05b-nyt-re-bert.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Relationship Extraction\n",
        "\n",
        "This notebook is similar to the `05-nyt-re-bert.ipynb` except that this is an implementation of scenario (f) in the slides. Instead of maxpooling over the subject and object spans, we just pull the encoding off the starting subject and object tags.\n",
        "\n",
        "In terms of results, this performs marginally better than scenario (e), MAXPOOLing the entity spans (including the entity marker tags), even though it appears to provide less information to the classifier head to make the prediction.\n",
        "\n",
        "Using the values from the starting span tags is faster though, so it is possible that it might be more feasible to fine tune this model for larger number of epochs to get better performance."
      ],
      "metadata": {
        "id": "3h5x4eJGyqsq"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f05i0ae5yTXg",
        "outputId": "be86a0c7-a6be-4b58-8ae2-72ca518bfc1d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.18.0-py3-none-any.whl (4.0 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.0 MB 5.1 MB/s \n",
            "\u001b[?25hCollecting datasets\n",
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            "  Downloading tokenizers-0.11.6-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.5 MB)\n",
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            "\u001b[?25hCollecting frozenlist>=1.1.1\n",
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            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.7.0)\n",
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            "Building wheels for collected packages: seqeval\n",
            "  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16180 sha256=2d9f469ef27e7b006471e9baba7da8dc8856df0b2e14a62f3500f5030d8419b3\n",
            "  Stored in directory: /root/.cache/pip/wheels/05/96/ee/7cac4e74f3b19e3158dce26a20a1c86b3533c43ec72a549fd7\n",
            "Successfully built seqeval\n",
            "Installing collected packages: multidict, frozenlist, yarl, urllib3, asynctest, async-timeout, aiosignal, pyyaml, fsspec, aiohttp, xxhash, tokenizers, sacremoses, responses, huggingface-hub, transformers, seqeval, datasets\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
            "    Uninstalling urllib3-1.24.3:\n",
            "      Successfully uninstalled urllib3-1.24.3\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed aiohttp-3.8.1 aiosignal-1.2.0 async-timeout-4.0.2 asynctest-0.13.0 datasets-2.0.0 frozenlist-1.3.0 fsspec-2022.3.0 huggingface-hub-0.5.1 multidict-6.0.2 pyyaml-6.0 responses-0.18.0 sacremoses-0.0.49 seqeval-1.2.2 tokenizers-0.11.6 transformers-4.18.0 urllib3-1.25.11 xxhash-3.0.0 yarl-1.7.2\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers datasets seqeval"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Imports"
      ],
      "metadata": {
        "id": "SF1qMkCqyi4i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import collections\n",
        "import json\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "import random\n",
        "import re\n",
        "import shutil\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "from collections import Counter, defaultdict\n",
        "from datasets import load_dataset, ClassLabel\n",
        "from sklearn.metrics import (\n",
        "    ConfusionMatrixDisplay, confusion_matrix, \n",
        "    classification_report, accuracy_score\n",
        ")\n",
        "from torch.utils.data import DataLoader, SubsetRandomSampler\n",
        "from torch.optim import AdamW\n",
        "\n",
        "from transformers import (\n",
        "    BertTokenizerFast, BertModel, BertForPreTraining, BertConfig, BertPreTrainedModel,\n",
        "    DataCollatorWithPadding,\n",
        "    get_scheduler\n",
        ")\n",
        "from transformers.modeling_outputs import SequenceClassifierOutput"
      ],
      "metadata": {
        "id": "Jqx3zIaHyZcO"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Mount Google Drive"
      ],
      "metadata": {
        "id": "Esf6Dldf0Gu0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BTlHrTXQ0BoY",
        "outputId": "f87940fb-ff21-4feb-fc2c-3a02ca34e3ab"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "GS_INPUT_DIR = \"/content/drive/MyDrive/odsc-2022-data/NYT\"\n",
        "DATA_DIR = \"./data\"\n",
        "\n",
        "BASE_MODEL_NAME = \"bert-base-cased\"\n",
        "MODEL_DIR = os.path.join(DATA_DIR, \"{:s}-nyt-re-v3\".format(BASE_MODEL_NAME))"
      ],
      "metadata": {
        "id": "9uz7rfPI0LDM"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!ls /content/drive/MyDrive/odsc-2022-data/NYT/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "szFMpxLd0SCB",
        "outputId": "bb94dc6b-b139-4eb6-b036-f7c58be5c06e"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "bert-base-cased-nyt-re-v1  test.json  train.json  valid.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Input Data Format"
      ],
      "metadata": {
        "id": "XB7qLtHK0Vdm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with open(os.path.join(GS_INPUT_DIR, \"train.json\"), \"r\") as fin:\n",
