Processing_the_data_(PyTorch).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dorbodwolf/huggingface_nlp_course/blob/main/Processing_the_data_(PyTorch).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mwfY3tXSnNce"
      },
      "source": [
        "# Processing the data (PyTorch)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0xWGGbAunNcf"
      },
      "source": [
        "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "pUJQN9conNcg",
        "outputId": "1b5cbc85-6161-401b-bbac-318412a80a15",
        "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-0.17.3 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]"
      ]
    },
    {
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
        },
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          "output_type": "display_data",
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            ],
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          "metadata": {}
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        {
          "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.weight', 'classifier.bias']\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": [
        "# 用两个样本走通训练流程\n",
        "\n",
        "import torch\n",
        "from transformers import AdamW, AutoTokenizer, AutoModelForSequenceClassification\n",
        "\n",
        "# Same as before\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",
        "\n",
        "# This is new 监督标签\n",
        "batch[\"labels\"] = torch.tensor([1, 1])\n",
        "\n",
        "optimizer = AdamW(model.parameters())\n",
        "loss = model(**batch).loss\n",
        "loss.backward()\n",
        "optimizer.step()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "help(load_dataset)"
      ],
      "metadata": {
        "id": "DhBTO14ioqz5",
        "outputId": "d46b36c2-4394-4891-9fe9-4c266db0f843",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Help on function load_dataset in module datasets.load:\n",
            "\n",
            "load_dataset(path: str, name: Optional[str] = None, data_dir: Optional[str] = None, data_files: Union[str, Sequence[str], Mapping[str, Union[str, Sequence[str]]], NoneType] = None, split: Union[str, datasets.splits.Split, NoneType] = None, cache_dir: Optional[str] = None, features: Optional[datasets.features.features.Features] = None, download_config: Optional[datasets.download.download_config.DownloadConfig] = None, download_mode: Union[datasets.download.download_manager.DownloadMode, str, NoneType] = None, verification_mode: Union[datasets.utils.info_utils.VerificationMode, str, NoneType] = None, ignore_verifications='deprecated', keep_in_memory: Optional[bool] = None, save_infos: bool = False, revision: Union[str, datasets.utils.version.Version, NoneType] = None, token: Union[bool, str, NoneType] = None, use_auth_token='deprecated', task='deprecated', streaming: bool = False, num_proc: Optional[int] = None, storage_options: Optional[Dict] = None, **config_kwargs) -> Union[datasets.dataset_dict.DatasetDict, datasets.arrow_dataset.Dataset, datasets.dataset_dict.IterableDatasetDict, datasets.iterable_dataset.IterableDataset]\n",
            "    Load a dataset from the Hugging Face Hub, or a local dataset.\n",
            "    \n",
            "    You can find the list of datasets on the [Hub](https://huggingface.co/datasets) or with [`huggingface_hub.list_datasets`].\n",
            "    \n",
            "    A dataset is a directory that contains:\n",
            "    \n",
            "    - some data files in generic formats (JSON, CSV, Parquet, text, etc.).\n",
            "    - and optionally a dataset script, if it requires some code to read the data files. This is used to load any kind of formats or structures.\n",
            "    \n",
            "    Note that dataset scripts can also download and read data files from anywhere - in case your data files already exist online.\n",
            "    \n",
            "    This function does the following under the hood:\n",
            "    \n",
            "        1. Download and import in the library the dataset script from `path` if it's not already cached inside the library.\n",
            "    \n",
            "            If the dataset has no dataset script, then a generic dataset script is imported instead (JSON, CSV, Parquet, text, etc.)\n",
            "    \n",
            "            Dataset scripts are small python scripts that define dataset builders. They define the citation, info and format of the dataset,\n",
            "            contain the path or URL to the original data files and the code to load examples from the original data files.\n",
            "    \n",
            "            You can find the complete list of datasets in the Datasets [Hub](https://huggingface.co/datasets).\n",
            "    \n",
            "        2. Run the dataset script which will:\n",
            "    \n",
            "            * Download the dataset file from the original URL (see the script) if it's not already available locally or cached.\n",
            "            * Process and cache the dataset in typed Arrow tables for caching.\n",
            "    \n",
            "                Arrow table are arbitrarily long, typed tables which can store nested objects and be mapped to numpy/pandas/python generic types.\n",
