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  },
  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/aydinmyilmaz/Transformers-Tutorials/blob/master/sentiment_fine_tuning_huggingface_v2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "C3tiei6D8wh1"
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    },
    {
      "cell_type": "code",
      "metadata": {
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        },
        "id": "4ruqK8H_EX-j",
        "outputId": "c0482bbb-dd17-4402-96a7-099ed3cc91ce"
      },
      "source": [
        "!pip install -qqq transformers"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 4.0 MB 4.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 6.5 MB 41.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 77 kB 4.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 596 kB 47.8 MB/s \n",
            "\u001b[K     |████████████████████████████████| 895 kB 48.8 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OM9GTsnZGMB0"
      },
      "source": [
        "import json\n",
        "from transformers import AutoTokenizer\n",
        "from transformers import TFAutoModelForSequenceClassification\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.losses import SparseCategoricalCrossentropy\n",
        "import numpy as np"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JjT86fYzF-Ji",
        "outputId": "b799079e-a463-4da6-c767-7934b431bdad"
      },
      "source": [
        "!wget --no-check-certificate \\\n",
        "    https://storage.googleapis.com/laurencemoroney-blog.appspot.com/sarcasm.json \\\n",
        "    -O /content/sarcasm.json"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-04-11 07:41:56--  https://storage.googleapis.com/laurencemoroney-blog.appspot.com/sarcasm.json\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 142.251.8.128, 64.233.189.128, 108.177.97.128, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|142.251.8.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 5643545 (5.4M) [application/json]\n",
            "Saving to: ‘/content/sarcasm.json’\n",
            "\n",
            "\r/content/sarcasm.js   0%[                    ]       0  --.-KB/s               \r/content/sarcasm.js 100%[===================>]   5.38M  --.-KB/s    in 0.03s   \n",
            "\n",
            "2022-04-11 07:41:56 (179 MB/s) - ‘/content/sarcasm.json’ saved [5643545/5643545]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ewAnHsoxGRRv"
      },
      "source": [
        "training_size = 20000\n",
        "\n",
        "with open(\"/content/sarcasm.json\", 'r') as f:\n",
        "    datastore = json.load(f)\n",
        "\n",
        "sentences = []\n",
        "labels = []\n",
        "urls = []\n",
        "for item in datastore:\n",
        "    sentences.append(item['headline'])\n",
        "    labels.append(item['is_sarcastic'])\n",
        "\n",
        "training_sentences = sentences[0:training_size]\n",
        "validation_sentences = sentences[training_size:]\n",
        "training_labels = labels[0:training_size]\n",
        "validation_labels = labels[training_size:]"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_sentences[0:5]"
      ],
      "metadata": {
        "id": "0dFYmYWQcOyZ",
        "outputId": "057a261f-a21b-4bf1-dac9-9905c849e722",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[\"former versace store clerk sues over secret 'black code' for minority shoppers\",\n",
              " \"the 'roseanne' revival catches up to our thorny political mood, for better and worse\",\n",
              " \"mom starting to fear son's web series closest thing she will have to grandchild\",\n",
              " 'boehner just wants wife to listen, not come up with alternative debt-reduction ideas',\n",
              " 'j.k. rowling wishes snape happy birthday in the most magical way']"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-E5rEGisGbi0",
        "outputId": "3dc0c26a-500c-4a8a-8d23-3c30f350ca05"
      },
      "source": [
        "print(len(training_sentences))\n",
        "print(len(validation_sentences))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "20000\n",
            "6709\n"
          ]
        }
      ]
    },
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        },
        "id": "dA0RCjScGesl",
        "outputId": "1e8ee6b0-0839-497c-aa5c-5cae3f0bd2bb"
      },
      "source": [
        "checkpoint = \"distilbert-base-uncased\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
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              "model_id": "d87ccddd733844fb926dbb5f76acef14"
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          "metadata": {}
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        {
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XU-6NeMwHN6t"
      },
      "source": [
        "train_encodings = tokenizer(training_sentences,\n",
        "                            truncation=True,\n",
        "                            padding=True,\n",
        "                            return_tensors='tf')\n",
        "val_encodings = tokenizer(validation_sentences,\n",
        "                          truncation=True,\n",
        "                          padding=True,\n",
        "                          return_tensors='tf')"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "imnUqYUhHxjh"
      },
      "source": [
        "train_dataset = tf.data.Dataset.from_tensor_slices((\n",
        "    dict(train_encodings),\n",
        "    training_labels\n",
        "))\n",
        "\n",
        "val_dataset = tf.data.Dataset.from_tensor_slices((\n",
        "    dict(val_encodings),\n",
        "    validation_labels\n",
        "))"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 157,
          "referenced_widgets": [
            "3766df86149c4bf0acab9810a55bad5b",
            "525ac01b05754eb59c82b408f35724d7",
            "c2cde21b86264a8a9afea4831de06ecc",
            "b9aaf4bc7412474897da86b538a01c6b",
            "37b7943cfe144cc5b900ccfebca274da",
            "3b61dd8544f841bfa2a7ac50d02ef791",
            "4d3ea60ba9fd4fffaf63d62d9c47aa12",
            "9fc89348edcc4b3083c2882b080df0af",
            "7a45982d5ac34bc18143fd04c9bdac99",
