single_text_classification_(nsmc)_BERT.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/seopbo/nlp_tutorials/blob/main/single_text_classification_(nsmc)_BERT.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HVfGeOOShhjS"
      },
      "source": [
        "# Single text classification - BERT\n",
        "- pre-trained language model로는 `klue/bert-base`를 사용합니다.\n",
        "  - https://huggingface.co/klue/bert-base\n",
        "- single text classification task를 수행하는 예시 데이터셋으로는 `nsmc`를 사용합니다.\n",
        "  - https://huggingface.co/datasets/nsmc"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZAWqkYl7h01y"
      },
      "source": [
        "## Setup\n",
        "어떠한 GPU가 할당되었는 지 아래의 코드 셀을 실행함으로써 확인할 수 있습니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aYIuMCwhNJuw",
        "outputId": "f0acc640-135f-4c20-a5d0-b5799d27e268"
      },
      "source": [
        "gpu_info = !nvidia-smi\n",
        "gpu_info = '\\n'.join(gpu_info)\n",
        "\n",
        "if gpu_info.find('failed') >= 0:\n",
        "    print('Not connected to a GPU')\n",
        "else:\n",
        "    print(gpu_info)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fri Dec 24 06:19:23 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 495.44       Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   36C    P0    26W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aIkAbBqQMdx8"
      },
      "source": [
        "아래의 코드 셀을 실행함으로써 본 노트북을 실행하기위한 library를 install하고 load합니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ASYkTRHbh4pE",
        "outputId": "6f94d4ae-9a7a-4168-d635-7104973477e6"
      },
      "source": [
        "!pip install torch\n",
        "!pip install transformers\n",
        "!pip install datasets\n",
        "!pip install -U scikit-learn\n",
        "\n",
        "import torch\n",
        "import transformers\n",
        "import datasets"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (1.10.0+cu111)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch) (3.10.0.2)\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.15.0-py3-none-any.whl (3.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.4 MB 7.5 MB/s \n",
            "\u001b[?25hCollecting huggingface-hub<1.0,>=0.1.0\n",
            "  Downloading huggingface_hub-0.2.1-py3-none-any.whl (61 kB)\n",
            "\u001b[K     |████████████████████████████████| 61 kB 282 kB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.62.3)\n",
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            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
            "Collecting sacremoses\n",
            "  Downloading sacremoses-0.0.46-py3-none-any.whl (895 kB)\n",
            "\u001b[K     |████████████████████████████████| 895 kB 50.3 MB/s \n",
            "\u001b[?25hCollecting tokenizers<0.11,>=0.10.1\n",
            "  Downloading tokenizers-0.10.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.3 MB 54.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Collecting pyyaml>=5.1\n",
            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
            "\u001b[K     |████████████████████████████████| 596 kB 55.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.4.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (3.10.0.2)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "enQEpWN6jxkj"
      },
      "source": [
        "## Preprocess data\n",
        "1. `klue/bert-base`가 사용한 subword tokenizer를 load합니다.\n",
        "2. `datasets` library를 이용하여 `nsmc`를 load합니다.\n",
        "3. 1의 subword tokenizer를 이용 `nsmc`의 data를 single text classification을 수행할 수 있는 형태, train example로 transform합니다.\n",
        "  - `[CLS] tok 1 ... tok N [SEP]`로 만들고, 이를 list_of_integers로 transform합니다.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1pOcjrBbujSa"
      },
      "source": [
        "`nsmc`를 load하고, `train_ds`, `valid_ds`, `test_ds`를 생성합니다"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        },
        "id": "ZBlJuuiBj0Aj",
        "outputId": "707cbccb-4b32-4197-c6f0-ce155bcf3b52"
      },
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "cs = load_dataset(\"nsmc\", split=\"train\")\n",
        "cs = cs.train_test_split(0.1)\n",
        "test_cs = load_dataset(\"nsmc\", split=\"test\")\n",
        "train_cs = cs[\"train\"]\n",
        "valid_cs = cs[\"test\"]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d02653d350ad4432bd0884ca10af76ef",
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c389c1e21c4640f58cf19a470041994a",
              "version_minor": 0,
              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using custom data configuration default\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset nsmc/default (download: 18.62 MiB, generated: 20.90 MiB, post-processed: Unknown size, total: 39.52 MiB) to /root/.cache/huggingface/datasets/nsmc/default/1.1.0/bfd4729bf1a67114e5267e6916b9e4807010aeb238e4a3c2b95fbfa3a014b5f3...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "53fda615de154e4b93ba772ed0538ebe",
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        {
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          "data": {
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              "model_id": "e49f9a6822d0495998dc55f3adac81db",
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a6ccfbd7b32b434695cb36994ea1098c",