            "                They can be directly accessed from disk, loaded in RAM or even streamed over the web.\n",
            "    \n",
            "        3. Return a dataset built from the requested splits in `split` (default: all).\n",
            "    \n",
            "    It also allows to load a dataset from a local directory or a dataset repository on the Hugging Face Hub without dataset script.\n",
            "    In this case, it automatically loads all the data files from the directory or the dataset repository.\n",
            "    \n",
            "    Args:\n",
            "    \n",
            "        path (`str`):\n",
            "            Path or name of the dataset.\n",
            "            Depending on `path`, the dataset builder that is used comes from a generic dataset script (JSON, CSV, Parquet, text etc.) or from the dataset script (a python file) inside the dataset directory.\n",
            "    \n",
            "            For local datasets:\n",
            "    \n",
            "            - if `path` is a local directory (containing data files only)\n",
            "              -> load a generic dataset builder (csv, json, text etc.) based on the content of the directory\n",
            "              e.g. `'./path/to/directory/with/my/csv/data'`.\n",
            "            - if `path` is a local dataset script or a directory containing a local dataset script (if the script has the same name as the directory)\n",
            "              -> load the dataset builder from the dataset script\n",
            "              e.g. `'./dataset/squad'` or `'./dataset/squad/squad.py'`.\n",
            "    \n",
            "            For datasets on the Hugging Face Hub (list all available datasets with [`huggingface_hub.list_datasets`])\n",
            "    \n",
            "            - if `path` is a dataset repository on the HF hub (containing data files only)\n",
            "              -> load a generic dataset builder (csv, text etc.) based on the content of the repository\n",
            "              e.g. `'username/dataset_name'`, a dataset repository on the HF hub containing your data files.\n",
            "            - if `path` is a dataset repository on the HF hub with a dataset script (if the script has the same name as the directory)\n",
            "              -> load the dataset builder from the dataset script in the dataset repository\n",
            "              e.g. `glue`, `squad`, `'username/dataset_name'`, a dataset repository on the HF hub containing a dataset script `'dataset_name.py'`.\n",
            "    \n",
            "        name (`str`, *optional*):\n",
            "            Defining the name of the dataset configuration.\n",
            "        data_dir (`str`, *optional*):\n",
            "            Defining the `data_dir` of the dataset configuration. If specified for the generic builders (csv, text etc.) or the Hub datasets and `data_files` is `None`,\n",
            "            the behavior is equal to passing `os.path.join(data_dir, **)` as `data_files` to reference all the files in a directory.\n",
            "        data_files (`str` or `Sequence` or `Mapping`, *optional*):\n",
            "            Path(s) to source data file(s).\n",
            "        split (`Split` or `str`):\n",
            "            Which split of the data to load.\n",
            "            If `None`, will return a `dict` with all splits (typically `datasets.Split.TRAIN` and `datasets.Split.TEST`).\n",
            "            If given, will return a single Dataset.\n",
            "            Splits can be combined and specified like in tensorflow-datasets.\n",
            "        cache_dir (`str`, *optional*):\n",
            "            Directory to read/write data. Defaults to `\"~/.cache/huggingface/datasets\"`.\n",
            "        features (`Features`, *optional*):\n",
            "            Set the features type to use for this dataset.\n",
            "        download_config ([`DownloadConfig`], *optional*):\n",
            "            Specific download configuration parameters.\n",
            "        download_mode ([`DownloadMode`] or `str`, defaults to `REUSE_DATASET_IF_EXISTS`):\n",
            "            Download/generate mode.\n",
            "        verification_mode ([`VerificationMode`] or `str`, defaults to `BASIC_CHECKS`):\n",
            "            Verification mode determining the checks to run on the downloaded/processed dataset information (checksums/size/splits/...).\n",
            "    \n",
            "            <Added version=\"2.9.1\"/>\n",
            "        ignore_verifications (`bool`, defaults to `False`):\n",
            "            Ignore the verifications of the downloaded/processed dataset information (checksums/size/splits/...).\n",
            "    \n",
            "            <Deprecated version=\"2.9.1\">\n",
            "    \n",
            "            `ignore_verifications` was deprecated in version 2.9.1 and will be removed in 3.0.0.\n",
            "            Please use `verification_mode` instead.\n",
            "    \n",
            "            </Deprecated>\n",
            "        keep_in_memory (`bool`, defaults to `None`):\n",
