03-gmb-ner-bert.ipynb

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      "source": [
        "# Named Entity Recognition\n",
        "\n",
        "In this notebook, we will explore how to implement a Named Entity Recognition Model using BERT and Pytorch.\n",
        "\n",
        "* Data for fine-tuning the NER comes from the [Groningen Meaning Bank (GMB)](https://gmb.let.rug.nl/) available on Kaggle as [Annotated Corpus for Named Entity Recognition](https://www.kaggle.com/datasets/abhinavwalia95/entity-annotated-corpus)\n",
        "* Data is provided in IOB format, and tokens are labeled with the following NER tags.\n",
        "  * Person (PER) - Person entities are limited to individuals that are human or have human characteristics, such as divine entities.\n",
        "  * Location (GEO) - Location entities are limited to geographical entities such as geographical areas and landmasses, bodies of water, and geological formations.\n",
        "  * Organization (ORG) - Organization entities are limited to corporations, agencies, and other groups of people defined by an established organizational structure.\n",
        "  * Geo-political Entity (GPE) - GPE entities are geographical regions defined by political and/or social groups. A GPE entity subsumes and does not distinguish between a city, a nation, its region, its government, or its people (LOC•ORG).\n",
        "  * Artifact (ART) - Artifacts are limited to manmade objects, structures and abstract entities, including buildings, facilities, art and scientific theories.\n",
        "  * Event (EVE) - Events are incidents and occasions that occur during a particular time.\n",
        "  * Natural Object (NAT) - Natural objects are entities that occur naturally and are not manmade, such as diseases, biological entities and other living things.\n",
        "  * Time (TIM) - Time entities are limited to references to certain temporal entities that have a name, such as the days of the week and months of a year. For all other temporal expressions the tagging layer timex is used (see below).\n",
        "* HuggingFace already provides a [BertForTokenClassification](https://huggingface.co/docs/transformers/main/en/model_doc/bert#transformers.BertForTokenClassification) that is suitable for NER so we will use that for our model.\n"
      ],
      "metadata": {
        "id": "9kqqn51WstFX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Environment Setup"
      ],
      "metadata": {
        "id": "QEQubLb4vNDP"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "NbIgHCmZsYdW",
        "outputId": "83029e21-b542-4782-99af-f030e55ae94b"
      },
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          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install transformers datasets seqeval"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Imports"
      ],
      "metadata": {
        "id": "a7RG6zemvdDV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import csv\n",
        "import json\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "import pandas as pd\n",
        "import random\n",
        "import shutil\n",
        "import torch\n",
        "\n",
        "from collections import Counter, defaultdict\n",
        "from datasets import load_dataset, ClassLabel\n",
        "from seqeval.metrics import classification_report, f1_score\n",
        "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix\n",
        "from torch.nn.functional import cross_entropy\n",
        "from torch.optim import AdamW\n",
        "from torch.utils.data import DataLoader, SubsetRandomSampler\n",
        "from transformers import (\n",
        "    AutoTokenizer, AutoModelForTokenClassification, AutoConfig,\n",
        "    DataCollatorForTokenClassification,\n",
        "    get_scheduler\n",
        ")"
      ],
      "metadata": {
        "id": "Doe6OQjKvVkC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Processing\n",
        "\n",
        "I downloaded the GMB dataset from Kaggle (it's the `ner_dataset.csv` file) and uploaded it into my Google Drive. Other approaches could be to download it directly from Kaggle (with Kaggle login) or download from Kaggle and upload to Google Storage (GCP).\n",
        "\n",
        "As you can see, the dataset provides both Part Of Speech (POS) tags as well as NER tags. Each line contains a token, its POS tag and NER tag. The first token of each sentence also contains a Sentence Number that follows the pattern \"Sentence: \\d+\".\n",
        "\n",
        "In this section we process this format into a JSON format that our HuggingFace `load_dataset` function can consume easily. Essentially, we convert it into a JSON structure as follows and write it out into JSON-L files. The JSON structure maps 1:1 to the Dataset format as we will see later.\n",
        "\n",
        "```\n",
        "{\n",
        "  \"tokens\": [\"Barack\", \"Obama\", ..., \"United\", \"States\", \".\"],\n",
        "  \"ner_tags\": [\"B-PER\", \"I-PER\", ..., \"B-GPE\", \"I-GPE\", \"O\"]\n",
        "}\n",
        "```\n",
        "\n",
        "We also split the dataset into three JSON-L files for training, validation, and test using a random-number generation technique."
