Hugging_Face_Datasets_overview_(Pytorch).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/boldbinarywisdom/Transformers/blob/main/Hugging_Face_Datasets_overview_(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": "ZFSzs_r4T3qe"
      },
      "source": [
        "This notebook regroups the code sample of the video below, which is a part of the [Hugging Face course](https://huggingface.co/course)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "r2P_TcioT3qg",
        "outputId": "4d927ac4-1c1c-4511-c65e-3ba9908615c0"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/_BZearw7f0w?rel=0&amp;controls=0&amp;showinfo=0\" frameborder=\"0\" allowfullscreen></iframe>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#@title\n",
        "from IPython.display import HTML\n",
        "\n",
        "HTML('<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/_BZearw7f0w?rel=0&amp;controls=0&amp;showinfo=0\" frameborder=\"0\" allowfullscreen></iframe>')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yHUhVIT5T3qi"
      },
      "source": [
        "Install the Transformers and Datasets libraries to run this notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IbdCqEBMT3qj",
        "outputId": "da03ca38-0d91-421c-86e7-c9e68e8f3655",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
            "  Downloading datasets-2.15.0-py3-none-any.whl (521 kB)\n",
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            "Collecting pyarrow-hotfix (from datasets)\n",
            "  Downloading pyarrow_hotfix-0.6-py3-none-any.whl (7.9 kB)\n",
            "Collecting dill<0.3.8,>=0.3.0 (from datasets)\n",
            "  Downloading dill-0.3.7-py3-none-any.whl (115 kB)\n",
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            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (1.5.3)\n",
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            "Collecting multiprocess (from datasets)\n",
            "  Downloading multiprocess-0.70.15-py310-none-any.whl (134 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: fsspec[http]<=2023.10.0,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n",
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            "Requirement already satisfied: huggingface-hub>=0.18.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.19.4)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (23.2)\n",
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            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (0.4.1)\n",
            "Collecting sentencepiece!=0.1.92,>=0.1.91 (from transformers[sentencepiece])\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m27.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: protobuf in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (3.20.3)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (23.1.0)\n",
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            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.18.0->datasets) (4.5.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2023.11.17)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.3.post1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
            "Installing collected packages: sentencepiece, pyarrow-hotfix, dill, multiprocess, datasets\n",
            "Successfully installed datasets-2.15.0 dill-0.3.7 multiprocess-0.70.15 pyarrow-hotfix-0.6 sentencepiece-0.1.99\n"
          ]
        }
      ],
      "source": [
        "! pip install datasets transformers[sentencepiece]"
      ]
    },
    {
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          "metadata": {}
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        {
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          "metadata": {}
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        {
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          "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",
      "source": [],
      "metadata": {
        "id": "pisfo1jkUTtl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4UIjVPLVT3qj",
        "outputId": "095dbfc6-2e7e-4535-8cda-2d76caea4e42",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
              "    num_rows: 3668\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "raw_datasets[\"train\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dd4CyWLxT3qk",
        "outputId": "66c185b7-6c60-4ec3-a0cc-283ab2c7f911",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'sentence1': 'The Nasdaq had a weekly gain of 17.27 , or 1.2 percent , closing at 1,520.15 on Friday .',\n",
              " 'sentence2': 'The tech-laced Nasdaq Composite .IXIC rallied 30.46 points , or 2.04 percent , to 1,520.15 .',\n",
              " 'label': 0,\n",
              " 'idx': 6}"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "raw_datasets[\"train\"][6]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q23F-lNeT3qk",
        "outputId": "467ddf37-c556-4a8c-a08c-250627bd8c73",
        "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",
              "  \"Yucaipa owned Dominick 's before selling the chain to Safeway in 1998 for $ 2.5 billion .\",\n",
              "  'They had published an advertisement on the Internet on June 10 , offering the cargo for sale , he added .',\n",
              "  'Around 0335 GMT , Tab shares were up 19 cents , or 4.4 % , at A $ 4.56 , having earlier set a record high of A $ 4.57 .',\n",
              "  'The stock rose $ 2.11 , or about 11 percent , to close Friday at $ 21.51 on the New York Stock Exchange .'],\n",
              " 'sentence2': ['Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .',\n",
              "  \"Yucaipa bought Dominick 's in 1995 for $ 693 million and sold it to Safeway for $ 1.8 billion in 1998 .\",\n",
              "  \"On June 10 , the ship 's owners had published an advertisement on the Internet , offering the explosives for sale .\",\n",
              "  'Tab shares jumped 20 cents , or 4.6 % , to set a record closing high at A $ 4.57 .',\n",
              "  'PG & E Corp. shares jumped $ 1.63 or 8 percent to $ 21.03 on the New York Stock Exchange on Friday .'],\n",
