + "style": "IPY_MODEL_cebe7c4bb7e447949431cf58deedef30", + "value": "Generating test split: 100%" + } + }, + "baa432ed76ae437e8946ebe41301453c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_1df30bb3175541a7b7a6541b38d51b89", + "placeholder": "​", + "style": "IPY_MODEL_e563fc2ca1ef4b668604bea1c50674e2", + "value": " 490M/490M [00:07<00:00, 80.9MB/s]" + } + }, + "baad5e47bfc940229c7c19cb8372b59c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "bb1ee26e35f04a429d3df1f7273b4603": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "bb888b3abe26402897177229c0a6ab01": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": "2", + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "bc7bb44709434398a99c395a01d2b3b6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "bcaced45f306473887ab7c07df871481": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "bcb3bc46a920467d8441c8fe2c0081f7": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "bd0e4a5ac7b5440a8b403a36fdaae181": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "be37899db94d4c15b8e194c03b057a45": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "bf2d692c9fd24236a6a760a293cb5a13": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": "hidden", + "width": null + } + }, + "bf3538bb8a33408088307bb3dba05a14": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": "2", + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "bf9d9715e7684c4e8ac29cbcabf1c600": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "bfc5138baadf4385a0ed6b87107fabec": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "bfc6976f3a4b4de3bd42aa1dc9ab73e5": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": "inline-flex", + "flex": null, + "flex_flow": "row wrap", + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": "hidden", + "width": "100%" + } + }, + "c08394b19b98474798fc290bc540ddd5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "c08e405d7915496089723b6d5e78d163": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "c0c9ce38c68446ddad343c8abc4aa3a9": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "c0cf700f7872458385250ba075a8edb1": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "c1f64aaadd484bb2900aaebadeb305d5": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "c1fe650a41b14253b0c398f09293310c": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "c21173370f63409b84b9f40abc202efd": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_2781fb98daf147638db891d565d2f194", + "placeholder": "​", + "style": "IPY_MODEL_da910c9d7bc94d6fabe99bfcd626d9a2", + "value": "Downloading (…)rocessor_config.json: 100%" + } + }, + "c253f1450fdc49939ad663b6f98b6929": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "c2a0bcc5ca8e42439ef2e153193714be": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "c2f3547e877a4f2dac7a1130e6f9acab": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_aef15ac1c7214177b7a890917b2a350d", + "max": 1, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_75e8cbe174904f91b3a9ee3f4572625b", + "value": 1 + } + }, + "c34474a8f453410f8f3dc39c835e3a2d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "c3735a62d26743cc88f0bb71712edaa2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "c3f54d242da14b689f6e213867b81cec": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "c419e4cb99994a5bb0cbccca9c0bc5c6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "c44ea4249c324b7cbbb69798e034cb85": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "c46ec088b99646f091f934536af46d5e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_2a459b1c958240d89f753fe695b457e0", + "max": 4742, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_7939062da2ee4ebabadb0a5f06ee3276", + "value": 4742 + } + }, + "c474aabdf84b459aae6c9c0de4526b47": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "c5083eb93376434e9a53fc44d7d215b9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_394b5ca6361e4d38ba6cca638de5cdcb", + "placeholder": "​", + "style": "IPY_MODEL_7b2f94dc358c4e5cb6662463e771bb2d", + "value": " 1.30M/1.30M [00:00<00:00, 24.7MB/s]" + } + }, + "c5810b986d064e93ac00ff911d3d09de": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_0d5904889d6246e4b0bd8223d9ce79ab", + "IPY_MODEL_09e57cfef8d04198b07337e6adfd3cfd", + "IPY_MODEL_9460488365d343e887fb88aafc4c434b" + ], + "layout": "IPY_MODEL_8ad84d92e3f2470084c072e64a46cfb3" + } + }, + "c5d372a9962c49ada49b96a66c5ecd7e": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": "inline-flex", + "flex": null, + "flex_flow": "row wrap", + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": "hidden", + "width": "100%" + } + }, + "c5f481bd341a48668518912efbd6a35a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "c5f9fb53d093461282bc9ebd4a68ead4": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "c61132d2ccbb404ea969d7701fbc3ed7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_1355cef1e865410182443c3b8ba73fd2", + "max": 3, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_ef4bc97031ee4e798ec0ccb3c1b76e2c", + "value": 3 + } + }, + "c65a6cc99b06467cbc7f4efe8d80aae0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "c663b10ffce1432c89a3948e324571b0": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "c6656f51d29747c7a831f8b4739b8cd7": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "c6e2d6abd785494ca143b5978811c326": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "c6e7bfb4e3144b16bcfd3fd12673d853": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "c75e6f8363fc4bd1ab7d1c6513f60dd5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_56c8c883282e4f74ae1c353455136380", + "IPY_MODEL_e13b3d868fe3450cac508a8799743696", + "IPY_MODEL_3a1168d83bbd44c887b93d20f8eb59a6" + ], + "layout": "IPY_MODEL_64f05915a7b040f9a73dfba9e02d249d" + } + }, + "c8d62278ed494aa1948d4ddac0b5e94c": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "c922e749c8464d2f9080b3bd2333d04f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "c97025e7d015445fafa406c4c1a89af7": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "c99fc92b5c87414faa6bafd1d91ce9c4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_dd41ea80146343bcbee92bed78324879", + "max": 100, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_04b01b03fcc143fa8633b52ec231f026", + "value": 100 + } + }, + "ca44a4c94aea499ea738f536eacf4b9b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_c5f9fb53d093461282bc9ebd4a68ead4", + "placeholder": "​", + "style": "IPY_MODEL_7ddc094c928a4b87a7d5895dc558b15b", + "value": "Validation DataLoader 0: 57%" + } + }, + "ca6747f101dc44fa8d3b85f668fff5ed": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "ca6fcf9c01fe4f2db382b6b0f8cb169d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "ca8a046ed39e45f79234a10f85ed09d4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_5b72a50beccd472399740c67d5b8a775", + "placeholder": "​", + "style": "IPY_MODEL_488139fa453f40e6895a0bac7d5cf840", + "value": " 100/100 [01:24<00:00, 1.19it/s]" + } + }, + "caf81cab2e1c45c38007dc88bc9a9e0e": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "cbf0235e60724033a400c2cb38b3971a": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "cc2651a0102d49c7b6a9ac6e82db8c9a": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "ccd7cb252ac34be1afa95313cd576555": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "cd21d646227b4d478abb70ba04588215": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_ac96d471b96049c4aace63a7d88ec166", + "placeholder": "​", + "style": "IPY_MODEL_86a4453fe0d7495aafe95e8f68f7a44c", + "value": " 100/100 [01:28<00:00, 1.13it/s]" + } + }, + "cd63bf0c96b6466586c66240f67b35a5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_5b515a9e88074259b1067880ad7aa637", + "max": 362, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_6dd394d212694b969b540123f515957a", + "value": 362 + } + }, + "cd80b89157374e759c3a24091fced46b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "cdf0d854eba142779798e0ac495c02af": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_297ba8af5a704c5ca34d0f406de3e918", + "placeholder": "​", + "style": "IPY_MODEL_95ec17f083be45d1a48d643e62f4dbf8", + "value": " 100/100 [00:58<00:00, 1.71it/s]" + } + }, + "ce88d1d6c0494245b812ae65ea2654ad": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_648f377c77da4818b1817ab95677d5bc", + "max": 100, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_ccd7cb252ac34be1afa95313cd576555", + "value": 100 + } + }, + "ce963a069b50408abc6cf6073857c5ee": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_21d179e89c8d47768585c9d9d3bd6e20", + "IPY_MODEL_f88cf9ec7d8b4faa80017e24f99d5dc8", + "IPY_MODEL_7a20309eef57435f9f35082d5285f444" + ], + "layout": "IPY_MODEL_6d669ab16e374f0faa089a183bd52532" + } + }, + "cebe7c4bb7e447949431cf58deedef30": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "cf862b504d86425f886ed6d19b55b05a": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "cfa9b34f74b541fc9c54b905cffa05ad": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "cfdf85f797074bc8af900f5623b63e67": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_a071ed8d726f45b4a07251b60793dc6a", + "placeholder": "​", + "style": "IPY_MODEL_5f71880b66d1458e923cc893a88f91bf", + "value": " 100/100 [01:25<00:00, 1.17it/s]" + } + }, + "d06409a05ec640f9a6a87a8925dd23c1": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "d135d783486d456494d15ef2a339ebff": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_74478f6d2baf47999e56952b01ba938f", + "IPY_MODEL_24e315cb82a747d6b9566b461db6aee9", + "IPY_MODEL_0d2f68be02e7495fab83926f99b93586" + ], + "layout": "IPY_MODEL_65277dbddbb34381b788a0ee526a0245" + } + }, + "d1837ece0226413489f3df0dfd7f3279": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "d216a5de8270482b90d297cf31a09d35": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "d22010093bc54928b96389c8cc48d409": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": "2", + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "d2d0f66f84874e679594bc856182c6c2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_b64f8b95e23c4baf87fcec7fb16aedfa", + "placeholder": "​", + "style": "IPY_MODEL_f9bf6bbc52ea46bc8204f3756f2f981b", + "value": "Validation DataLoader 0: 100%" + } + }, + "d34ff744caf646e88cb0606d7a6de704": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": "inline-flex", + "flex": null, + "flex_flow": "row wrap", + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": "hidden", + "width": "100%" + } + }, + "d3995239fdf54fabb7fb42417dd2d1e2": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "d3ff6bc3da3b4754af245dcbacd0914c": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "d494ec40fa104f16ace482fa5738e771": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_ba77336ed8a64ce1b7b3a561844b53ef", + "IPY_MODEL_02700c32e4d64a66818ac92209384b14", + "IPY_MODEL_9e5bbac1661d4385b06a3caa40cfce63" + ], + "layout": "IPY_MODEL_bf2d692c9fd24236a6a760a293cb5a13" + } + }, + "d510866a711a4f5bb98748690dc89fbe": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "d525b6ed32414b8583d8726d83dd747b": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": "inline-flex", + "flex": null, + "flex_flow": "row wrap", + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": "hidden", + "width": "100%" + } + }, + "d5667dae1b4d479db3745879a34b9ab5": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "d56998a0695e434bb7fe3857f5075083": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "d643c6ffb4964990899a47cc02a63b9d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_04fa08f418e74bfe8d274620cd07e448", + "IPY_MODEL_ae8b189059e94a7ab73fb256432c18df", + "IPY_MODEL_5915cae6f7ba4d5caa52ff5c8f854b38" + ], + "layout": "IPY_MODEL_3db9e9823c2d4f418cd6707058ff07ba" + } + }, + "d68dcd34129d4922b23a3bc96739814f": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "d6e90d985e8b4f7fa5d8258c72a8ba9e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_5053663ab6874c378d764dc8584b6aca", + "IPY_MODEL_873c26ebd5a0403885933700f87ae39f", + "IPY_MODEL_4058a3b2505f441f84f78fe4f7188aa8" + ], + "layout": "IPY_MODEL_d525b6ed32414b8583d8726d83dd747b" + } + }, + "d75985c018114da8943c9bad0c61869b": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "d80a188b254f4bd1be7b3c18442547ea": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "d91e67e21bfa49888bf6316c154bcf77": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": "hidden", + "width": null + } + }, + "da517c560a174ed3b3a0d342ab9531d4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_0d29cb058287441397849858958eecc5", + "IPY_MODEL_971fb37793b04b02916266e0606a8cd3", + "IPY_MODEL_f397c359a4844fe0b43cbd55f7e45e2c" + ], + "layout": "IPY_MODEL_5be79748747e48dc9557e635185b09b7" + } + }, + "da910c9d7bc94d6fabe99bfcd626d9a2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "db3b31f6234f4c0781e02e27c0a78139": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_c0c9ce38c68446ddad343c8abc4aa3a9", + "placeholder": "​", + "style": "IPY_MODEL_a7f7c95743214d17a1cd103c2add1b3c", + "value": " 809M/809M [01:03<00:00, 13.5MB/s]" + } + }, + "dbe52dd9651f42238a11351f10b22b4a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_3f76ca5381b64731af382a63b890292b", + "IPY_MODEL_5af452039cad49ee92c938aaffa25765", + "IPY_MODEL_05c1c99b4aff486a9c5eeadf9bd8230a" + ], + "layout": "IPY_MODEL_167cf75950f6434293f6809a84f998f3" + } + }, + "dcaa8093d0194d76871523cd07347bd6": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "dcbf40b9f3d04a29abb5b82cc4f4f43a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_acfeebabb13647ddbbe4372e192b235a", + "max": 489, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_b2f81adfc7624d0ebec30a6f49d3b0d3", + "value": 489 + } + }, + "dd0b6870c8b6435691009ca66fa71b09": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_eee6b3f385774227b35fc4d83eb0171f", + "placeholder": "​", + "style": "IPY_MODEL_f20da578968f43c3ac44efa032dbd6e2", + "value": " 3/3 [01:08<00:00, 22.16s/it]" + } + }, + "dd4114a8f79b4725951e78ab4f31d19b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "dd41ea80146343bcbee92bed78324879": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": "2", + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "dda5061d7d3d4a2cb166bb9473dc3fbb": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "ddd6a6f26e4a40de85db830c02834c4c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_646592d608724342ad765c7e7457740d", + "IPY_MODEL_c46ec088b99646f091f934536af46d5e", + "IPY_MODEL_3ce6f898ce604f258efab30dd9f65b79" + ], + "layout": "IPY_MODEL_38fff333701c4678b5c69863aa8992ac" + } + }, + "de3f7930ae454905be655bf33f045280": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": "inline-flex", + "flex": null, + "flex_flow": "row wrap", + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": "hidden", + "width": "100%" + } + }, + "def152f1a9e84c7e90e6e24f001f29a2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_140f7d5cb0fb4b4b8598d1552a429052", + "IPY_MODEL_ad391511f2ec4e3ebb69865afee7ae6b", + "IPY_MODEL_c5083eb93376434e9a53fc44d7d215b9" + ], + "layout": "IPY_MODEL_56fea1b142db47fbab0b118185587e76" + } + }, + "df11ed2a5b2f4264b82cfd95724e7a32": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_dcaa8093d0194d76871523cd07347bd6", + "max": 6, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_8544e6360d83437cb03e028051e96be4", + "value": 6 + } + }, + "e05ca35fa13a42e0bfb8daef4706d291": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "e0732c157cbf446f80cd1b4d52f7f6b8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_fad67a0d8f834d6eb9609fc0ecec94c8", + "placeholder": "​", + "style": "IPY_MODEL_9c081ba4b4b7409caef98b8c7754c9b6", + "value": " 100/100 [01:55<00:00, 1.16s/it]" + } + }, + "e0da7e4a45a846c0ac14269e5bbb77f8": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": "hidden", + "width": null + } + }, + "e0f0d44a4e834e9291b2c021fcb1dad9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_bf3538bb8a33408088307bb3dba05a14", + "max": 100, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_24c7acb091ab4037a6b4a2eb8a1085e3", + "value": 100 + } + }, + "e13b3d868fe3450cac508a8799743696": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_2a058b66d21642f3ab38b917a57ead4c", + "max": 355, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_0eccda6238eb4756863a6efeeadc9efb", + "value": 355 + } + }, + "e30df53a96fc474da072e8692a0d1d58": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "e32e4cee40f14064b9cd93b7688c3ff5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_e82ab023dc0e4a2dbe8da18f1b346ca0", + "IPY_MODEL_542c34c7d55c400a8f623232403fe250", + "IPY_MODEL_0759cda7bd084b02921689bc5e319ba5" + ], + "layout": "IPY_MODEL_543c3f05eb0547298950b7e7d8c64467" + } + }, + "e347c9815ff64f93884e65d882c8dc64": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_293e17ab3b4a462ba7d4adc2de35aa05", + "placeholder": "​", + "style": "IPY_MODEL_36c215478b2f415e904a24de4b91d9d7", + "value": "Computing checksums: 100%" + } + }, + "e45b9b8af30042d0a0f91ef4ec1aad76": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_11e6194ce58c4c05a4cf3c9c55b4acc4", + "placeholder": "​", + "style": "IPY_MODEL_5f177c38708d4492909da4b8ae0a4120", + "value": " 4.90k/4.90k [00:00<00:00, 102kB/s]" + } + }, + "e4dd0f4da33c453e886144de5c93aeb3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "e529a78e1aba43d980b2953aef3cdfba": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": "inline-flex", + "flex": null, + "flex_flow": "row wrap", + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": "hidden", + "width": "100%" + } + }, + "e5562397a34348be901b498203690a42": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_f3cb306bf10b448ab173861f392ad1d7", + "max": 100, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_52cbbbf6bc6643e9b43d844586ab453e", + "value": 100 + } + }, + "e563fc2ca1ef4b668604bea1c50674e2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "e6523a394c4442bd949cae3f7666b6d4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "e6b5096cac864d27ad88010c2180db29": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "e6f8227c5e914a279428bfc620591ef0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_79c3843d7f6045799cfc76b2bc4f0837", + "IPY_MODEL_33170adc2cbe4b3daa9073cee501d854", + "IPY_MODEL_8f6e8799b4e1454ea04bb9c94b70fdb8" + ], + "layout": "IPY_MODEL_9ea85c5be7104fdbb67f52414ba1a1bd" + } + }, + "e71b1fe193ba49afb42fe50f96663070": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "e797e849c5e0443e84af5c12061589d4": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "e82ab023dc0e4a2dbe8da18f1b346ca0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_f414756091e34756a98d812bcf299f69", + "placeholder": "​", + "style": "IPY_MODEL_d510866a711a4f5bb98748690dc89fbe", + "value": "Upload 1 LFS files: 100%" + } + }, + "e8dba2b5fdc94b5598e6ab9f75671cbb": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "e8f2e16d8a9f4091a2d6438892c731b4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "e9b2a001c8dc4591b00a21893c56dceb": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_a9ed7db85a364e6384f84f216080d24a", + "placeholder": "​", + "style": "IPY_MODEL_98c6ca051cde41cf806b1b8bd9caf6c9", + "value": " 1.30M/1.30M [00:00<00:00, 1.15MB/s]" + } + }, + "ea0303a1646b454cb26f023c0d8b7804": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_a4dc6daaa87e4a4689ffc623b0a3b259", + "IPY_MODEL_24b5e61aecd84402a6261b4b06146559", + "IPY_MODEL_db3b31f6234f4c0781e02e27c0a78139" + ], + "layout": "IPY_MODEL_c1f64aaadd484bb2900aaebadeb305d5" + } + }, + "ea7c0bd905074e5d8105bb82036bea1c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_3528f8245a2044c4b2037481bdaf39a7", + "placeholder": "​", + "style": "IPY_MODEL_047bdaa0ff9d4a4f8d6f46fca23bd0d2", + "value": " 100/100 [01:20<00:00, 1.24it/s]" + } + }, + "eb5f6ef011b44f7d8a62a35ee66899db": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_61223d9e086a42ab94927964a4455894", + "placeholder": "​", + "style": "IPY_MODEL_aeccbc9b9c3e4038874656c34f12d9f4", + "value": "Extracting data files: 100%" + } + }, + "ebeb73a097574a8597d3592329674e44": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "ec11d0d99c684e2c87c1bf1c05220a95": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "ec2fc9e744d9460f9063f9c0aac4c3d5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "ed76219299d54f908eff9c9678e1e00f": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "ed98bdb5c8364a6782a1883411acd5f5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "ee26b20d24b94233b242a490bbb528bd": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": "inline-flex", + "flex": null, + "flex_flow": "row wrap", + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": "hidden", + "width": "100%" + } + }, + "ee9eb1bc7197470487cf0dea02a3d8f9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_a4ddea7815394732a0125128fcd9e1f2", + "IPY_MODEL_3da1fd9a348f4c7bb07889c017cc0c2a", + "IPY_MODEL_62bad7effcf24059b293404071b83263" + ], + "layout": "IPY_MODEL_c5d372a9962c49ada49b96a66c5ecd7e" + } + }, + "ee9f3bdcf3814bbbac4e053b4c169704": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "eee6b3f385774227b35fc4d83eb0171f": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "ef4bc97031ee4e798ec0ccb3c1b76e2c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "ef6284ce29684d8b9b16875076cd579f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "efb2989e68d844bb875fbdbd5c79d780": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "efbbbb45e1554253bbf331525f219e4c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_ab1c3ba6663a40c383a57333f5b13ddb", + "IPY_MODEL_4664a4a1ab034098a12b6ad238c0391b", + "IPY_MODEL_257fa5076dd74486b7751bffa39c2ef8" + ], + "layout": "IPY_MODEL_b02d03f2ac8d47559d03eb83a01d467e" + } + }, + "effb0f0cff3c4cc6a8321d0c411a86f6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "f09e42ab2e9f4560aa68de42fbc4d4e2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "f0b290d10e694b4c86355bdba583261a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "f0d43f6407674c12b85a6292284b4ba4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_6412ab45ef6c45fd88462f76731675f9", + "placeholder": "​", + "style": "IPY_MODEL_f09e42ab2e9f4560aa68de42fbc4d4e2", + "value": "Downloading metadata: 100%" + } + }, + "f0e0d969d5f943c3ac158a6a443bceee": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_c21173370f63409b84b9f40abc202efd", + "IPY_MODEL_cd63bf0c96b6466586c66240f67b35a5", + "IPY_MODEL_ab6b00b5c38d40e4878e5de67bba6b95" + ], + "layout": "IPY_MODEL_0f2dc68b57614e79b95ddeb715f124af" + } + }, + "f16c7f25616a478ab6ba69ce88a69724": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_bcb3bc46a920467d8441c8fe2c0081f7", + "max": 1296245, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_212fc0393d3a4ea9a76571c9b636ae20", + "value": 1296245 + } + }, + "f1a2bee7a7dc408087833912c951119d": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "f20da578968f43c3ac44efa032dbd6e2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "f21af123c24f43498942dd4f3e91c54f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_c6656f51d29747c7a831f8b4739b8cd7", + "placeholder": "​", + "style": "IPY_MODEL_ed98bdb5c8364a6782a1883411acd5f5", + "value": " 518/518 [00:00<00:00, 36.1kB/s]" + } + }, + "f24b9f61a1fe4c3f86e5086676a5e5d5": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "f2d79a145b3c45389a473ae2fb94e0fc": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_e347c9815ff64f93884e65d882c8dc64", + "IPY_MODEL_df11ed2a5b2f4264b82cfd95724e7a32", + "IPY_MODEL_35560ff2bef140aea29689440aa8f741" + ], + "layout": "IPY_MODEL_36c149d0545d43d7ac1cb34f4e3c33bd" + } + }, + "f2e58d8539584eebae048ed68d50a641": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_3563de5d5e394c578b7667bdae8644e6", + "max": 809404185, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_2040593b2df341c68670f38512b423d0", + "value": 809404185 + } + }, + "f387bdb0ba094db2892ed92e786886f6": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "f397c359a4844fe0b43cbd55f7e45e2c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_c8d62278ed494aa1948d4ddac0b5e94c", + "placeholder": "​", + "style": "IPY_MODEL_9ef9d3b7c4e141c99299d21e270347af", + "value": " 0/0 [00:00<?, ?it/s]" + } + }, + "f3cb306bf10b448ab173861f392ad1d7": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": "2", + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "f414756091e34756a98d812bcf299f69": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "f51518a3bf3b4a7582dbe26fca21ca6e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_54e45826ac5f44d3b315e59391fbc347", + "placeholder": "​", + "style": "IPY_MODEL_f0b290d10e694b4c86355bdba583261a", + "value": "Downloading data: 100%" + } + }, + "f5449d2c5013488ab7396dc95604be0b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_1105d8a1da0146e0963254b901f6fd80", + "IPY_MODEL_c61132d2ccbb404ea969d7701fbc3ed7", + "IPY_MODEL_45684f2d86954453b3f7059f847028c4" + ], + "layout": "IPY_MODEL_053e5fb5ff954b1bb8099c07ad77c35d" + } + }, + "f578278adfb146e8bdf89810a39d43cb": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_8f12f7c6ea604de7ab155971ffea8275", + "placeholder": "​", + "style": "IPY_MODEL_165a9c0135cb45869feb49beacb8101a", + "value": " 355/355 [00:00<00:00, 20.0kB/s]" + } + }, + "f59a9d0b14c348d6b9c221744c256afc": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "f60b9e69e69e40189644c2cde41fae18": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "f6679d2887ae4c759e6fe58b01a9d546": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "f6c554c9119f4152bd747905b3b2ade3": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "f71dd43430aa4d4cb5168644a972df59": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_92f74f940eb74ceb89139e25dc1fe869", + "IPY_MODEL_b6168e1cb7354844a2ac17dee7a31168", + "IPY_MODEL_324b10e8d5ea4f1598111ce9321f5809" + ], + "layout": "IPY_MODEL_b3d5b8a11f8948d0bb7273e036841869" + } + }, + "f75aeb7b8cc74ef488ac24f02b208535": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_d5667dae1b4d479db3745879a34b9ab5", + "max": 100, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_0fb032afb0904c358aaa984c8556d0c8", + "value": 100 + } + }, + "f794d85eed554a6ea4dabf8420da0160": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "f88cf9ec7d8b4faa80017e24f99d5dc8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_7b93f73af3274920ab2b51418e95cff6", + "max": 1516, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_e4dd0f4da33c453e886144de5c93aeb3", + "value": 1516 + } + }, + "f8c2d38923c948cf8f2cf8c49ffb2c8d": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "f98e3325c6b64df7b028a71eb6c1aa5e": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": "2", + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "f9bf6bbc52ea46bc8204f3756f2f981b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "fa5080966e724cf2901654c57dbe8abd": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_cf862b504d86425f886ed6d19b55b05a", + "placeholder": "​", + "style": "IPY_MODEL_12636a29bb09482693911ac3c6ab429a", + "value": "Downloading data: 100%" + } + }, + "fad67a0d8f834d6eb9609fc0ecec94c8": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "fbed03f8b8ad47638a418b9f423e4c26": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "fc4c72e25d9a4b648c14d03560bba0a2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_58a4b18d859e4c47bc97f2e6ce8c6c73", + "IPY_MODEL_6a63b2bbe3f34bc6b9a4258a04ca9104", + "IPY_MODEL_9812cfa5ccd14b7aa756a383c793c2c5" + ], + "layout": "IPY_MODEL_4a2f2fa2885347aebb436467a49b8857" + } + }, + "fc88c486f35747cd8a3e0eeaf67ebfb6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "fcb2db503d9a49c3b45282bebec92322": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_1384b04401ee4d5e9a7ab9574e660ee6", + "IPY_MODEL_86b758931e9b4f6c81b2b5d61bb0ed86", + "IPY_MODEL_ca8a046ed39e45f79234a10f85ed09d4" + ], + "layout": "IPY_MODEL_9d0bb9e6cae5452a8f7909340d0ab21c" + } + }, + "fd3d2e91c7a64c2782727d2c29020fbc": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "fd4d064de57d469c977318add3069303": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "fdf8fb828c1d4ad6b864b9197a0aa486": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "fe0bc6d3aa0b4d88a63aa611af3bf65d": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "fe170e9c276f49b6927ffcbf3809de3e": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "fe52a9c056f843068d5588e488f490db": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "fe5fe724aaa14f2bb80e84fc589103fc": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "fe66b6883afa471eb687430534bd0820": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "fe92ba127a624524bfe051a6040b4c45": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "fee13e5677e84373a18a0f68feb13ab5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_7982ba1cd95e4347ad3fca29f38d0638", + "IPY_MODEL_f75aeb7b8cc74ef488ac24f02b208535", + "IPY_MODEL_008e77e9b3cb4cc4b37e875cab3f3d6c" + ], + "layout": "IPY_MODEL_e0da7e4a45a846c0ac14269e5bbb77f8" + } + }, + "ff830930e9e04bdaa73f57e4eafe1979": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + } + } + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/src/transformers/models/idefics2/fine_tune_idefics2_pl.ipynb b/src/transformers/models/idefics2/fine_tune_idefics2_pl.ipynb index bfbd8efd493c5a..5541a2e6b2e9b8 100644 --- a/src/transformers/models/idefics2/fine_tune_idefics2_pl.ipynb +++ b/src/transformers/models/idefics2/fine_tune_idefics2_pl.ipynb @@ -128,7 +128,7 @@ "output_type": "stream", "text": [ "`low_cpu_mem_usage` was None, now set to True since model is quantized.\n", - "Loading checkpoint shards: 100%|██████████| 7/7 [00:06<00:00, 1.12it/s]\n" + "Loading checkpoint shards: 100%|██████████| 7/7 [00:24<00:00, 3.45s/it]\n" ] } ], @@ -178,9 +178,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-04-30 09:34:36.912138: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "2024-04-30 12:39:37.461173: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2024-04-30 09:34:37.555749: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", + "2024-04-30 12:39:38.252248: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n" ] } @@ -423,8 +423,14 @@ "\n", " batch[\"images\"] = images\n", " batch[\"answers\"] = answers\n", + "\n", + " input_ids = batch[\"input_ids\"]\n", + " attention_mask = batch[\"attention_mask\"]\n", + " pixel_values = batch[\"pixel_values\"]\n", + " pixel_attention_mask = batch[\"pixel_attention_mask\"]\n", + " labels = batch[\"labels\"]\n", " \n", - " return batch\n", + " return input_ids, attention_mask, pixel_values, pixel_attention_mask, labels, images, answers\n", "\n", "# feel free to increase the batch size if you have a lot of memory\n", "# I'm fine-tuning on Colab and given the large image size, batch size > 1 is not feasible\n", @@ -453,28 +459,10 @@ "No chat template is set for this tokenizer, falling back to a default class-level template. This is very error-prone, because models are often trained with templates different from the class default! Default chat templates are a legacy feature and will be removed in Transformers v4.43, at which point any code depending on them will stop working. We recommend setting a valid chat template before then to ensure that this model continues working without issues.