      ],
      "metadata": {
        "id": "w0K5ENxYvkC8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "igfsE-sovhcj",
        "outputId": "bbfce081-01dc-4261-d49d-1e758ab120da"
      },
      "execution_count": null,
      "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": [
        "!ls /content/drive/MyDrive/odsc-2022-data/GMB/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YWykMdOqwILI",
        "outputId": "8af27309-792f-43f3-f211-5b25bd51a4a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "bert-base-cased-gmb-ner        ner.csv\t\txlm-roberta-base-gmb-ner\n",
            "distilbert-base-cased-gmb-ner  ner_dataset.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!head /content/drive/MyDrive/odsc-2022-data/GMB/ner_dataset.csv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C0f-eDDEwMz4",
        "outputId": "1f8ad9f6-4ea8-4a82-9fa3-ff1eebfa3431"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sentence #,Word,POS,Tag\r\n",
            "Sentence: 1,Thousands,NNS,O\r\n",
            ",of,IN,O\r\n",
            ",demonstrators,NNS,O\r\n",
            ",have,VBP,O\r\n",
            ",marched,VBN,O\r\n",
            ",through,IN,O\r\n",
            ",London,NNP,B-geo\r\n",
            ",to,TO,O\r\n",
            ",protest,VB,O\r\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "GS_DATA_DIR = \"/content/drive/MyDrive/odsc-2022-data/GMB/\"\n",
        "DATA_DIR = \"data\"\n",
        "\n",
        "NER_FILEPATH = os.path.join(GS_DATA_DIR, \"ner_dataset.csv\")\n",
        "\n",
        "OUTPUT_FILEPATHS = [\n",
        "  os.path.join(DATA_DIR, \"gmb-train.jsonl\"),\n",
        "  os.path.join(DATA_DIR, \"gmb-valid.jsonl\"),\n",
        "  os.path.join(DATA_DIR, \"gmb-test.jsonl\")\n",
        "]\n",
        "\n",
        "BASE_MODEL_NAME = \"bert-base-cased\"\n",
        "MODEL_DIR = os.path.join(DATA_DIR, \"{:s}-gmb-ner\".format(BASE_MODEL_NAME))"
      ],
      "metadata": {
        "id": "lbXeVwSowP_t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def write_output(tokens, labels, output_files, num_writter):\n",
        "  assert(len(tokens) == len(labels))\n",
        "  rec = json.dumps({ \"tokens\": tokens, \"ner_tags\": labels })\n",
        "  dice = random.random()\n",
        "  if dice <= 0.7:\n",
        "    output_files[0].write(\"{:s}\\n\".format(rec))\n",
        "    num_written[0] += 1\n",
        "  elif dice <= 0.8:\n",
        "    output_files[1].write(\"{:s}\\n\".format(rec))\n",
        "    num_written[1] += 1\n",
        "  else:\n",
        "    output_files[2].write(\"{:s}\\n\".format(rec))\n",
        "    num_written[2] += 1\n",
        "\n",
        "\n",
        "os.makedirs(DATA_DIR, exist_ok=True)\n",
        "output_files = [open(filepath, \"w\") for filepath in OUTPUT_FILEPATHS]\n",
        "num_written = [0, 0, 0]\n",
        "tokens, labels = [], []\n",
        "with open(NER_FILEPATH, \"r\", encoding=\"latin-1\") as fner:\n",
        "  csv_reader = csv.reader(fner)\n",
        "  next(csv_reader)  # skip header\n",
        "  for row in csv_reader:\n",
        "    if row[0].startswith(\"Sentence\") and len(tokens) > 0:\n",
        "      # write out current sentence to train / valid / test\n",
        "      write_output(tokens, labels, output_files, num_written)\n",
        "      tokens, labels = [], []\n",
        "    # accumulate tokens and labels\n",
        "    tokens.append(row[1])\n",
        "    labels.append(row[3])\n",
        "    # if num_written[0] > 1000:\n",
        "    #   break\n",
        "\n",
        "if len(tokens) > 0:\n",
        "  write_output(tokens, labels, output_files, num_written)\n",
        "\n",
        "[output_file.close() for output_file in output_files]\n",
        "print(num_written)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-1xyW8QLwWZx",
        "outputId": "2e3a1419-e67e-4e75-9797-b65be1c44157"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[33558, 4736, 9665]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dataset\n",
        "\n",
        "We now want to convert our three JSON-L files `train.jsonl`, `validation.jsonl` and `test.jsonl` files into our raw GMB dataset.\n",
        "\n",
        "First we produce a mapping of name to file path, and call `load_dataset` on it with `format=\"json\"`.\n",
        "\n",
        "This creates a DatasetDict object with three Datasets identified by the names specified in the `data_files` mapping."
