token_classification_(klue_ner)_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/token_classification_(klue_ner)_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": [
        "# Token classification - BERT\n",
        "- pre-trained language model로는 `klue/bert-base`를 사용합니다.\n",
        "  - https://huggingface.co/klue/bert-base\n",
        "- token classification task를 수행하는 예시 데이터셋으로는 klue의 ner를 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": "9c7497a4-61ab-49ac-c422-d34ed28ac8ca"
      },
      "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": [
            "Mon Dec 27 02:44:35 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    27W / 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": "85425a7f-199a-45d7-e2ea-87623f4a66f3"
      },
      "source": [
        "!pip install torch\n",
        "!pip install transformers\n",
        "!pip install datasets\n",
        "!pip install seqeval\n",
        "!pip install numpy\n",
        "!pip install scikit-learn\n",
        "\n",
        "from pprint import pprint\n",
        "import sklearn\n",
        "import torch\n",
        "import transformers\n",
        "import datasets"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "    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 klue의 ner를 load합니.\n",
        "3. 1의 subword tokenizer를 이용 klue ner의 data를 token classification을 수행할 수 있는 형태, train example로 transformation합니다.\n",
        "\n",
        "- `[CLS] tok 1 ... tok N [SEP]`로 만들고, 이를 list_of_integers로 transform합니다.\n",
        "- 기존 klue ner의 label이 character level로 달려있으므로, 이를 token level로 adaptation하는 function인 `relabel_to_token` function을 작성합니다.\n",
        "\n"
      ]
    },
    {
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            "d8d5c7f0534a42738bbd244d65b40f70"
          ]
        },
        "id": "ELOFARIx63xt",
        "outputId": "6258e53d-d4fc-462f-a1d0-d31bfa5e169a"
      },
      "source": [
        "from transformers import AutoTokenizer, AutoConfig\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"klue/bert-base\")\n",
        "\n",
        "print(tokenizer.__class__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6a81466b0870400b96bfa0e0c8a2c31c",
              "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": "f4cc19724f1341148206c481e355a959",
              "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": "d945a5173ee44b27990794da4e450858",
              "version_minor": 0,
              "version_major": 2
            },
            "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": "4c0847b79c5a4a4788438ce14411f304",
              "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": "df873ef1377e4254b3d6d6049ddb316d",
              "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165,
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          ]
        },
        "id": "noK16hrChYyH",
        "outputId": "4df0cdc6-5e1b-46c4-8157-1a58626600b1"
      },
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "cs = load_dataset(\"klue\", \"ner\", split=\"train\")\n",
        "cs = cs.train_test_split(0.1)\n",
        "test_cs = load_dataset(\"klue\", \"ner\", split=\"validation\")\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": "35bba075caba454abeecb803bc7f91d8",
              "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": "c8d5778deb114e5aac775e4663815b16",
              "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/ner (download: 4.11 MiB, generated: 23.68 MiB, post-processed: Unknown size, total: 27.79 MiB) to /root/.cache/huggingface/datasets/klue/ner/1.0.0/e0fc3bc3de3eb03be2c92d72fd04a60ecc71903f821619cb28ca0e1e29e4233e...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5db925fc69dd401b86406c660f493b25",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/4.31M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a364407b2ecf4f33bfec56e1dc2687fe",
              "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": "7dda286f755246cba9d88d198bec30db",
              "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/ner/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/ner/1.0.0/e0fc3bc3de3eb03be2c92d72fd04a60ecc71903f821619cb28ca0e1e29e4233e)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "klue_ner dataset을 확인해보면 character 단위로 BIO tag가 label 되어있는 것을 확인할 수 있습니다."