            "            Whether to copy the dataset in-memory. If `None`, the dataset\n",
            "            will not be copied in-memory unless explicitly enabled by setting `datasets.config.IN_MEMORY_MAX_SIZE` to\n",
            "            nonzero. See more details in the [improve performance](../cache#improve-performance) section.\n",
            "        save_infos (`bool`, defaults to `False`):\n",
            "            Save the dataset information (checksums/size/splits/...).\n",
            "        revision ([`Version`] or `str`, *optional*):\n",
            "            Version of the dataset script to load.\n",
            "            As datasets have their own git repository on the Datasets Hub, the default version \"main\" corresponds to their \"main\" branch.\n",
            "            You can specify a different version than the default \"main\" by using a commit SHA or a git tag of the dataset repository.\n",
            "        token (`str` or `bool`, *optional*):\n",
            "            Optional string or boolean to use as Bearer token for remote files on the Datasets Hub.\n",
            "            If `True`, or not specified, will get token from `\"~/.huggingface\"`.\n",
            "        use_auth_token (`str` or `bool`, *optional*):\n",
            "            Optional string or boolean to use as Bearer token for remote files on the Datasets Hub.\n",
            "            If `True`, or not specified, will get token from `\"~/.huggingface\"`.\n",
            "    \n",
            "            <Deprecated version=\"2.14.0\">\n",
            "    \n",
            "            `use_auth_token` was deprecated in favor of `token` in version 2.14.0 and will be removed in 3.0.0.\n",
            "    \n",
            "            </Deprecated>\n",
            "        task (`str`):\n",
            "            The task to prepare the dataset for during training and evaluation. Casts the dataset's [`Features`] to standardized column names and types as detailed in `datasets.tasks`.\n",
            "    \n",
            "            <Deprecated version=\"2.13.0\">\n",
            "    \n",
            "            `task` was deprecated in version 2.13.0 and will be removed in 3.0.0.\n",
            "    \n",
            "            </Deprecated>\n",
            "        streaming (`bool`, defaults to `False`):\n",
            "            If set to `True`, don't download the data files. Instead, it streams the data progressively while\n",
            "            iterating on the dataset. An [`IterableDataset`] or [`IterableDatasetDict`] is returned instead in this case.\n",
            "    \n",
            "            Note that streaming works for datasets that use data formats that support being iterated over like txt, csv, jsonl for example.\n",
            "            Json files may be downloaded completely. Also streaming from remote zip or gzip files is supported but other compressed formats\n",
            "            like rar and xz are not yet supported. The tgz format doesn't allow streaming.\n",
            "        num_proc (`int`, *optional*, defaults to `None`):\n",
            "            Number of processes when downloading and generating the dataset locally.\n",
            "            Multiprocessing is disabled by default.\n",
            "    \n",
            "            <Added version=\"2.7.0\"/>\n",
            "        storage_options (`dict`, *optional*, defaults to `None`):\n",
            "            **Experimental**. Key/value pairs to be passed on to the dataset file-system backend, if any.\n",
            "    \n",
            "            <Added version=\"2.11.0\"/>\n",
            "        **config_kwargs (additional keyword arguments):\n",
            "            Keyword arguments to be passed to the `BuilderConfig`\n",
            "            and used in the [`DatasetBuilder`].\n",
            "    \n",
            "    Returns:\n",
            "        [`Dataset`] or [`DatasetDict`]:\n",
            "        - if `split` is not `None`: the dataset requested,\n",
            "        - if `split` is `None`, a [`~datasets.DatasetDict`] with each split.\n",
            "    \n",
            "        or [`IterableDataset`] or [`IterableDatasetDict`]: if `streaming=True`\n",
            "    \n",
            "        - if `split` is not `None`, the dataset is requested\n",
            "        - if `split` is `None`, a [`~datasets.streaming.IterableDatasetDict`] with each split.\n",
            "    \n",
            "    Example:\n",
            "    \n",
            "    Load a dataset from the Hugging Face Hub:\n",
            "    \n",
            "    ```py\n",
            "    >>> from datasets import load_dataset\n",
            "    >>> ds = load_dataset('rotten_tomatoes', split='train')\n",
            "    \n",
            "    # Map data files to splits\n",
            "    >>> data_files = {'train': 'train.csv', 'test': 'test.csv'}\n",
            "    >>> ds = load_dataset('namespace/your_dataset_name', data_files=data_files)\n",
            "    ```\n",
            "    \n",
            "    Load a local dataset:\n",
            "    \n",
            "    ```py\n",
            "    # Load a CSV file\n",