\n", "No chat template is set for this tokenizer, falling back to a default class-level template. This is very error-prone, because models are often trained with templates different from the class default! Default chat templates are a legacy feature and will be removed in Transformers v4.43, at which point any code depending on them will stop working. We recommend setting a valid chat template before then to ensure that this model continues working without issues.\n" ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "input_ids torch.Size([2, 377])\n", - "attention_mask torch.Size([2, 377])\n", - "pixel_values torch.Size([2, 1, 3, 980, 653])\n", - "pixel_attention_mask torch.Size([2, 1, 980, 653])\n", - "labels torch.Size([2, 377])\n", - "images 2\n", - "answers 2\n" - ] } ], "source": [ - "batch = next(iter(train_dataloader))\n", - "for key, value in batch.items():\n", - " if isinstance(value, torch.Tensor):\n", - " print(key, value.shape)\n", - " else:\n", - " print(key, len(value))" + "input_ids, attention_mask, pixel_values, pixel_attention_mask, labels, images, answers = next(iter(train_dataloader))" ] }, { @@ -486,14 +474,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "TT20,000120,00020,00020,0000\n", - "KP BRANDING S11-11HYDROCOCO 250ML6,9002-3,90013,80013,8013,901( 1,236)9,90009,9002\n" + "[' User: Extract JSON. \\nAssistant:MILK PASTRY ROLL19,000CARAMEL PASTRY112,00021,00050,00029,000', ' User: Extract JSON. \\nAssistant:Viet Milk Coffee125.000+Hot+M25.00025.00030.0005.000']\n" ] } ], "source": [ - "print(batch[\"answers\"][0])\n", - "print(batch[\"answers\"][1])" + "print(processor.batch_decode(input_ids))" ] }, { @@ -529,10 +515,13 @@ "\n", " def training_step(self, batch, batch_idx):\n", "\n", - " del batch[\"images\"]\n", - " del batch[\"answers\"]\n", + " input_ids, attention_mask, pixel_values, pixel_attention_mask, labels, images, answers = batch\n", "\n", - " outputs = self.model(**batch)\n", + " outputs = self.model(input_ids=input_ids,\n", + " attention_mask=attention_mask,\n", + " pixel_values=pixel_values,\n", + " pixel_attention_mask=pixel_attention_mask,\n", + " labels=labels)\n", " loss = outputs.loss\n", " \n", " self.log(\"train_loss\", loss)\n", @@ -540,13 +529,12 @@ " return loss\n", "\n", " def validation_step(self, batch, batch_idx, dataset_idx=0):\n", - " # we feed the prompt to the model\n", - " batch_size = batch[\"pixel_values\"].shape[0]\n", - " images = batch[\"images\"]\n", - " answers = batch[\"answers\"]\n", + " \n", + " input_ids, attention_mask, pixel_values, pixel_attention_mask, labels, images, answers = batch\n", " texts = []\n", " \n", - " for _ in range(batch_size):\n", + " # we feed the prompt to the model\n", + " for _ in range(len(answers)):\n", " messages = [\n", " {\n", " \"role\": \"user\",\n", @@ -641,13 +629,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ + "/home/niels/python_projects/transformers/env/lib/python3.8/site-packages/lightning/fabric/connector.py:563: `precision=16` is supported for historical reasons but its usage is discouraged. Please set your precision to 16-mixed instead!\n", "Using 16bit Automatic Mixed Precision (AMP)\n", "GPU available: True (cuda), used: True\n", "TPU available: False, using: 0 TPU cores\n", @@ -673,7 +662,7 @@ { "data": { "text/html": [ - "Run data is saved locally in ./wandb/run-20240430_093846-prlaj2s9" + "Run data is saved locally in ./wandb/run-20240430_124040-t3hdwi09" ], "text/plain": [ "" @@ -685,7 +674,7 @@ { "data": { "text/html": [ - "Syncing run demo-run-cord to Weights & Biases (docs)
" + "Syncing run demo-run-cord to Weights & Biases (docs)
" ], "text/plain": [ "" @@ -709,7 +698,7 @@ { "data": { "text/html": [ - " View run at https://wandb.ai/nielsrogge/Idefics2/runs/prlaj2s9" + " View run at https://wandb.ai/nielsrogge/Idefics2/runs/t3hdwi09" ], "text/plain": [ "" @@ -752,33 +741,162 @@ ] }, { - "ename": "TypeError", - "evalue": "is_floating_point(): argument 'input' (position 1) must be Tensor, not list", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[18], line 35\u001b[0m\n\u001b[1;32m 20\u001b[0m early_stop_callback \u001b[38;5;241m=\u001b[39m EarlyStopping(monitor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mval_edit_distance\u001b[39m\u001b[38;5;124m\"\u001b[39m, patience\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 22\u001b[0m trainer \u001b[38;5;241m=\u001b[39m L\u001b[38;5;241m.\u001b[39mTrainer(\n\u001b[1;32m 23\u001b[0m accelerator\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgpu\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 24\u001b[0m devices\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 32\u001b[0m callbacks\u001b[38;5;241m=\u001b[39m[PushToHubCallback(), early_stop_callback],\n\u001b[1;32m 33\u001b[0m )\n\u001b[0;32m---> 35\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_module\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/pytorch/trainer/trainer.py:544\u001b[0m, in \u001b[0;36mTrainer.fit\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m 542\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mstatus \u001b[38;5;241m=\u001b[39m TrainerStatus\u001b[38;5;241m.\u001b[39mRUNNING\n\u001b[1;32m 543\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 544\u001b[0m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_and_handle_interrupt\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 545\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_impl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_dataloaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_dataloaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdatamodule\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\n\u001b[1;32m 546\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/pytorch/trainer/call.py:44\u001b[0m, in \u001b[0;36m_call_and_handle_interrupt\u001b[0;34m(trainer, trainer_fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39mlauncher \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 43\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39mlauncher\u001b[38;5;241m.\u001b[39mlaunch(trainer_fn, \u001b[38;5;241m*\u001b[39margs, trainer\u001b[38;5;241m=\u001b[39mtrainer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 44\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtrainer_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m _TunerExitException:\n\u001b[1;32m 47\u001b[0m _call_teardown_hook(trainer)\n", - "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/pytorch/trainer/trainer.py:580\u001b[0m, in \u001b[0;36mTrainer._fit_impl\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m 573\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mfn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 574\u001b[0m ckpt_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_checkpoint_connector\u001b[38;5;241m.\u001b[39m_select_ckpt_path(\n\u001b[1;32m 575\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mfn,\n\u001b[1;32m 576\u001b[0m ckpt_path,\n\u001b[1;32m 577\u001b[0m model_provided\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 578\u001b[0m model_connected\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlightning_module \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 579\u001b[0m )\n\u001b[0;32m--> 580\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mckpt_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 582\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mstopped\n\u001b[1;32m 583\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", - "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/pytorch/trainer/trainer.py:987\u001b[0m, in \u001b[0;36mTrainer._run\u001b[0;34m(self, model, ckpt_path)\u001b[0m\n\u001b[1;32m 982\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_signal_connector\u001b[38;5;241m.\u001b[39mregister_signal_handlers()\n\u001b[1;32m 984\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m 985\u001b[0m \u001b[38;5;66;03m# RUN THE TRAINER\u001b[39;00m\n\u001b[1;32m 986\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[0;32m--> 987\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_stage\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 989\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m 990\u001b[0m \u001b[38;5;66;03m# POST-Training CLEAN UP\u001b[39;00m\n\u001b[1;32m 991\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m 992\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: trainer tearing down\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/pytorch/trainer/trainer.py:1033\u001b[0m, in \u001b[0;36mTrainer._run_stage\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1031\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_run_sanity_check()\n\u001b[1;32m 1032\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mset_detect_anomaly(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_detect_anomaly):\n\u001b[0;32m-> 1033\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_loop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1034\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1035\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnexpected state \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/pytorch/loops/fit_loop.py:205\u001b[0m, in \u001b[0;36m_FitLoop.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 203\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 204\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_start()\n\u001b[0;32m--> 205\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madvance\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 206\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_end()\n\u001b[1;32m 207\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_restarting \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", - "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/pytorch/loops/fit_loop.py:363\u001b[0m, in \u001b[0;36m_FitLoop.advance\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 361\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_training_epoch\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 362\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data_fetcher \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 363\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mepoch_loop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_data_fetcher\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/pytorch/loops/training_epoch_loop.py:140\u001b[0m, in \u001b[0;36m_TrainingEpochLoop.run\u001b[0;34m(self, data_fetcher)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdone:\n\u001b[1;32m 139\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 140\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madvance\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_fetcher\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_end(data_fetcher)\n\u001b[1;32m 142\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_restarting \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", - "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/pytorch/loops/training_epoch_loop.py:223\u001b[0m, in \u001b[0;36m_TrainingEpochLoop.advance\u001b[0;34m(self, data_fetcher)\u001b[0m\n\u001b[1;32m 221\u001b[0m batch \u001b[38;5;241m=\u001b[39m trainer\u001b[38;5;241m.\u001b[39mprecision_plugin\u001b[38;5;241m.\u001b[39mconvert_input(batch)\n\u001b[1;32m 222\u001b[0m batch \u001b[38;5;241m=\u001b[39m trainer\u001b[38;5;241m.\u001b[39mlightning_module\u001b[38;5;241m.\u001b[39m_on_before_batch_transfer(batch, dataloader_idx\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m--> 223\u001b[0m batch \u001b[38;5;241m=\u001b[39m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_strategy_hook\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrainer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbatch_to_device\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdataloader_idx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 225\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbatch_progress\u001b[38;5;241m.\u001b[39mincrement_ready()\n\u001b[1;32m 226\u001b[0m trainer\u001b[38;5;241m.\u001b[39m_logger_connector\u001b[38;5;241m.\u001b[39mon_batch_start(batch)\n", - "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/pytorch/trainer/call.py:309\u001b[0m, in \u001b[0;36m_call_strategy_hook\u001b[0;34m(trainer, hook_name, *args, **kwargs)\u001b[0m\n\u001b[1;32m 306\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 308\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[Strategy]\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtrainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhook_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 309\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 311\u001b[0m \u001b[38;5;66;03m# restore current_fx when nested context\u001b[39;00m\n\u001b[1;32m 312\u001b[0m pl_module\u001b[38;5;241m.\u001b[39m_current_fx_name \u001b[38;5;241m=\u001b[39m prev_fx_name\n", - "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/pytorch/strategies/strategy.py:278\u001b[0m, in \u001b[0;36mStrategy.batch_to_device\u001b[0;34m(self, batch, device, dataloader_idx)\u001b[0m\n\u001b[1;32m 276\u001b[0m device \u001b[38;5;241m=\u001b[39m device \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mroot_device\n\u001b[1;32m 277\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m model \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 278\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_apply_batch_transfer_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdataloader_idx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdataloader_idx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 279\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m move_data_to_device(batch, device)\n", - "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/pytorch/core/module.py:347\u001b[0m, in \u001b[0;36mLightningModule._apply_batch_transfer_handler\u001b[0;34m(self, batch, device, dataloader_idx)\u001b[0m\n\u001b[1;32m 343\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_apply_batch_transfer_handler\u001b[39m(\n\u001b[1;32m 344\u001b[0m \u001b[38;5;28mself\u001b[39m, batch: Any, device: Optional[torch\u001b[38;5;241m.