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "3a4bdc8b175342a087575e1688362983",
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              "model_id": "75a4bbd5fa1e48d083af122e673dd7e2",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ea202382150f4611a3ce3661570b7514",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset nsmc downloaded and prepared to /root/.cache/huggingface/datasets/nsmc/default/1.1.0/bfd4729bf1a67114e5267e6916b9e4807010aeb238e4a3c2b95fbfa3a014b5f3. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using custom data configuration default\n",
            "Reusing dataset nsmc (/root/.cache/huggingface/datasets/nsmc/default/1.1.0/bfd4729bf1a67114e5267e6916b9e4807010aeb238e4a3c2b95fbfa3a014b5f3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FlDm8wmwt46R"
      },
      "source": [
        "transform을 위한 함수를 정의하고 적용합니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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          ]
        },
        "id": "-DPFvTMyTh-0",
        "outputId": "4d260e20-12aa-4059-f207-1c6105410bda"
      },
      "source": [
        "from transformers import AutoTokenizer, AutoConfig\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"klue/bert-base\")\n",
        "config = AutoConfig.from_pretrained(\"klue/bert-base\")\n",
        "\n",
        "print(tokenizer.__class__)\n",
        "print(config.__class__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cfae81071bd3480a985d5f851270f3df",
              "version_minor": 0,
              "version_major": 2
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "db11b4d660f343cd912b305b0dccbfc3",
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "58f6e47fd53f4c4e9f985fb3a957126a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "71b6d19438244f9194a2c6b728ea805d",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ac3db2d92268435c94a1eb43151966e1",
              "version_minor": 0,
              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'transformers.models.bert.tokenization_bert_fast.BertTokenizerFast'>\n",
            "<class 'transformers.models.bert.configuration_bert.BertConfig'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from typing import Union, List, Dict\n",
        "\n",
        "\n",
        "def transform(sentences: Union[str, List[str]], tokenizer) -> Dict[str, List[List[int]]]:\n",
        "    if isinstance(sentences, str):\n",
        "        sentences = [sentences]\n",
        "    return tokenizer(text=sentences, add_special_tokens=True, padding=False, truncation=False)\n",
        "\n",
        "samples = train_cs[:2]\n",
        "transformed_samples = transform(samples[\"document\"], tokenizer)\n",
        "\n",
        "print(samples)\n",
        "print(transformed_samples)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SfBMMBb0dcSw",
        "outputId": "e5a47582-c9c0-486e-e5a7-761e4d286e68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'id': ['8852339', '8694184'], 'document': ['평점이왜이렇게낮지?난 이거 또다운받고있다.3번째...정말잼있던데...끝에 견자단하고 대빵하고싸울땐 정말 가슴이뜨거워졌다.난정말로 잼있게봤다...최근댓글이니 내가 알바아닌줄은 알거다.', '재미없습니다 그리고 신음 소리땜에 당황했네요 아 물런 전 21살 영화가 참 재미없어보였는데 평점들 보고 재밌구나 하고 봤는데 젠장 재수없게 속았네요 이런게 도대체 뭐가 재밌다는건지 망할만한 영화였습니다 전 공짜로봐서다행입니다 이걸돈내고보면미친년'], 'label': [1, 0]}\n",
            "{'input_ids': [[2, 20609, 2052, 3132, 2052, 7633, 3264, 2118, 35, 720, 4647, 918, 20721, 2757, 2088, 2689, 2062, 18, 23, 2517, 3135, 18, 18, 18, 3944, 3468, 2689, 2414, 2147, 18, 18, 18, 711, 2170, 586, 2155, 2286, 19521, 823, 2625, 19521, 2935, 2177, 2355, 3944, 4494, 2052, 2751, 2180, 14578, 2062, 18, 720, 2287, 5466, 1530, 2689, 2318, 3072, 2062, 18, 18, 18, 3744, 3315, 2701, 2052, 2209, 732, 2116, 14321, 30428, 2776, 2073, 1381, 2180, 2062, 18, 3], [2, 19113, 2219, 3606, 3673, 12433, 3856, 3515, 2170, 7389, 2371, 2203, 2182, 1376, 1093, 2957, 1537, 4041, 2593, 3771, 2116, 1637, 19113, 15882, 2507, 13964, 20609, 2031, 4530, 7478, 6074, 6159, 1170, 13964, 21195, 10970, 2899, 2318, 1283, 2886, 2203, 2182, 3667, 2318, 6641, 1097, 2116, 7478, 4000, 2332, 2118, 22228, 2154, 2470, 3771, 2507, 2219, 3606, 1537, 12113, 2200, 2998, 2112, 2062, 2375, 12190, 7027, 2765, 2369, 2088, 2178, 2460, 2044, 2720, 2440, 3]], '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, 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, 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, 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, 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, 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, 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], [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, 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",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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          ]
        },
        "id": "68meGEPr9_zB",
        "outputId": "16901873-b2eb-488f-dc3a-6a11162589f1"
      },
      "source": [
        "train_ds = train_cs.map(lambda data: transform(data[\"document\"], tokenizer), remove_columns=[\"id\", \"document\"], batched=True).rename_column(\"label\", \"labels\")\n",
        "valid_ds = valid_cs.map(lambda data: transform(data[\"document\"], tokenizer), remove_columns=[\"id\", \"document\"], batched=True).rename_column(\"label\", \"labels\")\n",
