05a-nyt-re-bert.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Relationship Extraction\n",
        "\n",
        "This notebook is similar to `05-nyt-re-bert.ipynb` except that implements scenario (b) in the slides, i.e., we use the subject and object entity spans **without** the entity markers. \n",
        "\n",
        "Working without entity markers is implemented by marking the start and end tokens of entity spans using a special character, and then extracting the span indexes in the raw dataset itself (since the information is no longer available by the time we go to the encoded dataset).\n",
        "\n",
        "As noted also in the [Matching the Blanks paper](https://arxiv.org/abs/1906.03158), this model does not perform as well as the models implementing scenario (e) and (f)."
      ],
      "metadata": {
        "id": "3h5x4eJGyqsq"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
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          "base_uri": "https://localhost:8080/"
        },
        "id": "f05i0ae5yTXg",
        "outputId": "e21b4a00-deeb-4bfb-b2ee-e8c794e7be4a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.7/dist-packages (4.18.0)\n",
            "Requirement already satisfied: datasets in /usr/local/lib/python3.7/dist-packages (2.0.0)\n",
            "Requirement already satisfied: seqeval in /usr/local/lib/python3.7/dist-packages (1.2.2)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.1.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.5.1)\n",
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            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.21.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
            "Requirement already satisfied: sacremoses in /usr/local/lib/python3.7/dist-packages (from transformers) (0.0.49)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.13,>=0.11.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.11.6)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.11.3)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
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            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
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            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (3.10.0.2)\n",
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            "Requirement already satisfied: fsspec[http]>=2021.05.0 in /usr/local/lib/python3.7/dist-packages (from datasets) (2022.3.0)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2021.10.8)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.25.11)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: scikit-learn>=0.21.3 in /usr/local/lib/python3.7/dist-packages (from seqeval) (1.0.2)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.21.3->seqeval) (1.1.0)\n",
            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.21.3->seqeval) (1.4.1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.21.3->seqeval) (3.1.0)\n",
            "Requirement already satisfied: asynctest==0.13.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (0.13.0)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (21.4.0)\n",
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            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (1.7.2)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (1.3.0)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (4.0.2)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.7.0)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\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": "649bc7bd-fcf2-4534-e474-c313e6446cdb"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\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-v2\".format(BASE_MODEL_NAME))\n",
        "\n",
        "SUB_MARK = \"\\u00b9\"\n",
        "OBJ_MARK = \"\\u00b2\""
      ],
      "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": "e13b4b68-8c69-4f83-cddd-16830708754a"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "bert-base-cased-nyt-re-v1  test.json   valid.json\n",
            "bert-base-cased-nyt-re-v3  train.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": "27b330b3-597d-4fd9-8b49-9291e7a77b0d"
      },
      "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": "3552f2e9-d709-46fc-bca0-da37c2d71ce2"
      },
      "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-c988c5a8-ea95-4d8e-8dd1-f3ca90ec8cc5\">\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-c988c5a8-ea95-4d8e-8dd1-f3ca90ec8cc5')\"\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",
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              "  </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",
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              "      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-c988c5a8-ea95-4d8e-8dd1-f3ca90ec8cc5 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-c988c5a8-ea95-4d8e-8dd1-f3ca90ec8cc5');\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": "df6cf3f8-026e-4cce-9967-c9ad27e6adb3"
      },
      "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-183d5491-774a-412b-9c3a-f59ec389bfe3\">\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-183d5491-774a-412b-9c3a-f59ec389bfe3')\"\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",
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              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </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-183d5491-774a-412b-9c3a-f59ec389bfe3 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-183d5491-774a-412b-9c3a-f59ec389bfe3');\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\n",
        "\n",
        "In models for scenarios (e) and (f), we have simply extracted the tokens and tags in this step, and inserted entity markers within the tokens.\n",
        "\n",
        "Here since we are not going to provide entity markers, we will use similar logic to mark the start and end tokens in the subject and object span as we parse the file, then post-process the tokens to retrieve the span indexes (and reset the tokens)."
