processing_the_data.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/loganathanspr/nlp_course/blob/main/processing_the_data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zwT4NO5kpe81"
      },
      "source": [
        "# Processing the data (PyTorch)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E5xHJTXQpe82"
      },
      "source": [
        "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "_45GDFTEpe83",
        "outputId": "4cbb2d90-abd9-40c0-aae0-5ef6116313e9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: tokenizers, sentencepiece, safetensors, xxhash, dill, responses, multiprocess, huggingface-hub, transformers, datasets, evaluate\n",
            "Successfully installed datasets-2.14.5 dill-0.3.7 evaluate-0.4.0 huggingface-hub-0.17.2 multiprocess-0.70.15 responses-0.18.0 safetensors-0.3.3 sentencepiece-0.1.99 tokenizers-0.13.3 transformers-4.33.2 xxhash-3.3.0\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets evaluate transformers[sentencepiece]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "WLZPpjeRpe83",
        "outputId": "9639f4b7-4e7a-4a79-a23a-07b9f78f82d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266,
          "referenced_widgets": [
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        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "b3d8b6b95c8148fb97d997f9746a4d62"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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              "version_major": 2,
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              "model_id": "83f0826feca345bfacd066e90e4a240e"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from transformers import AdamW, AutoTokenizer, AutoModelForSequenceClassification\n",
        "\n",
        "checkpoint = \"bert-base-uncased\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
        "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
        "sequences = [\n",
        "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
        "    \"This course is amazing!\",\n",
        "]\n",
        "batch = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"pt\")\n",
        "batch[\"labels\"] = torch.tensor([1, 1])\n",
        "optimizer = AdamW(model.parameters())\n",
        "loss = model(**batch).loss\n",
        "loss.backward()\n",
        "optimizer.step()"
      ]
    },
    {
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            "f9479dd34fb4415981a75655b215c930",
            "00ebedb55d304537b58e827e99a9bd59"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/28.8k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a4f648039c654f4facc63e44c61597b3"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/28.7k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b243708d6d414b63b08f87752cd9327a"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/27.9k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d7c911c4a02c4aa28c673d1dcc62284b"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7107e42944f646b69dbd632c57edd1cd"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data: 0.00B [00:00, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "6af6838493444ac385656478f2cca44f"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data: 0.00B [00:00, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "37060291a9634f0286e4bfac87a951a8"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data: 0.00B [00:00, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "eddffc75f885428cb7cc3bfa302364a8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/3668 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4e687b28dd6e42d8830bc0a1dd6d75ed"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/408 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f9c9d59232aa48818d2d6ce19bf89248"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/1725 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "119f39de6378409f862047864eaa9f8e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
              "        num_rows: 3668\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
              "        num_rows: 408\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
              "        num_rows: 1725\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
        "raw_datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "8gUgmeBEpe84",
        "outputId": "c514a84e-bd42-452a-c5d0-b74cae7b0430",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'sentence1': 'Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .',\n",
              " 'sentence2': 'Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .',\n",
              " 'label': 1,\n",
              " 'idx': 0}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "raw_train_dataset = raw_datasets[\"train\"]\n",
        "raw_train_dataset[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "uIRSjFEspe85",
        "outputId": "c487b7e1-af4c-4c34-a894-013aab4c5346",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'sentence1': Value(dtype='string', id=None),\n",