      ],
      "metadata": {
        "id": "eEJQtJxiynAR"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "budcfQpF6aTu",
        "outputId": "d0954c0b-df48-4c26-8250-9b178a015811"
      },
      "source": [
        "# label의 목록\n",
        "list_of_labels = train_cs.features[\"ner_tags\"].feature.names\n",
        "print(list_of_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['B-DT', 'I-DT', 'B-LC', 'I-LC', 'B-OG', 'I-OG', 'B-PS', 'I-PS', 'B-QT', 'I-QT', 'B-TI', 'I-TI', 'O']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-3C6DeXmFA17",
        "outputId": "c64ee305-a024-40ca-ff5a-7aff0c5e3979"
      },
      "source": [
        "example = train_cs[4]\n",
        "original_sentence = example[\"sentence\"]\n",
        "original_clean_sentence = \"\".join(example[\"tokens\"]).replace(\"\\xa0\",\" \")\n",
        "original_clean_tokens = example[\"tokens\"]\n",
        "original_clean_labels = example[\"ner_tags\"]\n",
        "\n",
        "pprint(list(zip(original_clean_tokens, original_clean_labels)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('다', 12),\n",
            " ('음', 12),\n",
            " ('은', 12),\n",
            " (' ', 12),\n",
            " ('공', 2),\n",
            " ('주', 3),\n",
            " ('사', 3),\n",
            " ('대', 3),\n",
            " ('부', 3),\n",
            " ('고', 3),\n",
            " (' ', 12),\n",
            " ('대', 12),\n",
            " ('강', 12),\n",
            " ('당', 12),\n",
            " ('에', 12),\n",
            " (' ', 12),\n",
            " ('차', 12),\n",
            " ('려', 12),\n",
            " ('진', 12),\n",
            " (' ', 12),\n",
            " ('합', 12),\n",
            " ('동', 12),\n",
            " (' ', 12),\n",
            " ('분', 12),\n",
            " ('향', 12),\n",
            " ('소', 12),\n",
            " (' ', 12),\n",
            " ('모', 12),\n",
            " ('습', 12),\n",
            " ('입', 12),\n",
            " ('니', 12),\n",
            " ('다', 12),\n",
            " ('.', 12)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "위와 같이 character 단위로 labeling 되어있는 data를 token level로 adaptation하는 function인 `relabel_to_token` function을 작성합니다."
      ],
      "metadata": {
        "id": "cgO4iNHf0DaG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def relabel_to_token(original_clean_labels, offset_mappings):\n",
        "    labels_of_tokens = []\n",
        "\n",
        "    for offset_mapping in offset_mappings:\n",
        "\n",
        "        cur_start_offset, cur_end_offset = offset_mapping\n",
        "        if cur_start_offset == cur_end_offset:\n",
        "            labels_of_tokens.append(-100)\n",
        "            continue\n",
        "        labels_of_tokens.append(original_clean_labels[cur_start_offset])\n",
        "    return labels_of_tokens\n",
        "\n",
        "offset_mappings = tokenizer(original_clean_sentence, return_offsets_mapping=True, return_attention_mask=False, return_token_type_ids=False, add_special_tokens=False, padding=False, truncation=False)[\"offset_mapping\"]\n",
        "labels_of_tokens = relabel_to_token(original_clean_labels, offset_mappings)\n",
        "pprint(list(zip(tokenizer.tokenize(original_clean_sentence), labels_of_tokens)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RvhhI3M22y5b",
        "outputId": "6b6e82e7-5263-4bf9-da31-67a60318337c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('다음', 12),\n",
            " ('##은', 12),\n",
            " ('공주', 2),\n",
            " ('##사', 3),\n",
            " ('##대', 3),\n",
            " ('##부', 3),\n",
            " ('##고', 3),\n",
            " ('대강', 12),\n",
            " ('##당', 12),\n",
            " ('##에', 12),\n",
            " ('차려', 12),\n",
            " ('##진', 12),\n",
            " ('합동', 12),\n",
            " ('분향소', 12),\n",
            " ('모습', 12),\n",
            " ('##입니다', 12),\n",
            " ('.', 12)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "정의한 `relabel_to_token` function을 `transform` function 내부에서 사용합니다."
