05-nyt-re-bert.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Relationship Extraction\n",
        "\n",
        "In this notebook, we will implement a Relation Extraction Model using BERT and Pytorch.\n",
        "\n",
        "### Data\n",
        "\n",
        "* Data for fine-tuning the RE comes from the [New York Times (NYT) Relation Extraction Dataset](https://www.kaggle.com/datasets/daishinkan002/new-york-times-relation-extraction-dataset) available on Kaggle.Each sentence can have more than 1 relation.\n",
        "* This dataset contains 24 types of relations that may occur in a sentence.\n",
        "  * /location/location/contains\n",
        "  * /people/person/nationality\n",
        "  * /people/person/place_lived\n",
        "  * /business/person/company\n",
        "  * /location/country/capital\n",
        "  * /location/neighborhood/neighborhood_of\n",
        "  * /people/person/placeofbirth\n",
        "  * /location/country/administrative_divisions\n",
        "  * /location/administrative_division/country\n",
        "  * /people/deceasedperson/placeof_death\n",
        "  * /people/person/children\n",
        "  * /business/company/founders\n",
        "  * /business/company/place_founded\n",
        "  * /business/companyshareholder/majorshareholder_of\n",
        "  * /sports/sportsteamlocation/teams\n",
        "  * /sports/sports_team/location\n",
        "  * /business/company/major_shareholders\n",
        "  * /people/person/religion\n",
        "  * /business/company/advisors\n",
        "  * /people/ethnicity/geographic_distribution\n",
        "  * /people/person/ethnicity\n",
        "  * /people/ethnicity/people\n",
        "  * /people/person/profession\n",
        "  * /business/company/industry\n",
        "* The relationships connect three different entity types PERSON, LOCATION and ORGANIZATION. \n",
        "\n",
        "### Model\n",
        "\n",
        "* Unlike the `XXXForTokenClassification` model, which comes with a standard token classification head on top of some XXX transformer, there is no pre-built model available OOB at HuggingFace for Relation Extraction.\n",
        "* So we will build our own `XXXForRelationExtraction` model by composing a pretrained Transformer encoder with a classification head for classifying the relation type."
      ],
      "metadata": {
        "id": "3h5x4eJGyqsq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Environment Setup"
      ],
      "metadata": {
        "id": "mBaXqnaO3wR4"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f05i0ae5yTXg",
        "outputId": "a2b2c2ad-6845-484d-89d5-806f369dd428"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "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",
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            "\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",
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          ]
        }
      ],
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Processing\n",
        "\n",
        "### 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": "58432cab-9df7-4455-9ada-9389a18c6a7c"
      },
      "execution_count": null,
      "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-v1\".format(BASE_MODEL_NAME))"
      ],
      "metadata": {
        "id": "9uz7rfPI0LDM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!ls /content/drive/MyDrive/odsc-2022-data/NYT/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "szFMpxLd0SCB",
        "outputId": "77e7a514-2f0c-4878-c8ae-b9b744c7fd37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "bert-base-cased-nyt-re\ttest.json  train.json  valid.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Input Data Format\n",
        "\n",
        "Our dataset is pre-partitioned into train, validation and test splits.\n",
        "\n",
        "The `relationMentions` tag lists the relations and matched text of the entities, and the corresponding entity types can be found in the `entityMentions` tag.\n",
        "\n",
        "The sentence text can be found in `sentText`.\n"
      ],
      "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": "a43fb2cc-b8b8-44c0-fbac-0069f2614dfd"
      },
      "execution_count": null,
      "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\n",
        "\n",
        "Here we analyze the distribution of entity tags across the different splits to verify that it is approximately uniform across the splits."
      ],
      "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": "29652654-65e9-4434-cec9-9fe10c3a8a5e"
      },
      "execution_count": null,
      "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"
            ],
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              "          const element = document.querySelector('#df-2f684da4-38fb-4872-8737-7f7461964fff');\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",
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              "          dataTable['output_type'] = 'display_data';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Relation Types\n",
        "\n",
        "We analyze the distribution of relation types across different splits, and we find that while the distribution is uniform across splits, the distribution is very uneven across different relation types.\n",
        "\n",
        "Here we discard the relation types that are very heavily represented and those which are very lightly represented by filtering our dataset such that only relations with train split counts between 1000 and 10000 are retained.\n",
        "\n",
        "In real life, we might want to structure our relation extraction pipeline into a hierarchy of models to reflect a more uniform split, or do over/undersampling to attempt to make the dataset less imbalanced, or reduce the model into a set of binary classifiers. But here we take the easy way out and discard relations that are outside the range."
