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            "grid_template_columns": null,
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            "justify_content": null,
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            "left": null,
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            "object_fit": null,
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            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
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          }
        },
        "7110d0c856f4466b92a5e39421cc0234": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "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": ""
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ralfferreira/generate-abstract/blob/main/Abstract2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Gerando o abstract com o código do hugging face\n",
        "## TEM QUE MUDAR O AMBIENTE DE EXECUÇÃO PRA GPU ANTES DE RODAR"
      ],
      "metadata": {
        "id": "BeSLnuT5kYuI"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HvJm5NncaIJM",
        "outputId": "e7a2ecc5-3bb3-40a4-8aaf-65911ff3b041"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.8/dist-packages (4.25.1)\n",
            "Requirement already satisfied: datasets in /usr/local/lib/python3.8/dist-packages (2.8.0)\n",
            "Requirement already satisfied: evaluate in /usr/local/lib/python3.8/dist-packages (0.4.0)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.10.0 in /usr/local/lib/python3.8/dist-packages (from transformers) (0.11.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (1.21.6)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.8/dist-packages (from transformers) (4.64.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from transformers) (3.8.2)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (2022.6.2)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.8/dist-packages (from transformers) (0.13.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from transformers) (21.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub<1.0,>=0.10.0->transformers) (4.4.0)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n",
            "Requirement already satisfied: pyarrow>=6.0.0 in /usr/local/lib/python3.8/dist-packages (from datasets) (9.0.0)\n",
            "Requirement already satisfied: multiprocess in /usr/local/lib/python3.8/dist-packages (from datasets) (0.70.14)\n",
            "Requirement already satisfied: responses<0.19 in /usr/local/lib/python3.8/dist-packages (from datasets) (0.18.0)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (from datasets) (1.3.5)\n",
            "Requirement already satisfied: dill<0.3.7 in /usr/local/lib/python3.8/dist-packages (from datasets) (0.3.6)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.8/dist-packages (from datasets) (3.8.3)\n",
            "Requirement already satisfied: xxhash in /usr/local/lib/python3.8/dist-packages (from datasets) (3.1.0)\n",
            "Requirement already satisfied: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.8/dist-packages (from datasets) (2022.11.0)\n",
            "Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (2.1.1)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.3.3)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (4.0.2)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (6.0.3)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (22.1.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.3.1)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.8.2)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2022.12.7)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (1.25.11)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2022.6)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n"
          ]
        }
      ],
      "source": [
        "pip install transformers datasets evaluate"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "billsum = load_dataset(\"billsum\", split=\"ca_test\")\n",
        "billsum = billsum.train_test_split(test_size=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RBqvTuM-aUXg",
        "outputId": "29c175c7-cad8-45dd-f96a-d8221d15a791"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Found cached dataset billsum (/root/.cache/huggingface/datasets/billsum/default/3.0.0/75cf1719d38d6553aa0e0714c393c74579b083ae6e164b2543684e3e92e0c4cc)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"t5-small\")\n",
        "model = (\"t5-small\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SN_ENPGFaj0K",
        "outputId": "e5c118f6-a0f9-47f2-e5f0-4c5d5ee5f92c"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/transformers/models/t5/tokenization_t5_fast.py:155: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n",
            "For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n",
            "- Be aware that you SHOULD NOT rely on t5-small automatically truncating your input to 512 when padding/encoding.\n",
            "- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n",
            "- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prefix = \"summarize: \"\n",
        "\n",
        "\n",
        "def preprocess_function(examples):\n",