              " 'sentence2': Value(dtype='string', id=None),\n",
              " 'label': ClassLabel(names=['not_equivalent', 'equivalent'], id=None),\n",
              " 'idx': Value(dtype='int32', id=None)}"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "raw_train_dataset.features"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_datasets[\"train\"][14]"
      ],
      "metadata": {
        "id": "UtI8kczLxmA0",
        "outputId": "c0f83c8b-1686-40e5-8fe8-3ff65e5d7e71",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'sentence1': 'Gyorgy Heizler , head of the local disaster unit , said the coach was carrying 38 passengers .',\n",
              " 'sentence2': 'The head of the local disaster unit , Gyorgy Heizler , said the coach driver had failed to heed red stop lights .',\n",
              " 'label': 0,\n",
              " 'idx': 15}"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_datasets[\"validation\"][86]"
      ],
      "metadata": {
        "id": "v7wtLhKSxq8Z",
        "outputId": "eb1a44ae-89a3-4eca-d13c-7ce334ea2101",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'sentence1': 'He was arrested Friday night at an Alpharetta seafood restaurant while dining with his wife , singer Whitney Houston .',\n",
              " 'sentence2': 'He was arrested again Friday night at an Alpharetta restaurant where he was having dinner with his wife .',\n",
              " 'label': 1,\n",
              " 'idx': 796}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preprocessing the dataset"
      ],
      "metadata": {
        "id": "2oujP2U5xyii"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "cxEyFTPUpe85"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "checkpoint = \"bert-base-uncased\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
        "tokenized_sentences_1 = tokenizer(raw_datasets[\"train\"][\"sentence1\"])\n",
        "tokenized_sentences_2 = tokenizer(raw_datasets[\"train\"][\"sentence2\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "cfQ1HQPOpe85",
        "outputId": "5e8eae8b-7ce3-47e3-e8e3-56f7da12ff25",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [101, 2023, 2003, 1996, 2034, 6251, 1012, 102, 2023, 2003, 1996, 2117, 2028, 1012, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "inputs = tokenizer(\"This is the first sentence.\", \"This is the second one.\")\n",
        "inputs"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_datasets[\"train\"][14]"
      ],
      "metadata": {
        "id": "wMKVTJj2cF9_",
        "outputId": "4ec4ee25-16ef-4eb2-b068-59aed9c9d6af",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'sentence1': 'Gyorgy Heizler , head of the local disaster unit , said the coach was carrying 38 passengers .',\n",
              " 'sentence2': 'The head of the local disaster unit , Gyorgy Heizler , said the coach driver had failed to heed red stop lights .',\n",
              " 'label': 0,\n",
              " 'idx': 15}"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = tokenizer(raw_datasets[\"train\"][14][\"sentence1\"], raw_datasets[\"train\"][14][\"sentence2\"])\n",
        "inputs"
      ],
      "metadata": {
        "id": "pnVti4Z0ZEwC",
        "outputId": "9005059a-15d3-4228-97ba-b4c7f2692ba0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [101, 1043, 7677, 22637, 2002, 10993, 3917, 1010, 2132, 1997, 1996, 2334, 7071, 3131, 1010, 2056, 1996, 2873, 2001, 4755, 4229, 5467, 1012, 102, 1996, 2132, 1997, 1996, 2334, 7071, 3131, 1010, 1043, 7677, 22637, 2002, 10993, 3917, 1010, 2056, 1996, 2873, 4062, 2018, 3478, 2000, 18235, 2094, 2417, 2644, 4597, 1012, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 1, 1, 1, 1], '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, 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, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "agBoN8zSpe86",
        "outputId": "9de84419-ad88-44ad-f1e1-d6eb733b5526",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['[CLS]',\n",
              " 'g',\n",
              " '##yo',\n",
              " '##rgy',\n",
              " 'he',\n",
              " '##iz',\n",
              " '##ler',\n",
              " ',',\n",
              " 'head',\n",
              " 'of',\n",
              " 'the',\n",
              " 'local',\n",
              " 'disaster',\n",
              " 'unit',\n",
              " ',',\n",
              " 'said',\n",
              " 'the',\n",
              " 'coach',\n",
              " 'was',\n",
              " 'carrying',\n",
              " '38',\n",
              " 'passengers',\n",
              " '.',\n",
              " '[SEP]',\n",
              " 'the',\n",
              " 'head',\n",
              " 'of',\n",
              " 'the',\n",
              " 'local',\n",
              " 'disaster',\n",
              " 'unit',\n",
              " ',',\n",
              " 'g',\n",
              " '##yo',\n",
              " '##rgy',\n",
              " 'he',\n",
              " '##iz',\n",
              " '##ler',\n",
              " ',',\n",
              " 'said',\n",
              " 'the',\n",