      ],
      "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"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        },
        "id": "uIGnOA7oE6en",
        "outputId": "b1b29d2e-7859-4f4e-8634-ddab9a669221"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          train   valid    test\n",
              "contains                  45608  4032.0  4059.0\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\n",
              "founders                    712    61.0    63.0\n",
              "children                    441    46.0    42.0\n",
              "place_founded               360    37.0    36.0\n",
              "major_shareholder_of        249    21.0    33.0\n",
              "major_shareholders          249    21.0    33.0\n",
              "teams                       186    22.0    17.0\n",
              "location                    186    22.0    17.0\n",
              "religion                     59     7.0     5.0\n",
              "geographic_distribution      40     3.0     1.0\n",
              "advisors                     39     5.0     3.0\n",
              "ethnicity                    20     1.0     1.0\n",
              "people                       20     1.0     1.0\n",
              "profession                    2     NaN     NaN\n",
              "industry                      1     NaN     NaN"
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              "      <td>589.0</td>\n",
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              "      <td>708.0</td>\n",
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              "      <td>21.0</td>\n",
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              "    <tr>\n",
              "      <th>teams</th>\n",
              "      <td>186</td>\n",
              "      <td>22.0</td>\n",
              "      <td>17.0</td>\n",
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              "    <tr>\n",
              "      <th>location</th>\n",
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              "      <th>geographic_distribution</th>\n",
              "      <td>40</td>\n",
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              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>advisors</th>\n",
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              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
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              "    <tr>\n",
              "      <th>ethnicity</th>\n",
              "      <td>20</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>people</th>\n",
              "      <td>20</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>profession</th>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>industry</th>\n",
              "      <td>1</td>\n",
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              "      <td>NaN</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "            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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rel_df = rel_df[(rel_df[\"train\"] > 1000) & (rel_df[\"train\"] < 10000)]\n",
        "rel_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
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      },
      "execution_count": null,
      "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"
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          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Raw Dataset\n",
        "\n",
        "We introduce entity marker tags that wrap the entity mentions in the sentence. For example, the sentence:\n",
        "\n",
        "```Washington DC is the capital of the United States .```\n",
        "\n",
        "is converted to:\n",
        "\n",
        "```<S:LOC> Washington DC </S:LOC> is the capital of the <O:LOC> United States </O:LOC> .```\n",
        "\n",
        "We read the input data and write out the augmented sentence using the information from `sentText`, `entityMentions` and `relationMentions` and tokenize them (by space). Also, we extract the relation type from `relationMentions`. The temporary JSON files have records with the following keys: `{\"tokens\", \"label\"}`\n",
        "\n",
        "This is then converted to a raw HuggingFace dataset using the `load_dataset` function."
      ],
      "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": "1bbea5c4-d8c4-4d96-bdc0-1b9a7c0ed231"
      },
      "execution_count": null,
      "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"
      ],
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using custom data configuration default-5dd41866a152fa5c\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-5dd41866a152fa5c/0.0.0/ac0ca5f5289a6cf108e706efcf040422dbbfa8e658dee6a819f20d76bb84d26b...\n"
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        {
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          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"
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        {
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          "data": {
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              "Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]"
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            "Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/json/default-5dd41866a152fa5c/0.0.0/ac0ca5f5289a6cf108e706efcf040422dbbfa8e658dee6a819f20d76bb84d26b. Subsequent calls will reuse this data.\n"
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          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
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          "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": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Label Distribution\n",
        "\n",
        "Even with the filtering, the label distribution is not uniform. However, it is not as bad as it would be if no filtering was done."