        "    inputs = [prefix + doc for doc in examples[\"text\"]]\n",
        "    model_inputs = tokenizer(inputs, max_length=1024, truncation=True)\n",
        "\n",
        "    labels = tokenizer(text_target=examples[\"summary\"], max_length=128, truncation=True)\n",
        "\n",
        "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "    return model_inputs"
      ],
      "metadata": {
        "id": "ZfuX3yS4asQx"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_billsum = billsum.map(preprocess_function, batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "bf1de1bf566f4b35924cc85b3ab6a966",
            "f5d86f81bfbd4570a78c2aff1736233b",
            "300396764b024fe18a2fc929ce6b5483",
            "9ca9346813b74055b0a9807255b2cea4",
            "a705c6a2f45d48ea98d00fb3ae393ed6",
            "785630fb42cf4c26b7e87b2038718616",
            "2c3ca9994c4b4bd09616679ded93e030",
            "301869155f994c7dbf5b6fb731582368",
            "c66d193106fd4770a00660be44f7b10e",
            "ba347a011873470cb6a943a94d3d1475",
            "807785e102134e0995893547ef32a552",
            "a5fbf4431a114bc1b74bc9437e2f782a",
            "9a0394131bd9485d851c683612ae6a38",
            "28cb59d787e74380a109a493d2e14294",
            "f2c72043bf1246dea097fddcf73a18bc",
            "104bb756a2d74a2285502c27fbdfabef",
            "dc35c410ff14431fa04f3f4b440c6285",
            "f7e0e7e8b79f44aeb152a9d4ae085eb2",
            "8994b75c7f3f46f6b296013099a4d6a7",
            "aa4a67f6f5114ef2ae06ece458280214",
            "994394aeae6f4a81842656fd1ebc042a",
            "b2b35d448f164e24a671a64f38b4a10e"
          ]
        },
        "id": "Rxig4okeazaq",
        "outputId": "00743a9f-31fd-4719-864e-676a0e922d9e"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bf1de1bf566f4b35924cc85b3ab6a966"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a5fbf4431a114bc1b74bc9437e2f782a"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import DataCollatorForSeq2Seq\n",
        "\n",
        "data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model)"
      ],
      "metadata": {
        "id": "UZ6heqypa46y"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install rouge_score\n",
        "import evaluate\n",
        "\n",
        "rouge = evaluate.load(\"rouge\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x_gFiNWJbeVA",
        "outputId": "64905076-993f-4165-f9b6-b6e364cabd64"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: rouge_score in /usr/local/lib/python3.8/dist-packages (0.1.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from rouge_score) (1.21.6)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.8/dist-packages (from rouge_score) (3.7)\n",
            "Requirement already satisfied: absl-py in /usr/local/lib/python3.8/dist-packages (from rouge_score) (1.3.0)\n",
            "Requirement already satisfied: six>=1.14.0 in /usr/local/lib/python3.8/dist-packages (from rouge_score) (1.15.0)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.8/dist-packages (from nltk->rouge_score) (2022.6.2)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.8/dist-packages (from nltk->rouge_score) (7.1.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from nltk->rouge_score) (4.64.1)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.8/dist-packages (from nltk->rouge_score) (1.2.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    predictions, labels = eval_pred\n",
        "    decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)\n",
        "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
        "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
        "\n",
        "    result = rouge.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)\n",
        "\n",
        "    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in predictions]\n",
        "    result[\"gen_len\"] = np.mean(prediction_lens)\n",
        "\n",
        "    return {k: round(v, 4) for k, v in result.items()}"
      ],
      "metadata": {
        "id": "jZC2CAmJbp6D"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer\n",
        "\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(\"t5-large\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R_BCaFQNbuSI",
        "outputId": "82884ff5-4c70-4220-db87-818bafbdd740"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--t5-large/snapshots/3b572c1e76757a16eeb27770cf1be83b33895413/config.json\n",
            "Model config T5Config {\n",
            "  \"_name_or_path\": \"t5-large\",\n",
            "  \"architectures\": [\n",
            "    \"T5ForConditionalGeneration\"\n",
            "  ],\n",
            "  \"d_ff\": 4096,\n",
            "  \"d_kv\": 64,\n",
            "  \"d_model\": 1024,\n",
            "  \"decoder_start_token_id\": 0,\n",
            "  \"dense_act_fn\": \"relu\",\n",
            "  \"dropout_rate\": 0.1,\n",
            "  \"eos_token_id\": 1,\n",
            "  \"feed_forward_proj\": \"relu\",\n",
            "  \"initializer_factor\": 1.0,\n",
            "  \"is_encoder_decoder\": true,\n",
            "  \"is_gated_act\": false,\n",
            "  \"layer_norm_epsilon\": 1e-06,\n",
            "  \"model_type\": \"t5\",\n",
            "  \"n_positions\": 512,\n",