      ],
      "metadata": {
        "id": "7FK26Q1Vw5AY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_files = {\n",
        "    \"train\": OUTPUT_FILEPATHS[0],\n",
        "    \"validation\": OUTPUT_FILEPATHS[1],\n",
        "    \"test\": OUTPUT_FILEPATHS[2]\n",
        "}\n",
        "gmb_dataset = load_dataset(\"json\", data_files=data_files)\n",
        "gmb_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436,
          "referenced_widgets": [
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        "id": "vre-SI0Lwu7f",
        "outputId": "0f9d332a-1044-4890-a722-30da3e3ee050"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using custom data configuration default-19fa66c8eb224597\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-19fa66c8eb224597/0.0.0/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": "db9b74bea6f9452eb25ac4619370035f"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]"
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/json/default-19fa66c8eb224597/0.0.0/ac0ca5f5289a6cf108e706efcf040422dbbfa8e658dee6a819f20d76bb84d26b. Subsequent calls will reuse this data.\n"
          ]
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
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          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['tokens', 'ner_tags'],\n",
              "        num_rows: 33558\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['tokens', 'ner_tags'],\n",
              "        num_rows: 4736\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['tokens', 'ner_tags'],\n",
              "        num_rows: 9665\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# gmb_dataset[\"train\"][0]"
      ],
      "metadata": {
        "id": "DckiFt39w7Ds"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Distribution of Entity Types across splits\n",
        "\n",
        "We want to make sure that the validation and test sets look roughly similar to the training set in terms of distribution of data across the different classes. We can verify that by using the following code, which computes the distribution across the different classes across different splits.\n",
        "\n",
        "We see that the distribution of tags are roughly similar across splits, so validation and test sets should provide a good measure of the NER's generalization capabilities."
      ],
      "metadata": {
        "id": "fuXJwTgTxEkG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tag_freqs_by_split = defaultdict(Counter)\n",
        "for split, dataset in gmb_dataset.items():\n",
        "  for ner_tags in dataset[\"ner_tags\"]:\n",
        "    for tag in ner_tags:\n",
        "      if tag.startswith(\"B-\"):\n",
        "        tag = tag.replace(\"B-\", \"\")\n",
        "        tag_freqs_by_split[split][tag] += 1\n",
        "pd.DataFrame.from_dict(tag_freqs_by_split, orient=\"index\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "XHcwISULxBgG",
        "outputId": "3ca152a0-617e-4acd-d1e8-36f948b0822f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              per    geo    org    gpe    tim  art  nat  eve\n",
              "train       11913  26282  14143  11117  14168  292  146  214\n",
              "validation   1687   3806   1955   1539   2010   42   22   35\n",
              "test         3390   7556   4045   3214   4155   68   33   59"
            ],
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              "          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": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tokenization and Label Alignment\n",
        "\n",
        "We now have to deal with the subword tokenization alignment problem that come with using Transformers.\n",
        "\n",
        "Transformers use subword tokenization to split long words into their more common subwords, for example \"Washington\" could become \"Wash\", \"##ing\", and \"##ton\". Common subwords would be reused across different words. This helps to keep the vocabulary size down, or conversely, helps to cover a larger input vocabulary using the same size of tokenizer vocabulary.\n",
        "\n",
        "However, our input sequence of `tokens` is space separated (with some additional heuristics around punctuation that is already taken care of in the provided input) and our `ner_tags` are aligned with this space-separated token sequence.\n",
        "\n",
        "Since Transformers expect their input to be subword tokenized, the corresponding labels need to be aligned with the subword tokens as well. So for our \"Washington\" example, the corresponding labels will change from a single \"B-LOC\" to \"B-LOC\", \"IGN\", and \"IGN\", i.e. the trailing subwords would be ignored.\n",
        "\n",
        "To do the mapping, we need the Tokenizer's `word_ids()` function, that maps the original token ids onto the subword tokenized sequence, which is only available from the \"faster\" Rust-based Tokenizer implementations. Fortunately, the AutoTokenizer will choose version of the Tokenizer for us automatically. We will use this to construct an \"aligned\" version of the `ner_tags`.\n",
        "\n",
        "In addition, we can also take this opportunity to construct an \"encoded\" version of the GMB dataset, since we are calling the tokenizer on the input tokens anyway and as a result have access to the `input_ids`, `token_type_ids` and `attention_masks`."