              " 'coach',\n",
              " 'driver',\n",
              " 'had',\n",
              " 'failed',\n",
              " 'to',\n",
              " 'hee',\n",
              " '##d',\n",
              " 'red',\n",
              " 'stop',\n",
              " 'lights',\n",
              " '.',\n",
              " '[SEP]']"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "U_TwvmnGpe86"
      },
      "outputs": [],
      "source": [
        "tokenized_dataset = tokenizer(raw_datasets[\"train\"][\"sentence1\"], raw_datasets[\"train\"][\"sentence2\"], padding=True, truncation=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "yn8W6PuDpe86"
      },
      "outputs": [],
      "source": [
        "def tokenize_function(example):\n",
        "  return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "YJTRaogCpe86",
        "outputId": "967b820d-a17e-4093-b343-be3fd63f6587",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356,
          "referenced_widgets": [
            "8afb8497524346cab2434a8b767e95f3",
            "4d4aad65ebfe42eb84c60af36959989a",
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            "e989f010b3ef452d9b28ac5c63fd8dbf",
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            "faa679eb2cfc457fadf29a898dd3195b",
            "a7b0dcbfdc264b8a937417c50cc6fb9d",
            "ce2e47f5a5f74230ae9cff108aca8051",
            "15ef3cd385e049abbc1c0f33575ec12d",
            "9b93be0902df4bf9ae60970eec413234",
            "ba4bcd8d85274f7199f6f9fb435d789e",
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            "05aa3e6591d94ad88bc186770f13f042",
            "492fbede346c41e5924e42e9fc0b0132",
            "7babbef86e5149cc89ad5a3e560c1656",
            "bee17a3cfc1d4a03baee35f84fc6faab",
            "55afec307c3a4d1684f473ae55b3e4db",
            "3e49bb8efa1f4d1da05f35f95a076620",
            "7dbf6777b7b14fa8aa2b9542b4cf72c8",
            "597e04668a7b47709ffe4683293d16ae",
            "f70c43e1028342cfabf4c46c31ec2a87",
            "919333c8412541e0ab9420361e7c56be",
            "0c844472b1414a369a6d47da78378990",
            "434490df5ce84e9aa510dc3ce47bc139",
            "ef43f75d7af74509a8e91f783e988397",
            "304071ee7e184c329030ed5111169d41",
            "d9c7d029caeb497aa9a06194206077aa",
            "45f3bf12503748c38b0c433a3a5c1c48",
            "cbb9221d02f243b1b2ebc698af687c66",
            "dc5bb4c65c48446ab96889935ab202cb",
            "157184cd3c42406096e59f1a7762db60",
            "29a0e0617219499f8c3a56dd487237b9",
            "f99ab8fc104544f1a9e5b688d9afbf2d"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/3668 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8afb8497524346cab2434a8b767e95f3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/408 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6e72b26df3b944cb9522482d5ac7dd35"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1725 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0c844472b1414a369a6d47da78378990"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['sentence1', 'sentence2', 'label', 'idx', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 3668\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['sentence1', 'sentence2', 'label', 'idx', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 408\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['sentence1', 'sentence2', 'label', 'idx', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 1725\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
        "tokenized_datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dynamic padding"
      ],
      "metadata": {
        "id": "fsjv_ON2rXQO"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "nO4kvNW1pe86"
      },
      "outputs": [],
      "source": [
        "from transformers import DataCollatorWithPadding\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "a3mlLrXFpe86",
        "outputId": "979e73b3-718b-4b15-e83d-f92f2e776fe6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[50, 59, 47, 67, 59, 50, 62, 32]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "samples = tokenized_datasets[\"train\"][:8]\n",
        "samples = {k: v for k, v in samples.items() if k not in [\"idx\", \"sentence1\", \"sentence2\"]}\n",
        "[len(x) for x in samples[\"input_ids\"]]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "samples.keys()"
      ],
      "metadata": {
        "id": "5tkjS2Opwdax",
        "outputId": "02066efb-86f4-4799-fff8-995624d5664a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['label', 'input_ids', 'token_type_ids', 'attention_mask'])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch = data_collator(samples)\n",
        "{k: v.shape for k, v in batch.items()}"
      ],
      "metadata": {
        "id": "9kmmXcq-wSu9",
        "outputId": "7f5bf4c8-7137-43d5-e66a-99b8170b99da",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': torch.Size([8, 67]),\n",
              " 'token_type_ids': torch.Size([8, 67]),\n",
              " 'attention_mask': torch.Size([8, 67]),\n",