            "  \"num_decoder_layers\": 24,\n",
            "  \"num_heads\": 16,\n",
            "  \"num_layers\": 24,\n",
            "  \"output_past\": true,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"relative_attention_max_distance\": 128,\n",
            "  \"relative_attention_num_buckets\": 32,\n",
            "  \"task_specific_params\": {\n",
            "    \"summarization\": {\n",
            "      \"early_stopping\": true,\n",
            "      \"length_penalty\": 2.0,\n",
            "      \"max_length\": 200,\n",
            "      \"min_length\": 30,\n",
            "      \"no_repeat_ngram_size\": 3,\n",
            "      \"num_beams\": 4,\n",
            "      \"prefix\": \"summarize: \"\n",
            "    },\n",
            "    \"translation_en_to_de\": {\n",
            "      \"early_stopping\": true,\n",
            "      \"max_length\": 300,\n",
            "      \"num_beams\": 4,\n",
            "      \"prefix\": \"translate English to German: \"\n",
            "    },\n",
            "    \"translation_en_to_fr\": {\n",
            "      \"early_stopping\": true,\n",
            "      \"max_length\": 300,\n",
            "      \"num_beams\": 4,\n",
            "      \"prefix\": \"translate English to French: \"\n",
            "    },\n",
            "    \"translation_en_to_ro\": {\n",
            "      \"early_stopping\": true,\n",
            "      \"max_length\": 300,\n",
            "      \"num_beams\": 4,\n",
            "      \"prefix\": \"translate English to Romanian: \"\n",
            "    }\n",
            "  },\n",
            "  \"transformers_version\": \"4.25.1\",\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 32128\n",
            "}\n",
            "\n",
            "loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--t5-large/snapshots/3b572c1e76757a16eeb27770cf1be83b33895413/pytorch_model.bin\n",
            "All model checkpoint weights were used when initializing T5ForConditionalGeneration.\n",
            "\n",
            "All the weights of T5ForConditionalGeneration were initialized from the model checkpoint at t5-large.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use T5ForConditionalGeneration for predictions without further training.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_args = Seq2SeqTrainingArguments(\n",
        "    output_dir=\"test_dir\",\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    learning_rate=2e-5,\n",
        "    per_device_train_batch_size=8,\n",
        "    per_device_eval_batch_size=8,\n",
        "    weight_decay=0.01,\n",
        "    save_total_limit=3,\n",
        "    num_train_epochs=2,\n",
        "    predict_with_generate=True,\n",
        "    fp16=True,\n",
        ")\n",
        "\n",
        "trainer = Seq2SeqTrainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized_billsum[\"train\"],\n",
        "    eval_dataset=tokenized_billsum[\"test\"],\n",
        "    tokenizer=tokenizer,\n",
        "    data_collator=data_collator,\n",
        "    compute_metrics=compute_metrics,\n",
        ")\n",
        "\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "0kbdE0r5cjKq",
        "outputId": "30c3d051-2857-4ad7-fbbd-cbe3c935ab49"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "PyTorch: setting up devices\n",
            "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n",
            "Using cuda_amp half precision backend\n",
            "The following columns in the training set don't have a corresponding argument in `T5ForConditionalGeneration.forward` and have been ignored: text, title, summary. If text, title, summary are not expected by `T5ForConditionalGeneration.forward`,  you can safely ignore this message.\n",
            "***** Running training *****\n",
            "  Num examples = 989\n",
            "  Num Epochs = 2\n",
            "  Instantaneous batch size per device = 8\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 248\n",
            "  Number of trainable parameters = 737668096\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "OutOfMemoryError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mOutOfMemoryError\u001b[0m                          Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-27-9025082ffcac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     22\u001b[0m )\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m   1525\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_inner_training_loop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_train_batch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_find_batch_size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m         )\n\u001b[0;32m-> 1527\u001b[0;31m         return inner_training_loop(\n\u001b[0m\u001b[1;32m   1528\u001b[0m             \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1529\u001b[0m             \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   1773\u001b[0m                         \u001b[0mtr_loss_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1774\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1775\u001b[0;31m                     \u001b[0mtr_loss_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1776\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1777\u001b[0m                 if (\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtraining_step\u001b[0;34m(self, model, inputs)\u001b[0m\n\u001b[1;32m   2521\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2522\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_loss_context_manager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2523\u001b[0;31m             \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2524\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2525\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_gpu\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mcompute_loss\u001b[0;34m(self, model, inputs, return_outputs)\u001b[0m\n\u001b[1;32m   2553\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2554\u001b[0m             \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2555\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2556\u001b[0m         \u001b[0;31m# Save past state if it exists\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2557\u001b[0m         \u001b[0;31m# TODO: this needs to be fixed and made cleaner later.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1188\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1189\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1190\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1191\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1192\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/transformers/models/t5/modeling_t5.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, head_mask, decoder_head_mask, cross_attn_head_mask, encoder_outputs, past_key_values, inputs_embeds, decoder_inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m   1609\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mencoder_outputs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1610\u001b[0m             \u001b[0;31m# Convert encoder inputs in embeddings if needed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1611\u001b[0;31m             encoder_outputs = self.encoder(\n\u001b[0m\u001b[1;32m   1612\u001b[0m                 \u001b[0minput_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1613\u001b[0m                 \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1188\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1189\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1190\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1191\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1192\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/transformers/models/t5/modeling_t5.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, encoder_hidden_states, encoder_attention_mask, inputs_embeds, head_mask, cross_attn_head_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m   1038\u001b[0m                 )\n\u001b[1;32m   1039\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1040\u001b[0;31m                 layer_outputs = layer_module(\n\u001b[0m\u001b[1;32m   1041\u001b[0m                     \u001b[0mhidden_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1042\u001b[0m                     \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mextended_attention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1188\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1189\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1190\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1191\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1192\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/transformers/models/t5/modeling_t5.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, attention_mask, position_bias, encoder_hidden_states, encoder_attention_mask, encoder_decoder_position_bias, layer_head_mask, cross_attn_layer_head_mask, past_key_value, use_cache, output_attentions, return_dict)\u001b[0m\n\u001b[1;32m    671\u001b[0m             \u001b[0mself_attn_past_key_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcross_attn_past_key_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    672\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 673\u001b[0;31m         self_attention_outputs = self.layer[0](\n\u001b[0m\u001b[1;32m    674\u001b[0m             \u001b[0mhidden_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    675\u001b[0m             \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1188\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1189\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1190\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1191\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1192\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/transformers/models/t5/modeling_t5.