      ],
      "metadata": {
        "id": "ZembIEz_xUyc"
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      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_NAME)"
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    {
      "cell_type": "code",
      "source": [
        "tokens = tokenizer(gmb_dataset[\"train\"][0][\"tokens\"], is_split_into_words=True).tokens()\n",
        "tokens"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yN0r2Goxxed9",
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      },
      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['[CLS]',\n",
              " 'Families',\n",
              " 'of',\n",
              " 'soldiers',\n",
              " 'killed',\n",
              " 'in',\n",
              " 'the',\n",
              " 'conflict',\n",
              " 'joined',\n",
              " 'the',\n",
              " 'protesters',\n",
              " 'who',\n",
              " 'carried',\n",
              " 'banner',\n",
              " '##s',\n",
              " 'with',\n",
              " 'such',\n",
              " 'slogan',\n",
              " '##s',\n",
              " 'as',\n",
              " '\"',\n",
              " 'Bush',\n",
              " 'Number',\n",
              " 'One',\n",
              " 'Terror',\n",
              " '##ist',\n",
              " '\"',\n",
              " 'and',\n",
              " '\"',\n",
              " 'Stop',\n",
              " 'the',\n",
              " 'Bomb',\n",
              " '##ings',\n",
              " '.',\n",
              " '\"',\n",
              " '[SEP]']"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "input = tokenizer(gmb_dataset[\"train\"][0][\"tokens\"], is_split_into_words=True)\n",
        "word_ids = input.word_ids()\n",
        "pd.DataFrame([tokens, word_ids], index=[\"tokens\", \"word_ids\"])"
      ],
      "metadata": {
        "colab": {
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            "text/plain": [
              "             0         1   2         3       4   5    6         7       8   \\\n",
              "tokens    [CLS]  Families  of  soldiers  killed  in  the  conflict  joined   \n",
              "word_ids   None         0   1         2       3   4    5         6       7   \n",
              "\n",
              "           9   ...  26   27  28    29   30    31      32  33  34     35  \n",
              "tokens    the  ...   \"  and   \"  Stop  the  Bomb  ##ings   .   \"  [SEP]  \n",
              "word_ids    8  ...  22   23  24    25   26    27      27  28  29   None  \n",
              "\n",
              "[2 rows x 36 columns]"
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              "      <th>tokens</th>\n",
              "      <td>[CLS]</td>\n",
              "      <td>Families</td>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Class Label Mappings\n",
        "\n",
        "In the encoded dataset, since the input will be converted to arrays of floats, we also want to do the same thing with the labels.\n",
        "\n",
        "For this we construct a ClassLabel object and use its `int2str` and `str2int` methods to construct lookup tables from label name to label id and vice versa."