        "  for line in fin:\n",
        "    rec = json.loads(line.strip())\n",
        "    break\n",
        "\n",
        "print(json.dumps(rec, indent=2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9gx0rmEj0UDm",
        "outputId": "ab31b345-532e-4abd-d491-ff564cfca65c"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\n",
            "  \"sentText\": \"Massachusetts ASTON MAGNA Great Barrington ; also at Bard College , Annandale-on-Hudson , N.Y. , July 1-Aug .\",\n",
            "  \"articleId\": \"/m/vinci8/data1/riedel/projects/relation/kb/nyt1/docstore/nyt-2005-2006.backup/1669365.xml.pb\",\n",
            "  \"relationMentions\": [\n",
            "    {\n",
            "      \"em1Text\": \"Annandale-on-Hudson\",\n",
            "      \"em2Text\": \"Bard College\",\n",
            "      \"label\": \"/location/location/contains\"\n",
            "    }\n",
            "  ],\n",
            "  \"entityMentions\": [\n",
            "    {\n",
            "      \"start\": 1,\n",
            "      \"label\": \"ORGANIZATION\",\n",
            "      \"text\": \"Bard College\"\n",
            "    },\n",
            "    {\n",
            "      \"start\": 2,\n",
            "      \"label\": \"LOCATION\",\n",
            "      \"text\": \"Annandale-on-Hudson\"\n",
            "    }\n",
            "  ],\n",
            "  \"sentId\": \"1\"\n",
            "}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Entity Types"
      ],
      "metadata": {
        "id": "gQChhtHEE2Qq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "splits = [\"train\", \"valid\", \"test\"]\n",
        "ent_counts = collections.defaultdict(collections.Counter)\n",
        "for split in splits:\n",
        "  with open(os.path.join(GS_INPUT_DIR, \"{:s}.json\".format(split)), \"r\") as fin:\n",
        "    for json_line in fin:\n",
        "      json_dict = json.loads(json_line.strip())\n",
        "      for ent_mention in json_dict[\"entityMentions\"]:\n",
        "        label = ent_mention[\"label\"]\n",
        "        ent_counts[label][split] += 1\n",
        "\n",
        "ent_df = pd.DataFrame.from_dict(ent_counts, orient=\"index\").sort_values(\"train\", ascending=False)\n",
        "ent_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "sY6PMv9_EmnQ",
        "outputId": "87a00734-41d4-45ba-c9fe-f16ef677c7b6"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               train  valid   test\n",
              "LOCATION      119693  10703  10721\n",
              "PERSON         41367   3727   3727\n",
              "ORGANIZATION   16401   1493   1413"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c0649d5c-8f47-4bce-95bc-7b9b981eca18\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>train</th>\n",
              "      <th>valid</th>\n",
              "      <th>test</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>LOCATION</th>\n",
              "      <td>119693</td>\n",
              "      <td>10703</td>\n",
              "      <td>10721</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PERSON</th>\n",
              "      <td>41367</td>\n",
              "      <td>3727</td>\n",
              "      <td>3727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ORGANIZATION</th>\n",
              "      <td>16401</td>\n",
              "      <td>1493</td>\n",
              "      <td>1413</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c0649d5c-8f47-4bce-95bc-7b9b981eca18')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c0649d5c-8f47-4bce-95bc-7b9b981eca18 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c0649d5c-8f47-4bce-95bc-7b9b981eca18');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Relation Types"
      ],
      "metadata": {
        "id": "GZFA09W4E9Di"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rel_counts = collections.defaultdict(collections.Counter)\n",
        "for split in splits:\n",
        "  with open(os.path.join(GS_INPUT_DIR, \"{:s}.json\".format(split)), \"r\") as fin:\n",
        "    for json_line in fin:\n",
        "      json_dict = json.loads(json_line.strip())\n",
        "      for ent_mention in json_dict[\"relationMentions\"]:\n",
        "        label = ent_mention[\"label\"].split(\"/\")[-1]\n",
        "        rel_counts[label][split] += 1\n",
        "\n",
        "rel_df = pd.DataFrame.from_dict(rel_counts, orient=\"index\").sort_values(\"train\", ascending=False)\n",
        "rel_df = rel_df[(rel_df[\"train\"] > 1000) & (rel_df[\"train\"] < 10000)]\n",
        "rel_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "uIGnOA7oE6en",
        "outputId": "6ff20ee0-0d83-40ef-c436-c526960d5716"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          train  valid   test\n",
              "nationality                7198  643.0  589.0\n",
              "capital                    6718  616.0  708.0\n",
              "place_lived                6362  539.0  612.0\n",
              "country                    5658  558.0  580.0\n",
              "administrative_divisions   5658  558.0  580.0\n",
              "company                    4950  466.0  436.0\n",