      ],
      "metadata": {
        "id": "5k8ELMmd4Luy"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q9jNzoAD2eve"
      },
      "source": [
        "def transform(example, tokenizer):\n",
        "    original_clean_sentence = \"\".join(example[\"tokens\"]).replace(\"\\xa0\",\" \")\n",
        "    original_clean_labels = example[\"ner_tags\"]\n",
        "\n",
        "    encoded = tokenizer(original_clean_sentence, return_offsets_mapping=True, return_attention_mask=True, return_token_type_ids=True, add_special_tokens=True, padding=False, truncation=False)\n",
        "    labels = relabel_to_token(original_clean_labels, encoded[\"offset_mapping\"])\n",
        "    encoded.update({\"labels\": labels})\n",
        "    return encoded"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
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            "fd21c136383945ceb4463cd2ff787ef4",
            "381b4c3337964e5097b5a735ad595e86",
            "210d695d77e849608006c8a5e7933482"
          ]
        },
        "id": "-l8dJuoroxXd",
        "outputId": "1f733d0d-7fbd-4b58-8b1e-4135804125c3"
      },
      "source": [
        "train_ds = train_cs.map(lambda example: transform(example, tokenizer), remove_columns=[\"sentence\"], batched=False)\n",
        "valid_ds = valid_cs.map(lambda example: transform(example, tokenizer), remove_columns=[\"sentence\"], batched=False)\n",
        "test_ds = test_cs.map(lambda example: transform(example, tokenizer), remove_columns=[\"sentence\"], batched=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4cfec253693f4e6abfd604cf4bc9c3b4",
              "version_minor": 0,
              "version_major": 2
            },
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              "  0%|          | 0/18907 [00:00<?, ?ex/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "afa55a138de44a7da5cce93b91174263",
              "version_minor": 0,
              "version_major": 2
            },
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              "  0%|          | 0/2101 [00:00<?, ?ex/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "12e602b1a6b34baab2bff0c9299acc82",
              "version_minor": 0,
              "version_major": 2
            },
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              "  0%|          | 0/5000 [00:00<?, ?ex/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rccBa-zIsvq_"
      },
      "source": [
        "## Prepare model\n",
        "token classification을 수행하기위해서 `klue/bert-base` load합니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173,
          "referenced_widgets": [
            "66dddbc92cf043d5bcfc353c65e59774",
            "4fe6bc6b3771472b9ec950eff321d6d8",
            "6dec9b347c62429cabfb50f03f58a1a6",
            "8f86b59e93614e468c7715982d6b699a",
            "53df989515cb4f659a800295a0b2238f",
            "e622350cfab3407fa49596126aa9c588",
            "1429ceb7b233431c9a7b6e6536c767dc",
            "f30794ae2e1b4a07bd8999a2eaa40e8c",
            "48eddaa65d424069aa162de1dfd2bc55",
            "2409186b6f6c4912abc21cc5268d3c33",
            "0801d2b5145948558944cfd3caa6b71f"
          ]
        },
        "id": "OAAkXge5mapO",
        "outputId": "3595981e-477f-4d40-da52-8e68206bbce9"
      },
      "source": [
        "from transformers import AutoModelForTokenClassification\n",
        "\n",
        "model = AutoModelForTokenClassification.from_pretrained(\"klue/bert-base\", num_labels=13)\n",
        "\n",
        "print(model.__class__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "66dddbc92cf043d5bcfc353c65e59774",
              "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 BertForTokenClassification: ['cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.decoder.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight']\n",
            "- This IS expected if you are initializing BertForTokenClassification 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 BertForTokenClassification 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 BertForTokenClassification 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.BertForTokenClassification'>\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 DataCollatorForTokenClassification\n",
        "from datasets import load_metric\n",
        "\n",
        "metric = load_metric(\"seqeval\")\n",
        "\n",
        "\n",
        "def compute_metrics(p):\n",
        "    predictions, labels = p\n",
        "    predictions = np.argmax(predictions, axis=2)\n",
        "\n",
        "    # Remove ignored index (special tokens)\n",
        "    true_predictions = [\n",
        "        [list_of_labels[p] for (p, l) in zip(prediction, label) if l != -100]\n",
        "        for prediction, label in zip(predictions, labels)\n",
        "    ]\n",
        "    true_labels = [\n",
        "        [list_of_labels[l] for (p, l) in zip(prediction, label) if l != -100]\n",
        "        for prediction, label in zip(predictions, labels)\n",
        "    ]\n",
        "\n",
        "    results = metric.compute(predictions=true_predictions, references=true_labels)\n",
        "    return {\n",
        "        \"precision\": results[\"overall_precision\"],\n",
        "        \"recall\": results[\"overall_recall\"],\n",