      ],
      "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}\".format(SUB_MARK, sub_text, SUB_MARK)\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 = \"{:s}{:s}{:s}\".format(OBJ_MARK, obj_text, OBJ_MARK)\n",
        "          text_post = text[obj_end:]\n",
        "          text = text_pre + text_sub + text_mid + text_obj + text_post\n",
        "\n",
        "          tokens = text.split()\n",
        "          span_idxs = [\n",
        "            [i for i, tok in enumerate(tokens) if tok.startswith(SUB_MARK)][0],\n",
        "            [i for i, tok in enumerate(tokens) if tok.endswith(SUB_MARK)][0],\n",
        "            [i for i, tok in enumerate(tokens) if tok.startswith(OBJ_MARK)][0],\n",
        "            [i for i, tok in enumerate(tokens) if tok.endswith(OBJ_MARK)][0]\n",
        "          ]\n",
        "          if span_idxs[0] > span_idxs[1] or span_idxs[2] > span_idxs[3]:\n",
        "            continue\n",
        "          clean_tokens = [tok.replace(SUB_MARK, \"\").replace(OBJ_MARK, \"\") for tok in tokens]\n",
        "          output = {\n",
        "              \"tokens\": clean_tokens,\n",
        "              \"label\": label,\n",
        "              \"span_idxs\": span_idxs\n",
        "          }\n",
        "          \n",
        "          fout.write(json.dumps(output) + \"\\n\")\n",
        "        except Exception as e:\n",
        "          # raise e\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": "6f13fdb8-c3c2-4c1a-c0ea-10b38450882c"
      },
      "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": [
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            "03d6fffc61854ece88ecd0b736727a1e",
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            "ac14838aab6445769a8a7737fcdeafc5",
            "01a886b4ef4f4c9f94125f72db36667f"
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        },
        "id": "ZE0PQZbiFh_H",
        "outputId": "042f4a8a-f993-42cc-a29a-df17bbabf4d0"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using custom data configuration default-4b7395031582caa7\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-4b7395031582caa7/0.0.0/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": "3eb32e007ac2426a8ee7e29059573eba"
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          },
          "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": "bdbcb4076aca400fa65ca993d5877c86"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/json/default-4b7395031582caa7/0.0.0/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": "179933ae186f45e0a2401d1b0c8f2d31"
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          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['tokens', 'label', 'span_idxs'],\n",
              "        num_rows: 31986\n",
              "    })\n",
              "    valid: Dataset({\n",
              "        features: ['tokens', 'label', 'span_idxs'],\n",
              "        num_rows: 2916\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['tokens', 'label', 'span_idxs'],\n",
              "        num_rows: 3005\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": "2995375b-6a60-44fd-87cc-103422a2d37d"
      },
      "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, Charles, B., ...  place_of_birth   \n",
              "\n",
              "          span_idxs  \n",
              "0    [8, 9, 17, 17]  \n",
              "1  [19, 20, 26, 26]  \n",
              "2  [18, 20, 22, 22]  \n",
              "3  [18, 20, 22, 22]  \n",
              "4    [3, 5, 16, 18]  "
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>tokens</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>[Mr., Ferrer, still, holds, commanding, leads,...</td>\n",
              "      <td>place_lived</td>\n",
              "      <td>[18, 20, 22, 22]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>[Mr., Ferrer, still, holds, commanding, leads,...</td>\n",
              "      <td>place_lived</td>\n",
              "      <td>[18, 20, 22, 22]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>[United, States, Representative, Charles, B., ...</td>\n",
              "      <td>place_of_birth</td>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-a594e1a3-100c-4852-a121-153eaf3e0630');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          "metadata": {},
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    {
      "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": "3b3fd536-f636-4077-b4da-3472734f17c8"
      },
      "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": "e17582f2-763f-459d-f91a-e07fa7978d28"
      },
      "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\n",
        "\n",
        "Note **no need to add marker tokens**!"