              "neighborhood_of            4941  469.0  394.0\n",
              "place_of_birth             2813  228.0  270.0\n",
              "place_of_death             1752  133.0  136.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e44a8f23-426b-4b37-81fc-297d09436a0d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>train</th>\n",
              "      <th>valid</th>\n",
              "      <th>test</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>nationality</th>\n",
              "      <td>7198</td>\n",
              "      <td>643.0</td>\n",
              "      <td>589.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>capital</th>\n",
              "      <td>6718</td>\n",
              "      <td>616.0</td>\n",
              "      <td>708.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>place_lived</th>\n",
              "      <td>6362</td>\n",
              "      <td>539.0</td>\n",
              "      <td>612.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>country</th>\n",
              "      <td>5658</td>\n",
              "      <td>558.0</td>\n",
              "      <td>580.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>administrative_divisions</th>\n",
              "      <td>5658</td>\n",
              "      <td>558.0</td>\n",
              "      <td>580.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>company</th>\n",
              "      <td>4950</td>\n",
              "      <td>466.0</td>\n",
              "      <td>436.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>neighborhood_of</th>\n",
              "      <td>4941</td>\n",
              "      <td>469.0</td>\n",
              "      <td>394.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>place_of_birth</th>\n",
              "      <td>2813</td>\n",
              "      <td>228.0</td>\n",
              "      <td>270.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>place_of_death</th>\n",
              "      <td>1752</td>\n",
              "      <td>133.0</td>\n",
              "      <td>136.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e44a8f23-426b-4b37-81fc-297d09436a0d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e44a8f23-426b-4b37-81fc-297d09436a0d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e44a8f23-426b-4b37-81fc-297d09436a0d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Raw Dataset"
      ],
      "metadata": {
        "id": "_RgKJgvrFNuJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def reformat_json(infile, outfile, valid_relations):\n",
        "  print(\"reformating {:s} -> {:s}\".format(infile, outfile))\n",
        "  fout = open(outfile, \"w\")\n",
        "  with open(infile, \"r\") as fin:\n",
        "    for line in fin:\n",
        "      rec = json.loads(line.strip())\n",
        "      text = rec[\"sentText\"]\n",
        "      entities = {}\n",
        "      for entity_mention in rec[\"entityMentions\"]:\n",
        "        entity_text = entity_mention[\"text\"]\n",
        "        entity_type = entity_mention[\"label\"][0:3]\n",
        "        entities[entity_text] = entity_type\n",
        "      for relation_mention in rec[\"relationMentions\"]:\n",
        "        label = relation_mention[\"label\"].split(\"/\")[-1]\n",
        "        if label not in valid_relations:\n",
        "          continue\n",
        "        try:\n",
        "          sub_text = relation_mention[\"em1Text\"]\n",
        "          sub_type = entities[sub_text]\n",
        "          obj_text = relation_mention[\"em2Text\"]\n",
        "          obj_type = entities[obj_text]\n",
        "          \n",
        "          # assumption: em1Text == SUBJECT and occurs before em2Text == OBJECT\n",
        "          sub_start = text.find(sub_text)\n",
        "          sub_end = sub_start + len(sub_text)\n",
        "          text_pre = text[:sub_start]\n",
        "          text_sub = \"<S:{:s}> {:s} </S:{:s}>\".format(sub_type, sub_text, sub_type)\n",
        "          obj_start = text.find(obj_text, sub_end)\n",
        "          obj_end = obj_start + len(obj_text)\n",
        "          text_mid = text[sub_end:obj_start]\n",
        "          text_obj = \"<O:{:s}> {:s} </O:{:s}>\".format(obj_type, obj_text, obj_type)\n",
        "          text_post = text[obj_end:]\n",
        "          text = text_pre + text_sub + text_mid + text_obj + text_post\n",
        "\n",
        "          tokens = text.split()\n",
        "          output = {\n",
        "              \"tokens\": tokens,\n",
        "              \"label\": label\n",
        "          }\n",
        "          \n",
        "          fout.write(json.dumps(output) + \"\\n\")\n",
        "        except:\n",
        "          pass\n",
        "  fout.close()\n",
        "\n",
        "\n",
        "os.makedirs(DATA_DIR, exist_ok=True)\n",
        "\n",
        "valid_relations = set([\n",
        "  \"nationality\", \"capital\", \"place_lived\", \"country\", \"administrative_divisions\",\n",
        "  \"company\", \"neighborhood_of\", \"place_of_birth\", \"place_of_death\"\n",
        "])\n",
        "\n",