\u001b[39mdevice] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m, dataloader_idx: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m 345\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m 346\u001b[0m device \u001b[38;5;241m=\u001b[39m device \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice\n\u001b[0;32m--> 347\u001b[0m batch \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_batch_hook\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtransfer_batch_to_device\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdataloader_idx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 348\u001b[0m batch \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_batch_hook(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mon_after_batch_transfer\u001b[39m\u001b[38;5;124m\"\u001b[39m, batch, dataloader_idx)\n\u001b[1;32m 349\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m batch\n", - "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/pytorch/core/module.py:336\u001b[0m, in \u001b[0;36mLightningModule._call_batch_hook\u001b[0;34m(self, hook_name, *args)\u001b[0m\n\u001b[1;32m 333\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 334\u001b[0m trainer_method \u001b[38;5;241m=\u001b[39m call\u001b[38;5;241m.\u001b[39m_call_lightning_datamodule_hook\n\u001b[0;32m--> 336\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtrainer_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrainer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhook_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 337\u001b[0m hook \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, hook_name)\n\u001b[1;32m 338\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m hook(\u001b[38;5;241m*\u001b[39margs)\n", - "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/pytorch/trainer/call.py:157\u001b[0m, in \u001b[0;36m_call_lightning_module_hook\u001b[0;34m(trainer, hook_name, pl_module, *args, **kwargs)\u001b[0m\n\u001b[1;32m 154\u001b[0m pl_module\u001b[38;5;241m.\u001b[39m_current_fx_name \u001b[38;5;241m=\u001b[39m hook_name\n\u001b[1;32m 156\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[LightningModule]\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpl_module\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhook_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 157\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 159\u001b[0m \u001b[38;5;66;03m# restore current_fx when nested context\u001b[39;00m\n\u001b[1;32m 160\u001b[0m pl_module\u001b[38;5;241m.\u001b[39m_current_fx_name \u001b[38;5;241m=\u001b[39m prev_fx_name\n", - "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/pytorch/core/hooks.py:613\u001b[0m, in \u001b[0;36mDataHooks.transfer_batch_to_device\u001b[0;34m(self, batch, device, dataloader_idx)\u001b[0m\n\u001b[1;32m 562\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtransfer_batch_to_device\u001b[39m(\u001b[38;5;28mself\u001b[39m, batch: Any, device: torch\u001b[38;5;241m.\u001b[39mdevice, dataloader_idx: \u001b[38;5;28mint\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m 563\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Override this hook if your :class:`~torch.utils.data.DataLoader` returns tensors wrapped in a custom data\u001b[39;00m\n\u001b[1;32m 564\u001b[0m \u001b[38;5;124;03m structure.\u001b[39;00m\n\u001b[1;32m 565\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 611\u001b[0m \n\u001b[1;32m 612\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 613\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmove_data_to_device\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/fabric/utilities/apply_func.py:103\u001b[0m, in \u001b[0;36mmove_data_to_device\u001b[0;34m(batch, device)\u001b[0m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;66;03m# user wrongly implemented the `_TransferableDataType` and forgot to return `self`.\u001b[39;00m\n\u001b[1;32m 101\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m data\n\u001b[0;32m--> 103\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mapply_to_collection\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_TransferableDataType\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunction\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_to\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning_utilities/core/apply_func.py:64\u001b[0m, in \u001b[0;36mapply_to_collection\u001b[0;34m(data, dtype, function, wrong_dtype, include_none, allow_frozen, *args, **kwargs)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[38;5;66;03m# fast path for the most common cases:\u001b[39;00m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, dtype): \u001b[38;5;66;03m# single element\u001b[39;00m\n\u001b[0;32m---> 64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 65\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28mlist\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mall\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, dtype) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m data): \u001b[38;5;66;03m# 1d homogeneous list\u001b[39;00m\n\u001b[1;32m 66\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [function(x, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m data]\n", - "File \u001b[0;32m~/python_projects/transformers/env/lib/python3.8/site-packages/lightning/fabric/utilities/apply_func.py:97\u001b[0m, in \u001b[0;36mmove_data_to_device..batch_to\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m 95\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, Tensor) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(device, torch\u001b[38;5;241m.\u001b[39mdevice) \u001b[38;5;129;01mand\u001b[39;00m device\u001b[38;5;241m.\u001b[39mtype \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m _BLOCKING_DEVICE_TYPES:\n\u001b[1;32m 96\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnon_blocking\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m---> 97\u001b[0m data_output \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data_output \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 99\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m data_output\n", - "File \u001b[0;32m~/python_projects/transformers/src/transformers/feature_extraction_utils.py:229\u001b[0m, in \u001b[0;36mBatchFeature.to\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 226\u001b[0m \u001b[38;5;66;03m# We cast only floating point tensors to avoid issues with tokenizers casting `LongTensor` to `FloatTensor`\u001b[39;00m\n\u001b[1;32m 227\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m 228\u001b[0m \u001b[38;5;66;03m# check if v is a floating point\u001b[39;00m\n\u001b[0;32m--> 229\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mis_floating_point\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m 230\u001b[0m \u001b[38;5;66;03m# cast and send to device\u001b[39;00m\n\u001b[1;32m 231\u001b[0m new_data[k] \u001b[38;5;241m=\u001b[39m v\u001b[38;5;241m.\u001b[39mto(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 232\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m device \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "\u001b[0;31mTypeError\u001b[0m: is_floating_point(): argument 'input' (position 1) must be Tensor, not list" + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0: 20%|██ | 80/400 [01:02<04:10, 1.28it/s, v_num=wi09]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No chat template is set for this tokenizer, falling back to a default class-level template. This is very error-prone, because models are often trained with templates different from the class default! Default chat templates are a legacy feature and will be removed in Transformers v4.43, at which point any code depending on them will stop working. We recommend setting a valid chat template before then to ensure that this model continues working without issues.\n", + "No chat template is set for this tokenizer, falling back to a default class-level template. This is very error-prone, because models are often trained with templates different from the class default! Default chat templates are a legacy feature and will be removed in Transformers v4.43, at which point any code depending on them will stop working. We recommend setting a valid chat template before then to ensure that this model continues working without issues.\n", + "No chat template is set for this tokenizer, falling back to a default class-level template. This is very error-prone, because models are often trained with templates different from the class default! Default chat templates are a legacy feature and will be removed in Transformers v4.43, at which point any code depending on them will stop working. We recommend setting a valid chat template before then to ensure that this model continues working without issues.\n", + "No chat template is set for this tokenizer, falling back to a default class-level template. This is very error-prone, because models are often trained with templates different from the class default! Default chat templates are a legacy feature and will be removed in Transformers v4.43, at which point any code depending on them will stop working. We recommend setting a valid chat template before then to ensure that this model continues working without issues.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No chat template is set for this tokenizer, falling back to a default class-level template. This is very error-prone, because models are often trained with templates different from the class default! Default chat templates are a legacy feature and will be removed in Transformers v4.43, at which point any code depending on them will stop working. We recommend setting a valid chat template before then to ensure that this model continues working without issues.\n", + "/home/niels/python_projects/transformers/src/transformers/generation/utils.py:1542: UserWarning: You are calling .generate() with the `input_ids` being on a device type different than your model's device. `input_ids` is on cpu, whereas the model is on cuda. You may experience unexpected behaviors or slower generation. Please make sure that you have put `input_ids` to the correct device by calling for example input_ids = input_ids.to('cuda') before running `.generate()`.\n", + " warnings.warn(\n", + "The `seen_tokens` attribute is deprecated and will be removed in v4.41. Use the `cache_position` model input instead.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Prediction: REAL GANACHE116,500EGG TART1REAL GANACHE116,500EGG TART113,000PIZZA TOAST116,00045,50050,0004,500\n", + " Normed ED: 0.7002881844380403\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/niels/python_projects/transformers/env/lib/python3.8/site-packages/lightning/pytorch/utilities/data.py:77: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 2. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Prediction: S-Ovaltine120,000,000PB1S-Ovaltine 50%20,000120,00010% Tax Included18,1811,81820,000100,00080,000\n", + " Normed ED: 0.7617801047120419\n", + "Prediction: BBQ Chicken141,000- Sedang1BBQ Chicken141,000Sedang1041,00041,00050.000:9,0001\n", + " Normed ED: 0.7342105263157894\n", + "Prediction: POTATO SAUSAGE BREAD119,000OREO GREEN TEA SPADPOTATO SAUSAGE BREAD119,000OREO GREEN TEA SPREAD152,000WHITE CHOCO BANANA SPREAD152,000123,000123,000\n", + " Normed ED: 0.6833333333333333\n", + "Prediction: TALAM UNGU319,500-40,000%TALAM UNGU@65003X-7,80019,500MIKA KECIL@01X011,70011,70020,0008,3004.00xITEMs\n", + " Normed ED: 0.8148148148148148\n", + "Prediction: Serbu 1240,000Choco Peanut Bread2\n", + " Answer: Serbu 1240.000Choco Peanut Bread220.00060.00060.0000\n", + " Normed ED: 0.6209386281588448\n", + "Prediction: ES KOPI SUSU472.00072.0\n", + " Answer: ES KOPI SUSU472.00072.000072.000\n", + " Normed ED: 0.5144230769230769\n", + "Prediction: Arem Arem@12.0002x24.000Arem Arem@ 12.0002 x24.000Kroket@ 12.0001 x12.000Rp 36.000Rp 3.600Rp 39.600Rp 39.600\n", + " Normed ED: 0.747072599531616\n", + "Prediction: TOTAL CASH CHANGE20,000,0003220,000,00\n", + " Answer: TT20,000120,00020,000100,00080,000\n", + " Normed ED: 0.703862660944206\n", + "Prediction: BEEF C ROLL 3PCS110,000KAYA BRED<\n", + " Answer: beef C roll 3pcs10,000110,000kaya bred15,000115,00025,000100,00075,0002\n", + " Normed ED: 0.7900262467191601\n", + "Prediction: JAMUR210,000TAHU1<\n", + " Answer: JAMUR210,000TAHU15,00015,0001,50016,50020,0003,500\n", + " Normed ED: 0.7295081967213115\n", + "Prediction: RedVelvet Nutella1280,0005 Free Mini Candle5\n", + " Answer: RedVelvet Nutella1280,000Free Mini Candle.5Large Box1280,00028,000308,000308,000\n", + " Normed ED: 0.7256857855361596\n", + "Prediction: PAIN AU CHOCOLATE111,000CHOCO CUSTARD PASTRY<\n", + " Answer: PAIN AU CHOCOLATE111,000CHOCO CUSTARD PASTRY112,000MILK PASTRY ROLL19,000REAL CHEESE INSIDE BREAD113,500SAUSAGE BREAD115,000HAM CHEESE FLAT BREAD120,00080,500100,00019,500\n", + " Normed ED: 0.8174342105263158\n", + "Prediction: NASI MERAH/PUTIH5,0001x5,000NASI MERAH/PUTIH5.0001x5.000SAYUR4.0002x8.000KERUPUK/SAMBEL2.0001x2.000AYAM14.0001x14.000MINUMAN KEMASAN/REFILL6.0001x6.000Rp. 35.000\n", + " Normed ED: 0.8249158249158249\n", + "Prediction: ELEPHANT READ BEAN112,000CHAPSAL TWISTER DONUTELEPHANT READ BEAN12,000112,000chapsal twister donnut10,000110,00022,00022,00002\n", + " Normed ED: 0.764102564102564\n", + "Prediction: REDBEAN BREAD19,000FRANKFUT S/USAGE ROLLREDBEAN BRE/D19,000FRANKFRUT S/USAGE ROLL112,00021,00050,00029,000\n", + " Normed ED: 0.6426116838487973\n", + "Prediction: Nasi (MLY)16.0006.000Nasi (MLY)16.0006.0006.0006.000\n", + " Normed ED: 0.5375\n", + "Prediction: GRILLED BABY POTATO ( R150,500TRUFFLE CREAMGRILLED BABY POTATO (R150,500TRUFFLE CREAM176,000CARBONARA170,500ORIGINAL BREWED TEA246,000243,00014,58025,758283,338283,3385962\n", + " Normed ED: 0.8181818181818182\n", + "Prediction: Soft Gri 3 Tap117,272Top Dro1\n", + " Answer: Soft Ori 3 Top117,272Top Oreo0Top Oreo0Top Banana017,2721,72718,99918,999\n", + " Normed ED: 0.7336448598130841\n", + "Prediction: TOTAL CASH CHANGE20,000,0003320,000,00\n", + " Answer: TT20,000120,00020,000100,00080,000\n", + " Normed ED: 0.703862660944206\n", + "Prediction: Gojek Chicken195,000Chili Sauce H1Gojek Chicken Chilli Sauce H195,000Gojek Chicken Soy Sauce F1180,000Gojek French Fries170,000345,0000034,500379,500\n", + " Normed ED: 0.785140562248996\n", + "Prediction: STIX CINNAMON119,000CINNAMONSUGARSTIX CINNAMON19,000119,000CINNAMON SUGAR17,000117,000TRIPPLE CHEESE17,000117,00053,00053,000053,000\n", + " Normed ED: 0.8108614232209738\n", + "Prediction: THAI ICED TEA (L)116,36316,\n", + " Answer: THAI ICED TEA (L)16,363116.36316.3631,63617,9991\n", + " Normed ED: 0.6366559485530546\n", + "Prediction: MARBLE CASTELA122,00022,000\n", + " Answer: MARBLE CASTELA22,000122,00022,00022,00001\n", + " Normed ED: 0.6051660516605166\n", + "Prediction: SB 1 OR128,636Chokocha Fit1SB 1 OR128,636Chokocha Flt113,636Bbq Bento113,63655,9085,59261,50070,0008,5003\n", + " Normed ED: 0.7866108786610879\n", + "Prediction: CHOCOLATE ECLAIR226,00026,000\n", + " Answer: CHOCOLATE ECLAIR226,00026,00026,0000\n", + " Normed ED: 0.47596153846153844\n", + "Prediction: KENTHIR 2242.000KOL GORENG2\n", + " Answer: KENTHIR 2242.000KOL GORENG26.000TEH MANIS28.00056.00056.00056.0000\n", + " Normed ED: 0.7549019607843137\n", + "Prediction: 4003-Blueberry Fujji40.00040.0006001-Pl\n", + " Answer: 4003-Blueberry Fuji40.000x140.0006001-Plastic Bag Small0x1040.00050.00010.0002\n", + " Normed ED: 0.7551546391752577\n", + "Prediction: Thai Iced T.x1120.00020.