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, attention_mask, position_bias, layer_head_mask, past_key_value, use_cache, output_attentions)\u001b[0m\n\u001b[1;32m    577\u001b[0m     ):\n\u001b[1;32m    578\u001b[0m         \u001b[0mnormed_hidden_states\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer_norm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhidden_states\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 579\u001b[0;31m         attention_output = self.SelfAttention(\n\u001b[0m\u001b[1;32m    580\u001b[0m             \u001b[0mnormed_hidden_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    581\u001b[0m             \u001b[0mmask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1188\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1189\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1190\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1191\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1192\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/transformers/models/t5/modeling_t5.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, mask, key_value_states, position_bias, past_key_value, layer_head_mask, query_length, use_cache, output_attentions)\u001b[0m\n\u001b[1;32m    537\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    538\u001b[0m         \u001b[0mscores\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mposition_bias_masked\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 539\u001b[0;31m         attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(\n\u001b[0m\u001b[1;32m    540\u001b[0m             \u001b[0mscores\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    541\u001b[0m         )  # (batch_size, n_heads, seq_length, key_length)\n",
            "\u001b[0;31mOutOfMemoryError\u001b[0m: CUDA out of memory. Tried to allocate 512.00 MiB (GPU 0; 14.76 GiB total capacity; 13.48 GiB already allocated; 335.75 MiB free; 13.64 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "artigo=\"\"\"Recurrent models typically factor computation along the symbol positions of the input and output\n",
        "sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden\n",
        "states ht, as a function of the previous hidden state ht−1 and the input for position t. This inherently\n",
        "sequential nature precludes parallelization within training examples, which becomes critical at longer\n",
        "sequence lengths, as memory constraints limit batching across examples. Recent work has achieved\n",
        "significant improvements in computational efficiency through factorization tricks [21] and conditional\n",
        "computation [32], while also improving model performance in case of the latter. The fundamental\n",
        "constraint of sequential computation, however, remains.\n",
        "\n",
        "Attention mechanisms have become an integral part of compelling sequence modeling and transduc-\n",
        "tion models in various tasks, allowing modeling of dependencies without regard to their distance in\n",
        "the input or output sequences [2, 19]. In all but a few cases [27], however, such attention mechanisms\n",
        "are used in conjunction with a recurrent network.\n",
        "\n",
        "In this work we propose the Transformer, a model architecture eschewing recurrence and instead\n",
        "relying entirely on an attention mechanism to draw global dependencies between input and output.\n",
        "The Transformer allows for significantly more parallelization and can reach a new state of the art in\n",
        "translation quality after being trained for as little as twelve hours on eight P100 GPUs.\"\"\""
      ],
      "metadata": {
        "id": "Gs5mJCAMQDCX"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"t5-large\")\n",
        "inputs = tokenizer(artigo, return_tensors=\"pt\").input_ids"
      ],
      "metadata": {
        "id": "59GFJ4Tehzq0",
        "outputId": "31e75141-4732-47c1-c845-278bf65cdefa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "26f7784f37e74273b7d73c9c0c857a03",
            "27e0e33c4ca5468880b9a1dd2d0ddcdd",
            "c255faae7d884d18826dd4289ce932cd",
            "7145c59275de484fbdbb1a838590a401",
            "06390d3427714e7abdc7e5c94fbdd193",
            "656000297b0040acb249c905b98ed691",
            "ee927c6a15694b3cbbf0c94aaa2cc43f",
            "0f067859ae2245aaabae9e5f652cf8e0",
            "173b4dd6f51c4afbb75bbd22b6af4bd2",
            "7f97c172385f462ea76aee321060a047",
            "5589512a8c5542cf8eeb0e2e86d11330",
            "ff5073994fc14d7cb91acd84226cba37",
            "a46eb8ff4e304629b18859a9c48e1ad9",
            "b4f0625d440c42b0a22ae736125313df",
            "1adb0d7b2208459e9c38ed823832dd91",
            "ce14f19faea54abb8eb8f44dca303c5f",
            "f0dad9b2e37b4cc4bcfddb25fbc009a5",
            "9ae6f001d07f4200b9f58cdc48a4c114",
            "96b37da0229244a5b50504e06c71c0a2",
            "1a7beb736b84487e8c4251b9c73335b6",
            "4f1f4e098795469ea627409127b1a622",
            "7110d0c856f4466b92a5e39421cc0234"
          ]
        }
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Could not locate the tokenizer configuration file, will try to use the model config instead.\n",
            "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--t5-large/snapshots/3b572c1e76757a16eeb27770cf1be83b33895413/config.json\n",
            "Model config T5Config {\n",
            "  \"_name_or_path\": \"t5-large\",\n",
            "  \"architectures\": [\n",
            "    \"T5ForConditionalGeneration\"\n",
            "  ],\n",
            "  \"d_ff\": 4096,\n",
            "  \"d_kv\": 64,\n",
            "  \"d_model\": 1024,\n",
            "  \"decoder_start_token_id\": 0,\n",