      ],
      "metadata": {
        "id": "nMrSZrlPDeT-"
      }
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      "cell_type": "code",
      "source": [
        "nyt_dataset.set_format(type=\"pandas\")\n",
        "nyt_df = nyt_dataset[\"train\"][:]\n",
        "nyt_df.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 206
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      "execution_count": null,
      "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"
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              "      <th>4</th>\n",
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              "    <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-5bd7f51b-d0b2-47be-b972-9dffadb13454 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-5bd7f51b-d0b2-47be-b972-9dffadb13454');\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": 12
        }
      ]
    },
    {
      "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": "716576fc-e1fc-4bd6-ce57-be39a36266b4"
      },
      "execution_count": null,
      "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",
        "We were encountering errors where we were running out of CUDA Memory. This was happening even when I reduced the batch size from 32 to 8. The other reason this could be happening is that some batches might contain very long outlier sentences and this causes the batch to be too big for the GPU memory.\n",
        "\n",
        "Solution is to truncate the sentence size to a size that will not impact the accuracy too much. \n",
        "\n",
        "The box plots below shows that we won't lose too many samples if we 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": "f6f054e8-80c3-4da8-b10f-c9433c90570e"
      },
      "execution_count": null,
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tokenizer"
      ],
      "metadata": {
        "id": "YgnVGRXGGA03"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = BertTokenizerFast.from_pretrained(BASE_MODEL_NAME)\n",
        "vocab_size_orig = len(tokenizer.vocab)"
      ],
      "metadata": {
        "id": "T-WFzVDGF5bZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
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      "execution_count": null,
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    },
    {
      "cell_type": "markdown",
      "source": [
        "### Add Entity Marker Tokens\n",
        "\n",
        "Now that we added these entity marker tokens, we need to tell the tokenizer to treat them as unbreakable tokens, so we add these tokens to our tokenizer."
      ],
      "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)\n",
        "vocab_size_new = len(tokenizer.vocab)\n",
        "\n",
        "print(\"original vocab size:\", vocab_size_orig)\n",
        "print(\"new vocab size:\", vocab_size_new)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z2Ldh9b1GQU6",
        "outputId": "06e9ba32-60a7-43f8-859a-c5d2fa422f61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "original vocab size: 28996\n",
            "new vocab size: 29008\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Encoded Dataset\n",
        "\n",
        "As before, we create an encoded dataset that contains the output of the tokenizer and numeric labels.\n",
        "\n",
        "To compute numeric labels, we create a `ClassLabel` object with our relation tags, and use its built-in functions to create lookup tables from label to label id and vice versa.\n",
        "\n",
        "We discard sentences that are larger than `MAX_TOKENS` which we have decided should be 100 tokens (these are space delimited tokens prior to subword tokenization).\n",
        "\n",
        "In addition, we also compute and store the value of the entity marker tokens with respect to the positions _after subword tokenization_. This is written into a fixed size int vector consisting of 4 elements, and stored under the `span_idxs` key."
      ],
      "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": null,
      "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": "bd9d9079-90c4-4155-e11c-1eecd98f58aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'span_idxs', 'labels'])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "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"
      ],
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        "id": "OEpXrPdWGrNN",
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        {
          "output_type": "display_data",
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          "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": 20
        }
      ]
    },
    {
      "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": "50f7c21f-4ceb-47dd-c998-1dd563273635"
      },
      "execution_count": null,
      "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\n",
        "\n",
        "As we have done before in our NER notebooks (the ones using PyTorch native idioms), we build `DataLoaders` for each of training, validation and test splits by wrapping the corresponding encoded `Dataset`.\n",
        "\n",
        "We use a `DataColatorWithPadding` data collator to automatically pad our batch with the longest sentence in the batch.\n",
        "\n",
        "Also as before, we have used the `sampler` trick for doing quick development iterations by keeping the data volume down."
      ],
      "metadata": {
        "id": "1dvFBiA06T4v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "BATCH_SIZE = 16"
      ],
      "metadata": {
        "id": "SWSn_hxH6egZ"
      },
      "execution_count": null,
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model\n",
        "\n",
        "Since HuggingFace does not provide us with a built-in `XXXForRelationExtraction` model, we need to build our own. Our model is based on the implementation described in [A frustratingly easy approach for Entity and Relation Extraction](https://arxiv.org/abs/2010.12812) (Zhong and Chen 2020). The authors also provide code in the github repository [princeton-nlp/PURE](https://github.com/princeton-nlp/PURE) to support their paper, which we have consulted as well when building our model.\n",
        "\n",
        "The idea is to use a PreTrainedModel as the encoder and a small linear head consisting of `torch.nn.Dropout`, `torch.nn.LayerNorm` and `torch.nn.Linear` layers as the classifier head. The encoder should start with weights from the pretrained model and be fine-tuned from that point using our labeled data.\n",
        "\n",
        "Since our base model is BERT, we will use a `BertModel` as our encoder and subclass the full model from `BertPreTrainedModel` so we can inherit its weights via the `self.init_weights()` call. When instantiating the model, we will first instantiate the `BertConfig` class with the appropriate `from_pretrained` model, then instantiate our custom `BertForRelationExtraction` with the weights of its superclass also using `from_pretrained`.\n",
        "\n",
        "Finally, we will increase the size of the BERT encoder's embedding matrix to accomodate the extra entity marker tokens we added to the tokenizer vocabulary.\n",
        "\n",
        "The `forward` method takes a batch of examples from the encoded dataset, and passes the `input_ids`, `token_type_ids` and `attention_mask` into the BERT encoder (initialized with BertForPreTraining weights). Then the maxpool value across tokens within the entity marker spans (including the entity markers) are computed for the subject and object spans, concatenated and passed into the classifier head.\n",
        "\n",
        "The classifier head outputs a logits vector the size of the number of classes, and the argmax over the logits is the predicted relation class."