\n", + " Answer: Thai Iced T. .x120.00020.00020.00020.0000\n", + " Normed ED: 0.6412213740458015\n", + "Prediction: Viet Milk Coffee125.000+M0Viet Milk Coffee125.000+Hot+M25.00025.00025.0000\n", + " Normed ED: 0.6797583081570997\n", + "Prediction: VANILLA CHOCOL HEART CAKE1180,000VANILLA CHOCO HEART CAKE1180,000180,000180,000\n", + " Normed ED: 0.45226130653266333\n", + "Prediction: French Fries110,909Cheese Burger2French Fries110,909Cheese Burger236,364Milo19.09156,3645,63662,000100,00038,000\n", + " Normed ED: 0.7544642857142857\n", + "Prediction: WHOLE WHEAT PAN BREAD120,00020,00\n", + " Answer: WHOLE WHEAT PAN BREAD120,00020,000100,00080,000\n", + " Normed ED: 0.4840182648401826\n", + "Prediction: AIR MINERAL7,00077,000AIR MINERAL7,0007,0007,000\n", + " Normed ED: 0.47368421052631576\n", + "Prediction: Honey Mandarin2X13,00026,000Honey Mandarin13,0002 X26,00026,00030,0004,000\n", + " Normed ED: 0.5510204081632653\n", + "Prediction: KarageCurryTeishoku169,000Benton PlateKaraageCurryTeishoku169,000Lemon Plate119,000Ocha Hot115,000103,0007,72511,073121,79833\n", + " Normed ED: 0.7847082494969819\n" ] } ], diff --git a/src/transformers/models/idefics2/wandb/latest-run b/src/transformers/models/idefics2/wandb/latest-run index 9632092f974408..84f3d11f573af4 120000 --- a/src/transformers/models/idefics2/wandb/latest-run +++ b/src/transformers/models/idefics2/wandb/latest-run @@ -1 +1 @@ -run-20240430_093846-prlaj2s9 \ No newline at end of file +run-20240430_124040-t3hdwi09 \ No newline at end of file diff --git a/src/transformers/models/idefics2/wandb/run-20240430_093846-prlaj2s9/files/config.yaml b/src/transformers/models/idefics2/wandb/run-20240430_093846-prlaj2s9/files/config.yaml index ea95a051da444b..504c98560a679c 100644 --- a/src/transformers/models/idefics2/wandb/run-20240430_093846-prlaj2s9/files/config.yaml +++ b/src/transformers/models/idefics2/wandb/run-20240430_093846-prlaj2s9/files/config.yaml @@ -26,7 +26,23 @@ _wandb: - 71 - 98 - 103 + 2: + - 1 + - 2 + - 3 + - 5 + - 9 + - 11 + - 12 + - 49 + - 51 + - 53 + - 55 + - 71 + - 98 + - 103 3: + - 7 - 13 - 23 4: 3.8.10 @@ -36,3 +52,7 @@ _wandb: - 1 - 5 13: linux-x86_64 + m: + - 1: trainer/global_step + 6: + - 3 diff --git a/src/transformers/models/idefics2/wandb/run-20240430_120740-z6z7tlgg/files/config.yaml b/src/transformers/models/idefics2/wandb/run-20240430_120740-z6z7tlgg/files/config.yaml new file mode 100644 index 00000000000000..78e14ebc9d0fe2 --- /dev/null +++ b/src/transformers/models/idefics2/wandb/run-20240430_120740-z6z7tlgg/files/config.yaml @@ -0,0 +1,62 @@ +wandb_version: 1 + +_wandb: + desc: null + value: + python_version: 3.8.10 + cli_version: 0.16.6 + framework: huggingface + huggingface_version: 4.41.0.dev0 + is_jupyter_run: true + is_kaggle_kernel: false + start_time: 1714471660.0 + t: + 1: + - 1 + - 2 + - 3 + - 5 + - 11 + - 12 + - 49 + - 51 + - 53 + - 55 + - 71 + - 98 + 2: + - 1 + - 2 + - 3 + - 5 + - 11 + - 12 + - 49 + - 51 + - 53 + - 55 + - 71 + - 98 + 3: + - 7 + - 13 + - 23 + 4: 3.8.10 + 5: 0.16.6 + 6: 4.41.0.dev0 + 8: + - 1 + - 5 + 13: linux-x86_64 + m: + - 1: trainer/global_step + 6: + - 3 + - 1: train_loss + 5: 1 + 6: + - 1 + - 1: epoch + 5: 1 + 6: + - 1 diff --git a/src/transformers/models/idefics2/wandb/run-20240430_120740-z6z7tlgg/files/requirements.txt b/src/transformers/models/idefics2/wandb/run-20240430_120740-z6z7tlgg/files/requirements.txt new file mode 100644 index 00000000000000..f79e27fbf99175 --- /dev/null +++ b/src/transformers/models/idefics2/wandb/run-20240430_120740-z6z7tlgg/files/requirements.txt @@ -0,0 +1,307 @@ +APScheduler==3.10.4 +Babel==2.14.0 +Flask==3.0.2 +GitPython==3.1.18 +Jinja2==3.1.3 +Levenshtein==0.25.1 +Mako==1.3.2 +Markdown==3.6 +MarkupSafe==2.1.5 +PyYAML==6.0.1 +Pygments==2.17.2 +SQLAlchemy==2.0.28 +SudachiDict-core==20240109 +SudachiPy==0.6.8 +Werkzeug==3.0.1 +absl-py==2.1.0 +accelerate==0.28.0 +aiohttp==3.9.3 +aiosignal==1.3.1 +alembic==1.13.1 +annotated-types==0.6.0 +appdirs==1.4.4 +arrow==1.3.0 +asttokens==2.4.1 +astunparse==1.6.3 +async-timeout==4.0.3 +attrs==23.2.0 +audioread==3.0.1 +av==9.2.0 +backcall==0.2.0 +backoff==1.11.1 +backports.zoneinfo==0.2.1 +beautifulsoup4==4.12.3 +bibtexparser==2.0.0b7 +binaryornot==0.4.4 +bitsandbytes==0.42.0 +black==24.3.0 +blinker==1.7.0 +cached-property==1.5.2 +cachetools==5.3.3 +certifi==2024.2.2 +cffi==1.16.0 +chardet==5.2.0 +charset-normalizer==3.3.2 +chex==0.1.7 +click==8.1.7 +clldutils==3.22.2 +cmake==3.28.3 +codecarbon==1.2.0 +colorama==0.4.6 +coloredlogs==15.0.1 +colorlog==6.8.2 +comm==0.2.2 +cookiecutter==1.7.3 +csvw==3.3.0 +dash-bootstrap-components==1.5.0 +dash-core-components==2.0.0 +dash-html-components==2.0.0 +dash-table==5.0.0 +dash==2.16.1 +datasets==2.18.0 +debugpy==1.8.1 +decorator==5.1.1 +decord==0.6.0 +dill==0.3.4 +dlinfo==1.2.1 +dm-tree==0.1.8 +docker-pycreds==0.4.0 +einops==0.7.0 +etils==1.3.0 +evaluate==0.4.1 +exceptiongroup==1.2.0 +execnet==2.0.2 +executing==2.0.1 +faiss-cpu==1.8.0 +fastjsonschema==2.19.1 +filelock==3.13.1 +fire==0.6.0 +flatbuffers==24.3.7 +flax==0.7.0 +frozenlist==1.4.1 +fsspec==2024.3.0 +fugashi==1.3.1 +gast==0.4.0 +gitdb==4.0.11 +google-auth-oauthlib==1.0.0 +google-auth==2.28.2 +google-pasta==0.2.0 +greenlet==3.0.3 +grpcio==1.62.1 +h5py==3.11.0 +hf-doc-builder==0.5.0 +huggingface-hub==0.21.4 +humanfriendly==10.0 +hypothesis==6.99.8 +idna==3.6 +importlib_metadata==7.0.2 +importlib_resources==6.3.1 +iniconfig==2.0.0 +ipadic==1.0.0 +ipykernel==6.29.4 +ipython==8.12.3 +isodate==0.6.1 +isort==5.13.2 +itsdangerous==2.1.2 +jax==0.4.13 +jaxlib==0.4.13 +jedi==0.19.1 +jinja2-time==0.2.0 +joblib==1.3.2 +jsonschema-specifications==2023.12.1 +jsonschema==4.21.1 +jupyter_client==8.6.1 +jupyter_core==5.7.2 +kenlm==0.2.0 +keras-core==0.1.5 +keras-nlp==0.6.1 +keras==2.13.1 +language-tags==1.2.0 +lazy_loader==0.3 +libclang==18.1.1 +librosa==0.10.1 +lightning-utilities==0.11.2 +lightning==2.2.3 +lit==18.1.1 +llvmlite==0.41.1 +lxml==5.1.0 +markdown-it-py==3.0.0 +matplotlib-inline==0.1.7 +mdurl==0.1.2 +ml-dtypes==0.2.0 +mpmath==1.3.0 +msgpack==1.0.8 +multidict==6.0.5 +multiprocess==0.70.16 +mypy-extensions==1.0.0 +namex==0.0.8 +nbformat==5.10.3 +nest-asyncio==1.6.0 +networkx==3.1 +nltk==3.8.1 +numba==0.58.1 +numpy==1.24.4 +nvidia-cublas-cu11==11.10.3.66 +nvidia-cublas-cu12==12.1.3.1 +nvidia-cuda-cupti-cu11==11.7.101 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cuda-nvrtc-cu11==11.7.99 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-runtime-cu11==11.7.99 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cudnn-cu11==8.5.0.96 +nvidia-cudnn-cu12==8.9.2.26 +nvidia-cufft-cu11==10.9.0.58 +nvidia-cufft-cu12==11.0.2.54 +nvidia-curand-cu11==10.2.10.91 +nvidia-curand-cu12==10.3.2.106 +nvidia-cusolver-cu11==11.4.0.1 +nvidia-cusolver-cu12==11.4.5.107 +nvidia-cusparse-cu11==11.7.4.91 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-nccl-cu11==2.14.3 +nvidia-nccl-cu12==2.20.5 +nvidia-nvjitlink-cu12==12.4.99 +nvidia-nvtx-cu11==11.7.91 +nvidia-nvtx-cu12==12.1.105 +oauthlib==3.2.2 +onnx==1.15.0 +onnxconverter-common==1.14.0 +onnxruntime-tools==1.7.0 +onnxruntime==1.16.3 +opt-einsum==3.3.0 +optax==0.1.4 +optuna==3.6.0 +orbax-checkpoint==0.2.3 +packaging==24.0 +pandas==2.0.3 +parameterized==0.9.0 +parso==0.8.4 +pathspec==0.12.1 +peft==0.10.0 +pexpect==4.9.0 +phonemizer==3.2.1 +pickleshare==0.7.5 +pillow==10.2.0 +pip==20.0.2 +pkg_resources==0.0.0 +pkgutil_resolve_name==1.3.10 +plac==1.4.3 +platformdirs==4.2.0 +plotly==5.20.0 +pluggy==1.4.0 +pooch==1.8.1 +portalocker==2.0.0 +poyo==0.5.0 +prompt-toolkit==3.0.43 +protobuf==4.25.3 +psutil==5.9.8 +ptyprocess==0.7.0 +pure-eval==0.2.2 +py-cpuinfo==9.0.0 +py3nvml==0.2.7 +pyarrow-hotfix==0.6 +pyarrow==15.0.1 +pyasn1-modules==0.3.0 +pyasn1==0.5.1 +pycparser==2.21 +pyctcdecode==0.5.0 +pydantic==2.6.4 +pydantic_core==2.16.3 +pygtrie==2.5.0 +pylatexenc==2.10 +pynvml==11.5.0 +pyparsing==3.1.2 +pypng==0.20220715.0 +pytest-timeout==2.3.1 +pytest-xdist==3.5.0 +pytest==7.4.4 +python-dateutil==2.9.0.post0 +python-slugify==8.0.4 +pytorch-lightning==2.2.3 +pytz==2024.1 +pyzmq==26.0.2 +rapidfuzz==3.8.1 +ray==2.9.3 +rdflib==7.0.0 +referencing==0.34.0 +regex==2023.12.25 +requests-oauthlib==1.4.0 +requests==2.31.0 +responses==0.18.0 +retrying==1.3.4 +rfc3986==1.5.0 +rhoknp==1.3.0 +rich==13.7.1 +rjieba==0.1.11 +rouge-score==0.1.2 +rpds-py==0.18.0 +rsa==4.9 +ruff==0.1.5 +sacrebleu==1.5.1 +sacremoses==0.1.1 +safetensors==0.4.2 +scikit-learn==1.3.2 +scipy==1.10.1 +segments==2.2.1 +sentencepiece==0.1.99 +sentry-sdk==2.0.1 +setproctitle==1.3.3 +setuptools==44.0.0 +sigopt==8.8.2 +six==1.16.0 +smmap==5.0.1 +sortedcontainers==2.4.0 +soundfile==0.12.1 +soupsieve==2.5 +soxr==0.3.7 +stack-data==0.6.3 +sympy==1.12 +tabulate==0.9.0 +tenacity==8.2.3 +tensorboard-data-server==0.7.2 +tensorboard==2.14.0 +tensorboardX==2.6.2.2 +tensorflow-estimator==2.13.0 +tensorflow-hub==0.16.1 +tensorflow-io-gcs-filesystem==0.34.0 +tensorflow-text==2.13.0 +tensorflow==2.13.1 +tensorstore==0.1.45 +termcolor==2.4.0 +text-unidecode==1.3 +tf2onnx==1.16.1 +tf_keras==2.15.1 +threadpoolctl==3.3.0 +timeout-decorator==0.5.0 +timm==0.9.16 +tokenizers==0.19.1 +tomli==2.0.1 +toolz==0.12.1 +torch==2.3.0 +torchaudio==2.1.2 +torchmetrics==1.3.2 +torchvision==0.18.0 +tornado==6.4 +tqdm==4.66.2 +traitlets==5.14.2 +transformers==4.41.0.dev0 +triton==2.3.0 +types-python-dateutil==2.9.0.20240316 +typing_extensions==4.10.0 +tzdata==2024.1 +tzlocal==5.2 +unidic-lite==1.0.8 +unidic==1.1.0 +uritemplate==4.1.1 +urllib3==1.26.18 +wandb==0.16.6 +wasabi==0.10.1 +wcwidth==0.2.13 +wheel==0.43.0 +wrapt==1.16.0 +xformers==0.0.22.post7 +xmltodict==0.13.0 +xxhash==3.4.1 +yarl==1.9.4 +zipp==3.18.1 \ No newline at end of file diff --git a/src/transformers/models/idefics2/wandb/run-20240430_120740-z6z7tlgg/files/wandb-metadata.json b/src/transformers/models/idefics2/wandb/run-20240430_120740-z6z7tlgg/files/wandb-metadata.json new file mode 100644 index 00000000000000..1feaeedaf99c25 --- /dev/null +++ b/src/transformers/models/idefics2/wandb/run-20240430_120740-z6z7tlgg/files/wandb-metadata.json @@ -0,0 +1,703 @@ +{ + "os": "Linux-5.4.0-166-generic-x86_64-with-glibc2.29", + "python": "3.8.10", + "heartbeatAt": "2024-04-30T10:07:41.645094", + "startedAt": "2024-04-30T10:07:40.970500", + "docker": null, + "cuda": null, + "args": [], + "state": "running", + "program": "", + "codePathLocal": null, + "git": { + "remote": "git@github.com:NielsRogge/transformers.git", + "commit": "20a1cfa3db5bb5d96725d619a293500a81de0bc6" + }, + "email": "niels.rogge1@gmail.com", + "root": "/home/niels/python_projects/transformers", + "host": "hf-dgx-01", + "username": "niels", + "executable": "/home/niels/python_projects/transformers/env/bin/python", + "cpu_count": 64, + "cpu_count_logical": 128, + "cpu_freq": { + "current": 2296.4624374999994, + "min": 1500.0, + "max": 2250.0 + }, + "cpu_freq_per_core": [ + { + "current": 3270.243, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2258.021, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1643.702, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3308.325, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.339, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3279.169, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2185.469, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1664.269, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3285.146, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1643.745, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1643.974, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3289.931, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1644.382, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3292.799, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1643.079, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.234, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2148.386, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.93, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3279.711, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1643.775, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1673.221, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1642.602, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3292.191, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1646.519, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3278.846, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1646.492, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.984, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.704, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1708.665, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1707.002, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1949.356, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2351.421, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2432.55, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1643.791, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3286.429, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1642.533, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3288.869, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1646.2, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2292.926, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1644.728, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3271.923, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1643.024, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1647.888, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2895.577, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3272.423, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.855, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1646.518, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1643.6, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1954.402, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1643.611, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1644.29, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3269.275, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3273.733, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.38, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.342, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1643.159, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2291.926, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1711.177, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2435.85, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1704.652, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1646.497, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3273.645, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.366, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1837.308, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3272.962, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2325.155, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1644.783, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3284.115, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.512, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3295.013, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2273.35, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2150.698, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3273.126, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.959, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1635.358, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3272.046, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.641, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3277.674, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1644.243, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1644.267, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