        "for i, split in enumerate(splits):\n",
        "  reformat_json(os.path.join(GS_INPUT_DIR, \"{:s}.json\".format(split)),\n",
        "                os.path.join(DATA_DIR, \"{:s}.json\".format(split)), \n",
        "                valid_relations=valid_relations)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VtdRuYVSFCH_",
        "outputId": "fcf57ee6-10fb-452f-f658-f8bb7110daf4"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "reformating /content/drive/MyDrive/odsc-2022-data/NYT/train.json -> ./data/train.json\n",
            "reformating /content/drive/MyDrive/odsc-2022-data/NYT/valid.json -> ./data/valid.json\n",
            "reformating /content/drive/MyDrive/odsc-2022-data/NYT/test.json -> ./data/test.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_files = {split: os.path.join(DATA_DIR, \"{:s}.json\".format(split)) for split in splits}\n",
        "nyt_dataset = load_dataset(\"json\", data_files=data_files)\n",
        "nyt_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436,
          "referenced_widgets": [
            "e051fa3a4cac4cecb86d950e5b8e292f",
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            "1bc53d1772d54e9da44b5e7b8fc2e381",
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            "b24758797e02479da4b9f3068fe1d018",
            "1313e236803f4cdf94d14dc71f510d0a",
            "a85ca66c7ba14a708f14ebc820496572"
          ]
        },
        "id": "ZE0PQZbiFh_H",
        "outputId": "c75b7867-11c9-49c0-fb63-2e2ac1e69c13"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using custom data configuration default-f2756fef427e335d\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-f2756fef427e335d/0.0.0/ac0ca5f5289a6cf108e706efcf040422dbbfa8e658dee6a819f20d76bb84d26b...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e051fa3a4cac4cecb86d950e5b8e292f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "08a95c6f0aa04bb58be3373832dddbbe"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/json/default-f2756fef427e335d/0.0.0/ac0ca5f5289a6cf108e706efcf040422dbbfa8e658dee6a819f20d76bb84d26b. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "eca66980ddb04ffeb7bf57b6104578f8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['tokens', 'label'],\n",
              "        num_rows: 45860\n",
              "    })\n",
              "    valid: Dataset({\n",
              "        features: ['tokens', 'label'],\n",
              "        num_rows: 4182\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['tokens', 'label'],\n",
              "        num_rows: 4277\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Label Distribution"
      ],
      "metadata": {
        "id": "nMrSZrlPDeT-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nyt_dataset.set_format(type=\"pandas\")\n",
        "nyt_df = nyt_dataset[\"train\"][:]\n",
        "nyt_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "yu3AzGD6DhlF",
        "outputId": "ab57f429-7015-4684-9e2f-f09d8b489f0b"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              tokens           label\n",
              "0  [It, will, be, the, final, movie, credited, to...  place_of_birth\n",
              "1  [Under, pressure, from, Mr., Kerkorian, and, o...         company\n",
              "2  [Mr., Ferrer, still, holds, commanding, leads,...     place_lived\n",
              "3  [Mr., Ferrer, still, holds, commanding, leads,...     place_lived\n",
              "4  [United, States, Representative, <S:PER>, Char...  place_of_birth"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-faaa9c5d-b80f-49b4-90f6-e3588464fc8c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tokens</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>[It, will, be, the, final, movie, credited, to...</td>\n",
              "      <td>place_of_birth</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>[Under, pressure, from, Mr., Kerkorian, and, o...</td>\n",
              "      <td>company</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>[Mr., Ferrer, still, holds, commanding, leads,...</td>\n",
              "      <td>place_lived</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>[Mr., Ferrer, still, holds, commanding, leads,...</td>\n",
              "      <td>place_lived</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>[United, States, Representative, &lt;S:PER&gt;, Char...</td>\n",
              "      <td>place_of_birth</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-faaa9c5d-b80f-49b4-90f6-e3588464fc8c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-faaa9c5d-b80f-49b4-90f6-e3588464fc8c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-faaa9c5d-b80f-49b4-90f6-e3588464fc8c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nyt_df[\"label\"].value_counts(ascending=True).plot.barh()\n",
        "plt.title(\"Relation Label Frequency\")\n",
        "_ = plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "XwXVk9-MEf29",
        "outputId": "75a688ab-da3f-49ef-eee6-601caed87d04"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Sentence Length Distribution\n",
        "\n",
        "Box plots below shows that we can safely ignore sentences that are over 100 tokens in length."