            "  \"dense_act_fn\": \"relu\",\n",
            "  \"dropout_rate\": 0.1,\n",
            "  \"eos_token_id\": 1,\n",
            "  \"feed_forward_proj\": \"relu\",\n",
            "  \"initializer_factor\": 1.0,\n",
            "  \"is_encoder_decoder\": true,\n",
            "  \"is_gated_act\": false,\n",
            "  \"layer_norm_epsilon\": 1e-06,\n",
            "  \"model_type\": \"t5\",\n",
            "  \"n_positions\": 512,\n",
            "  \"num_decoder_layers\": 24,\n",
            "  \"num_heads\": 16,\n",
            "  \"num_layers\": 24,\n",
            "  \"output_past\": true,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"relative_attention_max_distance\": 128,\n",
            "  \"relative_attention_num_buckets\": 32,\n",
            "  \"task_specific_params\": {\n",
            "    \"summarization\": {\n",
            "      \"early_stopping\": true,\n",
            "      \"length_penalty\": 2.0,\n",
            "      \"max_length\": 200,\n",
            "      \"min_length\": 30,\n",
            "      \"no_repeat_ngram_size\": 3,\n",
            "      \"num_beams\": 4,\n",
            "      \"prefix\": \"summarize: \"\n",
            "    },\n",
            "    \"translation_en_to_de\": {\n",
            "      \"early_stopping\": true,\n",
            "      \"max_length\": 300,\n",
            "      \"num_beams\": 4,\n",
            "      \"prefix\": \"translate English to German: \"\n",
            "    },\n",
            "    \"translation_en_to_fr\": {\n",
            "      \"early_stopping\": true,\n",
            "      \"max_length\": 300,\n",
            "      \"num_beams\": 4,\n",
            "      \"prefix\": \"translate English to French: \"\n",
            "    },\n",
            "    \"translation_en_to_ro\": {\n",
            "      \"early_stopping\": true,\n",
            "      \"max_length\": 300,\n",
            "      \"num_beams\": 4,\n",
            "      \"prefix\": \"translate English to Romanian: \"\n",
            "    }\n",
            "  },\n",
            "  \"transformers_version\": \"4.25.1\",\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 32128\n",
            "}\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/792k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "26f7784f37e74273b7d73c9c0c857a03"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.39M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ff5073994fc14d7cb91acd84226cba37"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "loading file spiece.model from cache at /root/.cache/huggingface/hub/models--t5-large/snapshots/3b572c1e76757a16eeb27770cf1be83b33895413/spiece.model\n",
            "loading file tokenizer.json from cache at /root/.cache/huggingface/hub/models--t5-large/snapshots/3b572c1e76757a16eeb27770cf1be83b33895413/tokenizer.json\n",
            "loading file added_tokens.json from cache at None\n",
            "loading file special_tokens_map.json from cache at None\n",
            "loading file tokenizer_config.json from cache at None\n",
            "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--t5-large/snapshots/3b572c1e76757a16eeb27770cf1be83b33895413/config.json\n",
            "Model config T5Config {\n",
            "  \"_name_or_path\": \"t5-large\",\n",
            "  \"architectures\": [\n",
            "    \"T5ForConditionalGeneration\"\n",
            "  ],\n",
            "  \"d_ff\": 4096,\n",
            "  \"d_kv\": 64,\n",
            "  \"d_model\": 1024,\n",
            "  \"decoder_start_token_id\": 0,\n",
            "  \"dense_act_fn\": \"relu\",\n",
            "  \"dropout_rate\": 0.1,\n",
            "  \"eos_token_id\": 1,\n",
            "  \"feed_forward_proj\": \"relu\",\n",
            "  \"initializer_factor\": 1.0,\n",
            "  \"is_encoder_decoder\": true,\n",
            "  \"is_gated_act\": false,\n",
            "  \"layer_norm_epsilon\": 1e-06,\n",
            "  \"model_type\": \"t5\",\n",
            "  \"n_positions\": 512,\n",
            "  \"num_decoder_layers\": 24,\n",
            "  \"num_heads\": 16,\n",
            "  \"num_layers\": 24,\n",
            "  \"output_past\": true,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"relative_attention_max_distance\": 128,\n",
            "  \"relative_attention_num_buckets\": 32,\n",
            "  \"task_specific_params\": {\n",
            "    \"summarization\": {\n",
            "      \"early_stopping\": true,\n",
            "      \"length_penalty\": 2.0,\n",
            "      \"max_length\": 200,\n",
            "      \"min_length\": 30,\n",
            "      \"no_repeat_ngram_size\": 3,\n",
            "      \"num_beams\": 4,\n",
            "      \"prefix\": \"summarize: \"\n",
            "    },\n",
            "    \"translation_en_to_de\": {\n",
            "      \"early_stopping\": true,\n",
            "      \"max_length\": 300,\n",
            "      \"num_beams\": 4,\n",
            "      \"prefix\": \"translate English to German: \"\n",
            "    },\n",
            "    \"translation_en_to_fr\": {\n",
            "      \"early_stopping\": true,\n",
            "      \"max_length\": 300,\n",