        "test_ds = test_cs.map(lambda data: transform(data[\"document\"], tokenizer), remove_columns=[\"id\", \"document\"], batched=True).rename_column(\"label\", \"labels\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "207c0141d8fc4b2e9f279ce2e666e8b7",
              "version_minor": 0,
              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "86c0d7196e654930af1e547ea88b2c80",
              "version_minor": 0,
              "version_major": 2
            },
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              "  0%|          | 0/15 [00:00<?, ?ba/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cebd95c87bb848bc818640d0f5644254",
              "version_minor": 0,
              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vbhh6YZ2vbb5"
      },
      "source": [
        "## Prepare model\n",
        "single text classification을 수행하기위해서 `klue/bert-base`를 load합니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173,
          "referenced_widgets": [
            "d3af396b44be4f9395d59eef158a94ca",
            "a8a73d4db279462ab24949183c03c629",
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          ]
        },
        "id": "P1zYxEolvdjH",
        "outputId": "550e7f36-3884-48c4-a5cd-f459f6df44e9"
      },
      "source": [
        "from transformers import  AutoModelForSequenceClassification\n",
        "\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\"klue/bert-base\", num_labels=2)\n",
        "\n",
        "print(model.__class__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d3af396b44be4f9395d59eef158a94ca",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/424M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at klue/bert-base were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.decoder.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias']\n",
            "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at klue/bert-base 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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'transformers.models.bert.modeling_bert.BertForSequenceClassification'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GhMh1EP_x6ki"
      },
      "source": [
        "## Train model\n",
        "`Trainer` class를 이용하여 train합니다.\n",
        "\n",
        "- https://huggingface.co/transformers/custom_datasets.html?highlight=trainer#fine-tuning-with-trainer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Fe10emURec0"
      },
      "source": [
        "import numpy as np\n",
        "from transformers.data.data_collator import DataCollatorWithPadding\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "def compute_metrics(p):    \n",
        "    pred, labels = p\n",
        "    pred = np.argmax(pred, axis=1)\n",
        "    accuracy = accuracy_score(y_true=labels, y_pred=pred)\n",
        "    return {\"accuracy\": accuracy}\n",
        "\n",
        "\n",
        "batchify = DataCollatorWithPadding(\n",
        "    tokenizer=tokenizer,\n",
        "    padding=\"longest\",\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# mini-batch 구성확인\n",
        "batchify(train_ds[:2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NzTvCY-AenRQ",
        "outputId": "a01ef62e-cdc6-4e85-baff-284c09142c85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'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, 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],\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, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 0, 0, 0]]), 'input_ids': tensor([[    2, 20609,  2052,  3132,  2052,  7633,  3264,  2118,    35,   720,\n",
              "          4647,   918, 20721,  2757,  2088,  2689,  2062,    18,    23,  2517,\n",
              "          3135,    18,    18,    18,  3944,  3468,  2689,  2414,  2147,    18,\n",
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              "         19521,  2935,  2177,  2355,  3944,  4494,  2052,  2751,  2180, 14578,\n",
              "          2062,    18,   720,  2287,  5466,  1530,  2689,  2318,  3072,  2062,\n",
              "            18,    18,    18,  3744,  3315,  2701,  2052,  2209,   732,  2116,\n",
              "         14321, 30428,  2776,  2073,  1381,  2180,  2062,    18,     3],\n",
              "        [    2, 19113,  2219,  3606,  3673, 12433,  3856,  3515,  2170,  7389,\n",
              "          2371,  2203,  2182,  1376,  1093,  2957,  1537,  4041,  2593,  3771,\n",
              "          2116,  1637, 19113, 15882,  2507, 13964, 20609,  2031,  4530,  7478,\n",
              "          6074,  6159,  1170, 13964, 21195, 10970,  2899,  2318,  1283,  2886,\n",
              "          2203,  2182,  3667,  2318,  6641,  1097,  2116,  7478,  4000,  2332,\n",
              "          2118, 22228,  2154,  2470,  3771,  2507,  2219,  3606,  1537, 12113,\n",
              "          2200,  2998,  2112,  2062,  2375, 12190,  7027,  2765,  2369,  2088,\n",
              "          2178,  2460,  2044,  2720,  2440,     3,     0,     0,     0]]), 'labels': tensor([1, 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, 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, 0, 0, 0, 0, 0, 0, 0,\n",
              "         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, 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]])}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "cAknoPtowJpJ",
        "outputId": "fd5ab00f-cd83-45db-817c-2f2030ad5b2b"
      },
      "source": [