      ],
      "metadata": {
        "id": "RVhkS5m_IivZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "entity_types = set()\n",
        "for ner_tags in gmb_dataset[\"train\"][\"ner_tags\"]:\n",
        "  for ner_tag in ner_tags:\n",
        "    if ner_tag.startswith(\"B-\"):\n",
        "      entity_types.add(ner_tag.replace(\"B-\", \"\"))\n",
        "entity_types = sorted(list(entity_types))\n",
        "\n",
        "tag_names = []\n",
        "for entity_type in entity_types:\n",
        "  tag_names.append(\"B-{:s}\".format(entity_type))\n",
        "  tag_names.append(\"I-{:s}\".format(entity_type))\n",
        "tag_names.append(\"O\")\n",
        "\n",
        "tags = ClassLabel(names=tag_names)\n",
        "label2id = {name: tags.str2int(name) for name in tag_names}\n",
        "id2label = {id: tags.int2str(id) for id in range(len(tag_names))}\n",
        "\n",
        "# label2id, id2label"
      ],
      "metadata": {
        "id": "mm-AzDPqIh8M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We now define the `tokenize_and_align_labels` which takes a batch of records from the input raw dataset, i.e. the input `examples` object is a list of dictionaries with keys `{\"tokens\", \"ner_tags\"}`.\n",
        "\n",
        "The output of this function is a list of tokenized input objects, which are dictionaries with keys `{\"input_ids\", \"token_type_ids\", \"attention_masks\", \"labels\"}`. The first three keys are from the output of the tokenizer, and the last one is a numeric representation of the input `ner_tags` aligned to the subworded tokens.\n",
        "\n",
        "We convert the raw dataset to the encoded one by calling `map` on the raw dataset with `tokenize_and_align_labels` as the function. The `map` call is applied batch-wise, and the original keys `ner_tags` and `tokens` are removed from the encoded dataset."
      ],
      "metadata": {
        "id": "48c9ioBcB4gx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_and_align_labels(examples):\n",
        "  tokenized_inputs = tokenizer(examples[\"tokens\"], \n",
        "                               truncation=True, \n",
        "                               is_split_into_words=True)\n",
        "  aligned_batch_labels = []\n",
        "  for idx, labels in enumerate(examples[\"ner_tags\"]):\n",
        "    word_ids = tokenized_inputs.word_ids(batch_index=idx)\n",
        "    prev_word_id = None\n",
        "    aligned_labels = []\n",
        "    for word_id in word_ids:\n",
        "      if word_id is None or word_id == prev_word_id:\n",
        "        aligned_labels.append(-100)   # IGNore tag\n",
        "      else:\n",
        "        aligned_labels.append(label2id[labels[word_id]])\n",
        "      prev_word_id = word_id\n",
        "    aligned_batch_labels.append(aligned_labels)\n",
        "  tokenized_inputs[\"labels\"] = aligned_batch_labels\n",
        "  return tokenized_inputs\n",
        "\n",
        "\n",
        "tokens = gmb_dataset[\"train\"][0][\"tokens\"]\n",
        "ner_tags = gmb_dataset[\"train\"][0][\"ner_tags\"]\n",
        "aligned_labels = tokenize_and_align_labels(gmb_dataset[\"train\"][0:1])[\"labels\"][0]\n",
        "len(tokens), len(ner_tags), len(aligned_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yoQxZo7G0NHV",
        "outputId": "0fac1145-cb3e-4cfa-bc23-1e04d0aea9c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(30, 30, 36)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encoded_gmb_dataset = gmb_dataset.map(tokenize_and_align_labels,\n",
        "                                      batched=True,\n",
        "                                      remove_columns=[\"ner_tags\", \"tokens\"])\n",
        "encoded_gmb_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363,
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              "  0%|          | 0/34 [00:00<?, ?ba/s]"
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            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 33558\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 4736\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 9665\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## DataLoader\n",
        "\n",
        "PyTorch models are trained using outputs from a DataLoader, which is created by wrapping the Dataset. We will use the encoded Dataset splits to create the corresponding DataLoaders for training, validation and test.\n",
        "\n",
        "We define a `DataCollatorForTokenClassification` which will take care of padding each batch of the encoded dataset. Even though we are feeding it arrays of numbers, the number of tokens for each sentence in the batch can be different, and hence results in a \"jagged\" array. By setting `padding=\"longest\"` we ensure that each batch results in non-jagged matrices padded with padding characters.\n",
        "\n",
        "The use of the `sampler` parameter is to help during testing. Since the training run time can be quite long, we want to be able to iterate rapidly during development by training on a small sample of the data (1000 training examples in our case). To switch between \"development\" and \"run\" modes, comment the `shuffle` and uncomment the `sampler` parameter respectively."
