bert.ipynb

{
 "cells": [
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   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7GLAEFyULRwd",
    "outputId": "019eaf6a-c009-45fb-b809-cadbc49ff2ec"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting transformers\n",
      "  Downloading transformers-4.33.3-py3-none-any.whl (7.6 MB)\n",
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      "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.2)\n",
      "Collecting huggingface-hub<1.0,>=0.15.1 (from transformers)\n",
      "  Downloading huggingface_hub-0.17.3-py3-none-any.whl (295 kB)\n",
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      "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.23.5)\n",
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      "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n",
      "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
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      "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
      "  Downloading safetensors-0.3.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m75.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.1)\n",
      "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.15.1->transformers) (2023.6.0)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.15.1->transformers) (4.5.0)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.2.0)\n",
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      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.7.22)\n",
      "Installing collected packages: tokenizers, safetensors, huggingface-hub, transformers\n",
      "Successfully installed huggingface-hub-0.17.3 safetensors-0.3.3 tokenizers-0.13.3 transformers-4.33.3\n"
     ]
    }
   ],
   "source": [
    "! pip install transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 201,
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    },
    "id": "h5hIlRXOLe05",
    "outputId": "f1586eba-e0c7-487b-91cd-c417ae05b4b7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No model was supplied, defaulted to distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english).\n",
      "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "85228b09a4e34ac2a50382bafc0a1cdc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)lve/main/config.json:   0%|          | 0.00/629 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "23cec940d41c41c8a735975cb807e7c8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "11e97b82288b49d9a84553e241a91b08",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)okenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cbc5a20a663e44f39ec08f2e4678cd12",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from transformers import pipeline #it calls the transformer\n",
    "classifer=pipeline('sentiment-analysis') #default model distilbert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "G9RpJoxPs1iQ",
    "outputId": "16f852c6-0f55-4cc0-dc0a-568ad635d50e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No model was supplied, defaulted to distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english).\n",
      "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
     ]
    }
   ],
   "source": [
    "classifer=pipeline('sentiment-analysis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 181
    },
    "id": "Oyem5lqTs6dg",
    "outputId": "5123b945-675d-4e59-d075-3ef417b8479d"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-35e2b53bf478>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mclassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Esperamos que no lo odie.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'classifier' is not defined"
     ]
    }
   ],
   "source": [
    "classifier(\"Esperamos que no lo odie.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9t8XthOQMlDQ",
    "outputId": "09d02136-712d-4160-c13f-aabcb616d371"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'label': 'POSITIVE', 'score': 0.9998078942298889}]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifer('we are very happy to show you thransformers library')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MWsTfywEMtz0",
    "outputId": "d0bd35c2-b488-4e39-e1c8-97882032dccd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'label': 'NEGATIVE', 'score': 0.9920217990875244}]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifer('we are sad to show you thransformers library')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZIc1z37xM_OB",
    "outputId": "323a38f8-d1d7-4015-b26d-7d1a448011e6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label: POSITIVE, with score: 0.9998\n",
      "label: NEGATIVE, with score: 0.5309\n"
     ]
    }
   ],
   "source": [
    "results = classifer([\"We are very happy to show you the 🤗 Transformers library.\",\n",
    "           \"We hope you don't hate it.\"])\n",
    "for result in results:\n",
    "    print(f\"label: {result['label']}, with score: {round(result['score'], 4)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0zJNi7-PNP2o"
   },
   "outputs": [],
   "source": [
    "classifier = pipeline('sentiment-analysis', model=\"nlptown/bert-base-multilingual-uncased-sentiment\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bgEnfKdvPdgh",
    "outputId": "975cd887-b73d-4d01-acf7-c73858fa92b3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'label': '3 stars', 'score': 0.3368820548057556}]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier(\"Esperamos que no lo odie.\") #france also support by this model whice we have used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nzEASg3yPeiq",
    "outputId": "aa904b1f-50e3-4fc5-ca91-ae1b67a61187"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'label': 'NEGATIVE', 'score': 0.9920217990875244}]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifer('we are sad to show you thransformers library')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qID7qXGXPwpe"
   },
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "T1Z9tFr4tJ7v",
    "outputId": "b4213ed0-fc6a-4757-eaa3-20a4cb47d1c3"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All PyTorch model weights were used when initializing TFBertForSequenceClassification.\n",
      "\n",
      "All the weights of TFBertForSequenceClassification were initialized from the PyTorch model.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertForSequenceClassification for predictions without further training.\n"
     ]
    }
   ],
   "source": [
    "model_name = \"nlptown/bert-base-multilingual-uncased-sentiment\"\n",
    "# This model only exists in PyTorch, so we use the `from_pt` flag to import that model in TensorFlow.\n",
    "model = TFAutoModelForSequenceClassification.from_pretrained(model_name, from_pt=True)\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Vb_Ea4SftJ-3",