      ],
      "metadata": {
        "id": "LMj6KayQDip_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nyt_df[\"num_tokens\"] = nyt_df[\"tokens\"].apply(len)\n",
        "nyt_df.boxplot(\"num_tokens\", by=\"label\", grid=False, showfliers=False)\n",
        "plt.suptitle(\"\")\n",
        "plt.xlabel(\"\")\n",
        "plt.xticks(rotation=90)\n",
        "_ = plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "id": "pLq1ZPYmDnS2",
        "outputId": "dcd82f41-865d-414a-babf-9578a97dd484"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nyt_dataset.reset_format()"
      ],
      "metadata": {
        "id": "XvYXld9iG5AB"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tokenizer"
      ],
      "metadata": {
        "id": "YgnVGRXGGA03"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = BertTokenizerFast.from_pretrained(BASE_MODEL_NAME)"
      ],
      "metadata": {
        "id": "T-WFzVDGF5bZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "dac1501190a440a199b8e16a8f87d3fc",
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          ]
        },
        "outputId": "07049060-59b1-47d5-f7fc-fb4a3b88b2a0"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/208k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "version_major": 2,
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              "model_id": "cebbe6a54a144a87b7e532c07d7581a0"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "da2248ddbdbe47dd91106abc0b929d6b"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Add Entity Marker Tokens"
      ],
      "metadata": {
        "id": "o_zH8_jcGRoh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "marker_tokens = []\n",
        "entity_types = [\"LOC\", \"PER\", \"ORG\"]\n",
        "for entity_type in entity_types:\n",
        "  marker_tokens.append(\"<S:{:s}>\".format(entity_type))\n",
        "  marker_tokens.append(\"</S:{:s}>\".format(entity_type))\n",
        "  marker_tokens.append(\"<O:{:s}>\".format(entity_type))\n",
        "  marker_tokens.append(\"</O:{:s}>\".format(entity_type))\n",
        "\n",
        "tokenizer.add_tokens(marker_tokens)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z2Ldh9b1GQU6",
        "outputId": "33644ce6-791c-4528-b51f-66bb35418f01"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Encoded Dataset"
      ],
      "metadata": {
        "id": "mBD3SiXGH7dY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "valid_relations = sorted(list(valid_relations))\n",
        "rel_tags = ClassLabel(names=valid_relations)\n",
        "label2id = {name: rel_tags.str2int(name) for name in valid_relations}\n",
        "id2label = {id: rel_tags.int2str(id) for id in range(len(valid_relations))}\n",
        "\n",
        "# label2id, id2label"
      ],
      "metadata": {
        "id": "tkLoF1gII5rT"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def encode_data(examples):\n",
        "  tokenized_inputs = tokenizer(examples[\"tokens\"],\n",
        "                               is_split_into_words=True,\n",
        "                               truncation=True)\n",
        "  span_idxs = []\n",
        "  for input_id in tokenized_inputs.input_ids:\n",
        "    tokens = tokenizer.convert_ids_to_tokens(input_id)\n",
        "    span_idxs.append([\n",
        "      [idx for idx, token in enumerate(tokens) if token.startswith(\"<S:\")][0],\n",
        "      [idx for idx, token in enumerate(tokens) if token.startswith(\"</S:\")][0],\n",
        "      [idx for idx, token in enumerate(tokens) if token.startswith(\"<O:\")][0],\n",
        "      [idx for idx, token in enumerate(tokens) if token.startswith(\"</O:\")][0]\n",
        "    ])\n",
        "  tokenized_inputs[\"span_idxs\"] = span_idxs\n",
        "  tokenized_inputs[\"labels\"] = [label2id[label] for label in examples[\"label\"]]\n",
        "  return tokenized_inputs\n",
        "\n",
        "encoded = encode_data(nyt_dataset[\"train\"][0:5])\n",
        "encoded.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K4vroA1XGaw5",
        "outputId": "5b2ad369-9be1-4b6f-907b-baae7f6cfa5c"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'span_idxs', 'labels'])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "MAX_LENGTH = 100\n",
        "encoded_nyt_dataset = (nyt_dataset\n",
        "                       .filter(lambda example: len(example[\"tokens\"]) < MAX_LENGTH)\n",
        "                       .map(encode_data, batched=True, remove_columns=[\"tokens\", \"label\"]))\n",
        "encoded_nyt_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459,
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        "id": "OEpXrPdWGrNN",
        "outputId": "2a1d6703-292e-445f-9cd8-d654412437e8"
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      "execution_count": 19,
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        {
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        {
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "8bad8de7e288422ab1c39d614beb8ea2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['input_ids', 'token_type_ids', 'attention_mask', 'span_idxs', 'labels'],\n",
              "        num_rows: 40126\n",
              "    })\n",
              "    valid: Dataset({\n",
              "        features: ['input_ids', 'token_type_ids', 'attention_mask', 'span_idxs', 'labels'],\n",