      ],
      "metadata": {
        "id": "WXAMRrF26-9r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "BATCH_SIZE = 24"
      ],
      "metadata": {
        "id": "ui_-aN2A70Bp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "collate_fn = DataCollatorForTokenClassification(tokenizer, padding=\"longest\", return_tensors=\"pt\")\n",
        "\n",
        "train_dl = DataLoader(encoded_gmb_dataset[\"train\"], \n",
        "                      shuffle=True, \n",
        "                      # sampler=SubsetRandomSampler(np.random.randint(0, encoded_gmb_dataset[\"train\"].num_rows, 1000).tolist()),\n",
        "                      batch_size=BATCH_SIZE, \n",
        "                      collate_fn=collate_fn)\n",
        "valid_dl = DataLoader(encoded_gmb_dataset[\"validation\"], \n",
        "                      shuffle=False, \n",
        "                      # sampler=SubsetRandomSampler(np.random.randint(0, encoded_gmb_dataset[\"validation\"].num_rows, 200).tolist()),                      \n",
        "                      batch_size=BATCH_SIZE, \n",
        "                      collate_fn=collate_fn)\n",
        "test_dl = DataLoader(encoded_gmb_dataset[\"test\"],\n",
        "                     shuffle=False,\n",
        "                    #  sampler=SubsetRandomSampler(np.random.randint(0, encoded_gmb_dataset[\"test\"].num_rows, 100).tolist()),                     \n",
        "                     batch_size=BATCH_SIZE, \n",
        "                     collate_fn=collate_fn)"
      ],
      "metadata": {
        "id": "FUAk2DIl7Azu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model\n",
        "\n",
        "This step discovers if we have a GPU available. If we do, the model is instantiated and assigned to the GPU, otherwise it continues to process on the CPU.\n",
        "\n",
        "In general, while in development mode, it is preferable to work with the CPU (i.e do not set the GPU to the notebook via Runtime :: Change Runtime Type). Once you are satisfied that your model is working, then enable GPU. This saves on GPU utilization across Colab (and since access to GPU is based on availability, also potentially increases the chance that you will have access to the GPU when you need it).\n",
        "\n",
        "In the `AutoModelForTokenClassification.from_pretrained` call, we instantiate a `BertForTokenClassification` object from HuggingFace. This is a model with a BERT encoder and a head consisting of a `torch.nn.Dropout` and a `torch.nn.Linear` layer for classifying the output of the pre-trained BERT encoder into one of `num_labels` possible NER tags.\n",
        "\n",
        "__NOTE:__ remember to set the `num_labels` parameter, otherwise the model will default to a binary classifier, and you will see a hard to understand error when you do the training."
      ],
      "metadata": {
        "id": "dOArgpIE6BLc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ],
      "metadata": {
        "id": "u3sW6pLh6vsx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForTokenClassification.from_pretrained(BASE_MODEL_NAME,\n",
        "                                                        num_labels=len(tag_names),\n",
        "                                                        id2label=id2label,\n",
        "                                                        label2id=label2id)\n",
        "model = model.to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158,
          "referenced_widgets": [
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        "id": "WdN-jp3C48v7",
        "outputId": "913f38b7-4076-4908-ce0a-65591769b9a9"
      },
      "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": "3baabf23f3044d78a46b906732aed177"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForTokenClassification: ['cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias']\n",
            "- This IS expected if you are initializing BertForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.weight', 'classifier.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": "markdown",
      "source": [
        "## Training Loop\n",
        "\n",
        "In this section we define all the functions and objects that will be needed to do the training. The training loop is constructed as follows:\n",
        "\n",
        "```\n",
        "for epoch in num_epochs:\n",
        "    do_train()\n",
        "    do_eval()\n",
        "```\n",
        "\n",
        "The `align_predictions` function is the reverse of `tokenize_and_align_labels` function. It will remove the labels and predictions corresponding to the IGN tags or \"##word\" tokens and align the labels and predictions.\n",
        "\n",
        "We also want to compute the F1-score since it makes more sense to compute this for NER tasks. In the `do_eval` function, we will just call the `compute_f1_score` function so its cleaner and more generic."