2862.147, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2828.998, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3272.298, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2945.001, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3047.366, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1715.297, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3272.452, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2070.978, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3266.589, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2120.919, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3247.007, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3271.258, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1819.614, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1728.017, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3257.389, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2173.478, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1678.57, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1632.148, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3278.917, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1676.822, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3276.474, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1632.715, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1634.016, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1630.956, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3261.95, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3076.884, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1630.801, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1634.077, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3259.796, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1632.969, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1633.441, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1630.95, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2141.263, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1633.148, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1679.228, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3264.692, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3271.754, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1632.456, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1633.525, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1632.224, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3281.607, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1777.97, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1650.754, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1781.772, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1632.825, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3264.092, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1631.408, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3253.546, + "min": 1500.0, + "max": 2250.0 + } + ], + "disk": { + "/": { + "total": 1757.8785285949707, + "used": 1616.2449836730957 + } + }, + "gpu": "NVIDIA A100-SXM4-80GB", + "gpu_count": 5, + "gpu_devices": [ + { + "name": "NVIDIA A100-SXM4-80GB", + "memory_total": 85899345920 + }, + { + "name": "NVIDIA A100-SXM4-80GB", + "memory_total": 85899345920 + }, + { + "name": "NVIDIA A100-SXM4-80GB", + "memory_total": 85899345920 + }, + { + "name": "NVIDIA DGX Display", + "memory_total": 4294967296 + }, + { + "name": "NVIDIA A100-SXM4-80GB", + "memory_total": 85899345920 + } + ], + "memory": { + "total": 503.5396919250488 + } +} diff --git a/src/transformers/models/idefics2/wandb/run-20240430_120740-z6z7tlgg/files/wandb-summary.json b/src/transformers/models/idefics2/wandb/run-20240430_120740-z6z7tlgg/files/wandb-summary.json new file mode 100644 index 00000000000000..2b996351d772a6 --- /dev/null +++ b/src/transformers/models/idefics2/wandb/run-20240430_120740-z6z7tlgg/files/wandb-summary.json @@ -0,0 +1 @@ +{"train_loss": 7.635411020601168e-05, "epoch": 0, "trainer/global_step": 49, "_timestamp": 1714471726.2381043, "_runtime": 65.26272630691528, "_step": 0} \ No newline at end of file diff --git a/src/transformers/models/idefics2/wandb/run-20240430_120740-z6z7tlgg/run-z6z7tlgg.wandb b/src/transformers/models/idefics2/wandb/run-20240430_120740-z6z7tlgg/run-z6z7tlgg.wandb new file mode 100644 index 0000000000000000000000000000000000000000..23220915c1f1320a6770b860623f17f054730cb2 GIT binary patch literal 60520 zcmeHQ3wTu3wPr|yali+lRmVrCzN&2)&TC#Nihw96D8BGj3zK9BGfgsSCPA=RF^C{4 zf(in@VpWVU3|Gmbaj@F3Ixh~w+58$w?4SvFEiSt*wosl zc*;=c23mXDJ%!3g*Q6(9s`J{xdjxk2_L*>gs-dQ;HPHLl7Y^U8CS9K!)YQ^2s5;kF zlihXlq(L>cQwJ%~MZfa*XRTh*XV0#Cu6U_rY2d8jLFIux1N#P!2%Hs|7?@KMSX>fl zF9~$Q|CI*j_X&i`CI>nLfrI<#kwipQe$q#YsIf>)>uW>|HKIgn(o>Y-rRC5*+pjLu z&@y$<)I@AT%(yEsKKPT`O!G1D|C3v)BGtM2%9Aqby8NlR+S>Hs=B89bep0TfKHZd$ zz*hzwp(!b~dQwWSsft!rMQf6=xE8I6smYX<)T4@$N~kqyrK&n6(Rp5OUHSen87102 z!M%e683lvt)6J=xRC6kl&F32K=zG<)hbR=b!A{ zUY`uR4~)UC*=@bW_?;WCexW4vZtq|?JUx6;sy>Z>pFXV-{x0xmdU#x_DOH~j4(j=z zpaLV+=W5b*@V8}Lj>|QT$TietnlrhERNe7u(8Lt}Z}FnclzByB$YEIYi z2Qxbl=Q9_i!$*uAeq;}3ZOU)DO7oyJ!R%4xfdPHXKQQg-t*;LYRqVzdBhye@3H*pZ z)5FT(p`a^DrJ@WOS`i+aX&#(EZ|C*py^9yVIt@ndn;qd8dB$z+!$X>5C>o?$>Vkm?sXvGGv1#!at*(rL{$4qsAjx0`^jRWdgQVDLh}ICe%Z5_;rxto zwf5RaMsJ6zxS&c^kt#J|Qf2tC0QX!tTR~7E8PukaiwWwgJy-k@=Wl;Lf1jI7E&aSB zupO!rHdWdXC0Q9wnpF8=t457`yBtv+Aff8|+fyrwiK=}3*j*6SfgIJp%@z1u$4$gq zxmIA(=1NDd4AWZ0;y$RAIyiF!qB=-Mwe{noqB^MUA9v3OR6op~!z}w3jH_**Ja~CY zXq3~kGp^=2EW4`MWEn`7Zf=4x=4*aKkLt^hP8fi&!V;_*8$T^Bteek#a2U?vkJudc zE&qzK)qed)Kak1N(X>ls*^apyMY1$bmLL1zsWT^Bji?TmP|bK})jq{UwK(v_(}?O2 zj_Pa1)#BH$`t|lu*{-@8N3K+JAB-zLtiod!zW52EI#fcn>W%e9MYZc4qmD&&Ka8vU zj7u?ax$(tE{uWnCXg(}eB+Dz;WQ`}oxes{hfA<#JaEZ7I}giz0rUmd$Wwdm zfv|qeVO`AF>RS8eOTA^b4A(NW9d|W3L`hhiVO;Y;wHDn}{ZE8-gaoVO_20f+R9K-eh9HQ$-*+j-X%#+JDq`oh+@k)uvRSU-_qbv*pw?}`fR zFUP$05ZZ^Ia{F)zW2@uQ&8KdUEZck6z|Trl8m3Dc*L_f}i7&T2ho~xKRNL-2t(d6R zx7_g?oWenT3TH8{R;}OoBry+e&9eP>4IC{Xi))r2WKI9o)~yIDBEi~r=b}|bg>`<} zrOav#=CCehY;~@?qTS0pNbAsc;WhBI5|z5?!pHr1+j%Rs@*-qQkzmca_?6xBK+wOd&m6!)NS+*0efv=UQ)XW%i+>cl3UbV|}2rDMR+A?$5%%Z{? zJ+G@j!isZPmov7uTzB_eVzpwp4kgvH-FS`pTKp`+7q-4?`sf3Mm5^YyZ~CCPux>fw zlUET|lEb=!v93`ax zi0UYg>MF+7sz+|W$OF~oP77~4@j7$0RQ@LB2UjiLX3n8{2q}kzj3F^g(f9sn0(;716b=CO(&HplZGV3>S4>n>*FF13oWkSz6fR_3 zZMLd14;iY}sDC!8|Q~c<+m9Z93$d5`=Yv1Z&k3vtKAGtPh{P^Erfd zB8PPyV{87x5AGr6!OfQK#T(4kQWWzA(T^js8|q`N2y2W4YvYp6dy5L|w|kv63hl#K zZXcGIY~6m@@v?nzlVv;c2KZX>$|#Qk`a#x*_ve`{JxM~f?a2*AMfLph%g5mqp3J9k zDdTF@^PBG5u6eMXcmsSbAd5SuevoxhS<~(aYn%jY+vXK;TrLH@-E;VD!n%6aX}u8E zDIC`IjIB-2&R#^!LlSNLWlkG!yYUA2TEG^6KlFpGm#Z%PJ;FLwg4O=&i^YXCy!C~V z2juVF$EFvqBj&-)mhHzI;A;U}+%xrqtuKc68iBA*mtf8RYVP8q!aAknxF^tR zox$zHO^mI@tM9LrHQr5@?Zz8$-U?*-XyOl9F{Tfq8ZV)mG3TSAqI&Iuf8L5ycqX61 zn@!Dn=p$ktRLJio>(KV%4e+(%;+V$|%^Hwc5JgyLNw7NqvIf#=oK|Z`VDXm=KVZ#| zy`dtkvpK9bldT&UERn622Q1r-kAklSZ24&8->%wfxIQ zcFuDyhjj~MYs&{Ww#w$gWNVJY#v8UD9|d0v*y0YeAF%G4xyyKD>*o@z&epe!3#(7x zs-oG_70(^)q45$~wi_RX^Hw0s#|^QydD(D8^$Q7A`^I^P6cg3Tecom3 zZ~~vg+st)%$7k1inFon1+l!AfU(4XfJbvVAWfQmGgPJu_g0=S1)eDOXYsK>9*$6Ae zVcpKy+II7$UlCX=XIWy)cH^VW*RsxA{a~x&ftNZFR+R*+ZOew@!m6vwEk#(>9M&?% z*5U;Zeno7&n=RX6jxt|M=Px~eu$5apd>z87kzjSqp8IuCVO8JotJMntRyunw%Ufl#=k;69B_x#~zgluS2Gi=?KcGIdyJICeOEO;04F2`!S0h4u{g zF~89oDAS505mFOgcv5x3ehPsf;qAz(fc6@Vx~z`Rf*gAi1f)NyU{XMu|z zBvpVxVOlI9rO2sa2BpZUxdx$#4J!1{ARNImONcSu6eVo5lpJj|5_;sxOj3Se-vw`c zAUpxMiiDstcy0|9o{K3(oCPi=95FQ!O({q4q^JtKDJ6+H??OsgLPPCnOL+qA#D&It zCPYD7BW6npRj8njwuCB>g3+~lrhc{DR;5%yTf1kKP=%-*J-fMzp@U;SkxIA%ol$I1 zlqz^hOW0C3RnB24RbWCLkSSH%Ip?4VRebLSV(VFcZs1B|M=tE;L;uM3sT90*}KYnR?oiE`#eLuXpfy{AOieSj|QHD=zP6L^{8OfLu)%8Ri z{mzgPJhh~sfRu+?)+R}58;Ybv;IN{ILDdU~s8 z=s2n=37B>9T^AG9cM*k}h=CWXgnkh0=VS%uRgdEP#X-Tnyq;gk_cGgAPudy)C00WG zQ|p=F(?mqGKDDsR-?dQkPX`2dHSxfIK?%WIVX+Mvi$m7m{Jt7tpD_>v&5tzmqcYrg z6wqzfukQFB|HyEBv{x{QBPo&QbbTW{D^SmD*Z_HaNy^)(RvUON0RH%=n)oz{CYpb$ zTc5%^BYbzDIjX@wErpkk#F_Wr`ZEmlWqF|QE*638x^>>gy|*8Mv<}EsywwVhnsk2D z)=RTx?*$u2GL6usROxZpPS_^;iDS4lrh&k^^JxBvbeCylMEn z-KHS0dI{K!b*~f~*tpkzaR~xz;K1%NqulNP^-{_9b<8>`2gcZOrp}Wv{LryuKf7fG z0?SFj7O(lN*uct8op}_VS2XhTisg*4`IkP~=uMcoQek>HY8{pXVeCj#^YPfJ_M~7V z;`)_@Yty2ue^p#ueb0Gq)H1-;G~kv%@X%n8-4!^YE|-T5cW-zj)1!u}Dw+~gR5g}J z4^pDRzQM9I-dEQe2q?jj=N+5i`54LIkAlGoaDyORBbY*4JwT1Ch7nhB`_}Y?su+6k zpkVm~xM~%@)f(szAqOp{fm~N&Nj0h^a4dFdCyq6s*P`*qJb_x5=UR6ko7_!vAAgG+ z`mozF;Lyi8yVmJG%q}~EwMy;$SPnm(S^5eBYnFh``E2H2iVUoL_vb%EU@aV22V-o` zU)QfCCfAEG>xds1W5=*oxt$-3#XnonfxxCnz-Dyau&&6!KELSIsR-%U~GKjhdBf`RRY#N>(`2gc<%L5n5adEJ<2-khkNgsdr!yO0B0$r$Ur z^x6f)Sa>mJ9rgob%yHxwyZNGGOW&CMcLequ30PO#pNb9a$^oCT{)h5--N=mlKsaj&Lt9Uis<^W3vXA^tuLs%?L=yRxNw>iXtN$cEp;wNZG{!ZZA3+ zXI(4T@3(!*?6(t@m1(Fv&(aS&_Opsd_eNl?GO)HS*M^D@mjK zwz*x@U1TNeur{5X*`7aM0T&17%-jL|;Ox9}V+lkyS4Ot^4@(~=JHU61On>jit!QX3 zw+!tQjI$XJd^SurXI{W;*B|eJ1IqZO=?7)?ZI@Ocuq$L>o8Ne9_|5>sE7$x5cH6df z)i{?|T66iNY0Z}3zmixpFUoA^Ut^w`!t+-BpiF<`=pl$~o{X$>{#V6DcFTr^O^EC& z3)w2h+2VCKz)k$pu4!>5Zb*O~!1n*~PB@rN?r8YI*~7+LY&Pf1$l7MUS8QbWpMT9H z?y|C=7m?^(T&Y;a%R5);MH)z84tGm!CCf*LHi=It7T+cUjB2jkzI1bQ+uJ& zxW+OXtIf4}^RlC5HDgyLzy{!?%zg#H`{95xUhU`yWxZS5S`pZ_GO)F)w-y=L(AtUj z;#@AY=JFZFSm)BOmJw^_MVb8yfcKsOW&F+D56U*oPQQxC7RkukUVHM6A|tzcm;e4T zBD>B)wuW)G_R4E7C)Ui1Gy5Gt1K%trE>HD?vv9xL-$Z1KWn>)>{i)c1CcG2kAv~olCdM8%TZ(%S_{4(kvpL(Z-&Erp7?JnHY{{ z5V3SB-Y3Z)f`k)0zs{9u#c=XuNsmM;J9C)kS(PCik@=}oZXj7oShQgnUg>>tPrfoex(^!AcTwt zo{35`tYn5fGOX-GUMQ82pVK*K8fAzLYQd!pvE*S35n+fUvZREfG8jk^DY>xVB}kY7 zl@t`Z3K^iOV2KtoK)w|$6b{%`$-q^fCEe0Wh7tpkS*(VXBWId4r13~-u@FQgx$2ao z1m??I-4c$lBm+RSdZE3ifCjgJi&H6$9i0vqDd{jA$7HbJDo#c~mhl_sVQY6bOv1 zSSPfG0kGQsMZ&E=w&u|y259LhR3J(Ezz?g^Wq4>61K<%l;woZ*bt|?`TEtKWzLUKH zx~dp>Ww1FWwLxfYIeD%s2K?n?A*2h8X%{z8?lK0aV$w1ORFVR946v>osrw4u%UEM8 ztz4mYh$)Jd8Lp6p2AX20-z)Cz9#lniOn5@O=_sxsngr$#>Y_wjN)^;kIC`ea#0#9v zOn8M@BW?hB=04FT?sIY>A&RxMAW`QUroi#AF6Wx<1EMV@3RK3X!o$60#-4+sL^0zk zdZt8ypJEe5J#5767?%(QQg{oodiHR#nICPUa(UTcnCzaZn~n8*jxqg$`#SZne%Rti|RU5R90eS`pUNk0LmBFNeyXV!>JluxmxHWq>*cHIa-*4cG{j9j;}NTk-k?E5l!uepvMhEN?TZ zVKoW7I~28=;goq#g>DO!7AR_f*94g0?u!~sxpz^+-e#$TUDQwtMGctY&nhQ&sH%aN zBq+Qj0X97Ly5C-fWc|J(u2?89~)PCG$bQE-+cfvDFOrjEc8e*TwTL@%;jG0zZO0#P0M2|o(_0+Q{tJ=aE+cEZ z^tSayMiw45`f)V0%Pd3tBI9iHhW4{%L+eGEeb|n_Nf4_vd`umB>E3OJBCtDTVC_r( zU1VTG`fpi+bJ=dq<$A{0mJhZ*L9CeldWG^w!+U|dgWKVl?W}mj>k2(NnK6)C7-YbS8vU_D@?f+bUrEJZ< zb7Tt-xMwXIjpdfnc*V44FFv=gv;YJ!adGGLaKb)p$6s~;%6xP*&O7_c;}F<=GO#UQ zUneJ=f9Jps+IQ@&CjhXHKz1OzT+w7~M%Sm{H3ovMfrCTgYGabBLE*Muov1SOcq~YSK+%r4n@N+u`hiuSc##&N^kzR{i_!9&@&2{lETv z23*tmaG-KMCc123)@s#lAFc~PD3aXM*;h?QlS)*9f?5pg38|In5>-JAH+jN!1))O= z+|^mGC-Bk>I4dAmbwd2J`#cvo=*F{`9m{ipXeiJ1AQ5Nt;v6m`0cVI;LgbgrXk1>$ z;}kBV5mXlS!H6vs{38app>s zTtFtywrK2KI2xv+yN)-O$R!HZDs`v*NP>x7gzPOe9mi+w^$vs}QEfOX;z(@mO-)vx

@o^g)+F(T;O0257^kC zt~3|CCA#~oIzh?Am~KmCli<3llcx@vV-kjd1ibRhlo5ht5+Bjd(t!)TuDMrTzM|sL144(k>?`V^UQM*W}fD` zz@8)~#g*p5Q)ta0$tq*YXXWb3XC9~X1PGGqM_@mPT)rvc$(w_9;D5WE6au1 z3ciNCvRqg-@9(> zgbf}xIw2;$G-mX`4r-+{xYAr$(t(gtOT-cfgw$=UAwNkBOjM~1lJ}ThHS-zZWkuS% zVXfI$R4T+^16Ryp;7y4-@ny<|bh$bErM@-Z2I)u(Ayk=`%z9dvd(F+KIV|L z2=d4x2m2{HCPcxoY(eslDNi7^n^Kylhxp zHjUKHhJ-+e!cjLHh6O`mDGhbB%ix_A-We>+E?*m73~*E6nB7#NXUm0zDrScL&2fPo z;RM`YXy4X}UO7D6Ud|+!UEt0u$Z}Dw+!L=GJN?XZQR3D}R$hci5P=+q@8-r%IbUT* zZ|sy?{lB=eGlBkL_sK2r5(4raPi}#^{RYV`;9oF1$Xi%fc9yc6Q0a}G;Ak@<*o~mh8$0b!?bMbin3Mix&z$&&0fF*IAhvcG#MVA% zv9(PrUilhhYdyH8*JI&>F;(fpkHZ5SXhlVUj@PL{5n577!Yee~_Ir>mHN$tisgN#Z zD7p^5Jvb?jU%dfWi2n)=iKW4Ys8}Mhr#VIBW#1_x`@T~|HhrgvY-COmHO&r$)G#|l zWCL?{NOmtK;(jXKzzePYlG+vEg~r6Bb_MK<>&I&xwtxo2q&^GqLc?Lsb~G9$$qMj7 zE54+51$c1>17T911@wi6!K8Ktc%eZsXFD1LlVk;Wp%E~tT>)Nb08DCEKwoJ1 zOKMlZ?$R1BXFD4Gl4J$!E{%Oj?F!gk8u^mi6|lRmxR*nhXxvNcvj8uysF$Np8ufDa zNrPUJtbo4IkeAf1fWFXpm(;F+zR+lw)UE(8G}gu1S?};P)FlhOG)A=aqDBb4bVdlh z$Pq#>tr0>mdW6u+&Iq9wK|<(dX)K|q>nntX7^2}ViCb563$;~rTZkcgdMtNah#`7< zEVnGg5WOTpYU$|&TaV)D1q)#z9im|^dCWo#(U_LpvJgY`{Ft@uCZrYL-VG-z{DujZ zm+oQzNNXuL;tGW3Jb$BARiSi=9o%)3_<;e#@2HaN`xw_W5!AD)zP_7ZrQs!4RuB_@ z2nJsIG|-uDew4-(dq=qVr&gGf{U(k0rJ5b8-XgzA1AZ(Pzk9{EN}23i4{x!=itfT& ziuHRka(K&CxP=GV{6j0cRSJ0#qPZ2^V$rd4ewh1s1jM!;4OBjUD8#nDVzI6Ex3+c_ zifut|5)kF}6IAf-4_PO2P@7eK@9hj~m#bP7ylN;pEwJLDtGKZvwVUP_3)XJ>vEn&Y z>vxUB3WBgW>^)G3yuBZyf~-3&8H;HKoXPKa@uuABKO?NopVUQ#WlZ3rEHxR|;|eQN z&aAs0&gf;e#kOTqCIxsJrcL2E5y%+yK7;C(kgf{rKnTtppY zIP|te)V0AoYdC)59GAj@7#b*+k4ZegQi#TAR+rQ@#?&?|FiJRq15J_oN7E{uSE^9I zxD=vkB2|F;#ih`bxPghD`m#%TEHsoC+%+v3B?jFrOGcQ1K1DtR&8RE|D`tbC)HrU9 zp)PSLta34;%O!R^p(`*9>0=gBmqTn$AxoEW1%}~%F42xM1L+DvMw!7wcAhOmona}1 zf<}gM5^bbzYQ%`H&@k#RACV?f;*pieP@=KnQph#6dv&iRkq+Yu4m*Bvn&wf$?2Ggg z8XPW#w;-Y5G>sA(3c_8{VYm;`BAn(>!lAin=ZX%at>9|viVmYiuw=x@K%chEO^kRz znmt(x;ahQo6AcfS!nbXtRBhnAPM~owD3N_cI~E4L91P}HtbC6Mu}ENa-3U1yEvzA!$pfzAI%C zXcNnn#)wNHASrrv1&QH8J8MLmG3iuASCANQII|KP$-6i1X+qyzQDXS{S1};f8c1Ui zGO9I>Me2$YqjSjTm)-~l^DFvvMTs4h(kO8$q(KX1qnVOyPv8T(!o-d_bmd7p%ohz4 zmqMnnV9k{#i9a6A>85dF_8XbjV$)M%Td6Bn z>|n+tRxGg4{76POp9)v3*g*_Uj)Y|>Nb`sl+vB-clUUlWSTS1!r@(O@wu4QdpZ5;quB5luT6(k%*?w-C}j3NCT7g~Zx`PC0JxqKX3#XJ_2G5+ZR2 z6N6HNVQi{;X z3n8Hhrp3{c>X7bW-W4w9!AV$E5tX{epzV(AOu~@mIht^(bIf--E_73bI~^CgDYBo% zTS^hCm=IEm(280s!sQ~v+lt!VJzw$nHuk;gEr=vN7O4CqW=UN- zd-aMwd#>F&_uT*tM^c!AMl`DEF>pdLU@U~Z05P77K>||@(wsoNotw5*9sCEl8Eog` z&}^l!A6~Cd*XNq1MH;Q&SBI%)iL1_^S9kpQdYrxKct*ZpizQc!yre!0XpAf5g?(C2NHj)%LRZksfyNc|a>@$mjAu$D zM?-^N1Wkd}^nFs~1ltF6%r6aj$&9*UUXD5Zk57x_I^(JkhWmMJo9_yFIb{VbFf9;v zt~FQ4%h@McPtJC(ke8z!4S7lJ3alnq#LL-d0Xst@Ueb64c%cC=sa=7c>HEY;vOf84 z=L&W?=Fb)Ea?0qdxKma@UtGa1XS)LW;t}i;J3XxfcIt#H*yS9rfWCa67zuG4#fu;- z_TjqV9+|a-o^G%Zdg+c3dMS?(dilXZ=%qeF=(TqXp_joCLNCuo2n#XvePU#;8pG_7 z=E+UzR877$&^sA>@r1coPb>|TKOU%jy&U3Pvss+0^NruFDcMe}DxT2b>J?R`v8osp z@poR($*) zk*4+2U~yS>gN35=B89bep0TfKHZcboNkyhxGq!0zXYfJgY%i@^q|I6^)FMk={(44CpR}_ z8fphmN>w#wss~r+8XBORTvMcR8eGG#SB57xLR3DJhif*EQezXtnS40k(%6`5YEIXL zVVLmbOdh_TsZP~}!ARzF4f$|YOLI8WoDa9;VGwKq!Sw1}OB4L7CK4W32VYBv^XcaB zH25$q`VTkf!fI?#eP$|jooUE7gU$U>5AF>eb?Qx9n&5iHCj;4^3FbDvb>DLI(BYQCL-Fx@Bq#g!d70AZK!0oU9BOUtj z@#D@2r>3Mbb*ZYlbY=MTrj~U0i0YP_)R7fHQ@Y0ds0U;IYQurC9E|x?Ae#_l&i`cI zq|lM4%VQpws>`QwG@zg|tb|cqoohtZg)lgG~i%?I-KshF-o7hT9g_$7S)Y1@6)uC514VDV5p!yXPL5OP<7gDOBI-L*aCWR-SiQSH2V@wR6pQ*z|3;GvshGmSMM#7^fng4)) zH{_bbHR*h&DP2<$h6P?1o@m0FlxfK1Cm(U-#Bfy_Yyco*gEVJgIl=mpMv3%5z4C`& z{nb>M?KOeyFu{H2wU1{)+L)6^3>!1y)L|!`h&ntdJmQRFhfNqi`n1u*$BdkC?8xz> zM~pnJ67IFFP%E?wy}~%J2g83ls`p;^!Q4L^$bO1*e>tT&gKmpAQb|`JbQyBh}|>(sl6n=$iDTOm$v6F4r_7*HDwe`;Sv~ z$EO?8P3$gY_zIl6hlhv1(TEV2*281*xlV6NWg5_Eg&WNWF?&bRNx6nWu-kMeK0VhA zdjfBjtkChgS{>_w3+NAGLteJQv6w&2ITM zr)}d6vsVW~6}zzqVWy8YOj5y3hcLB~5^F$5zUbJdf(xzpGSKzQeGx2_)g*0aX}g69L-J%EG0<-avumiO)& zvGbiBy717+c+2?<)-C5|XKUNmj@a?