            "    >>> from datasets import load_dataset\n",
            "    >>> ds = load_dataset('csv', data_files='path/to/local/my_dataset.csv')\n",
            "    \n",
            "    # Load a JSON file\n",
            "    >>> from datasets import load_dataset\n",
            "    >>> ds = load_dataset('json', data_files='path/to/local/my_dataset.json')\n",
            "    \n",
            "    # Load from a local loading script\n",
            "    >>> from datasets import load_dataset\n",
            "    >>> ds = load_dataset('path/to/local/loading_script/loading_script.py', split='train')\n",
            "    ```\n",
            "    \n",
            "    Load an [`~datasets.IterableDataset`]:\n",
            "    \n",
            "    ```py\n",
            "    >>> from datasets import load_dataset\n",
            "    >>> ds = load_dataset('rotten_tomatoes', split='train', streaming=True)\n",
            "    ```\n",
            "    \n",
            "    Load an image dataset with the `ImageFolder` dataset builder:\n",
            "    \n",
            "    ```py\n",
            "    >>> from datasets import load_dataset\n",
            "    >>> ds = load_dataset('imagefolder', data_dir='/path/to/images', split='train')\n",
            "    ```\n",
            "\n"
          ]
        }
      ]
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              "    train: Dataset({\n",
              "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
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              "    })\n",
              "})"
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      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
        "raw_datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tlj8oROXnNch",
        "outputId": "08d7d0be-b11d-4b6c-e5f7-d9aafda6c4ec",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'sentence1': 'Rudder was most recently senior vice president for the Developer & Platform Evangelism Business .',\n",
              " 'sentence2': 'Senior Vice President Eric Rudder , formerly head of the Developer and Platform Evangelism unit , will lead the new entity .',\n",
              " 'label': 0,\n",
              " 'idx': 16}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "raw_train_dataset = raw_datasets[\"train\"]\n",
        "raw_train_dataset[15]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_validation_dataset = raw_datasets['validation']\n",
        "raw_validation_dataset[87]"
      ],
      "metadata": {
        "id": "yLmTPXUjqTeH",
        "outputId": "5f639a3b-a46f-4d79-c903-8adc254f0bac",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'sentence1': 'However , EPA officials would not confirm the 20 percent figure .',\n",
              " 'sentence2': 'Only in the past few weeks have officials settled on the 20 percent figure .',\n",
              " 'label': 0,\n",
              " 'idx': 812}"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dHNcfWinnNch",
        "outputId": "26faca9f-3af0-4b5d-f97a-dc592cd211f6",
        "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": 6
        }
      ],
      "source": [
        "raw_train_dataset.features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qNFEj81XnNch"
      },
      "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": null,
      "metadata": {
        "id": "FwmuWCadnNci",
        "outputId": "19f554af-a767-44fe-ccde-320aa22f4441",
        "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": 10
        }
      ],
      "source": [
        "inputs = tokenizer(\"This is the first sentence.\", \"This is the second one.\")\n",
        "inputs"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = tokenizer(raw_train_dataset[15]['sentence1'], raw_train_dataset[15]['sentence2'])\n",
        "inputs"
      ],
      "metadata": {
        "id": "-127-bUsrP88",
        "outputId": "8c9adfa8-80cd-419f-bb5b-a7c5ce805add",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [101, 24049, 2001, 2087, 3728, 3026, 3580, 2343, 2005, 1996, 9722, 1004, 4132, 9340, 12439, 2964, 2449, 1012, 102, 3026, 3580, 2343, 4388, 24049, 1010, 3839, 2132, 1997, 1996, 9722, 1998, 4132, 9340, 12439, 2964, 3131, 1010, 2097, 2599, 1996, 2047, 9178, 1012, 102], 'token_type_ids': [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], '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]}"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_sentences_1 = tokenizer(raw_datasets[\"train\"][15][\"sentence1\"])\n",
        "tokenized_sentences_2 = tokenizer(raw_datasets[\"train\"][15][\"sentence2\"])\n",
        "print(tokenized_sentences_1, tokenized_sentences_2)"
      ],
      "metadata": {
        "id": "uhVIl0v7rlHy",
        "outputId": "334a2828-7295-4065-ac76-28c40242238d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': [101, 24049, 2001, 2087, 3728, 3026, 3580, 2343, 2005, 1996, 9722, 1004, 4132, 9340, 12439, 2964, 2449, 1012, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]} {'input_ids': [101, 3026, 3580, 2343, 4388, 24049, 1010, 3839, 2132, 1997, 1996, 9722, 1998, 4132, 9340, 12439, 2964, 3131, 1010, 2097, 2599, 1996, 2047, 9178, 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, 0, 0], '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]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K2D5BnFgnNci",
