pairwise_text_classification_(klue_nli)_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/pairwise_text_classification_(klue_nli)_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": "8sJoxEU0fJ5f"
      },
      "source": [
        "# Pairwise text classification - BERT\n",
        "- pre-trained language model로는 `klue/bert-base`를 사용합니다.\n",
        "  - https://huggingface.co/klue/bert-base\n",
        "- pairwise text classification task를 수행하는 예시 데이터셋으로는 klue의 nli를 사용합니다.\n",
        "  - https://huggingface.co/datasets/klue"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z8-sxczRgiNj"
      },
      "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": "bbvZuVIdgim6",
        "outputId": "48a9bf05-2537-4994-c470-6e0ac5325ec6"
      },
      "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": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fri Dec 24 06:09:20 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   37C    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": "qA_Guaj4g-ME"
      },
      "source": [
        "아래의 코드 셀을 실행함으로써 본 노트북을 실행하기위한 library를 install하고 load합니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D3Djk64xhAPw",
        "outputId": "43ac2566-4995-4aeb-9756-300ff72cdaf0"
      },
      "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": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v8uwFSCVhTlv"
      },
      "source": [
        "## Preprocess data\n",
        "1. `klue/bert-base`가 사용한 subword tokenizer를 load합니다.\n",
        "2. `datasets` library를 이용하여 klue nli를 load합니다.\n",
        "3. 1의 subword tokenizer를 이용 klue nli의 data를 pairwise text classification을 수행할 수 있는 형태, train example로 transform합니다.\n",
        "  - `[CLS] premise_tokens [SEP] hypothesis_tokens [SEP]`\n"
      ]
    },
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            "0390863819294a2fa5af2074264255a3",
            "b639ecfb48f24ecea2f70ed39b09a402",
            "028d07aa1e4149ca8b7c3b991d59ad53",
            "766f88d9be0a4e1aaef2602298e4916d",
            "1a5a8a8d13cf4e35ac8cc7a2d5d06133",
            "1854b455c535421ba940978828ff958e",
            "f2905940fa844f81aeb1cc4c38a2d9c1",
            "a57ca24eb15c4d94a48374e9f57fe33c",
            "43b9cf06c2ba43f09ae763329864fcd5",
            "4dd3f29c4be7476ab40dc77b5c6e8fe1",
            "02fda083c6e74180a17fb5d1b3ec49a0",
            "f93a6ad30b0c44c4aac51d8b91e2bc18",
            "46a6994705b6422590ff0ef4d9c18a13",
            "5ef252d800fa454884f18b1821bdba3d",
            "f2121f2075b745eca6dd949e8c044952",
            "2489f3223af5425e95b07d32b65ef1f4",
            "c966787f123740b3a9a32a339229ec44",
            "3d9af7778a824b539a6d20209b70928a",
            "90bfa8fbcd784ad79f8371a9be37bf00"
          ]
        },
        "id": "noK16hrChYyH",
        "outputId": "4c7952e0-1a11-413c-a30d-f6ec3da62506"
      },
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "cs = load_dataset(\"klue\", \"nli\", split=\"train\")\n",
        "cs = cs.train_test_split(0.1)\n",
        "test_cs = load_dataset(\"klue\", \"nli\", split=\"validation\")\n",
        "train_cs = cs[\"train\"]\n",
        "valid_cs = cs[\"test\"]"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "60f004ad31a549f4b64fb1d54476abed",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/5.21k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5403d68cde9a482ea52ed1305208c5b9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/2.93k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset klue/nli (download: 1.20 MiB, generated: 6.10 MiB, post-processed: Unknown size, total: 7.30 MiB) to /root/.cache/huggingface/datasets/klue/nli/1.0.0/e0fc3bc3de3eb03be2c92d72fd04a60ecc71903f821619cb28ca0e1e29e4233e...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0170bf3ff4ea4439aeda256e8ac7d590",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.26M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cd2fd1f6fd884529a1fdbce48772ac1e",
              "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": "43b9cf06c2ba43f09ae763329864fcd5",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset klue downloaded and prepared to /root/.cache/huggingface/datasets/klue/nli/1.0.0/e0fc3bc3de3eb03be2c92d72fd04a60ecc71903f821619cb28ca0e1e29e4233e. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Reusing dataset klue (/root/.cache/huggingface/datasets/klue/nli/1.0.0/e0fc3bc3de3eb03be2c92d72fd04a60ecc71903f821619cb28ca0e1e29e4233e)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1TnyhdO0hbGC",
        "outputId": "5522dfd8-b553-4e8a-efa9-88aed1dc4781"
      },
      "source": [
        "cs[\"train\"][:2]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'guid': ['klue-nli-v1_train_14265', 'klue-nli-v1_train_13962'],\n",
              " 'hypothesis': ['시민들은 오전 10시쯤 일어난 사고로 인해 두려움을 느꼈다.',\n",
              "  '영화가 시작한지 10분만에 시계를 쳐다보았다.'],\n",
              " 'label': [0, 0],\n",
              " 'premise': ['오전 10시경 일어난 사고였기에 출근길만큼 큰 불편을 겪지는 않았지만, 회사를 불문한 연이은 탈선 사고에 시민들이 두려움을 느끼는 것은 사실이다.',\n",
              "  '영화시작 10분만에 시계보기 시작한 첫 영화'],\n",
              " 'source': ['wikinews', 'NSMC']}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PZauV4B6kMPE"
      },
      "source": [
        "transform을 위한 함수를 정의하고 적용합니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212,
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            "a02f2ac8c6614475a02d1cde83e6b2a6",