**LA!CT-D&e6~KGMFz_nyitv;P4K4LY;SJ+G zxE8$a%w@wy1F+u(vI87Xx%t60BYSo?b@-l{$lC8^);3@N;>ew6?W511T7ckQl)-I! z^VOb#d$X", + "codePathLocal": null, + "git": { + "remote": "git@github.com:NielsRogge/transformers.git", + "commit": "20a1cfa3db5bb5d96725d619a293500a81de0bc6" + }, + "email": "niels.rogge1@gmail.com", + "root": "/home/niels/python_projects/transformers", + "host": "hf-dgx-01", + "username": "niels", + "executable": "/home/niels/python_projects/transformers/env/bin/python", + "cpu_count": 64, + "cpu_count_logical": 128, + "cpu_freq": { + "current": 2330.1935546875, + "min": 1500.0, + "max": 2250.0 + }, + "cpu_freq_per_core": [ + { + "current": 3248.85, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1924.257, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1629.468, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1633.62, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3274.28, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1631.533, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1632.18, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1632.321, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1632.904, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3249.889, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1708.02, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1632.981, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1632.376, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1631.985, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3248.543, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3265.69, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3252.183, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1631.691, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1628.627, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1631.867, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3249.246, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1633.647, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1941.286, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1631.409, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1896.11, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3268.393, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1632.806, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2371.076, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3251.734, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1631.484, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1634.126, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1631.685, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1727.381, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2830.271, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1724.947, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1727.167, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3269.844, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1644.068, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2753.869, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1879.467, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3257.789, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1682.423, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2615.127, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1945.778, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1632.652, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2389.953, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3292.077, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1631.961, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3248.955, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3268.35, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3274.615, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1632.603, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3282.733, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1631.717, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3248.365, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3248.873, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3254.632, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1630.768, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1635.285, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1634.22, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3258.564, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.295, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1634.154, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1632.584, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3246.807, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1735.124, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1630.531, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1631.712, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3258.726, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1632.182, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1633.696, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1633.25, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1632.698, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3269.765, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1634.8, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1632.917, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1634.78, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1634.442, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2082.525, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3247.42, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3266.862, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1632.968, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2649.266, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1622.986, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3278.679, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1629.874, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3268.616, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1633.309, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2159.964, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3251.674, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1630.454, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1649.097, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3272.428, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1645.107, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1631.531, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1629.763, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2025.869, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2278.369, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1720.438, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1726.798, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3248.801, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1632.929, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3260.68, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2990.887, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2832.635, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1704.089, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2693.407, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2088.429, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1597.782, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2902.498, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3248.374, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1697.111, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3028.075, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1852.767, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1832.301, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3235.716, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3285.426, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 2197.554, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3245.66, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3282.193, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3279.416, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1639.283, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1638.016, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1638.133, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 3246.508, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1636.997, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1623.936, + "min": 1500.0, + "max": 2250.0 + }, + { + "current": 1639.282, + "min": 1500.0, + "max": 2250.0 + } + ], + "disk": { + "/": { + "total": 1757.8785285949707, + "used": 1640.9605255126953 + } + }, + "gpu": "NVIDIA A100-SXM4-80GB", + "gpu_count": 5, + "gpu_devices": [ + { + "name": "NVIDIA A100-SXM4-80GB", + "memory_total": 85899345920 + }, + { + "name": "NVIDIA A100-SXM4-80GB", + "memory_total": 85899345920 + }, + { + "name": "NVIDIA A100-SXM4-80GB", + "memory_total": 85899345920 + }, + { + "name": "NVIDIA DGX Display", + "memory_total": 4294967296 + }, + { + "name": "NVIDIA A100-SXM4-80GB", + "memory_total": 85899345920 + } + ], + "memory": { + "total": 503.5396919250488 + } +} diff --git a/src/transformers/models/idefics2/wandb/run-20240430_124040-t3hdwi09/files/wandb-summary.json b/src/transformers/models/idefics2/wandb/run-20240430_124040-t3hdwi09/files/wandb-summary.json new file mode 100644 index 00000000000000..badd13293b538a --- /dev/null +++ b/src/transformers/models/idefics2/wandb/run-20240430_124040-t3hdwi09/files/wandb-summary.json @@ -0,0 +1 @@ +{"train_loss": 0.04636861011385918, "epoch": 0, "trainer/global_step": 49, "_timestamp": 1714473681.9160814, "_runtime": 41.268248319625854, "_step": 0} \ No newline at end of file diff --git a/src/transformers/models/idefics2/wandb/run-20240430_124040-t3hdwi09/run-t3hdwi09.wandb b/src/transformers/models/idefics2/wandb/run-20240430_124040-t3hdwi09/run-t3hdwi09.wandb new file mode 100644 index 0000000000000000000000000000000000000000..6f9d7a44ea13a670d62e5cf9138d457a32b90a07 GIT binary patch literal 115590 zcmeEv31AgPwzfARnhCg!%jjfAz2h6#fpGUF&n;kBWR=C;!H`@e*N`wtP*9u+C>UH3 zx40v2aY1nbg9^kAg1AIcK~zLkKt%zU5%>Q)Rn=A9RW}#Xxnth{zuz|xrR#R}sjp6* zI^E|xrwUIwtk;K&clqkzvA0Ec@#K1@dJf6=h681>&=_A?Ni1Fx3YPgxd~ts;7AqSw zCQ?>fQWk!|v&(bc^73+PgJa9iFZab~bj|&?pU0D%HPz$W@!f}XyI@>tO|azL(uwh! zF_(F!W~rX?bNiPi#*~-V1P<9VZ}+@iN1b0%SvGpAC+otCdhK487+2M!dO~H7(yHpR zTX>*I+@c!0$V1S6_iYQVL|`2VpJMi-P;jVl~no~Wo9R&~xfiJrC9C6zT} zs;b8&s%r}Hlf91!_)7exV@iT$qr;;|hs)xTXdqk`@yAO7@nG2JD~b8b628%;5eJ-S zRV~l`6DA`&uz%iudHXO3dW=idmXwv$mJ}pwsw#I~6Wa1He$nOhi8~tV-v1#ec1lqk z!}@CG;unTM=$z4i%-aW0l~*RJd!AEKHM*o?R84JSd?4$9ol`$YuP0Y5-DMtn-SzYf zIt;~bF^@aFXg?TRA9?z|5fFQ~(Fg7AIqHyg4{z`}Z1HJnDqvll%8B9$x4@!`I#4 zJ`X-=>CG_9;2`qrO zm$ySt3s;|1iPH5sVyH@Qky6f1LK{0YsxQ3c#jy=>qr-~_UbpQ&a8#fI^~h;!S$gZQ zS^3>}XX7ZZJf{!<$$ut$eLatZviJ&pzN3%p?me!&wr9;*?N^w;%$SsO0Uq5od4lcH zGhf_IhnjK|KslmzJJ5TJ4Hjdj@&pm=;$=~%C?5aZsd=X=|a zKo?KLq$vz^NxMRpsaG@Q|;eV|pnf4Si&1KlsuXy|7th6!( z?AZ7Ng@F*0Da^3xpiH^{UcQE8+RGu+_D|+r(s?r7^w1RrB-8JOOb;`hRxi5ZI*$`4 zzjF}<6;7d}eeuF@gy9s4k`8qcr;D~-av;gHw?n2)^WN-4nT{B`HJ@bKN67RD!)adY z=X28I6jC^a2`68S;S>#I0H-^j|91t+w68;^y86fN>O7h5YdU42W|L5Va0pd@$ETes)INv4 zeE8K6YX9VE%xZF^TE1G?nC*lrphFc=P(_dSMGHe=rc<$S2B0eXxbJ-=)d3Evw!eAJ z{hcY*tCt5SkyL*aQa#GFYSWCHf6CecR#6SB0AGKxSO%~<^pfNLMpETFq}uxY#!i*$ z%V`@fCaL};q;eZ)AJAgwG)MU zWAR0sW zIHan(`Rh)V>e@fd{u@o73JbBG;p_0OD>iGsmWh_lkH^b&xG>1whm4>V z8XsFnVnrNc&AxtB=ZZCH-YLhDSWzL?B8Jx1dp1qe(2CGXbm*4NkH`Bo(4x=o{sMm{ z{P1PfaGV;tsL4Z~yQK&2E`!DL+1dH|lUz^V*R%eYcbQUb&cQOrr9kME#=4yw7CD^&x9j?SpNfO zkXT1M#9IB+E4O#5SeMT``9Tuv7$MdYhF0AN_byAN@|WAKyz=A&c%z1HiItcM-}I9i zxwA>EV;y2``r^CJ73-?Y|NR|_b(|2Znd?^TJ#{-oOL_7Eyio(KAh%nYKLae>Mg{2mg&~^jbDJ*;`nZAqNQAUj+Xeg<&&&aT+K0qC z!6DY_2VZLJRIy?+o^GW#jAHSI@f<^I>mAoj+7VdFl@H(pM7Yq;U#b0>_{e1#LFLE?*bB8^EW=D-)%?ZP z$BB70ASAkw5%;(peiVhtB!En{fat$qAHP329rY<@hxKZFXyL=%DC%EXtzdw%!M=_J+&r&u3MZ|qdD zRtKL*&`v#4?9?p`txZ?{aFo;6GQm=Ad=Q@>02cqWl@YL7`#s56Q71d3YW{Z7-#SyO z6MmT5m8S3%F@+qfb&GCa=u~(=sJ_}dR9?IQOIWQ;eA#OJ=!)w|tWzCg&0M^>bHyr& z9&izfb(#=sIn%6l8}51DsqiV#Qf_<@Umu`a+>H-p0uiTu_49x2T(KVP z^ALN>FA*C5GDEBN(sw31HJ&k-9bhRpK8SA*nFVX*M9&?x4o5qrYFzTrMV%?tnMc0c zKvIn>&B_~n#x5jPnM10k?TdzXrc~3u{%P$jNR>#Q z$(WNItJU9qG%q{qI`&lStYibqzN^5TPdp9Wm~YkWr5;r2(Ly_N(U;}EQV%M*`w zvS4Qx_Pl`vJ4XoiD$}lpJFi>maU#aJru9z5lqW9`6PsctzQ~vM{{CPRY^+nTOSW~k zU~9K;zK{ef7lOUU5UYP^<#RiMn9ZHXR|x2s@VPQ!>zB0#29aP%r(kpMh0o zpA_u)m5VA!uyciAtsJpwzclTHU^br~@74;jOc=5C>v|tXf>k&K+w|#c3p-h`B_Aa2 zz6OGgOP;|pdtPUV&7N}2NjsiNn_JIbuLaR2BcAH0Xh93fRq2qcsbNhg%hk7a@Ch`L zRbnDJUYlChJg%8nOZ6oUe6lyJT>B7v#SV$DHGL!l^o^B2VZ5ns$7$~!nOcNB_5 z(68_Vlye9~Vu4UB=#NJN;aE6C4`cNP=wW;BGI8ObUmy^x1&D93)iCeJ@88XGg2)*D z*rV(qs&|ph&+fzJrSFhnH4ee*7p{1(lLcEa|CobFuv#J5N`~0>*Qc)43|lJ1)cZ(? zeHRF3@a^rpUmm%O1e@Rxtm&awI$JPMcK0F6cAl_oZ!*Lh8>gPUV--{HBq8=~K+w-J z;y7-p*|(PDI^Q8z)6XkAS*{u5XKthy%Zcp8GB5WnzC%5Fz7sCSFxFP2dMydD_uU|; z&1M9y$;1Er6~Q%0z{SOCxcNm*?|*B#9{+jLbkh3^gx+(x8h?8IZOu|xf=TWyHYoLC zA^;Pit4wTApJeABL4sZA5NzI>Ygcu$U|agEd!GcGEChR-E7-h8Uv6V6?TD!tlMwq# zASjL*QL*MZ;aU>xB8OnB7hLvACkyt`$*cO1?*CKh{wju8%anz?re2eFz?AzRVxJ0F z-XQJE8KAQN@#1j}B-g(ja?O78;@vt|uD3@1*PLr1SDi=9BnPZ@$va5Vb2w+I7NvSG z5r_$|F%yWL@ztk`NwO(U$(r83=dDhb?3;i6c@s%C)sk#AQ?i-QZG1=5e#X*<<+p2@ zdNm2LFD!yfn-ML0H}v{+lI&tlvbB${>14@{TweST8H`IjVgbCvFq^k-*#M`SS&&^} z1s*1QBes_l_Q8PV)MH_0g2p#!*Sg*$*fghLGhe@cVCM?fG(B_=&E<4!E;(ZBrhEo! zlVfG3g3RXevo8h#5i-pLWEVVB`W;DjsZ+A;S1)+AlO-FmY})=LS-mCM8m48;-z+Q!PXtC`K`=a>nfEEAZG`10w~NU|AD$(r6;1nO0%O15Wti19 z--XIWJCw}k_OlNyfed06|NG>DB-!Oo$=1#Nsem|bB>_8!A*_57tEmhTWV z<@<-(w-%9cpOGD)IOZuQ%&v4w*0{W(rIRJA_J2Qt4DD5xp?#lWw)KU_&P)xNWO8E% zP~Lx-y^f0{fs8;l`og0wCc& za{t5Zg2IwhOkG7lL=4x8~?OIBCbl1-RhXG!(}!>sk0oAjVb zD#_G4K$v~WV8#71VKX}bJ<|cB}c4f#^T2`o55J#SeY`1 zV6r!=UID`F-Q3SJ5HkW zW;0SrrrrSrX5t%mCal?oAN}hplI#|zWZUOII-`>%`|nwS2WbJ^YAt~E46}yT1s6G2 zW-7?kD?pgNoTH*Gqvd8I1*-JvqxK=eZgUD&_razA>|DWqcyQx3n#FCHsTLDyW`RP;y>Lc?A{t9@J~9=Xb$qG!*w2M8o-e=IzRVG1b#05Gjb| zADH)h`+e0lHFU>r;dnS&5Dnp>``DYuC#p-CU|k?mB)?l;cIo&D=*m|Rk3;(3<8L^` z0Dq2>zsv~)3WDMMeBS7HHg*XXgaTGuKx+#Z1S9!>#zGoAXoNu!lM>SL zaWrc9@ENo2)BtimQ%49>B9}@?Lx>HOOca}xkcJWPlh}qeh|ne{C9DHPMg1*o!x}(2 zF}51SBVjxo*rMBj1yEQAh^-W%mthT{96v0-9A{WV$T?2zQqG5BCz21<5Q4b~szwbU zGK|uKMKpxOGsT=mOpKr>0)>c%5m1oEB%&dN$yTN>qCu1cTNsI3&_{HPf?-;_mP8sx z7>qJR5e*`0RQ+lgw-LW`Oh*%~#BAdy<4FiOsD#g@&~ zI;w$$XR3ilHH^^6HhX@j2~k){t3$-P6*C&uAc75(@I^I%a$tV2M8&jVdFVq?1_i`H zXJeosDz!7FBV@-mW+D_&&omLj<{?%ZHe$y!bKnB{ff>A-Yun2%ps zf-Pxvh_LikqYe>sd#pP(i118k(SdpUU<~jYHHhSvCYm*npq~*;xM(y%Vh(|5HBn*` zi&hOLu}_Fb4JJG_rk<+9!~m8unK&_XFFH1X3XwvHRt+g8lLeISUkkHsJ=d=R1(dOF zWUG#pcSD;}YBRaRbeZo!!a6KYD%Nes2#X>;PSf?y0A z`5yM>zVtfmj}V_m9Z@+W! zSf6+uFRG}hDy3tya2H%5yg479cOdFt+WAa|@>oi{YTWeN#ao+xO%5S%byr6U@ zS1sXjm0qF5RZC29b)UT7ae?qB=_mYMvYfwnN@H&nclZnVMJyyZMdD&WTqMkW(0tZ+ zkY8j%h)W^crG4}A=wZHs+5}}uh}gxD{3*U-M8{ru#~nR=`^*PtYqW_}pC)agc1D8ni;@eO_?&yt zG5Il)>|Up2b)SCO*^<4O*XK%#XWVDSGd8eqle2%AG1?i=NClY{Y4_8ie8_0CAj?E- zs^-)g`6O7QQ?R-juXeIvy-$DmIGW4*t-0igZTjX09Cl=<00dZB5A6fE6=(-!i~}Po z&SfOf^2oWr8$^;l;FN6p^PhFLWO*+R|CS_s(2{H;{{nvbil>~P=2K#3McO51A~`n` zD|7zC7ygMPd&nu->PN4Bx|1b4;{My1HJfWmwuxcZ^5A<*oz_fZCd&&<_QtGGJ1}F! zAyKU_BYHNydF@ez*~3oBwl?0b5$Aq$$&PvI4^vKrWRG}~`>?cmj#=Zyb*OFQadA@V z=^Hoy{d{yd&yzgf)?w@CE$Fad_mfL1%5hFI4s-pF99Ft#zqbw>h^Ig5N$zKRdc*Yf zczX7q&M0dC>7VDHG5j_{=rJckvsb<_JKGgPN0*;Belj|I+>6(4`wiTK?hg+h(A{1)k(E z8%#|%{)7&Xx54B+0q1THtSTu>RJU;sY(_AfRrkYbgxQl$%v$c6HODn(&0jYjkmbp3 z@+8+~Bg!f4)km%9@wM#1Ro>FECAD7i11s=e~} zyL-o!RNx@=bG)NVO3%f0Z;2P@w3ke%sP&dsl+@Jps7RcL6PB%>3cMree*EveMD-+Z zqPn`Ox(D8}DihtkqZ6ei6KWFt=;9i0Np-?oHKsOE>1A2MIMeuioUJZ-Z6=i+6j0Rj+4g&Dk>m`w;Dm( z^YFm(N;;@np4{#|4`)Eb(*!c-k1fa9zvHXQ@${0)N#0VNBaP>dPvGomfPn*}AyP*3Qt%A2UHAwg@|U+|b z`op0>p?73WqWYAQ>Pp~I=sm4!0#krep3;M z(!cY{OMw#|BCd1IH9bh zfC)IR1R6XMk@WIJB_24TlKxUkr*Dr=j4e5@ys8?#ReNhHs?LXjP$Mt!4y{1X=(gnC zgjY=}r1RoFoC%J}vkwlF&$PR$8md!k4RAEnPQXJ8GcggF?>JCgg5o_!{dYla)e(o6 zLh{3pgm6r)kP1&&d(XCEdv*cNVLzWwLQMhv#}O{H=iK8izj86`)l;72bUHGet4>2b zoq~Mc2%5N>M51yOGa{I}lG@tp^3fBxnZX*u7}*i#ZLCOIg#H3j7Y54Nr6r|f6Qjmg z)v!a`&$i}@R0HF$sl|sSH%nkhzv_J#VGvJylG8lc1-j;LVJl$k9Swu>Yll`R%F0W* zGjVjysLFB24l6Di=K@93Y4~_@*A>tdSgpep4YRYe6-m8KvU=m`;+J~9$pZ>S=XTLoUyfJU+EWB)um(E zb_WxMHoS$D(7qRq7Q(-nL`E1EzPu9N3^!?`OKMBUj;eu!bvE~HuyvJ;8(n_RgsKTO z@HHwbunpivhPpMZ}x0geT>ssLKyj-6Kpzk*zpapg7GE=ng%E z?q2e>V2LZn6jW55b3{Qw0bDTo;A7N(9*JF{{nOTb_56M`ZO=KUt?}kpPj_W|YWG{p zN(MY{ePiP0xaqaJGw>xxz1$f-IV-+nKZo4k)AGuB0dpd@BdEhtsLyKuK+8}0ZpZ)( z^aW3HU)y46`tg=CT)xo!`Kg(=E<}$ndXiIU>*<#JE&rTw^QHx!