              "        num_rows: 3656\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['input_ids', 'token_type_ids', 'attention_mask', 'span_idxs', 'labels'],\n",
              "        num_rows: 3713\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rec = encoded_nyt_dataset[\"train\"][0:5]\n",
        "print(\"rec.labels:\", rec[\"labels\"])\n",
        "print(\"rec.input_ids:\", len(rec[\"input_ids\"]), len(rec[\"input_ids\"][0]))\n",
        "print(\"rec.span_idxs:\", rec[\"span_idxs\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KhP0StSssVsV",
        "outputId": "45ea36eb-7b0b-4fe9-d8f0-2b70a4a623f3"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "rec.labels: [7, 2, 6, 6, 7]\n",
            "rec.input_ids: 5 44\n",
            "rec.span_idxs: [[9, 13, 21, 25], [31, 36, 42, 44], [22, 28, 30, 32], [22, 29, 32, 34], [4, 10, 23, 27]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## DataLoader"
      ],
      "metadata": {
        "id": "1dvFBiA06T4v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "BATCH_SIZE = 16"
      ],
      "metadata": {
        "id": "SWSn_hxH6egZ"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "collate_fn = DataCollatorWithPadding(tokenizer, padding=\"longest\", return_tensors=\"pt\")\n",
        "\n",
        "train_dl = DataLoader(encoded_nyt_dataset[\"train\"], \n",
        "                      shuffle=True, \n",
        "                      # sampler=SubsetRandomSampler(np.random.randint(0, encoded_nyt_dataset[\"train\"].num_rows, 1000).tolist()),\n",
        "                      batch_size=BATCH_SIZE, \n",
        "                      collate_fn=collate_fn)\n",
        "valid_dl = DataLoader(encoded_nyt_dataset[\"valid\"], \n",
        "                      shuffle=False, \n",
        "                      # sampler=SubsetRandomSampler(np.random.randint(0, encoded_nyt_dataset[\"valid\"].num_rows, 200).tolist()),\n",
        "                      batch_size=BATCH_SIZE, \n",
        "                      collate_fn=collate_fn)\n",
        "test_dl = DataLoader(encoded_nyt_dataset[\"test\"], \n",
        "                     shuffle=False,\n",
        "                    #  sampler=SubsetRandomSampler(np.random.randint(0, encoded_nyt_dataset[\"test\"].num_rows, 100).tolist()),\n",
        "                     batch_size=BATCH_SIZE, \n",
        "                     collate_fn=collate_fn)"
      ],
      "metadata": {
        "id": "6ZFu3lra6V8S"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model"
      ],
      "metadata": {
        "id": "YRv2wT9qKYcu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ],
      "metadata": {
        "id": "Mke3xB586GeJ"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class BertForRelationExtraction(BertPreTrainedModel):\n",
        "  def __init__(self, config, num_labels):\n",
        "    super(BertForRelationExtraction, self).__init__(config)\n",
        "    self.num_labels = num_labels\n",
        "    # body\n",
        "    self.bert = BertModel(config)\n",
        "    # head\n",
        "    self.dropout = nn.Dropout(config.hidden_dropout_prob)\n",
        "    self.layer_norm = nn.LayerNorm(config.hidden_size * 2)\n",
        "    self.linear = nn.Linear(config.hidden_size * 2, self.num_labels)\n",
        "    self.init_weights()\n",
        "\n",
        "  def forward(self, input_ids, token_type_ids, attention_mask,\n",
        "              span_idxs, labels=None):\n",
        "    outputs = (\n",
        "        self.bert(input_ids, token_type_ids=token_type_ids,\n",
        "                  attention_mask=attention_mask,\n",
        "                  output_hidden_states=False)\n",
        "            .last_hidden_state)\n",
        "            \n",
        "    sub_maxpool, obj_maxpool = [], []\n",
        "    for bid in range(outputs.size(0)):\n",
        "      # just use the encodings off the <S:XXX> and <O:XXX> tags\n",
        "      sub_span = outputs[bid, span_idxs[bid, 0], :].unsqueeze(0)\n",
        "      obj_span = outputs[bid, span_idxs[bid, 2], :].unsqueeze(0)\n",
        "      sub_maxpool.append(sub_span)\n",
        "      obj_maxpool.append(obj_span)\n",
        "\n",
        "    sub_emb = torch.cat(sub_maxpool, dim=0)\n",
        "    obj_emb = torch.cat(obj_maxpool, dim=0)\n",
        "    rel_input = torch.cat((sub_emb, obj_emb), dim=-1)\n",
        "\n",
        "    rel_input = self.layer_norm(rel_input)\n",
        "    rel_input = self.dropout(rel_input)\n",
        "    logits = self.linear(rel_input)\n",
        "\n",
        "    if labels is not None:\n",
        "      loss_fn = nn.CrossEntropyLoss()\n",
        "      loss = loss_fn(logits.view(-1, self.num_labels), labels.view(-1))\n",
        "      return SequenceClassifierOutput(loss, logits)\n",
        "    else:\n",
        "      return SequenceClassifierOutput(None, logits)\n",
        "\n",
        "\n",
        "# config = BertConfig.from_pretrained(BASE_MODEL_NAME)\n",
        "# model = BertForRelationExtraction.from_pretrained(BASE_MODEL_NAME, \n",
        "#                                                   config=config,\n",
        "#                                                   num_labels=len(valid_relations))\n",
        "# model.bert.resize_token_embeddings(len(tokenizer.vocab))\n",
        "# for batch in train_dl:\n",
        "#   outputs = model(**batch)\n",
        "#   break\n",
        "# print(\"loss:\", outputs.loss, \"logits.size:\", outputs.logits.size())"