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            "e1232716a98643adbe9b56508babdc31"
          ]
        },
        "id": "rhSXDe4rkNi9",
        "outputId": "e8dd7d32-4518-4701-f520-da7165a2b816"
      },
      "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": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8e255aad702e4e13897dd830c3edcb49",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/289 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b3c9160a1b9e4554868d019be2a85030",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/425 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "971f3bc6ae174844adef7ed0aa15beee",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "Downloading:   0%|          | 0.00/243k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f4638fd96b8944e09f03ec07afe95098",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/483k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3a252c75705c4151add55939d5a82d87",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/125 [00:00<?, ?B/s]"
            ]
          },
          "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(hypotheses: Union[str, List[str]], premises: Union[str, List[str]], tokenizer) -> Dict[str, List[List[int]]]:\n",
        "    if isinstance(hypotheses, str):\n",
        "        hypotheses = [hypotheses]\n",
        "    if isinstance(premises, str):\n",
        "        premises = [premises]\n",
        "    return tokenizer(text=premises, text_pair=hypotheses, add_special_tokens=True, padding=False, truncation=False)\n",
        "\n",
        "samples = train_cs[:2]\n",
        "transformed_samples = transform(samples[\"hypothesis\"], samples[\"premise\"], tokenizer)\n",
        "\n",
        "print(samples)\n",
        "print(transformed_samples)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nRnXXNyDEDY1",
        "outputId": "3a34c100-c10f-4a4c-8846-c2a42a210d84"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'guid': ['klue-nli-v1_train_14265', 'klue-nli-v1_train_13962'], 'source': ['wikinews', 'NSMC'], 'premise': ['오전 10시경 일어난 사고였기에 출근길만큼 큰 불편을 겪지는 않았지만, 회사를 불문한 연이은 탈선 사고에 시민들이 두려움을 느끼는 것은 사실이다.', '영화시작 10분만에 시계보기 시작한 첫 영화'], 'hypothesis': ['시민들은 오전 10시쯤 일어난 사고로 인해 두려움을 느꼈다.', '영화가 시작한지 10분만에 시계를 쳐다보았다.'], 'label': [0, 0]}\n",
            "{'input_ids': [[2, 4400, 3633, 2067, 2382, 6657, 4022, 2507, 12551, 21383, 22883, 1751, 5153, 2069, 585, 2118, 2259, 1380, 2886, 3683, 16, 3769, 2138, 14294, 2470, 9913, 2073, 1764, 2020, 4022, 2170, 3857, 7285, 7391, 2069, 4491, 2259, 575, 2073, 3669, 28674, 18, 3, 3857, 2031, 2073, 4400, 3633, 2067, 3353, 6657, 4022, 2200, 4534, 7391, 2069, 6227, 2062, 18, 3], [2, 3771, 2067, 2333, 3633, 2377, 2154, 2170, 6974, 24374, 3670, 2470, 1656, 3771, 3, 3771, 2116, 3670, 2470, 2118, 3633, 2377, 2154, 2170, 6974, 2138, 6969, 2886, 2062, 18, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [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]], '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]]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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            "ba46211c75b84ebeb0deeff05925be4a",
            "e64dffb196c245989386e55c4b20f2f4",
            "1e36db3be3744236bd29c96d4f8f3a84",
            "d009240b251e4cc886a23391c6dfc6a4",
            "aa5da192b0b8417a8940860f71a860b6",
            "9d161bc6be74417288e851cf6ab2fa55",
            "f09dbb9d2c464bb7b6e710f8d8e82383",
            "75b3e7f628ba48b1bed564f26abc6e79"
          ]
        },
        "id": "-l8dJuoroxXd",
        "outputId": "49222685-2a07-456d-faeb-0e5a57841380"
      },
      "source": [
        "train_ds = train_cs.map(lambda data: transform(data[\"hypothesis\"], data[\"premise\"], tokenizer), remove_columns=[\"guid\", \"source\", \"hypothesis\", \"premise\"], batched=True).rename_column(\"label\", \"labels\")\n",
        "valid_ds = valid_cs.map(lambda data: transform(data[\"hypothesis\"], data[\"premise\"], tokenizer), remove_columns=[\"guid\", \"source\", \"hypothesis\", \"premise\"], batched=True).rename_column(\"label\", \"labels\")\n",
        "test_ds = test_cs.map(lambda data: transform(data[\"hypothesis\"], data[\"premise\"], tokenizer), remove_columns=[\"guid\", \"source\", \"hypothesis\", \"premise\"], batched=True).rename_column(\"label\", \"labels\")"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8646e976406b49749cf5e8008ac80f56",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/23 [00:00<?, ?ba/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "811e314d98f046ccbc5280411405a350",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?ba/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bba7c292754f4098b195af91e9db08a5",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?ba/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rccBa-zIsvq_"
      },
      "source": [
        "## Prepare model\n",
        "pairwise text classification을 수행하기위한 `klue/bert-base`를 load합니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173,
          "referenced_widgets": [
            "efa74c0b9e5d4bfeb0ef8d08e74c8f34",
            "29489a29a6e64abdb4e626305240c1ea",
            "ba12d41565024a5ea3fbf44f5add9168",
            "946a7427ee3e4448bde6fda6ae71c9ee",
            "612d0ee22ac640fa89fec39f3ec92a92",
            "f95b71278c314015ad7c4cb6eecdc076",
            "19d32644185841e1ba018b685a41231c",
            "337fb0909f9f4a43ad276b2df964181c",
            "a989d7eb37954425b97254dadeede014",