{3>(r8QxkzdEt3)xcZg^Y{eu<2)r z@8*SU^74hPZ(+$U^CWM}wgJ+x{#W#PS$5kSk3t3&mkw3vOsFic<)i0_P>Z@o_BiFn zt$r+_JFld6QsuFz-Rd1tGSS<=vb17CS)z>k<1O@G)}8^Fg$>6O@E7|g;g5Cq#~Ain z1AbatGSTuJy2F-Im+BJz&CxO6CFnNVqQs3#a{E;psN9(lNU#(`U8G;wwx*c+iR~&A zi0y~%SC$uc3!F~M(&AK>)wfM~(w(xj);_)Z9CWzcll0kiWp>@g=uqFCBK~O1=l6x8 z{(vv!kGEg^KNJt2PvXDi6ux3ovv_*FdiW{y1@L9- z3m{*Ob_y#dQ|(&55kegtd>Ea$Ng7XFpKYrJB8*ARB%sTufc z>h4?6yTj(}vjknf>PcS8P3Moybhf^~cwSci1k-f(>UEO0H(uuPJ%hBE+r89_A0_QH zr_yFOdw7Q@%HYDK=%oEhRk=7Yfpp+CO9%KM>grZw5N>p!@7$07xe^An)swu18&KY3 zbJH61Sf9p#W(uD+(Q&*O+24t{Sv!^h-5L9-W=FT5J7pi$?QPme@G`~xw`m>=#Prc_-RjzvGfT?6Y->}Rl*OX0Ptwo2r z`3!~-@(%|hL4PGxKIP9gDEI>lf2;yRb&e>caULmj^9?6B$6uMoX*vviit z3$>8>e9JPQ9FOf=A7(+PH0IMdr!XkZC(EY^Q$9@x%xCR|=jW37eA_agT(YJ`Z$dJ6 z=5xk7Pkr2iE?0SyGr9Ska>MhF?z&g~ta&)p_MfKt96Dq~(TE}5;YA~d7xgXn_QH$) z2`NmVW0511RI{V%>&K8`#Y4P(hZPqO_Kqkn@(v%0wF8^XI&HspZG7#oyO1)hwv>U7 zX7)47F&a0@aLK^2U6;U)zJu+B+fm-*%uiOJ$8XYn0r`&uwKK@$tc5m_Kc!#)5yjr# z{f6`&;_ZdcSc3_DSDo4nc1l;&kHQE-4Bgh!SGC&GlIk+HZu|nI{szMZ(}ZPvQHNFnBSun6qzNh{K0% zIe|Q3uq)m(z>|x_h=_Keo-x=B3LlL4A~7%nV>?P248|!BBb>jNN*T-nK@5D(HgX0d z{&1|o&se~Wcqvj5WhNl8V&f`jko~RgoI&tAMWaUBC60YSCiv;BT_%CJQRD1Vf*&Wu z5hYaPqUIoRBS^$smcyuVQFD+QggZqpqQ*tdMv9u%q!Ce(3MDfib+RtFrfnt-A_Glw z;B`_nNSlgsZcv)zah05a;W|jOEWuF{(hWv4QpIE{fL}w0n4&U`1#02ww~OPU8ag@1 zDDtVnXaFH42Mkn^XyMl&V*E#ux!~8>%sCJrRCUp+)0uNfBKc6A&)fwpN14T9@R=$4 z12tf&3kIDT`7aVcoy|lYzXOjgN_*u>S~0P4V9^)rwu|=;t~ZTZ;(xz02gFgU0k*R8S+6*jbJ;}bO$vp z%E45q=>{j%*)LX;Ou^N22a!J@^+Zn{ZyZh45>!V zP}71?jXFfkXjt89mKt{g1uL&mg9!Fku0cI@5I4fuh;bU3gUE(erby2mq&ZXl>N*rJ z2*~Wfu(r-}V4M`XM($t?d$G*NHgX4%b|{rW&mE+!V0EX7kxH7=lLxy%UTGKf+(C4U z)GaGtQB$Mr2zE08pq@F%Qu!rD@G+d)$rAOfZZ(YT>&!?TgjJO~ZzK+4=aG*@#)b2d zm~N?IM&e+=mbhgk4kFu5b!)COl}Q%W0D>h#)`w8ssJ_lnCO`?K1BC1~IoEpTAaYie zKt|#qN*c&Jjl@Chby7==#KB-eTnc0)4#EeKAYgGgv9;S1J#i5046d92!N?nAo4VC# zB4itiju6(M+m6hhx^$6J*Vb!s|)G%6`Q2JV9+AD?9kqX)K_k223qV$0f zS;s2E3nTDW!^akzlW5hTLh%tYk)lyY3g&^9s%X`bLfW9ZRYwZiuW~32FB{gwcCU^U za}A_WI#PHKvE8d9g_LhKL%OF;TBT-J_q1URnV$3E>Yg^XYL$wb!qc|r@M%bqnYHB= znoz;-wT5dr+epJwKpNgQ6ItG?Lxr^&XghZhx7m{eW$s`XtW+wckur4<8&NoaZ(Hi% zu0c3xC_LfJ9Sj8gw2;!|4#IoDJnls9ApfDr?6VThY*~mv7q+0&QwPJK?u6n&Ul>l4 zZ>LiSop*`sK4XyfjJ+5_ub71Yv3Y~29|8xm?Yu$WE|UJNkvdZWGnY1qJ+SSxLEb1b z|3um#Hc4MblLmu4x4Mm_LDnh~0j;FLE_e~3tVos!D02p(Re)7owQ>e?@b(BtUvyw4 z4R%3VZe->ar=0m1KHh!kNnXg9HudaN+om7ii03EG*u;pUfklJ7BM0{#>5NDO>92Z3 zLgQoFBfB`63!keMd8GYcY)?D)sUUHo)>~XCE_ma$>*B7yul{TQL+(5p-0Y7$$xFJ} zkZAtwvv~ejX`;EQAW^iJ<0AqEb&2R7V8R4XwP6MWTMCky;Lr4kc8_tYrSm%O6d&>9 z7M;=J%&2hBp#B4jyaS4R_aCUHNIm>`77kW_7sB}M!tg$|xbe=!i~auY;3m>zUB!#l z?RpGD@@W+1rp?L2iQ^|8L zn0XgbLO-!6pbW<>C?9r{#Z}~BG`)0Jue9M0^91m)75?CdrL$8NI}rZJjXpGyxQd@z zTtzO})*oN!Ls~R+UzHhbBrYLGkZJl_ThPin|KSPgqrim2qG(Z*`#RWhH+<0$rMuvr{kpy@7 zA7VbpBjl#5P5Y;Q(x&}m_Sa$i@LV1=ZvPxE9lPv%n!_)gbGYfzFI>*y-#$F+I|}Q5 zX@zz96mETE3p&({S_CyVg0V;-h9Dcr?y>fZ|I3Q92TA-bPVwt*-{w;M@RHHB)ZtcV zhb|J0pMFFkU_RoA1#ZJy-cZ7SV*&3ou@V4AS5-#P$Q(y(#DgtTm|Aa9UPm+OFK zTd`l=?C3e);m@gfX-~3D-&vN4&u8O}AEHCeGKFyvZXoE3 zM&kU>_G`~GgJ&E;;%|3~Kl`)wF2(Qrz!`I>!|$CPwm!5CN7%Yi$1%_Eb_iLfA1uqn z@o0MdmiM#LTc#9;>_sih#LvBC$HjHPGJPEiY$wa~qh*=6Y}@aB0~nw zfAS)gO`~{A?)#AII#~&te>I6yddv2Mo%>oAEE6^Te@9j*my4@_+lEh9fnM*%r~JQ z7DR-jC$Ez1j_y+$W4bpkZ?@&4;0boi>iC_#fga=5DC&y_!tr=G=#Tg#?OzxB&+YvdNk1h^)J73n zyl$mS>HFP!i)@;_$U~riQ2)lVx8iU%s^!%Idz-cIwnyn5_mV7;kH+KxH7Q(>ySl+Ro8{#4VtgS z@{acFQ`PVbHq!J@x2B&Dp#IrQ&T=*V-=EMr=Qy~3mu4j|<%!_D!{#@ppOyb@8t-p} zE$3Q|S$!!}*8~n`^1S-7Y4cLrA$0?5mLC*4J@swqU-rC_W~JUaD>L7#cQ-4$4j#Nu z0{zX%N*37Ev~JqvXBigCM=z_9h%XN3(C5NJeen40jU@YJPT9Awy2738vBs;rQh%2_ z`)m06+HzMK+O25ZzsW#dVHqf{pqn#s+1PyVF ziXQz3UrrK@^?){_`)!_F0v3d9(-{(sw`);zwGz~zb5Veu5e9V*vLN4DcWUT>5=|v` z`JEa#*~Cg04oQhxi(j$%H7nlQhHlNY5@-zqvYobO5Rjw9v=R6joCr`0B+W^u zL}Y^zEpOBSA{M8dF`a*m(674DR3Kja6_Zh*(~on&^NY#DZ*=}~4t~xU91J0ofE>3m zXbl1~!q;-3Isuu@r$ETyAKO`32LCu>qsr<0<1T(6Z{cUqkL{-r==5WdPO$)mp6L8z zCyU)6ATvIY7>jXq35eE`FlZ2vu^yzjDUL2tG+2Xx43$^in#;^~r>;e?dvby51Y`%Z z&)^?NY{bYE{9_n50fKRM2}^faVpZTW~!a$Smz&u%O>$N=*M7hNlvA4b_p8g z7NFCQb6~wAQpF7ZG0q*6Wa@~fMU?SFY!9iwc8d=ZOJR4TW4 zHg27R3|B(xA3RaVlmR`(8V5UHZ&YUw=@ewAMK(CdA=@5daFChOi*XtpWRyu)JY!vl zVCdyCG)Tzy`7}t#I3G=Jg*pit+)VYzbO;$7WQ;_*Y4CG&_^^80<{)EQ6syzVAY(9! zw`p*YnYV87Hhnru%)gPXx+1atP_~+yWPh%{*c=pBp9YnK3~X?a!P{3Haoxlx?j#R7jyyQW*}nLnRH58wZUj zUbW$J!xN=_PH>|&s4#&@p^&XQR?J2nkm?AN>_C8y?M%%>rZY@J^vco(5g8|nIf%%3 z6(<*dU;7~@y9J{#r=gg|4^DN8348@)`tA%9nf>rT&O{beX6rx<8`U`nz7!69+0iK` zVVtsxGfcuL)P&c%ojt|GdDs68DzZ3B#ehN(qW1{*z#c43MctZA))(d@19)az>vD2`DtwwKl^9>c=KO?%a||CEn4fD5CIJ zJ^I5I=IUD8Pmw>~*$WhXV=)y z6!H6b%))V`NC#pTKYDTxB@)YxS>kjHF5LF(*Sap%SG-4H0~yZ-X*}mL<2mn&4KHM+ zs&aZ_(V&sT)QU~b8&0?EM-=tbczMd=wqK);K68)Zq)|6n3c$r*_xyHix_&$Lezpi54oPP%EiN(+`?BcBlf8aX6dLxD)#mecur}Yv z7rgWX*~{6Mz2xGxPJa{Pxg-4zIBsz5%jojXtmJi^^!GTkmyK7fU!0vk#bfeNh9$~Q zPE?$SgIfmUbcKqF5~uN0#?M;f0af1Dbeh86KGlhYcTjm{xwkjYW~k7LO}kLD#lx<8 zn6zS!r4@Wcbzl925xFs;zE39(*#r}MS5|T!H=zQf$9_eRx25qvGl7*YX)rxh1!0;A z+Q955KMX9XJ}2SrRmIqZDw{=36E}!ET@lqbo9Rq%AZGcy?X|r@pSsma@6)UG*?y@v z5fa?8x8F={hz@$2^yhA;{xm)`Wr;iesrL z9+IyL`7)5Z=h>Oa_9G?Ew299T5M#`SCCmENc#tzPMQ z_08TRhvNX46TCxlLdmexESuf_)p6|iKlUL?)q@tLN{nRsf>T`?*JRw+v;f@dhq98t z@T_CrVf|}Or{-_&LS)X=r@#DW7;&4b45qcO;o#0TKcfulKX8D6nthkC@NTQGG{tLC zEfuuSe#OPZi@p5^5AT1%&KAU#?x(rywy(0T?xWkz73zmvcp+OK=;_Xt>Q%0@>GY>` z$KgQi=m4CZIc5MQf<-r(a>#FeegC6rG0b%?hIPL_XL(b~A^ zRDGj}`J*9Qp==kaf!|+Pw~~Z^#3}st&lkHBeqw&sSJdA;XMgqe&68YNh6R;lCfXADDESZ>k z+zyh4tZJ}YgcIa5BGR$vY2i)qz*HqRJrdjn5h=;KGUC8?rlB4N&Y>h68Ra*^z(}-` zqz!{_3HmODMN{)F!P-%DE`x80*oP!5fg0zCOta~dY!JQVE01ammV{2Yq=+et!%)23 zhyjBtq^j!~Az(Y3CKcmS;%fwe!DNyk8S!7Rz2sPo@NcxhC&{A*gqF@-rl$w-FXs%>A8I()V0HP9xRFq3OF*@Zk2NXHQWHLyX zV4kX5wS`9Nt19&xtV_m)74$xxbqT(J)O&+;3F@iDS0`OMxrn+QVr+Dwo(AVqFeBKC zF*uiCF-jdv!MRifDT8wf+KakVLkRU(QO8SQpcj1w#Zj(copR}9G8vppMy(R#G&q-b zn__S-LBo-HqH`{(l&l;|SU1(okXg?(ZFN|6tJ8NFgWI|@1w!gh%@}b;igl;H(im;T zqK|3@8^mWtQq*=bC$>7{5+EqPph3B`8?h9WOB?qw1wxV~mV$8^RgES>wr+KVzz>xR z$>3Wu?Gx}Bd`mRS$u{_wFn_XJgKx<)7DTr?-xBaCPMcox74#!=FW3sy*_PnD$@1m~ z*%B*7;$swih3S*#!XR71&PbwRRM#NzQRNPxlP$42iPpem)wz}|y~1kKY!IuqCcrV6 zmf&bgWjC0X4g#mHK6V6*YOf9gr>;KmBIItbQ!T038mdQH;_CR=RYd1nIt;NvwG=h# z7RLdMnQFD4mYyYBHK!U~L+53y4ig+aMVU)c@YP<@ zRX5gff(Z&vit}m8#I|kuSQ98v+hnV$OpIzRI@YiPjy`#-;b4ochu^Au*jS9@p@>Ek zC|GmZssqKQR<`O21sHAj>OjFhsOd9&Y;Y;bfC{NJq}a|c8#SPSv6?)?$7T#AOJd#0 zw$>zns_taNGST7@jT%sRsv3>qWW#T@aUpe_uqu@@8g90zjV&H)DwLYM6pl9hk+w;e zfT7J|uas_=l4A)vX#`}h-PoPwl~X;tAx4U~`am+ zTmz7XcSy4I$0EU?FA#@oGkx(sFV|;v3G6)A(spwo|9=M8QViH)FiP5~;9Bk;j{EVI zBm%-Eu}THs(c;sB??_ZiSnz$F%tC%}j@6u+Xeq8Lq9r>mB{k1dP&_Rur+Vnc3LBkZ zKl%z7nKB1NOZKMmd#5z^#tc#|S+<=)wZwN2_B!Yk!J=BSR)cEEULnO*Rr{5B2!d2; z)qd@QB`3?oTBToe@Z}&By>8~_@lP~S;=h_l=Z!Oj}<^YsVope*~5!Yvs1TJ zZ?cwGMdcwV2d_k9K43ucu#rPY4(K_&Xi%@>l*MGR!1>L_llpr;7rTF7}&s+>?(Xx0eI!qg~w<=#C7-OBlhhU62fDgeK)M*SBhB{cM@r8vA_fp9I87pMZ#jCq~ z#wypusIJ@ZcoAJL0&kKNqn5I*q5jFO;8CQA*%uEi9*Vj=BfNCHrMFjc(O|Wp+X&jH z2_U1y&(Prr{Ej%K|L_sT!@MU989Y){xoN+Cy*6)AKB>cEOC9)7TJFBQ*&Typk41g% ze;o5p6^wOHkrr&u1jA=_&@;P4AoA$HVQE;~G=iv{D!fO#X{6cY4rlvkbVmL6eP~9PJ7;v>j89z7 z=v{AgkCONJlI1<}32k~{BRbT)$4Dp;@%wRpb1W2##-qXZi~r=T{ck4mS2)FQnD(hl z@v|>HiqYy{c6QkM`6c(e)9{aPKlihnA>k`o$x`+<$?>RL)PxRCN<*s;+GzEPSr20; z>U{ePLq2BgI#}H?Id{hGWb9tGjGb74Z?r-{cgF7HmscJ@{^M)Xe_YA^$1Oi>%}V7z z4lb!F_Z~55;AuxH_aybgOY_d>Y^4V#8_-1stx3em3I2d)2qj6={yL*wyZ#qS8 zSaXX@ky{Qwi>c^a&JO4OGV5n|D*EokUWbv*dfT#D9D{XFzKRah+AJGMUGdamvqS}O z*en!$4C2kVV-DJZKRj1YHfxn-v$%kpF8v$=y0cja-aTLdIcTeegVr^74YOIzE9UH( z>T77JdrS~jIgoW>@v9gL;O>LZ4Y?9*ppv9~wgqS+U* z_FJoyzqy|AP~Wk5sC*E!r_S5cm4CMTp#6@Wg^!_YvXW~!L!Ecn^1xGj=C`Ez7Mc>C z%%YPjp~vEhsD=6V~T|Q1@_*AHn6gjvuO2q(HcPG#q0_ zx1IIJ1F>NDV36OMK0V3$C8NuOGt1FHeSq@X(vs>j^}*Dw-+JsmXI@Bz>vx@sUbk?O zJ4OFT<*a4&g8rVxKIeMA^}{85>RxNi@Ay9#XCrwhJas4u|Gp;t+-Kbhzwa&E0@UAH zXMe5B<{ai~)h`H4JL4ww_d!;2G-IE0FdDbD9+uwx+t}xdfDZF73X=N^!$D^LJIFu( z>-AsVP5kq97XO^fSJ$%jFqhW<@xOGv{an=h{V=QW6XKu0&;8X8zTP4K9Iq}YeG-f~ z`R8CpQlYEg+X?2?H8q2JAv%sQI0AU=gjsv@_(XMSqOul2P}D{vPLswz&jZ0c77T;) z=u1UEkNa>ei9ZU$E@<3#`ne=^YotBYscD;j4z@Fikdl5*Aw?>(mXdys0&G<9k+o^8 z2BJnWA|Pwj$R8Ao1$n29YQOf1z8dobwe2tr@kEXIk%J&Kcsp${&_UFa6gYz*1MZyU z2J8Ho91x8ZFWaD?10YFQGbrd7i)3RNb;xjvrzA7$?3f(z4cYm?j2~uD z(18PL&k9zRK|x0pTo&mx=rK49SrWMnb_{}9s!^lu@$?4qOoM@LugIE$90OvzpqA+j z^c*lL)QR&3HwKC;OZDj#bWW!iyfd8|gR*uK1ik1ltw>r%3|yUp4(gF)1Eyl2v!ZqE zPLqKiqM~C0BAtOwWH%|1K|p8303nf1KnL?kPPRcmM+rWOtzPpt2YgQ0Evs8?rGXYE z8LT?{9IH_A2o3rE6AeTW?Tw2+^5i13tgvGaCeSRAiG783c4pi)u9Kj0z#W<}ao@s3`FoLS&{?&1t>n zFZNzFqk73-fQuz6@G}_bSP*h8=?wH7>=`O|$Dp7iEk!C|3JSU+=^GSuY+CA09V6Id zxe4eTbj+GshABwsD(%N0q1&rq8zgj=Iwv4XK|)syew~DlNsGv#=qz+tF&lSVUubX% z)SVjH6FY>;urYY(Z2J(>qWZo=b@5Dvt-(Ww<&@a!6@SU|kvosUL+AN?)B85HCqU`H#wn!YZZq^-b z?25KJwz{JY2h&~D7I0RN7YLY~9s{+A#afVK!WC zR&QJmSH}uSs-6vJ+rim4sOYdUl9g|G+v4N|E|l(WV>~vZx(*bqo~oQ|xZ5xWbmWcb zSW_w@-NM+)W=*Yf;M(J5G2?7>m{X07PNd_Q#zsex8DxhB@hZI^QEx4ZvLrO)Jsfp( zWBG^W9q6Rya;~!RyZ<;BT|B_zG&=+!YraD&x(_AHqH%u=HM`qU@7HnH{|pZL-}3Tl zN(>|f2fbS;igN*xW3^}@Y3P2 z^PGIAlGz(GM?uHO2#j1%#I0(-UEtTUmr$n!oPo|8#T{0yUs@PGs~d}dPQR2F)6>tX zK6P;aqI+I`9pCWQXC-gqgqU^gn_JV1O;bGi(^C{*8(uZ0*4wwb+#B?cC>g(_kN5s) z_dt|=b~YmbBdW%GPpGbacGvIQ2}AAvdO_|_p5-TTN2#_?gbY<>~Lazl|B`stP% zvk`*%I4ik=^9^~AGoNThk4+~6%6rrkAj9v1e$inLj1Ar@pzowsf9u$t5N;LM(CkO z(GAD8U$x#of7(l=TAw&oYxT7&U8>gV52nu`uFeLFtHU*`;pMl`p~lsT27{3hzIDfA zsGuJUxhNPM+%++e#NX%?zwXslF2!G*oWTkxZ*q3HY1KPdxm#*)Mvgh1;@+QHac{BG zF2C&R9dLDQOm9W@M&Lsnbcn!5oB|*I4i>>|_-@vEihF-%#l5+JP228=fbQbnm%U%| z@-yi2^Q`10+zxMGc6j!R#SK~iv9nl%%=WT!jUsf(3u7VB+5UBL(Yb%vg%oSEr3ZWj z>)w71BXDDKZyr4Q$KyfK`2wj?oT9^fZ2k3h^!WczWA&Al#Ns8PV41(f7xxEav9d8^ zB4wo|W#Q+#<>lqp2FI42UyhayqYt2Y`O-Nrt>3S7IWNgeAN)53X}4G`YCbKiZ+;sc z>MZJxp2ym!XxAK)eydaZy0xoZN*W-T8KZc#8kdN?yY= z7kQ73@863ae@WvhIzS?@D$)(rnSzoIGiSfUt0oEj`s`>T@Rn+EUQF+j_lknRm8D}* zLb*I)JKMsV4yTM@@(A9v=nY<&m8zGHdJ{x)mblS$B9ZT2S~a1vR%~F*71pj91ReGx zCuhgO%?X0Q!xIqO>yHMbvcjW-Vcr3MbK;+W*Ov^|cbbA;f4@rwJ?QBlo+JCc-Ll_Y zKbx06hz@o89Rr`i7mfr1p;+8aE#MdCqpRh*=Gx|(ThQN+S;;cSvf=$TeY@e0`NPu?!$VGDxa5Yzev6vxfkJ+Xm0;L~oSp z7H`|_c$ECH`KS@Y`VSpSBv@AUSM3v-delx@{&1U|OLXAaGWgp(S#jHU0Cl_lDq+8a zlt%q~lUFoxGZ?d)M3__!fE7_s8V_(=_fQ`W5x}_V3N< z)7~Qn?lAiYXVU~D?At;6{5ag#{zbB>%akRw-(Q-Y+}k#otshK3)|H_j{>#DHSK_Ne zeRlFK`l?`|vwG<@$L2quCe<+wOg5`1p;6b`w)MnVb%mpKpXm-Oht=Ob5{xkO)ZTBZ z(Kf!R+7rE5PKEtKM-1U%do>NAIi8`-@%-!D&2f0fQNJS(_p4*iIPzuYN&^TGyq!vAIH=K0j$70&+Vt-k6cR|@~^RX;Bw z2lvWs;ox#>&~npDC#AQ6P8zvnlEVgyLgspnior3oxP!S`kbh~!si7j7UT`H##1ZhiiZ89ORu9t+372R4c1V5hZ7BnOE#%+8N) zlSob^2aQPHHH3tWAddYb_MchEJctYu7>5uDf#4q5ndDRfj&Zj&_8k#1*ny=~rrKmp zfdU}z)5$J)Lc}T^uF*FTDl- zmmDxaP>)Y^s*^TQFILi5b$$y*;j@^02D!yfa5K0qg6zSlNhvtwARY=LmBAqgt(^wK z@6<^^c9xaSYq4q_@;h~W*b%u_w;DiT`6;fW!5|0flGSODT2Mt)@?LdLi-SC9a9Thf zkwj*l(vpKJ&5E0uib8HKt+i4D2o9x&{)_T^MPM;j6RA zp_`2A#2O76IS84Gu&mR_iPS)udDVWLzKd4os}mZelegGde|CD*r3 zBnQ(^(lZSnImmjF#v3wCQ9v#Vy^J|@fmwAvP7NVqZL4`P>X?g68Fs7ABL~S(3S^Wq zhy9ba({&m-TTbd$%~X@>N?p)tcL6` z!6k>@}+lts~bI+FphQxu4Ia6Du^2pZ2qaT?bRIc!l;rYFK_f>&cQIoIjU1*xC8osm zWd{1E8mEpBb}ZZXWM`>j8@R<42S>!N((izi9 z1rg_nIL;> z2Al0#^07MM7uh@X#vyLi4K`L6%byU<+O~!Re-x|Qr(0|!2iRJ5T>^|`I7G)fRG@My z8nW(YbI;Yf*Ko9*q-ztZDCrY_uI_4MKxs(iOk@ixokX4kKMnRwN~J-CT{CVQk&YE+ zfxXzV$v0@^fLS4#!r=x5-4aXpxQUbvkI)h;wa+bY*1*caPGO_B>uxtJctnj!ce}~H zs~I+^%kx&B{Zk~5Y4-@_yqe0GbXEjjgqN!~RcKvsP$0>{*snn(`MA}x^|?Wu|6qB6Rw zW{#9VSc}x<%uOaavb_x^IeQ_-Cle=+TQJF4V+WYz;?@waoX(D9Nm0t2zmYoyP^HY# znvzMLqLMi_ijF^80~KYl~|;|(7kwt>-Eud#wqd>Hi)Tza=VI_uzr zM!Yu$0f$-H$%i?Ok@wjA^bGWPOPYv7TL`JpfMQr%x4T$J2q`L60 zi{eDjzA4+HXHVzGZQA;KQ_{1C_wxF^LlncM4Ymi)s24#xlY-HEY}L6{AS!tKl-tTh z3)V>cRqM2c%bz4oxY^PKK7!_#zQPFHFlGwJ&Z`T9F>_0H@@JkA!+YHR?l@0&N+&%7NwYt*9YQx>bah4^e z!nb60cg%2!_$5=(#(Qy?XvMm0(?rLtPVBrGbYeHtEKY5|Aom3qIW+HiZupLr@m8lY z&c5|Km&!Q&%Q?rAnZC_3(_(!t`W_u>W;z;+L_mJA{uOWkT6*h=fBr(^->!*2^GBEB zzx1~$W2wVCoE_FZan*cxi}UGE;>*cQ&$i4o$7A-B&!WS0W;(2q(k)&(%(N))4Ks}s z6X?&5MSZ8dc-E(6rth@OG#7F8+nXSwJ2QRKhMeN((dC@%fZ=bW_aCo72XD7GVp=oIS0X@E#rrzyugpo8SDcwwodz&_lak?G$s{_ou z_|5k(Blz!e!e95sPcGqqen{b{vFPyL?4%zQhieFNTf9*;gHaa_OS@QFZ?mE2U=i64^jt=k7PM%`} zq<+)G4cYl4)9|jt2JhOUUc*F*qTKKyQQ?sM$&Q(*+SB)3OD5_8%S7>6Yx(d+2f9;V!-n38yiU*JA*MFFo*&>w^ZJH*8Q=Q2btmr2! z7j7CbWT3b2kYUAx`_d}2_0oRL`uu@a=MX#TA&Z?P#J}�#|maVNm#)FJYJFW+xYr z7uhwJci4JcLqY!CX`IOaEHH$V!}8w7K4 z_j|FD++n2=nd)#9D@d;bK{@r8U+SMz%&uc=DPh=A07#l9?Goyk*U4tPbs3w}9Z`H5c z1g)f|w!GBatK=N-@Np#-6>2V2|IEize{K75?1$n>HY=ObJ}S}LwvS2^`AA}G+n;VrON#4$s-S;cjzYT9hz%Q z;}h%Aq5cky!aiV22jl)|I1rAyFr!139&jg#ztAau{na146o1t%BNE=+tlX!v3-3G} z9yXVL=1q$)+ac!{rHVkT4CU{cx2rk>7sUU* zH{=hZv`4@XIt4<*Hp(xf+@zxXQl-hT-{S6S3>QRWeHL3*3m|tvaGsbbtWS-WKx8mg z%2}f&OxPIcWNL}%~4sd zROkNYpiU-3kjtx~gomn%qy`y5lpW-E8YnRc1tUl2B7l}8=|ct)0o+u{^D)T3M8mZP zYOoL(W6omXY7o)HsDTk_1YZMQJwuuAcEj)-K#G+aD;3< zYr2$!iDrcv`DjxtIYk%M3Ba^86gL~&r-SsFgDFu{YcPPLgoO}UX8_Ym_ervvK>-Gx zS*oH=0j4tga(WC7Fd9|28cw(q*2R)YhY3T)zF@T)Fo7Z`wM{1hQ|)0Mi6{1u&H|>% zQ#E-F9x$UJh{qZ{V1h}~_VjwvWSS(x1`inHQH6^Q9xxc4it>z?Lg&Pi#HwclDW9#d zw!s8Oy<|B(1`{|=oJlcQoe50ak;)2s8Cp9qUF4Ygi>D7^S9RR;5KSNWyrk zYScU^h{R4J5xiJ!&qFJ!R9j?DgCxxAnOkH|og_^23WF`1b)?YDY=L;P<{=PNs`Rf< zb2f9J*UYqvn>DaNeq}~Tv}!8FeOxJ*p;Sn!vpreEi_MavzhX@|_Y!grZQNPy)di2n zBC#F~1e`acH)dM7+=g%NAUGR|d3Z3r*NJ;|!0>ZeIoYfMmIJpCX@;^{LyRp5+n`fG zhHMW@O$@>?ycAl)Tr%BB$3(NtAkk_9h9he%xpY5WtM6@i>TrA&+B#pDxv#Vu#gjF} zFjYR=lXbx0*symjakB9}uP>4ddHZO46|SsYM#5*P~$Y z&Fj0cgJYj|eg$k@`RcXqzI)%j^!(i@uKbJ@R~8@A@4E6jPx|nOQSQL1c!2PSI605X zy-@fAC51Z_{y5Y-<5G$%FS6pwT*$gvk3mRxapgA;>OY)GeNvk(kv&Q9LLDcHQn)yuy`kF(NHu>tRZDZ(r{qb9A9lqe~u)cWLD(QXW6zNUN@;3T`tQ` z)^p2t53_7DpZ#)vcB(U>PcBc?dIyzPpx$iNm@$cjiX>8SGKFnJW}<_a!B`c<%78`u z8#@A;n?}CmS!}WWM(t^DC96B!V(9}PMawmf{K;=)85f?lY7|+5mz*;{ zyY*tslbf0UcHK=A$TF_5EF+)z#%7$YaU3&^vjY6#s4pIO5vaXn+U=~&-^)(% z>tDXqrTEnc&OeLrc*WUa!x#6hafiol#ry3~mhn}~GIBhcuUd-^({TwSCfQl=ohN&P zq6#-GBMuEfE$$8$`rCT;21a*&&9aPK$W2#&3nATE#(_O9e&kh*yEQv`1GkJ1G0WK6 z`t_xr{EJf@-f}W%Y6Axrp{ytIp1u8wi-wIr2Ht?;9oa~;610t0;ozV-Icn{jb?-KPvqh9)k2+wa?gl9gD%}-s14t2tF zI1q^7Jd;Q`7>GoI!C?EfruD=hyOI2FIpuFzd$~*bSC5=Kk~(}_>+r{q?r`@Gviyxi zm)YoWRd%wR5uQ09^><%C+mnAv8p3lV72#QMo?)g$v1y!Yf?9lZ!b!(oO;3LA)WgVu zTWy&sE@s06b0MZXGd20m1r2mqz&qK=EBFF@jG3v%$CtmDmFlp7_SBU3`;cG74gfNV zWxdBH5;X~LuL;%XB&wYjjxn*?uV#V$2Nsc5tg*C$k7VBU>o5{GTY2#Bm0R~m65YGm z$qzV9kN4R8{(AKIavGZ6Zv~Qr`xvD1rXhmTL>nIW4zuX+u{h2gaB=(2Z6e2_$gX&- zb9W&hU1iLLtu#M%i6nLGPARQ#Bw$s|){U=Z`}uwH?1DC(#$e3q#!d|Eh*j@2NG1Z6Tw)&N9jzkCsjA(P3JnoQf~fV-c8CbUY&#Yp-5ugko8BC5uF<6c^Cqpt|BDxQ$elcXR!s#ggFb)qDmst%erc#0WdjBadYvH&6ckfeCrEYyQAw5v)cKL9j;A<|x*fv&ll@|Irn4haB2B88PL5=H zB4@>5M+!^CyVXn)bY5~|i~^~4n-S9B0qTnWW>6zRLSlp&_Dqu+34WDSYl9le3Sx`Z zZBio<*p+o+b!sFql4S{XW~6;_>%2%zr>beI(;_iY#S;(f*4j=YH%O7pB8ydKkRt6w zS(6lLU-$+o63jNKng%Hn#B0d~lcY$C4GC7Fx>MWE$k0m;r%r|hM9Rb&Tu2bSP_Ipl zR4+bA~8zA zgY?PutMedvUVtT$w$O6Gb5qzGG)Q|DW1R*GLvI_4Zmm&gP_AH|1POFmxplS-4GyH8 zm#K3g0llr}s16a#pWGpI1|*)THUXUgnFC!$LW_W2r$BZ=aMz2|YC+7o^j=l6)VYsX70hyQ=9y1hZrEpSLTNZb&ls^=+^X+vSg1BIbf{2LSGDSX z_6|#=+V9oW$tjiLX=CDvlgwo{Ty0SNY=um9UmNR60jWFNU7%#N>O{{PR@hT*9h*?G zL>_Ug4i$T|vpP1sZJwgWZ`Ivxwp+JNcf<@SIwyoSq- zomWl1;d3)j;efoo*h?}NSV8UB>O@Id%~Vfz0PJFjWal#uO^98Y>Z5{6-3Hx}6=2^x zZ#Q;pdF52ku6Qk`9Dx1oY{y)`4@r)+_5WW}A2ki#5zZ#`>VFU2u}5A$Q3EX+pXZe`$Jwg`nj*bN@k^OFPP{wd`H%vn(x@I8mZ#ZmZYGj;tdfzah>rf ze96>RQdwi>7>}r?4$nZ)4=u)H4xCh;z2hWZT6I!ctKczOoX0M3(nA)xF;KvN+6At# zyco1DVj_qWn#ZTj>J&*D!ItY=7cIlLypP1`Y~6C3*r&HmGv9wRJO3iHP{AqvhM-L9 zDTvjM@D42+?CmwIxab6DG$RG4MSiX}zqF_P2F?zCZ~rIWl}Cm5pf9o?TVG`PU>cs= zhQYWAhTwoHa1}_Cm%zY=m_~XqCiA5|2LljbP}1txTC==8?;*F z18lVjBwi-;O$8c=zFNfG*vtah-Z3yW0?sU%nv-#5%)&u(TkH#6?+Yw$phqnmTN#^x ziyI0L$op|JegSA=@(XNY5*lwEx5+*rkKHZq3TPw4SLj9tZjNK%@@B`Z<{)gvjYoc7 zYNdj2VqQvOQDz>Z!~qo~MiCfA3h*#s)WyC?OAxSTF2s?ar00{FoRT%5I_Mj$4uW{4 z;ndV1vg)Ax<2n)Ez{b&c=B&w(yK5l^{+KxSNhR-{r2^^_$OSmaTw)pVS9)(lV#{XmVO4XbC77ei-sarp47A29{WD?>2whgr4?H_!>+Zk{Xw%~hD2i65|-ibwt>8Z}8 zz=P;QQZtKz2fvr17G#(mJTpsJR)A(eq~kup2b$+81mq-^pl*I3y##yp?3f6sru+}9 X*dRXHFlippC*<_+%59UBJeU{(c}$WG literal 0 HcmV?d00001