        "outputId": "95d9b502-cafe-4f6f-a2fc-18b1331e1bfa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['[CLS]',\n",
              " 'rudder',\n",
              " 'was',\n",
              " 'most',\n",
              " 'recently',\n",
              " 'senior',\n",
              " 'vice',\n",
              " 'president',\n",
              " 'for',\n",
              " 'the',\n",
              " 'developer',\n",
              " '&',\n",
              " 'platform',\n",
              " 'evan',\n",
              " '##gel',\n",
              " '##ism',\n",
              " 'business',\n",
              " '.',\n",
              " '[SEP]',\n",
              " 'senior',\n",
              " 'vice',\n",
              " 'president',\n",
              " 'eric',\n",
              " 'rudder',\n",
              " ',',\n",
              " 'formerly',\n",
              " 'head',\n",
              " 'of',\n",
              " 'the',\n",
              " 'developer',\n",
              " 'and',\n",
              " 'platform',\n",
              " 'evan',\n",
              " '##gel',\n",
              " '##ism',\n",
              " 'unit',\n",
              " ',',\n",
              " 'will',\n",
              " 'lead',\n",
              " 'the',\n",
              " 'new',\n",
              " 'entity',\n",
              " '.',\n",
              " '[SEP]']"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R6OcQ1PUnNci"
      },
      "outputs": [],
      "source": [
        "tokenized_dataset = tokenizer(\n",
        "    raw_datasets[\"train\"][\"sentence1\"],\n",
        "    raw_datasets[\"train\"][\"sentence2\"],\n",
        "    padding=True,\n",
        "    truncation=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eDOxY4pYnNci"
      },
      "outputs": [],
      "source": [
        "def tokenize_function(example):\n",
        "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(raw_datasets)"
      ],
      "metadata": {
        "id": "WEE6NjqLuuYs",
        "outputId": "e07cfa6e-fe44-4dfe-a079-7f9347be6391",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datasets.dataset_dict.DatasetDict"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(raw_datasets.items())"
      ],
      "metadata": {
        "id": "oNi1J1FgxeNp",
        "outputId": "1aa16e04-5dfd-4430-cc7f-5c08cbc39b3f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_items([('train', Dataset({\n",
            "    features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
            "    num_rows: 3668\n",
            "})), ('validation', Dataset({\n",
            "    features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
            "    num_rows: 408\n",
            "})), ('test', Dataset({\n",
            "    features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
            "    num_rows: 1725\n",
            "}))])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for k, ds in raw_datasets.items():\n",
        "  print(type(ds))"
      ],
      "metadata": {
        "id": "3uMMTt-KxBcc",
        "outputId": "ef48fe33-e899-4470-979b-d8222f8ebc90",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'datasets.arrow_dataset.Dataset'>\n",
            "<class 'datasets.arrow_dataset.Dataset'>\n",
            "<class 'datasets.arrow_dataset.Dataset'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yk1HEUwinNci",
        "outputId": "66b71cc6-289a-40a4-ac48-eaa0bfec495d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363,
          "referenced_widgets": [
            "36b3fb1dc72f45e397a6c267b16e4fec",
            "9419965e839f4d049e799b8d9cdecf45",
            "222fdd9a2918455b9d7c58552068c06c",
            "3bbe6740241f4c0b8e0d1f0dd018120b",
            "b3a3e4d85f3c4c7eb01ba5e0f2717923",
            "936d69b8286d4875b9a9a532cb9deeac",
            "acbf3d0d3f5f41adb032ad9a0de5b2e4",
            "abe59eb5b47a4111b29f41109f03680e",
            "cd983f2e84c74a10abc0245eb5d70d98",
            "c2c5b9f1b0164a37b4f3d300c09363f1",
            "66ff2eca62f847eb9d95072b2281e81a",
            "443c4248f3514fca8e8b692a9a5922ab",
            "16aa0135a3bb4b9d9393cc4b01bc0ca8",
            "7fbde975701c499f95361fdb5a602520",
            "3fe2768a83d946ad9d6d140455d4636b",
            "0038e25754df4d3e938971407ec1fdab",
            "f91b2272882647a8806377722c12dd9e",
            "2731d636a6724fa2a05c630a5e841b7e",
            "f0050d49f69e414595308b9aaf0f1437",
            "798dfa9383c7469d9e9fc9727e0e4f48",
            "75dc467db194414e8cc7b94990709402",
            "907371903ca04d7f8e70b57800148737",
            "d88ac7a95e40464d995f02e7aa04ff72",
            "bbbcfcc4c67241a9929be3034cafb12f",
            "dc81538520d940fd92d084b5ee70c6cd",
            "122539f9377b46dc9c8d2784c05f3b72",
            "b9a8da8e4a4845c48034cf63d9928229",
            "8307178dfe5b4b379799e8284118f6c8",
            "cadab868327445d7b2b896f1a9b7b339",
            "a1734d058487448087e174e4e5492971",
            "20b0fbc2b50a448aa9fbc26cbe6628b4",
            "2bc7ae2439114bfe8712ae13c4046b99",
            "5f4e3cebdbba433ebcf9915d3cd0904e"
          ]
        }
      },
      "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": "36b3fb1dc72f45e397a6c267b16e4fec"
            }
          },
          "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": "443c4248f3514fca8e8b692a9a5922ab"