              " 'labels': torch.Size([8])}"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch.items()"
      ],
      "metadata": {
        "id": "Gv19EuJRwyvB",
        "outputId": "3ccb0f9e-2fb6-45cf-cdbe-23dae7a052dd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_items([('input_ids', tensor([[  101,  2572,  3217,  5831,  5496,  2010,  2567,  1010,  3183,  2002,\n",
              "          2170,  1000,  1996,  7409,  1000,  1010,  1997,  9969,  4487, 23809,\n",
              "          3436,  2010,  3350,  1012,   102,  7727,  2000,  2032,  2004,  2069,\n",
              "          1000,  1996,  7409,  1000,  1010,  2572,  3217,  5831,  5496,  2010,\n",
              "          2567,  1997,  9969,  4487, 23809,  3436,  2010,  3350,  1012,   102,\n",
              "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0],\n",
              "        [  101,  9805,  3540, 11514,  2050,  3079, 11282,  2243,  1005,  1055,\n",
              "          2077,  4855,  1996,  4677,  2000,  3647,  4576,  1999,  2687,  2005,\n",
              "          1002,  1016,  1012,  1019,  4551,  1012,   102,  9805,  3540, 11514,\n",
              "          2050,  4149, 11282,  2243,  1005,  1055,  1999,  2786,  2005,  1002,\n",
              "          6353,  2509,  2454,  1998,  2853,  2009,  2000,  3647,  4576,  2005,\n",
              "          1002,  1015,  1012,  1022,  4551,  1999,  2687,  1012,   102,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0],\n",
              "        [  101,  2027,  2018,  2405,  2019, 15147,  2006,  1996,  4274,  2006,\n",
              "          2238,  2184,  1010,  5378,  1996,  6636,  2005,  5096,  1010,  2002,\n",
              "          2794,  1012,   102,  2006,  2238,  2184,  1010,  1996,  2911,  1005,\n",
              "          1055,  5608,  2018,  2405,  2019, 15147,  2006,  1996,  4274,  1010,\n",
              "          5378,  1996, 14792,  2005,  5096,  1012,   102,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0],\n",
              "        [  101,  2105,  6021, 19481, 13938,  2102,  1010, 21628,  6661,  2020,\n",
              "          2039,  2539, 16653,  1010,  2030,  1018,  1012,  1018,  1003,  1010,\n",
              "          2012,  1037,  1002,  1018,  1012,  5179,  1010,  2383,  3041,  2275,\n",
              "          1037,  2501,  2152,  1997,  1037,  1002,  1018,  1012,  5401,  1012,\n",
              "           102, 21628,  6661,  5598,  2322, 16653,  1010,  2030,  1018,  1012,\n",
              "          1020,  1003,  1010,  2000,  2275,  1037,  2501,  5494,  2152,  2012,\n",
              "          1037,  1002,  1018,  1012,  5401,  1012,   102],\n",
              "        [  101,  1996,  4518,  3123,  1002,  1016,  1012,  2340,  1010,  2030,\n",
              "          2055,  2340,  3867,  1010,  2000,  2485,  5958,  2012,  1002,  2538,\n",
              "          1012,  4868,  2006,  1996,  2047,  2259,  4518,  3863,  1012,   102,\n",
              "         18720,  1004,  1041, 13058,  1012,  6661,  5598,  1002,  1015,  1012,\n",
              "          6191,  2030,  1022,  3867,  2000,  1002,  2538,  1012,  6021,  2006,\n",
              "          1996,  2047,  2259,  4518,  3863,  2006,  5958,  1012,   102,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0],\n",
              "        [  101,  6599,  1999,  1996,  2034,  4284,  1997,  1996,  2095,  3333,\n",
              "          2321,  3867,  2013,  1996,  2168,  2558,  1037,  2095,  3041,  1012,\n",
              "           102,  2007,  1996,  9446,  5689,  2058,  5954,  1005,  1055,  2194,\n",
              "          1010,  6599,  1996,  2034,  4284,  1997,  1996,  2095,  3333,  2321,\n",
              "          3867,  2013,  1996,  2168,  2558,  1037,  2095,  3041,  1012,   102,\n",
              "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0],\n",
              "        [  101,  1996, 17235,  2850,  4160,  2018,  1037,  4882,  5114,  1997,\n",
              "          2459,  1012,  2676,  1010,  2030,  1015,  1012,  1016,  3867,  1010,\n",
              "          5494,  2012,  1015,  1010, 19611,  1012,  2321,  2006,  5958,  1012,\n",
              "           102,  1996,  6627,  1011, 17958, 17235,  2850,  4160, 12490,  1012,\n",
              "         11814,  2594, 24356,  2382,  1012,  4805,  2685,  1010,  2030,  1016,\n",
              "          1012,  5840,  3867,  1010,  2000,  1015,  1010, 19611,  1012,  2321,\n",
              "          1012,   102,     0,     0,     0,     0,     0],\n",
              "        [  101,  1996,  4966,  1011, 10507,  2050,  2059, 12068,  2000,  1996,\n",
              "          2110,  4259,  2457,  1012,   102,  1996,  4966, 10507,  2050, 12068,\n",
              "          2008,  3247,  2000,  1996,  1057,  1012,  1055,  1012,  4259,  2457,\n",
              "          1012,   102,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0]])), ('token_type_ids', tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 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, 0, 0, 0, 0, 0],\n",