      ],
      "metadata": {
        "id": "YRv2wT9qKYcu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ],
      "metadata": {
        "id": "Mke3xB586GeJ"
      },
      "execution_count": null,
      "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 includes 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",
        "# 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": null,
      "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": null,
      "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": [
            "6ad4bd94296b42a3a7194fd1d0622122",
            "fc4d7fa36b774858b9e06f2437f66de6",
            "e17be3b0a4304057ab965db0a0817423",
            "ef538dea83444da59cd63425911058c2",
            "e1e16892a1cd4c3ba2d57201249de4ce",
            "bc4e80086e2148c69b29f39f7aa45029",
            "80bcb4d6759c418798be477caeaedfb3",
            "05fc3d22f27b4b6f8c45b3e4e26b66fd",
            "621e156c7dd6468f8a955dcc37eb420f",
            "38e810cc80234b05bafbe393d1bac879",
            "605893081bcc4cb4b4bca2250ec97b82"
          ]
        },
        "outputId": "04d3074b-0b78-4a6b-a8e5-5af940f60239"
      },
      "execution_count": null,
      "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": "6ad4bd94296b42a3a7194fd1d0622122"
            }
          },
          "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.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight']\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.bias', 'layer_norm.weight', 'linear.bias', 'linear.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": null,
      "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": "93f73635-bd4c-4eae-b60c-18e9fc5fa8a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EPOCH 1, train loss: 1069.598, val loss: 72.279, val-acc: 0.84689\n",
            "EPOCH 2, train loss: 701.549, val loss: 69.721, val-acc: 0.84798\n",
            "EPOCH 3, train loss: 541.000, val loss: 62.365, val-acc: 0.85972\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": "e7c134f1-a85e-419f-b24f-6875ec5eeff8"
      },
      "execution_count": null,
      "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": "f7b4ae88-eb1d-4f95-af96-c84768353719"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test accuracy: 0.853\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": "e0ba92d7-2b17-4f59-8dbd-6fe9db4134fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                          precision    recall  f1-score   support\n",
            "\n",
            "administrative_divisions       0.69      0.83      0.75       463\n",
            "                 capital       0.85      0.72      0.78       597\n",
            "                 company       0.97      0.99      0.98       409\n",
            "                 country       0.98      0.99      0.98       458\n",
            "             nationality       0.90      0.96      0.93       527\n",
            "         neighborhood_of       0.99      1.00      1.00       364\n",
            "             place_lived       0.86      0.83      0.85       562\n",
            "          place_of_birth       0.57      0.53      0.55       223\n",
            "          place_of_death       0.48      0.41      0.44       110\n",
            "\n",
            "                accuracy                           0.85      3713\n",
            "               macro avg       0.81      0.81      0.81      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": "a26c82be-b8cd-4670-ac94-1c4283e9fb5c"
      },
      "execution_count": null,
      "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-v1/ /content/drive/MyDrive/odsc-2022-data/NYT/"
      ],
      "metadata": {
        "id": "OpRI94Sl9sHM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!ls /content/drive/MyDrive/odsc-2022-data/NYT/bert-base-cased-nyt-re-v1/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lY2vfHf0-DV_",
        "outputId": "c871f5e0-2d8f-4aa5-e802-50436972f06b"
      },
      "execution_count": null,
      "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": null,
      "outputs": []
    }
  ]
}