        "        \"f1\": results[\"overall_f1\"],\n",
        "        \"accuracy\": results[\"overall_accuracy\"],\n",
        "    }\n",
        "\n",
        "\n",
        "batchify = DataCollatorForTokenClassification(\n",
        "    tokenizer=tokenizer,\n",
        "    padding=True\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 679
        },
        "id": "Brl_Z371mC82",
        "outputId": "b6c5d00e-ec46-4d5c-eebe-30bd2c2e04d1"
      },
      "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": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "PyTorch: setting up devices\n",
            "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n",
            "The following columns in the training set  don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: offset_mapping, ner_tags, tokens.\n",
            "***** Running training *****\n",
            "  Num examples = 18907\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 = 1182\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1182' max='1182' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1182/1182 04:47, 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>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.069500</td>\n",
              "      <td>0.066514</td>\n",
              "      <td>0.870283</td>\n",
              "      <td>0.899321</td>\n",
              "      <td>0.884564</td>\n",
              "      <td>0.979727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.030300</td>\n",
              "      <td>0.067305</td>\n",
              "      <td>0.884119</td>\n",
              "      <td>0.902813</td>\n",
              "      <td>0.893368</td>\n",
              "      <td>0.980515</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the evaluation set  don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: offset_mapping, ner_tags, tokens.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2101\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-591\n",
            "Configuration saved in ./results/checkpoint-591/config.json\n",
            "Model weights saved in ./results/checkpoint-591/pytorch_model.bin\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: offset_mapping, ner_tags, tokens.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2101\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-1182\n",
            "Configuration saved in ./results/checkpoint-1182/config.json\n",
            "Model weights saved in ./results/checkpoint-1182/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=1182, training_loss=0.04516238271055488, metrics={'train_runtime': 288.2896, 'train_samples_per_second': 131.167, 'train_steps_per_second': 4.1, 'total_flos': 1254452265351564.0, 'train_loss': 0.04516238271055488, 'epoch': 2.0})"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "84to5-iqEnZ0"
      },
      "source": [
        "## Evaulate model\n",
        "학습된 model을 entity-level, char-level로 평가합니다. entity-level로 평가시에는 위에서 정의한 `compute_metrics` function을 사용하면 되지만, char-level로 평가하기위해서는 별도의 function을 정의해야합니다."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### entity-level로 test_ds에 대해서 성능을 계산\n"
      ],
      "metadata": {
        "id": "Qjq3IOn1JKJu"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        },
        "id": "-jenC9DZnjZ5",
        "outputId": "98d407ef-4606-4f10-805c-ec4a30f064b8"
      },
      "source": [
        "trainer.evaluate(test_ds)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the evaluation set  don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: offset_mapping, ner_tags, tokens.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 5000\n",
            "  Batch size = 32\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='157' max='157' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [157/157 00:11]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'epoch': 2.0,\n",
              " 'eval_accuracy': 0.977719109546625,\n",
              " 'eval_f1': 0.8845402539416771,\n",
              " 'eval_loss': 0.07692207396030426,\n",
              " 'eval_precision': 0.8778647095478779,\n",
              " 'eval_recall': 0.8913181019332161,\n",
              " 'eval_runtime': 15.6802,\n",
              " 'eval_samples_per_second': 318.873,\n",
              " 'eval_steps_per_second': 10.013}"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### char-level로 test_ds에 대해서 성능을 계산"
      ],
      "metadata": {
        "id": "dGGte2hfJVCy"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "id": "s7-VO3DADsoU",
        "outputId": "13757821-fa8b-4c27-e6f4-178dd7337bc7"
      },
      "source": [
        "# prediction만 수행해놓기\n",
        "test_result = trainer.predict(test_ds)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the test set  don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: offset_mapping, ner_tags, tokens.\n",
            "***** Running Prediction *****\n",
            "  Num examples = 5000\n",
            "  Batch size = 32\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='314' max='157' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [157/157 34:01]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "`relabel_to_char` function을 정의합니다. 해당 function은 model이 token-level로 prediction한 label을 다시 char-level로 변경하는 function입니다."