              " 'label': [1, 0, 1, 0, 1],\n",
              " 'idx': [0, 1, 2, 3, 4]}"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "raw_datasets[\"train\"][:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A-hlAegmT3ql",
        "outputId": "e9076516-cfec-4355-bdd1-5810d1eabf08",
        "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": 7
        }
      ],
      "source": [
        "raw_datasets[\"train\"].features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/436k [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/3668 [00:00<?, ? examples/s]"
            ],
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              "version_minor": 0,
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          "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,
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          "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": "ebc9ada1c7d74838a67352118b503453"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'train': ['sentence1', 'sentence2', 'label', 'idx', 'input_ids', 'token_type_ids', 'attention_mask'], 'validation': ['sentence1', 'sentence2', 'label', 'idx', 'input_ids', 'token_type_ids', 'attention_mask'], 'test': ['sentence1', 'sentence2', 'label', 'idx', 'input_ids', 'token_type_ids', 'attention_mask']}\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "checkpoint = \"bert-base-cased\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
        "\n",
        "def tokenize_function(example):\n",
        "    return tokenizer(\n",
        "        example[\"sentence1\"], example[\"sentence2\"], padding=\"max_length\", truncation=True, max_length=128\n",
        "    )\n",
        "\n",
        "tokenized_datasets = raw_datasets.map(tokenize_function)\n",
        "print(tokenized_datasets.column_names)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w3wALGYlT3ql",
        "outputId": "d7312a07-5417-43ac-a3b3-f74df162060a",
        "colab": {
          "referenced_widgets": [
            "a982bbb10e9648fe94b80ab7f25e4ca0"
          ]
        }
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Loading cached processed dataset at /home/sgugger/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-2b2682faffe74c3f.arrow\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a982bbb10e9648fe94b80ab7f25e4ca0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Loading cached processed dataset at /home/sgugger/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-754363c6c40d803c.arrow\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "checkpoint = \"bert-base-cased\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
        "\n",
        "def tokenize_function(examples):\n",
        "    return tokenizer(\n",
        "        examples[\"sentence1\"], examples[\"sentence2\"], padding=\"max_length\", truncation=True, max_length=128\n",
        "    )\n",
        "\n",
        "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Zr5cxcbET3qm",
        "outputId": "24221633-375d-44d0-8467-6f58dd5b50f5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['labels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "    num_rows: 3668\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "tokenized_datasets = tokenized_datasets.remove_columns([\"idx\", \"sentence1\", \"sentence2\"])\n",
        "tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
        "tokenized_datasets = tokenized_datasets.with_format(\"torch\")\n",
        "tokenized_datasets[\"train\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "99X3rp1aT3qm"
      },
      "outputs": [],
      "source": [
        "small_train_dataset = tokenized_datasets[\"train\"].select(range(100))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aHQ3VrFgT3qm",
        "outputId": "a23a10a2-8c65-46ea-d4ff-445d682951d9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['labels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "    num_rows: 100\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "small_train_dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "small_train_dataset[:100]"
      ],
      "metadata": {
        "id": "5O9YKYjNV_Pg",
        "outputId": "6569e80b-b76e-471e-edc9-aaf33773fd00",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'labels': tensor([1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1,\n",
              "         1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
              "         1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
              "         0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1,\n",
              "         1, 1, 1, 0]),\n",
              " 'input_ids': tensor([[  101,  7277,  2180,  ...,     0,     0,     0],\n",
              "         [  101, 10684,  2599,  ...,     0,     0,     0],\n",
              "         [  101,  1220,  1125,  ...,     0,     0,     0],\n",
              "         ...,\n",
              "         [  101,  1109,  9555,  ...,     0,     0,     0],\n",
              "         [  101, 15794,  7877,  ...,     0,     0,     0],\n",
              "         [  101,  1109,  2221,  ...,     0,     0,     0]]),\n",
              " 'token_type_ids': tensor([[0, 0, 0,  ..., 0, 0, 0],\n",
              "         [0, 0, 0,  ..., 0, 0, 0],\n",
              "         [0, 0, 0,  ..., 0, 0, 0],\n",
              "         ...,\n",
              "         [0, 0, 0,  ..., 0, 0, 0],\n",
              "         [0, 0, 0,  ..., 0, 0, 0],\n",
              "         [0, 0, 0,  ..., 0, 0, 0]]),\n",
              " 'attention_mask': tensor([[1, 1, 1,  ..., 0, 0, 0],\n",
              "         [1, 1, 1,  ..., 0, 0, 0],\n",
              "         [1, 1, 1,  ..., 0, 0, 0],\n",
              "         ...,\n",
              "         [1, 1, 1,  ..., 0, 0, 0],\n",
              "         [1, 1, 1,  ..., 0, 0, 0],\n",
              "         [1, 1, 1,  ..., 0, 0, 0]])}"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-KfXFi1fWC-w"
      },
      "execution_count": null,
      "outputs": []
    }
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