      ],
      "metadata": {
        "id": "qy379w4A8Fy8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "LEARNING_RATE = 5e-5\n",
        "WEIGHT_DECAY = 1e-2\n",
        "NUM_EPOCHS = 3"
      ],
      "metadata": {
        "id": "uxj29i8a6hiN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "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": "VHDdwUGI8TcP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def align_predictions(labels_cpu, preds_cpu):\n",
        "  # remove -100 labels from score computation\n",
        "  batch_size, seq_len = preds_cpu.shape\n",
        "  labels_list, preds_list = [], []\n",
        "  for bid in range(batch_size):\n",
        "    example_labels, example_preds = [], []\n",
        "    for sid in range(seq_len):\n",
        "      # ignore label -100\n",
        "      if labels_cpu[bid, sid] != -100:\n",
        "        example_labels.append(id2label[labels_cpu[bid, sid]])\n",
        "        example_preds.append(id2label[preds_cpu[bid, sid]])\n",
        "    labels_list.append(example_labels)\n",
        "    preds_list.append(example_preds)\n",
        "  return labels_list, preds_list\n",
        "\n",
        "\n",
        "def compute_f1_score(labels, logits):\n",
        "  # convert logits to predictions and move to CPU\n",
        "  preds_cpu = torch.argmax(logits, dim=-1).cpu().numpy()\n",
        "  labels_cpu = labels.cpu().numpy()\n",
        "  labels_list, preds_list = align_predictions(labels_cpu, preds_cpu)\n",
        "  # seqeval.metrics.f1_score takes list of list of tags\n",
        "  return f1_score(labels_list, preds_list)\n",
        "\n",
        "\n",
        "# for batch in train_dl:\n",
        "#   batch = {k: v.to(device) for k, v in batch.items()}\n",
        "#   outputs = model(**batch)\n",
        "#   break\n",
        "# logits = outputs.logits\n",
        "# labels = batch[\"labels\"]\n",
        "# print(\"labels.shape:\", labels.size(), \"logits.shape:\", logits.size())\n",
        "# score = compute_f1_score(labels, logits)\n",
        "# print(\"f1-score:\", score)"
      ],
      "metadata": {
        "id": "WLko-5b5-ruQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "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_f1_score(batch[\"labels\"], outputs.logits)\n",
        "    num_batches += 1\n",
        "\n",
        "  eval_score /= num_batches\n",
        "\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()"
      ],
      "metadata": {
        "id": "YmJCfM8YDnDP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training / Fine-tuning"
      ],
      "metadata": {
        "id": "5MXjDM0WI9Oq"
      }
    },
    {
      "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}, f1-score: {:.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": "Lqs9ZWTWHReK",
        "outputId": "8940b8c5-f637-4c84-b33b-e1ca370da8cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EPOCH 1, train loss: 181.750, val loss: 18.164, f1-score: 0.82775\n",
            "EPOCH 2, train loss: 103.318, val loss: 17.367, f1-score: 0.83533\n",
            "EPOCH 3, train loss: 69.847, val loss: 18.082, f1-score: 0.83781\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(\"f1-score\")\n",
        "plt.legend(loc=\"best\")\n",
        "\n",
        "plt.tight_layout()\n",
        "_ = plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "2FyKuIl5H7yY",
        "outputId": "e67ab4f5-b1cf-470b-b46e-83490d8c9a56"
      },
      "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\n",
        "\n",
        "We evaluate our trained model against the test split in two ways. \n",
        "\n",
        "1. Compute the precision, recall, and f1-score for each NER tag and produce the classification report. We use the `seqeval.classification_report` function for this, since it takes list of lists for labels and prediction, unlike the `sklearn.metrics.classification_report` which takes a pair of lists.\n",
        "2. Confusion matrix -- a more graphical and easier to understand report of model performance. We can see for which NER tags the model is doing well, and for which ones it is making mistakes.\n",
        "\n"
      ],
      "metadata": {