      ],
      "metadata": {
        "id": "B_X95sU84_Z-"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training Loop"
      ],
      "metadata": {
        "id": "xgI-XvqQXhhq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "LEARNING_RATE = 5e-5\n",
        "WEIGHT_DECAY = 1e-2\n",
        "NUM_EPOCHS = 3"
      ],
      "metadata": {
        "id": "n_1N0TfQcq82"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "config = BertConfig.from_pretrained(BASE_MODEL_NAME)\n",
        "model = BertForRelationExtraction.from_pretrained(BASE_MODEL_NAME, \n",
        "                                                  config=config,\n",
        "                                                  num_labels=len(valid_relations))\n",
        "model.bert.resize_token_embeddings(len(tokenizer.vocab))\n",
        "model = model.to(device)\n",
        "\n",
        "optimizer = AdamW(model.parameters(),\n",
        "                  lr=LEARNING_RATE,\n",
        "                  weight_decay=WEIGHT_DECAY)\n",
        "\n",
        "num_training_steps = NUM_EPOCHS * len(train_dl)\n",
        "lr_scheduler = get_scheduler(\"linear\",\n",
        "                             optimizer=optimizer,\n",
        "                             num_warmup_steps=0,\n",
        "                             num_training_steps=num_training_steps)"
      ],
      "metadata": {
        "id": "Bawjik5Xctf7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158,
          "referenced_widgets": [
            "76d0bb1dfb014e1c96dcf668645e22fb",
            "032bdd4dfa3c42c8a124cd51f889835e",
            "e5984008fe4846abb0fb5b0e44947bfb",
            "fe8213e758374d4fa17d440d6162851e",
            "acacad2205b845d187814ac6e902b8c3",
            "e3bd432cbce942f7a8077d30b7a42dbf",
            "0389e1d723364238a397b9251bbdd2e5",
            "ec73fc685b5c4603b8b4747940fd60a3",
            "895dd66b5b32414bb99e4b8fb113d296",
            "fc4e90ef6ba642b4be4cb8e3c21f4bf8",
            "5a511bd2283448019be0a27b961edcce"
          ]
        },
        "outputId": "1f7da24b-8547-4ef5-fd06-729078f7da3b"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/416M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "76d0bb1dfb014e1c96dcf668645e22fb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForRelationExtraction: ['cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias']\n",
            "- This IS expected if you are initializing BertForRelationExtraction 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 BertForRelationExtraction 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 BertForRelationExtraction were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['linear.bias', 'linear.weight', 'layer_norm.bias', 'layer_norm.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_accuracy(labels, logits):\n",
        "  preds_cpu = torch.argmax(logits, dim=-1).cpu().numpy()\n",
        "  labels_cpu = labels.cpu().numpy()\n",
        "  return accuracy_score(labels_cpu, preds_cpu)\n",
        "\n",
        "\n",
        "def do_train(model, train_dl):\n",
        "  train_loss = 0\n",
        "  model.train()\n",
        "  for bid, batch in enumerate(train_dl):\n",
        "    batch = {k: v.to(device) for k, v in batch.items()}\n",
        "    outputs = model(**batch)\n",
        "    loss = outputs.loss\n",
        "    train_loss += loss.detach().cpu().numpy()\n",
        "    loss.backward()\n",
        "\n",
        "    optimizer.step()\n",
        "    lr_scheduler.step()\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "  return train_loss\n",
        "\n",
        "\n",
        "def do_eval(model, eval_dl):\n",
        "  model.eval()\n",
        "  eval_loss, eval_score, num_batches = 0, 0, 0\n",
        "  for bid, batch in enumerate(eval_dl):\n",
        "    batch = {k: v.to(device) for k, v in batch.items()}\n",
        "    with torch.no_grad():\n",
        "      outputs = model(**batch)\n",
        "\n",
        "    loss = outputs.loss\n",
        "\n",
        "    eval_loss += loss.detach().cpu().numpy()\n",
        "    eval_score += compute_accuracy(batch[\"labels\"], outputs.logits)\n",
        "    num_batches += 1\n",
        "\n",
        "  eval_score /= num_batches\n",
        "  return eval_loss, eval_score\n",
        "\n",
        "\n",
        "def save_checkpoint(model, model_dir, epoch):\n",
        "  model.save_pretrained(os.path.join(MODEL_DIR, \"ckpt-{:d}\".format(epoch)))\n",
        "\n",
        "\n",
        "def save_training_history(history, model_dir, epoch):\n",
        "  fhist = open(os.path.join(MODEL_DIR, \"history.tsv\"), \"w\")\n",
        "  for epoch, train_loss, eval_loss, eval_score in history:\n",
        "    fhist.write(\"{:d}\\t{:.5f}\\t{:.5f}\\t{:.5f}\\n\".format(\n",
        "        epoch, train_loss, eval_loss, eval_score))\n",
        "  fhist.close()\n"
      ],
      "metadata": {
        "id": "r5vzzKQ132gO"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training / Fine-tuning"
      ],
      "metadata": {
        "id": "3y1g0Ce7zjk5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "if os.path.exists(MODEL_DIR):\n",
        "  shutil.rmtree(MODEL_DIR)\n",
        "  os.makedirs(MODEL_DIR)\n",
        "\n",
        "history = []\n",
        "\n",
        "for epoch in range(NUM_EPOCHS):\n",
        "  train_loss = do_train(model, train_dl)\n",