            }
          },
          "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": "d88ac7a95e40464d995f02e7aa04ff72"
            }
          },
          "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": 16
        }
      ],
      "source": [
        "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
        "tokenized_datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Y-eixPYjnNci"
      },
      "outputs": [],
      "source": [
        "from transformers import DataCollatorWithPadding\n",
        "\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UBD1eXa_nNci",
        "outputId": "dafd4017-3631-4a67-f5c8-e309ba66924a",
        "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": 22
        }
      ],
      "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",
      "execution_count": null,
      "metadata": {
        "id": "1hvVSbn7nNci",
        "outputId": "1d826c7b-1297-43f4-90c9-59d39460bf50",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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": 23
        }
      ],
      "source": [
        "batch = data_collator(samples)\n",
        "{k: v.shape for k, v in batch.items()}"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# For a harder challenge, try to write a preprocessing function that works on any of the GLUE tasks.\n",
        "# 挑战：编写一个预处理函数，适应不同的GLUE任务。\n",
        "# 测试sst2 和 mnli都可以通过\n",
        "\n",
        "from datasets import load_dataset\n",
        "from transformers import AutoTokenizer\n",
        "from functools import partial\n",
        "\n",
        "def tokenize_function(example, keys_of_ds):\n",
        "    \"\"\"\n",
        "    Args:\n",
        "      example:\n",
        "      keys_of_ds: 通过输入的数据集的column名称来自适应需要tokenizer的feature\n",
        "    \"\"\"\n",
        "\n",
        "    tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
        "    # print(keys_of_ds)\n",
        "    assert len(keys_of_ds) < 3\n",
        "    assert len(keys_of_ds) > 0\n",
        "    if len(keys_of_ds) > 1:\n",
        "      return tokenizer(example[f\"{keys_of_ds[0]}\"], example[f\"{keys_of_ds[1]}\"], truncation=True)\n",
        "    else:\n",
        "      return tokenizer(example[f\"{keys_of_ds[0]}\"], truncation=True)\n",
        "\n",
        "def preprocess_glue(subset):\n",
        "  \"\"\"\n",
        "  Args:\n",
        "    checkpoint:\n",
        "    subset: GLUE的任意一个任务数据集\n",
        "  \"\"\"\n",
        "  raw_datasets = load_dataset(\"glue\", subset)\n",
        "  keys_of_ds = list(raw_datasets['train'].features.keys())\n",
        "  keys_of_ds.pop(-1)\n",
        "  keys_of_ds.pop(-1)\n",
        "  print(keys_of_ds)\n",
        "  tokenized_datasets = raw_datasets.map(partial(tokenize_function, keys_of_ds = keys_of_ds), batched=True)\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "  # test\n",
        "  checkpoint = \"bert-base-uncased\"\n",
        "  subset = 'mnli' # sst2\n",
        "  preprocess_glue(subset)"
      ],
      "metadata": {
        "id": "Z8B4dpFL08aj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627,
          "referenced_widgets": [
            "5e294a3044664c34bef41843a0c21a1c",
            "50ae8ca618d747a2ac59b575ea35f01a",
            "ffd9dcf8f67e4cf38ae7b8574bc9361a",
            "a5ce1c00830e47f08aba3913de492bca",
            "4700782a0dcf4de6b057a575d3163556",
            "50bc862bd8c2492381d3b198b22c57b6",
            "358ea7db76b6492da1c48825234af36c",
            "ae57e4885a044b4db46748b1fc05cbb8",
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            "2944dfc818a64c10bfad800d1fade392",
            "c5033fa92008458b8d5b74cb69fc3460"
          ]
        },
        "outputId": "42396a40-ba54-44f9-ee51-a6381cd958f2"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/313M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5e294a3044664c34bef41843a0c21a1c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/392702 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a01a2c47106c4125a3bfd2a6d751b18b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation_matched split:   0%|          | 0/9815 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e9f5b87ef850455cab12f4c0902508eb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation_mismatched split:   0%|          | 0/9832 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ec7335bf2e614b51910085c3b6c2d514"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test_matched split:   0%|          | 0/9796 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "15db10fa58e54b10a1eeba0b293cb042"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test_mismatched split:   0%|          | 0/9847 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "abe4986c3ba545a18625650a1816d818"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['premise', 'hypothesis']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/392702 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c690c3113ea446588d2b4995f40da1bf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-40-dcd0c14601e6>\u001b[0m in \u001b[0;36m<cell line: 33>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     35\u001b[0m   \u001b[0mcheckpoint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"bert-base-uncased\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m   \u001b[0msubset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'mnli'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m   \u001b[0mpreprocess_glue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubset\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<ipython-input-40-dcd0c14601e6>\u001b[0m in \u001b[0;36mpreprocess_glue\u001b[0;34m(subset)\u001b[0m\n\u001b[1;32m     29\u001b[0m   \u001b[0mkeys_of_ds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\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[1;32m     30\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys_of_ds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m   \u001b[0mtokenized_datasets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mraw_datasets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpartial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtokenize_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys_of_ds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkeys_of_ds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatched\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\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     32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m__name__\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"__main__\"\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/python3.10/dist-packages/datasets/dataset_dict.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc)\u001b[0m\n\u001b[1;32m    851\u001b[0m             \u001b[0mcache_file_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    852\u001b[0m         return DatasetDict(\n\u001b[0;32m--> 853\u001b[0;31m             {\n\u001b[0m\u001b[1;32m    854\u001b[0m                 k: dataset.map(\n\u001b[1;32m    855\u001b[0m                     \u001b[0mfunction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfunction\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/python3.10/dist-packages/datasets/dataset_dict.py\u001b[0m in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    852\u001b[0m         return DatasetDict(\n\u001b[1;32m    853\u001b[0m             {\n\u001b[0;32m--> 854\u001b[0;31m                 k: dataset.map(\n\u001b[0m\u001b[1;32m    855\u001b[0m                     \u001b[0mfunction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    856\u001b[0m                     \u001b[0mwith_indices\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwith_indices\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/python3.10/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    590\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"Dataset\"\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"self\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    591\u001b[0m         \u001b[0;31m# apply actual function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 592\u001b[0;31m         \u001b[0mout\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Dataset\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"DatasetDict\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\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    593\u001b[0m         \u001b[0mdatasets\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Dataset\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    594\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdatasets\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/python3.10/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    555\u001b[0m         }\n\u001b[1;32m    556\u001b[0m         \u001b[0;31m# apply actual function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 557\u001b[0;31m         \u001b[0mout\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Dataset\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"DatasetDict\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\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    558\u001b[0m         \u001b[0mdatasets\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Dataset\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    559\u001b[0m         \u001b[0;31m# re-apply format to the output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\u001b[0m\n\u001b[1;32m   3095\u001b[0m                     \u001b[0mdesc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdesc\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m\"Map\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3096\u001b[0m                 ) as pbar:\n\u001b[0;32m-> 3097\u001b[0;31m                     \u001b[0;32mfor\u001b[0m \u001b[0mrank\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontent\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mDataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_map_single\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mdataset_kwargs\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[1;32m   3098\u001b[0m                         \u001b[0;32mif\u001b[0m \u001b[0mdone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3099\u001b[0m                             \u001b[0mshards_done\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36m_map_single\u001b[0;34m(shard, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset)\u001b[0m\n\u001b[1;32m   3472\u001b[0m                         )  # Something simpler?