      ],
      "metadata": {
        "id": "YgnVGRXGGA03"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = BertTokenizerFast.from_pretrained(BASE_MODEL_NAME)"
      ],
      "metadata": {
        "id": "T-WFzVDGF5bZ"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "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": 16,
      "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",
        "  tokenized_inputs[\"span_idxs\"] = examples[\"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": "df8c93aa-0aa7-42d6-a3c3-50f126f28bba"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'span_idxs', 'labels'])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "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,
          "referenced_widgets": [
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        },
        "id": "OEpXrPdWGrNN",
        "outputId": "d3793ffc-d84f-455a-db36-5d6b20479295"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
        },
        {
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          },
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        {
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        {
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          "data": {
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            "application/vnd.jupyter.widget-view+json": {
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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": "5169e32639df4130889268a7673749f5"
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          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['span_idxs', 'input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 30601\n",
              "    })\n",
              "    valid: Dataset({\n",
              "        features: ['span_idxs', 'input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 2767\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['span_idxs', 'input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 2844\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "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": "2e067d9a-ea82-48cd-86f2-3afaa9768563"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "rec.labels: [7, 2, 6, 6, 7]\n",
            "rec.input_ids: 5 40\n",
            "rec.span_idxs: [[8, 9, 17, 17], [19, 20, 26, 26], [18, 20, 22, 22], [18, 20, 22, 22], [3, 5, 16, 18]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## DataLoader"
      ],
      "metadata": {
        "id": "1dvFBiA06T4v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "BATCH_SIZE = 16"
      ],
      "metadata": {
        "id": "SWSn_hxH6egZ"
      },
      "execution_count": 20,
      "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": 21,
      "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": 22,
      "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",
        "      # span excludes entity markers, maxpool across span\n",
        "      sub_span = torch.max(outputs[bid, span_idxs[bid, 0]:span_idxs[bid, 1]+1, :], \n",
        "                           dim=0, keepdim=True).values\n",
        "      obj_span = torch.max(outputs[bid, span_idxs[bid, 2]:span_idxs[bid, 3]+1, :],\n",
        "                           dim=0, keepdim=True).values\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",
        "# 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": 23,
      "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": 24,
      "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/"
        },
        "outputId": "7df4c6db-d1f1-4dbe-dc06-842dfb3dddae"
      },
      "execution_count": 25,
      "outputs": [
        {
          "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.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.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: ['layer_norm.weight', 'layer_norm.bias', 'linear.weight', 'linear.bias']\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": 26,
      "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": "5c0b8b86-a5b4-4633-efd5-d576bdcb13a2"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EPOCH 1, train loss: 1257.972, val loss: 88.095, val-acc: 0.77878\n",
            "EPOCH 2, train loss: 857.723, val loss: 78.880, val-acc: 0.78960\n",
            "EPOCH 3, train loss: 685.971, val loss: 74.801, val-acc: 0.79754\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": "742df09c-a234-484a-93b4-7910a8ebb8fa"
      },
      "execution_count": 28,
      "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": "65a3b4a6-5c22-43f1-9653-35b75817c061"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test accuracy: 0.801\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": "dc23cb74-1b12-4b7d-b668-89d93d926a43"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                          precision    recall  f1-score   support\n",
            "\n",
            "administrative_divisions       0.59      0.68      0.63       323\n",
            "                 capital       0.83      0.62      0.71       397\n",
            "                 company       0.98      0.96      0.97       339\n",
            "                 country       0.69      0.83      0.76       332\n",
            "             nationality       0.91      0.92      0.91       423\n",
            "         neighborhood_of       0.99      0.98      0.99       321\n",
            "             place_lived       0.84      0.82      0.83       454\n",
            "          place_of_birth       0.55      0.48      0.51       173\n",
            "          place_of_death       0.48      0.57      0.52        82\n",
            "\n",
            "                accuracy                           0.80      2844\n",
            "               macro avg       0.76      0.76      0.76      2844\n",
            "            weighted avg       0.81      0.80      0.80      2844\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": "e1602f22-e57a-4cfd-9e36-81b30c1c3df9"
      },
      "execution_count": 31,
      "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-v2/ /content/drive/MyDrive/odsc-2022-data/NYT/"
      ],
      "metadata": {
        "id": "OpRI94Sl9sHM"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!ls /content/drive/MyDrive/odsc-2022-data/NYT/bert-base-cased-nyt-re-v2/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lY2vfHf0-DV_",
        "outputId": "60d3ce29-88d5-4865-c770-36f7b3e55e6c"
      },
      "execution_count": 33,
      "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": 33,
      "outputs": []
    }
  ]
}