        "  eval_loss, eval_score = do_eval(model, valid_dl)\n",
        "  history.append((epoch + 1, train_loss, eval_loss, eval_score))\n",
        "  print(\"EPOCH {:d}, train loss: {:.3f}, val loss: {:.3f}, val-acc: {:.5f}\".format(\n",
        "      epoch + 1, train_loss, eval_loss, eval_score))\n",
        "  save_checkpoint(model, MODEL_DIR, epoch + 1)\n",
        "  save_training_history(history, MODEL_DIR, epoch + 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mpSlg-Q8zjHh",
        "outputId": "32799bd0-89b6-430b-e204-4f9888660f89"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EPOCH 1, train loss: 1063.409, val loss: 73.926, val-acc: 0.85180\n",
            "EPOCH 2, train loss: 712.425, val loss: 66.152, val-acc: 0.85207\n",
            "EPOCH 3, train loss: 548.724, val loss: 65.063, val-acc: 0.85753\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplot(2, 1, 1)\n",
        "plt.plot([train_loss for _, train_loss, _, _ in history], label=\"train\")\n",
        "plt.plot([eval_loss for _, _, eval_loss, _ in history], label=\"validation\")\n",
        "plt.xlabel(\"epochs\")\n",
        "plt.ylabel(\"loss\")\n",
        "plt.legend(loc=\"best\")\n",
        "\n",
        "plt.subplot(2, 1, 2)\n",
        "plt.plot([eval_score for _, _, _, eval_score in history], label=\"validation\")\n",
        "plt.xlabel(\"epochs\")\n",
        "plt.ylabel(\"accuracy\")\n",
        "plt.legend(loc=\"best\")\n",
        "\n",
        "plt.tight_layout()\n",
        "_ = plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "gURDgSgCzqm1",
        "outputId": "4a949f8f-9760-4445-f59b-b755a59d21b1"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluation"
      ],
      "metadata": {
        "id": "P_XUtlO9zuve"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ytrue, ypred = [], []\n",
        "for batch in test_dl:\n",
        "    batch = {k: v.to(device) for k, v in batch.items()}\n",
        "    with torch.no_grad():\n",
        "      outputs = model(**batch)\n",
        "      predictions = torch.argmax(outputs.logits, dim=-1).cpu().numpy()\n",
        "      labels = batch[\"labels\"].cpu().numpy()\n",
        "      ytrue.extend(labels)\n",
        "      ypred.extend(predictions)\n",
        "\n",
        "print(\"test accuracy: {:.3f}\".format(accuracy_score(ytrue, ypred)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7NdiMpOqzuQt",
        "outputId": "50b14b12-1d5f-4184-9f54-e058adf6a72a"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test accuracy: 0.854\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(ytrue, ypred, target_names=valid_relations))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ljyhg_CXLm1W",
        "outputId": "4fd055d9-ba90-4eec-b880-3aedb1e69c11"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                          precision    recall  f1-score   support\n",
            "\n",
            "administrative_divisions       0.69      0.84      0.76       463\n",
            "                 capital       0.86      0.72      0.78       597\n",
            "                 company       0.98      0.98      0.98       409\n",
            "                 country       0.98      0.98      0.98       458\n",
            "             nationality       0.88      0.97      0.92       527\n",
            "         neighborhood_of       0.99      0.99      0.99       364\n",
            "             place_lived       0.83      0.86      0.85       562\n",
            "          place_of_birth       0.63      0.43      0.51       223\n",
            "          place_of_death       0.46      0.38      0.42       110\n",
            "\n",
            "                accuracy                           0.85      3713\n",
            "               macro avg       0.81      0.80      0.80      3713\n",
            "            weighted avg       0.85      0.85      0.85      3713\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_confusion_matrix(ytrue, ypreds, labels):\n",
        "  cm = confusion_matrix(ytrue, ypreds, normalize=\"true\")\n",
        "  fig, ax = plt.subplots(figsize=(12, 12))\n",
        "  disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
        "  disp.plot(cmap=\"Blues\", values_format=\"0.2f\", ax=ax, colorbar=False)\n",
        "  plt.title(\"Normalized Confusion Matrix\")\n",
        "  _ = plt.show()\n",
        "\n",
        "\n",
        "plot_confusion_matrix(ytrue, ypred, valid_relations)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        },
        "id": "iY05xS59Egww",
        "outputId": "b28c3cd7-82e4-4584-8bc5-330be8a98587"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x864 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Save Model"
      ],
      "metadata": {
        "id": "arJiYWXE95C3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!cp -r data/bert-base-cased-nyt-re-v3/ /content/drive/MyDrive/odsc-2022-data/NYT/"
      ],
      "metadata": {
        "id": "OpRI94Sl9sHM"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!ls /content/drive/MyDrive/odsc-2022-data/NYT/bert-base-cased-nyt-re-v3/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lY2vfHf0-DV_",
        "outputId": "da7407e0-5448-44d4-8c59-bbb256bb272c"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ckpt-1\tckpt-2\tckpt-3\thistory.tsv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "KpFMeXtc-XIC"
      },
      "execution_count": 34,
      "outputs": []
    }
  ]
}