\n\u001b[1;32m   3473\u001b[0m                         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3474\u001b[0;31m                             batch = apply_function_on_filtered_inputs(\n\u001b[0m\u001b[1;32m   3475\u001b[0m                                 \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3476\u001b[0m                                 \u001b[0mindices\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/python3.10/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36mapply_function_on_filtered_inputs\u001b[0;34m(pa_inputs, indices, check_same_num_examples, offset)\u001b[0m\n\u001b[1;32m   3351\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mwith_rank\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3352\u001b[0m                 \u001b[0madditional_args\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mrank\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-> 3353\u001b[0;31m             \u001b[0mprocessed_inputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mfn_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0madditional_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfn_kwargs\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   3354\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocessed_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLazyDict\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[1;32m   3355\u001b[0m                 processed_inputs = {\n",
            "\u001b[0;32m<ipython-input-40-dcd0c14601e6>\u001b[0m in \u001b[0;36mtokenize_function\u001b[0;34m(example, keys_of_ds)\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys_of_ds\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys_of_ds\u001b[0m\u001b[0;34m)\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---> 16\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexample\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34mf\"{keys_of_ds[0]}\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexample\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34mf\"{keys_of_ds[1]}\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtruncation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\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     17\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     18\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexample\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34mf\"{keys_of_ds[0]}\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtruncation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\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/python3.10/dist-packages/transformers/tokenization_utils_base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, text, text_pair, text_target, text_pair_target, add_special_tokens, padding, truncation, max_length, stride, is_split_into_words, pad_to_multiple_of, return_tensors, return_token_type_ids, return_attention_mask, return_overflowing_tokens, return_special_tokens_mask, return_offsets_mapping, return_length, verbose, **kwargs)\u001b[0m\n\u001b[1;32m   2600\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_in_target_context_manager\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2601\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_switch_to_input_mode\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-> 2602\u001b[0;31m             \u001b[0mencodings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_one\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtext_pair\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtext_pair\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mall_kwargs\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   2603\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtext_target\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\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   2604\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_switch_to_target_mode\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/python3.10/dist-packages/transformers/tokenization_utils_base.py\u001b[0m in \u001b[0;36m_call_one\u001b[0;34m(self, text, text_pair, add_special_tokens, padding, truncation, max_length, stride, is_split_into_words, pad_to_multiple_of, return_tensors, return_token_type_ids, return_attention_mask, return_overflowing_tokens, return_special_tokens_mask, return_offsets_mapping, return_length, verbose, **kwargs)\u001b[0m\n\u001b[1;32m   2686\u001b[0m                 )\n\u001b[1;32m   2687\u001b[0m             \u001b[0mbatch_text_or_text_pairs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtext_pair\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtext_pair\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mtext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2688\u001b[0;31m             return self.batch_encode_plus(\n\u001b[0m\u001b[1;32m   2689\u001b[0m                 \u001b[0mbatch_text_or_text_pairs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_text_or_text_pairs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2690\u001b[0m                 \u001b[0madd_special_tokens\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0madd_special_tokens\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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