      ],
      "metadata": {
        "id": "KK9exh15RFCr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def relabel_to_char(predicted_label, offset_mappings, list_of_labels):\n",
        "    labels_of_chars = []\n",
        "    prev_end_offset = 0\n",
        "\n",
        "    for label, offset_mapping in zip(predicted_label, offset_mappings):\n",
        "        cur_start_offset, cur_end_offset = offset_mapping\n",
        "\n",
        "        if prev_end_offset != cur_start_offset:\n",
        "            labels_of_chars.append(12)\n",
        "\n",
        "        for idx in range(cur_end_offset - cur_start_offset):\n",
        "            if idx != 0:\n",
        "                if list_of_labels[prev_label].startswith(\"B\"):\n",
        "                    labels_of_chars.append(label + 1)\n",
        "                else:\n",
        "                    labels_of_chars.append(label)\n",
        "            else:\n",
        "                labels_of_chars.append(label)\n",
        "                prev_label = label\n",
        "    \n",
        "        prev_end_offset = cur_end_offset\n",
        "    return labels_of_chars\n",
        "\n",
        "predicted_label =  [_ for _ in test_result.label_ids[4] if _ != -100]\n",
        "example =\"\".join(test_ds[\"tokens\"][4]).replace(\"\\xa0\",\" \")\n",
        "trues = test_ds[\"ner_tags\"][4]\n",
        "preds = relabel_to_char(predicted_label, tokenizer(example, return_offsets_mapping=True, add_special_tokens=False)[\"offset_mapping\"], list_of_labels)\n",
        "print(example)\n",
        "print(trues, \"\\n\", len(trues))\n",
        "print(preds, \"\\n\", len(preds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DiIpGDZUakYa",
        "outputId": "e898994a-4550-4d07-f9dc-5f4c4e20a080"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "유시진 대위(송중기)와 의사 강모연 팀장(송혜교)의 흔적이라도 엿볼 수 있을까'하는 마음에 촬영지를 찾은 관광객들은 아쉬운 발걸음을 되돌리고 있다.\n",
            "[6, 7, 7, 12, 12, 12, 12, 6, 7, 7, 12, 12, 12, 12, 12, 12, 6, 7, 7, 12, 12, 12, 12, 6, 7, 7, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12] \n",
            " 82\n",
            "[6, 7, 7, 12, 12, 12, 12, 6, 7, 7, 12, 12, 12, 12, 12, 12, 6, 7, 7, 12, 12, 12, 12, 6, 7, 7, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12] \n",
            " 82\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "`relabel_to_char` function을 이용하여 token-level로 prediction된 결과를 char-level로 변환합니다."
      ],
      "metadata": {
        "id": "eCgMQhx6bn34"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trues = []\n",
        "preds = []\n",
        "\n",
        "for i in range(len(test_result.label_ids)):\n",
        "    if test_ds[\"tokens\"][i][-1] == \" \":\n",
        "        continue\n",
        "\n",
        "    true = test_ds[\"ner_tags\"][i]\n",
        "    example =\"\".join(test_ds[\"tokens\"][i]).replace(\"\\xa0\",\" \")\n",
        "    offset_mapping = tokenizer(example, return_offsets_mapping=True, add_special_tokens=False)[\"offset_mapping\"]\n",
        "    predicted_label =  [_ for _ in test_result.label_ids[i] if _ != -100]\n",
        "    pred = relabel_to_char(predicted_label, offset_mapping, list_of_labels)\n",
        "\n",
        "    if len(true) != len(pred):\n",
        "        print(i)\n",
        "    trues.extend(true)\n",
        "    preds.extend(pred)\n",
        "\n",
        "    if (i + 1) % 100 == 0:\n",
        "        print(f\"{i+1} / {len(test_result.label_ids)}\")\n",
        "else:\n",
        "    print(f\"{i+1} / {len(test_result.label_ids)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IvwqWo43bZzh",
        "outputId": "2988fa41-5704-4f9e-d3c9-6dfc3c27ef33"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LnvGi7oSKWzX",
        "outputId": "66001411-3245-4c9c-aa92-e6b5dce36b3f"
      },
      "source": [
        "sklearn.metrics.f1_score(trues, preds, labels=list(range(len(list_of_labels))), average=\"macro\", zero_division=True) * 100.0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "97.65262555798931"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    }
  ]
}