              "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
              "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 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, 0, 0, 0, 0, 0],\n",
              "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 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, 1, 1, 1, 0, 0, 0, 0, 0],\n",
              "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])), ('attention_mask', tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 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, 0, 0, 0, 0, 0],\n",
              "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1],\n",
              "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 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, 0, 0, 0, 0, 0],\n",
              "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 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, 1, 1, 1, 0, 0, 0, 0, 0],\n",
              "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])), ('labels', tensor([1, 0, 1, 0, 1, 1, 0, 1]))])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Test"
      ],
      "metadata": {
        "id": "dcK8CWhT6fXj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "raw_datasets = load_dataset(\"glue\", \"sst2\")\n",
        "raw_datasets"
      ],
      "metadata": {
        "id": "lcEkSFue6hkK",
        "outputId": "693e644c-bf97-46d4-ed6e-245cab075a39",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388,
          "referenced_widgets": [
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              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['sentence', 'label', 'idx'],\n",
              "        num_rows: 67349\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['sentence', 'label', 'idx'],\n",
              "        num_rows: 872\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['sentence', 'label', 'idx'],\n",
              "        num_rows: 1821\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
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      ]
    },
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      "source": [
        "raw_datasets[\"train\"][0]"
      ],
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        "id": "UCRllQGB6k4Q",
        "outputId": "5aa87694-728f-4ae6-90ee-3d3b662d5d23",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'sentence': 'hide new secretions from the parental units ',\n",
              " 'label': 0,\n",
              " 'idx': 0}"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
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      "source": [
        "raw_datasets[\"train\"].features"
      ],
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        "id": "czuL1mKy6wPz",
        "outputId": "199a9675-f7a5-4f76-8a36-8cafeed3c66d",
        "colab": {
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        }
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'sentence': Value(dtype='string', id=None),\n",
              " 'label': ClassLabel(names=['negative', 'positive'], id=None),\n",
              " 'idx': Value(dtype='int32', id=None)}"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
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      "source": [
        "def tokenize_function_sst2(example):\n",
        "  return tokenizer(example[\"sentence\"], truncation=True)"
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        "tokenized_datasets = raw_datasets.map(tokenize_function_sst2, batched=True)\n",
        "tokenized_datasets"
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              "DatasetDict({\n",
              "    train: Dataset({\n",
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              "        num_rows: 67349\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['sentence', 'label', 'idx', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 872\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['sentence', 'label', 'idx', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 1821\n",
              "    })\n",
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            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import DataCollatorWithPadding\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
        "samples = tokenized_datasets[\"train\"][:10]\n",
        "samples = {k: v for k, v in samples.items() if k not in [\"sentence\", \"idx\"]}\n",
        "batch = data_collator(samples)\n",
        "{k: v.shape for k, v in batch.items()}"
      ],
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        "id": "023FMEHc7P-K",
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      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': torch.Size([10, 29]),\n",
              " 'token_type_ids': torch.Size([10, 29]),\n",
              " 'attention_mask': torch.Size([10, 29]),\n",
              " 'labels': torch.Size([10])}"
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          "execution_count": 32
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