    "outputId": "35331cff-7046-4173-bb5b-f8808aab19de"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'label': '4 stars', 'score': 0.4229269027709961}]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier(\"I am a good boy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "x2gcifJltKCZ",
    "outputId": "239025ca-9000-4359-b1b9-b2ef7dfcf055"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All PyTorch model weights were used when initializing TFDistilBertForSequenceClassification.\n",
      "\n",
      "All the weights of TFDistilBertForSequenceClassification were initialized from the PyTorch model.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFDistilBertForSequenceClassification for predictions without further training.\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
    "model_name = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
    "tf_model = TFAutoModelForSequenceClassification.from_pretrained(model_name)\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pOsIcIhkukPE",
    "outputId": "765f9f3b-0b28-4bb7-ecf3-3ccce16c75c4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_ids': [101, 2057, 2024, 2200, 3407, 2000, 2265, 2017, 1996, 100, 19081, 3075, 1012, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer(\"We are very happy to show you the 🤗 Transformers library.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142,
     "referenced_widgets": [
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    },
    "id": "Kx8ndXXXukSv",
    "outputId": "770bbd79-f814-462e-8540-4a67de7dcd1b"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a4789ac46454401b93cd51fec05ff767",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading pytorch_model.bin:   0%|          | 0.00/268M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All PyTorch model weights were used when initializing TFDistilBertForSequenceClassification.\n",
      "\n",
      "All the weights of TFDistilBertForSequenceClassification were initialized from the PyTorch model.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFDistilBertForSequenceClassification for predictions without further training.\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
    "model_name = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
    "tf_model = TFAutoModelForSequenceClassification.from_pretrained(model_name, from_pt=True)\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BjbHJSGKvGMj",
    "outputId": "bb23eb4f-7068-4b54-9625-b0f0a73741a8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_ids': [101, 2057, 2024, 2200, 3407, 2000, 2265, 2017, 1996, 100, 19081, 3075, 1012, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer(\"We are very happy to show you the 🤗 Transformers library.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "kDUJN8Q_vGQL"
   },
   "outputs": [],
   "source": [
    "tf_batch = tokenizer(\n",
    "    [\"We are very happy to show you the 🤗 Transformers library.\", \"We hope you don't hate it.\"],\n",
    "    padding=True,\n",
    "    truncation=True, #if ther any space it will remove\n",
    "\n",
    "    max_length=512,\n",
    "    return_tensors=\"tf\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OBQxh_35ukWn",
    "outputId": "693bb37e-1ca1-49e9-cc5c-5cbe8c9372ad"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_ids': <tf.Tensor: shape=(2, 14), dtype=int32, numpy=\n",
       "array([[  101,  2057,  2024,  2200,  3407,  2000,  2265,  2017,  1996,\n",
       "          100, 19081,  3075,  1012,   102],\n",
       "       [  101,  2057,  3246,  2017,  2123,  1005,  1056,  5223,  2009,\n",
       "         1012,   102,     0,     0,     0]], dtype=int32)>, 'attention_mask': <tf.Tensor: shape=(2, 14), dtype=int32, numpy=\n",
       "array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]], dtype=int32)>}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf_batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JDGh_yhfvpeX",
    "outputId": "2ca3d20e-4f35-469d-c132-761cd44e8747"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input_ids: [[101, 2057, 2024, 2200, 3407, 2000, 2265, 2017, 1996, 100, 19081, 3075, 1012, 102], [101, 2057, 3246, 2017, 2123, 1005, 1056, 5223, 2009, 1012, 102, 0, 0, 0]]\n",
      "attention_mask: [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]]\n"
     ]
    }
   ],
   "source": [
    "for key, value in tf_batch.items():\n",
    "    print(f\"{key}: {value.numpy().tolist()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8ejMQDbgvphw"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ihwi8sILvplJ"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "u6ekjSnUm9pT"
   },
   "outputs": [],
   "source": [
    "from transformers import pipeline"
   ]
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    },
    "id": "WEzQ7-Gym9ss",
    "outputId": "41261bdc-dc29-422f-adf4-83544ef494d7"
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   "outputs": [
    {
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       "Downloading (…)lve/main/config.json:   0%|          | 0.00/596 [00:00<?, ?B/s]"
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       "Downloading pytorch_model.bin:   0%|          | 0.00/2.63G [00:00<?, ?B/s]"
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       "Downloading (…)okenizer_config.json:   0%|          | 0.00/682 [00:00<?, ?B/s]"
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      "text/plain": [
       "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
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     },
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    },
    {
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      "text/plain": [
       "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
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     "metadata": {},
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    {
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      "text/plain": [
       "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/221 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "generator=pipeline('text-generation',model='facebook/opt-iml-max-1.3b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    },
    "id": "NYA5dSH4nlyG",
    "outputId": "fa7b06f1-3fe4-4751-8f9b-3c9144acd64e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1260: UserWarning: Using the model-agnostic default `max_length` (=20) to control thegeneration length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'generated_text': 'what is the capital of USA?\\nWashington, D.C.'}"
      ]
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     "execution_count": 5,
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    }
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   "outputs": [
    {
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      "text/plain": [
       "[{'generated_text': 'what is the capital of USA?\\nWashington, D.C.'}]"
      ]
     },
     "execution_count": 6,
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