            "f2f026cb4a974f8bb1c1fb43a7b16956",
            "5aba02b9858447a9a0b67d83ff8defcd"
          ]
        },
        "id": "IQx-CuS4KLCC",
        "outputId": "999d71a7-3bc7-4219-8c4f-bbdbdd824f84"
      },
      "source": [
        "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/347M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3766df86149c4bf0acab9810a55bad5b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some layers from the model checkpoint at distilbert-base-uncased were not used when initializing TFDistilBertForSequenceClassification: ['vocab_layer_norm', 'vocab_transform', 'activation_13', 'vocab_projector']\n",
            "- This IS expected if you are initializing TFDistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFDistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some layers of TFDistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['dropout_19', 'pre_classifier', 'classifier']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i94PipEXQ_Vr",
        "outputId": "b7656acc-7e17-4e7b-ae0a-3db79cbc2e07"
      },
      "source": [
        "test_sentence = \"former versace store clerk sues over secret 'black code' for minority shoppers\"\n",
        "test_sentence_sarcasm = \"Amphibious pitcher makes debut\"\n",
        "\n",
        "predict_input = tokenizer.encode(test_sentence,\n",
        "                                 truncation=True,\n",
        "                                 padding=True,\n",
        "                                 return_tensors=\"tf\")\n",
        "tf_output = model.predict(predict_input)[0]\n",
        "tf_prediction = tf.nn.softmax(tf_output, axis=1).numpy()[0]\n",
        "print(tf_prediction)\n",
        "\n",
        "predict_input_s = tokenizer.encode(test_sentence_sarcasm,\n",
        "                                   truncation=True,\n",
        "                                   padding=True,\n",
        "                                   return_tensors=\"tf\")\n",
        "tf_output_s = model.predict(predict_input_s)[0]\n",
        "tf_prediction_s = tf.nn.softmax(tf_output_s, axis=1).numpy()[0]\n",
        "print(tf_prediction_s)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.53186345 0.46813658]\n",
            "[0.5347489  0.46525115]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6-dx5E3zKW0e"
      },
      "source": [
        "model.compile(\n",
        "    optimizer=tf.keras.optimizers.Adam(learning_rate=5e-5),\n",
        "    loss=SparseCategoricalCrossentropy(from_logits=True),\n",
        "    metrics=['accuracy'],\n",
        ")"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WtbjM57XK6Gb",
        "outputId": "6294bc7c-25a5-4a69-c1c6-c2ea89ea6c4d"
      },
      "source": [
        "model.fit(train_dataset.shuffle(100).batch(16),\n",
        "          epochs=3,\n",
        "          batch_size=16,\n",
        "          validation_data=val_dataset.shuffle(100).batch(16))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "1250/1250 [==============================] - 352s 273ms/step - loss: 0.2632 - accuracy: 0.8887 - val_loss: 0.2036 - val_accuracy: 0.9191\n",
            "Epoch 2/3\n",
            "1250/1250 [==============================] - 339s 271ms/step - loss: 0.0925 - accuracy: 0.9667 - val_loss: 0.2557 - val_accuracy: 0.9191\n",
            "Epoch 3/3\n",
            "1250/1250 [==============================] - 341s 272ms/step - loss: 0.0404 - accuracy: 0.9869 - val_loss: 0.3280 - val_accuracy: 0.9146\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f6e3f668290>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IBkW92ZyU9yT"
      },
      "source": [
        "model.save_pretrained(\"/content/sentiment_custom_model\")"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D1X6b2jUXzBB"
      },
      "source": [
        "#### Load saved model and run predict function"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ATMCwBZBX8KB",
        "outputId": "c16d7974-6d8b-4ef8-d45f-3bdca229c44b"
      },
      "source": [
        "loaded_model = TFAutoModelForSequenceClassification.from_pretrained(\"/content/sentiment_custom_model\")"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some layers from the model checkpoint at /content/sentiment_custom_model were not used when initializing TFDistilBertForSequenceClassification: ['dropout_19']\n",
            "- This IS expected if you are initializing TFDistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFDistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some layers of TFDistilBertForSequenceClassification were not initialized from the model checkpoint at /content/sentiment_custom_model and are newly initialized: ['dropout_39']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A60AVunIZsdg"
      },
      "source": [
        "np.set_printoptions(formatter={'float_kind':'{:f}'.format})"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "guuqXzg5aJDS",
        "outputId": "d573dbe8-7f5a-4923-9ba2-75c7f0b55d3f"
      },
      "source": [
        "test_sentence =\"former versace store clerk sues over secret 'black code' for minority shoppers\"\n",
        "test_sentence_sarcasm = \"Amphibious pitcher makes debut\"\n",
        "\n",
        "predict_input = tokenizer.encode(test_sentence,\n",
        "                                 truncation=True,\n",
        "                                 padding=True,\n",
        "                                 return_tensors=\"tf\")\n",
        "tf_output = loaded_model.predict(predict_input)[0]\n",
        "tf_prediction = tf.nn.softmax(tf_output, axis=1).numpy()[0]\n",
        "print(tf_prediction)\n",
        "\n",
        "predict_input_s = tokenizer.encode(test_sentence_sarcasm,\n",
        "                                   truncation=True,\n",
        "                                   padding=True,\n",
        "                                   return_tensors=\"tf\")\n",
        "tf_output_s = model.predict(predict_input_s)[0]\n",
        "tf_prediction_s = tf.nn.softmax(tf_output_s, axis=1).numpy()[0]\n",
        "print(tf_prediction_s)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.999385 0.000615]\n",
            "[0.003198 0.996802]\n"
          ]
        }
      ]
    }
  ]
}