            "      \"num_beams\": 4,\n",
            "      \"prefix\": \"translate English to French: \"\n",
            "    },\n",
            "    \"translation_en_to_ro\": {\n",
            "      \"early_stopping\": true,\n",
            "      \"max_length\": 300,\n",
            "      \"num_beams\": 4,\n",
            "      \"prefix\": \"translate English to Romanian: \"\n",
            "    }\n",
            "  },\n",
            "  \"transformers_version\": \"4.25.1\",\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 32128\n",
            "}\n",
            "\n",
            "/usr/local/lib/python3.8/dist-packages/transformers/models/t5/tokenization_t5_fast.py:155: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n",
            "For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n",
            "- Be aware that you SHOULD NOT rely on t5-large automatically truncating your input to 512 when padding/encoding.\n",
            "- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n",
            "- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForSeq2SeqLM\n",
        "\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(\"t5-large\")\n",
        "outputs = model.generate(inputs, max_new_tokens=800, do_sample=False)"
      ],
      "metadata": {
        "id": "EbDVAe99iA7p",
        "outputId": "2d801e16-c4b1-4329-ba89-fc2ff306c9d2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--t5-large/snapshots/3b572c1e76757a16eeb27770cf1be83b33895413/config.json\n",
            "Model config T5Config {\n",
            "  \"_name_or_path\": \"t5-large\",\n",
            "  \"architectures\": [\n",
            "    \"T5ForConditionalGeneration\"\n",
            "  ],\n",
            "  \"d_ff\": 4096,\n",
            "  \"d_kv\": 64,\n",
            "  \"d_model\": 1024,\n",
            "  \"decoder_start_token_id\": 0,\n",
            "  \"dense_act_fn\": \"relu\",\n",
            "  \"dropout_rate\": 0.1,\n",
            "  \"eos_token_id\": 1,\n",
            "  \"feed_forward_proj\": \"relu\",\n",
            "  \"initializer_factor\": 1.0,\n",
            "  \"is_encoder_decoder\": true,\n",
            "  \"is_gated_act\": false,\n",
            "  \"layer_norm_epsilon\": 1e-06,\n",
            "  \"model_type\": \"t5\",\n",
            "  \"n_positions\": 512,\n",
            "  \"num_decoder_layers\": 24,\n",
            "  \"num_heads\": 16,\n",
            "  \"num_layers\": 24,\n",
            "  \"output_past\": true,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"relative_attention_max_distance\": 128,\n",
            "  \"relative_attention_num_buckets\": 32,\n",
            "  \"task_specific_params\": {\n",
            "    \"summarization\": {\n",
            "      \"early_stopping\": true,\n",
            "      \"length_penalty\": 2.0,\n",
            "      \"max_length\": 200,\n",
            "      \"min_length\": 30,\n",
            "      \"no_repeat_ngram_size\": 3,\n",
            "      \"num_beams\": 4,\n",
            "      \"prefix\": \"summarize: \"\n",
            "    },\n",
            "    \"translation_en_to_de\": {\n",
            "      \"early_stopping\": true,\n",
            "      \"max_length\": 300,\n",
            "      \"num_beams\": 4,\n",
            "      \"prefix\": \"translate English to German: \"\n",
            "    },\n",
            "    \"translation_en_to_fr\": {\n",
            "      \"early_stopping\": true,\n",
            "      \"max_length\": 300,\n",
            "      \"num_beams\": 4,\n",
            "      \"prefix\": \"translate English to French: \"\n",
            "    },\n",
            "    \"translation_en_to_ro\": {\n",
            "      \"early_stopping\": true,\n",
            "      \"max_length\": 300,\n",
            "      \"num_beams\": 4,\n",
            "      \"prefix\": \"translate English to Romanian: \"\n",
            "    }\n",
            "  },\n",
            "  \"transformers_version\": \"4.25.1\",\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 32128\n",
            "}\n",
            "\n",
            "loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--t5-large/snapshots/3b572c1e76757a16eeb27770cf1be83b33895413/pytorch_model.bin\n",
            "All model checkpoint weights were used when initializing T5ForConditionalGeneration.\n",
            "\n",
            "All the weights of T5ForConditionalGeneration were initialized from the model checkpoint at t5-large.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use T5ForConditionalGeneration for predictions without further training.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.decode(outputs[0], skip_special_tokens=True)"
      ],
      "metadata": {
        "id": "LDRIDOGkiGot",
        "outputId": "a038ce5e-58e7-43eb-948c-38f339a3fce4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'. The Transformer is a new generation of recurrent models that can be trained on GPUs. The Transformer can be trained on any number of GPUs and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs. The Transformer is implemented as a model architecture using the attention mechanism..tt         The Transformer is a re'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    }
  ]
}