        "from transformers import Trainer, TrainingArguments\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir='./results',          \n",
        "    evaluation_strategy=\"steps\",\n",
        "    eval_steps=1000,\n",
        "    per_device_train_batch_size=32, \n",
        "    per_device_eval_batch_size=32,\n",
        "    learning_rate=1e-4,\n",
        "    weight_decay=0.01,\n",
        "    adam_beta1=.9,\n",
        "    adam_beta2=.95,\n",
        "    adam_epsilon=1e-8,\n",
        "    max_grad_norm=1.,\n",
        "    num_train_epochs=2,    \n",
        "    lr_scheduler_type=\"linear\",\n",
        "    warmup_steps=100,\n",
        "    logging_dir='./logs',\n",
        "    logging_strategy=\"steps\",\n",
        "    logging_first_step=True,\n",
        "    logging_steps=100,\n",
        "    save_strategy=\"epoch\",\n",
        "    seed=42,\n",
        "    dataloader_drop_last=False,\n",
        "    dataloader_num_workers=2\n",
        ")\n",
        "\n",
        "trainer = Trainer(\n",
        "    args=training_args,\n",
        "    data_collator=batchify,\n",
        "    model=model,\n",
        "    train_dataset=train_ds,\n",
        "    eval_dataset=valid_ds,\n",
        "    compute_metrics=compute_metrics\n",
        ")\n",
        "\n",
        "trainer.train()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running training *****\n",
            "  Num examples = 135000\n",
            "  Num Epochs = 2\n",
            "  Instantaneous batch size per device = 32\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 32\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 8438\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='8438' max='8438' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [8438/8438 41:12, Epoch 2/2]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.324000</td>\n",
              "      <td>0.306126</td>\n",
              "      <td>0.871400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.311300</td>\n",
              "      <td>0.303569</td>\n",
              "      <td>0.875667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>0.283000</td>\n",
              "      <td>0.309051</td>\n",
              "      <td>0.879133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4000</td>\n",
              "      <td>0.296700</td>\n",
              "      <td>0.279373</td>\n",
              "      <td>0.886000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5000</td>\n",
              "      <td>0.229600</td>\n",
              "      <td>0.299405</td>\n",
              "      <td>0.885000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6000</td>\n",
              "      <td>0.205000</td>\n",
              "      <td>0.292465</td>\n",
              "      <td>0.893133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7000</td>\n",
              "      <td>0.224000</td>\n",
              "      <td>0.270097</td>\n",
              "      <td>0.896333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8000</td>\n",
              "      <td>0.202400</td>\n",
              "      <td>0.270413</td>\n",
              "      <td>0.897067</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 15000\n",
            "  Batch size = 32\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 15000\n",
            "  Batch size = 32\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 15000\n",
            "  Batch size = 32\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 15000\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-4219\n",
            "Configuration saved in ./results/checkpoint-4219/config.json\n",
            "Model weights saved in ./results/checkpoint-4219/pytorch_model.bin\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 15000\n",
            "  Batch size = 32\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 15000\n",
            "  Batch size = 32\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 15000\n",
            "  Batch size = 32\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 15000\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-8438\n",
            "Configuration saved in ./results/checkpoint-8438/config.json\n",
            "Model weights saved in ./results/checkpoint-8438/pytorch_model.bin\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=8438, training_loss=0.2721697393964608, metrics={'train_runtime': 2472.9911, 'train_samples_per_second': 109.18, 'train_steps_per_second': 3.412, 'total_flos': 1.016783628996192e+16, 'train_loss': 0.2721697393964608, 'epoch': 2.0})"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "id": "VWOYNiWiotGQ",
        "outputId": "9a3191ac-d836-4f4d-9ef0-703755b86311"
      },
      "source": [
        "trainer.evaluate(test_ds)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 50000\n",
            "  Batch size = 32\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1563' max='1563' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1563/1563 02:09]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'epoch': 2.0,\n",
              " 'eval_accuracy': 0.89758,\n",
              " 'eval_loss': 0.27248018980026245,\n",
              " 'eval_runtime': 129.9628,\n",
              " 'eval_samples_per_second': 384.725,\n",
              " 'eval_steps_per_second': 12.027}"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    }
  ]
}