            "fc8be71ec6154b69bfab1283e74a06e7",
            "ff23f82d662945a5ab4c629cc2098b19"
          ]
        },
        "id": "OAAkXge5mapO",
        "outputId": "4bbe93c9-7da3-47e2-8245-2356a1e02146"
      },
      "source": [
        "from transformers import AutoModelForSequenceClassification\n",
        "\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\"klue/bert-base\", num_labels=3)\n",
        "print(model.__class__)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "efa74c0b9e5d4bfeb0ef8d08e74c8f34",
              "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.decoder.bias', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.seq_relationship.weight']\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": "zurAlg2TtooX"
      },
      "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": "NGKtGBx4lMdP"
      },
      "source": [
        "import numpy as np\n",
        "from transformers.data.data_collator import DataCollatorWithPadding\n",
        "from sklearn.metrics import accuracy_score\n",
        "\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": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# mini-batch 구성확인\n",
        "batchify(train_ds[:2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Ep0-DLdGibr",
        "outputId": "feb314cb-8ed4-415c-c596-2dd9ce94a41b"
      },
      "execution_count": 11,
      "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],\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, 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]]), 'input_ids': tensor([[    2,  4400,  3633,  2067,  2382,  6657,  4022,  2507, 12551, 21383,\n",
              "         22883,  1751,  5153,  2069,   585,  2118,  2259,  1380,  2886,  3683,\n",
              "            16,  3769,  2138, 14294,  2470,  9913,  2073,  1764,  2020,  4022,\n",
              "          2170,  3857,  7285,  7391,  2069,  4491,  2259,   575,  2073,  3669,\n",
              "         28674,    18,     3,  3857,  2031,  2073,  4400,  3633,  2067,  3353,\n",
              "          6657,  4022,  2200,  4534,  7391,  2069,  6227,  2062,    18,     3],\n",
              "        [    2,  3771,  2067,  2333,  3633,  2377,  2154,  2170,  6974, 24374,\n",
              "          3670,  2470,  1656,  3771,     3,  3771,  2116,  3670,  2470,  2118,\n",
              "          3633,  2377,  2154,  2170,  6974,  2138,  6969,  2886,  2062,    18,\n",
              "             3,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0]]), 'labels': tensor([0, 0]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
              "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])}"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 572
        },
        "id": "Brl_Z371mC82",
        "outputId": "68382dba-2842-42b8-8c6a-0319e7f90c85"
      },
      "source": [
        "from transformers import Trainer, TrainingArguments\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir='./results',     \n",
        "    evaluation_strategy=\"epoch\",\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": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running training *****\n",
            "  Num examples = 22498\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 = 1408\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1408' max='1408' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1408/1408 05:54, Epoch 2/2]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</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>1</td>\n",
              "      <td>0.506700</td>\n",
              "      <td>0.449375</td>\n",
              "      <td>0.823600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.260300</td>\n",
              "      <td>0.427146</td>\n",
              "      <td>0.851200</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 = 2500\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-704\n",
            "Configuration saved in ./results/checkpoint-704/config.json\n",
            "Model weights saved in ./results/checkpoint-704/pytorch_model.bin\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2500\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-1408\n",
            "Configuration saved in ./results/checkpoint-1408/config.json\n",
            "Model weights saved in ./results/checkpoint-1408/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=1408, training_loss=0.45023471320217306, metrics={'train_runtime': 354.8486, 'train_samples_per_second': 126.803, 'train_steps_per_second': 3.968, 'total_flos': 1592668684552680.0, 'train_loss': 0.45023471320217306, 'epoch': 2.0})"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "id": "-jenC9DZnjZ5",
        "outputId": "9291af6b-738c-476d-939c-38a87ca8f5a0"
      },
      "source": [
        "trainer.evaluate(test_ds)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 3000\n",
            "  Batch size = 32\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='94' max='94' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [94/94 00:06]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'epoch': 2.0,\n",
              " 'eval_accuracy': 0.8046666666666666,\n",
              " 'eval_loss': 0.5251408219337463,\n",
              " 'eval_runtime': 7.3011,\n",
              " 'eval_samples_per_second': 410.897,\n",
              " 'eval_steps_per_second': 12.875}"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    }
  ]
}