        "id": "fjIoIM8ONW8r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_label_and_predicted_tags(batch):\n",
        "  batch = {k: v.to(device) for k, v in batch.items()}\n",
        "  with torch.no_grad():\n",
        "    outputs = model(**batch)\n",
        "  preds_cpu = torch.argmax(outputs.logits, dim=-1).cpu().numpy()\n",
        "  labels_cpu = batch[\"labels\"].cpu().numpy()\n",
        "  labels_list, preds_list = align_predictions(labels_cpu, preds_cpu)  \n",
        "  return labels_list, preds_list\n",
        "\n",
        "\n",
        "id2label[-100] = \"IGN\"\n",
        "test_labels_list, test_preds_list = [], []\n",
        "for batch in test_dl:\n",
        "  labels_list, preds_list = get_label_and_predicted_tags(batch)\n",
        "  for labels, preds in zip(labels_list, preds_list):\n",
        "    test_labels_list.append(labels)\n",
        "    test_preds_list.append(preds)\n",
        "\n",
        "print(classification_report(test_labels_list, test_preds_list))"
      ],
      "metadata": {
        "id": "-03ghPoINRYd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c8913d5d-b6e5-4957-d430-73a76276506f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         art       0.28      0.24      0.25        68\n",
            "         eve       0.36      0.44      0.39        59\n",
            "         geo       0.87      0.91      0.89      7556\n",
            "         gpe       0.96      0.94      0.95      3214\n",
            "         nat       0.57      0.24      0.34        33\n",
            "         org       0.73      0.72      0.73      4045\n",
            "         per       0.79      0.81      0.80      3390\n",
            "         tim       0.88      0.87      0.88      4155\n",
            "\n",
            "   micro avg       0.84      0.85      0.85     22520\n",
            "   macro avg       0.68      0.65      0.65     22520\n",
            "weighted avg       0.84      0.85      0.85     22520\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",
        "flat_test_labels, flat_test_preds = [], []\n",
        "for test_labels, test_preds in zip(test_labels_list, test_preds_list):\n",
        "  flat_test_labels.extend(test_labels)\n",
        "  flat_test_preds.extend(test_preds)\n",
        "plot_confusion_matrix(flat_test_labels, flat_test_preds, tag_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        },
        "id": "9eLetoihSOl7",
        "outputId": "a0207d1e-503c-4830-9674-2b31107c4219"
      },
      "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": [
        "## Usage\n",
        "\n",
        "Finally, we show how the trained model can be used for inference, taking in a sentence and predicting the NERs in it."
      ],
      "metadata": {
        "id": "lK-nvMiMTss5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def align_tokens_and_predicted_labels(toks_cpu, preds_cpu):\n",
        "  aligned_toks, aligned_preds = [], []\n",
        "  prev_tok = None\n",
        "  for tok, pred in zip(toks_cpu, preds_cpu):\n",
        "    if tok.startswith(\"##\") and prev_tok is not None:\n",
        "      prev_tok += tok[2:]\n",
        "    else:\n",
        "      if prev_tok is not None:\n",
        "        aligned_toks.append(prev_tok)\n",
        "        aligned_preds.append(id2label[prev_pred])\n",
        "      prev_tok = tok\n",
        "      prev_pred = pred\n",
        "  if prev_tok is not None:\n",
        "    aligned_toks.append(prev_tok)\n",
        "    aligned_preds.append(id2label[prev_pred])\n",
        "  return aligned_toks, aligned_preds\n",
        "\n",
        "\n",
        "def predict(texts):\n",
        "  aligned_tok_list, aligned_pred_list = [], []\n",
        "  for text in texts:\n",
        "    inputs = tokenizer(text, return_tensors=\"pt\").to(device)\n",
        "    outputs = model(**inputs)\n",
        "    tokens_cpu = tokenizer.convert_ids_to_tokens(inputs.input_ids.view(-1))\n",
        "    preds_cpu = torch.argmax(outputs.logits, dim=-1)[0].cpu().numpy()\n",
        "\n",
        "    aligned_toks, aligned_preds = align_tokens_and_predicted_labels(tokens_cpu, preds_cpu)\n",
        "\n",
        "    aligned_tok_list.append(aligned_toks)\n",
        "    aligned_pred_list.append(aligned_preds)\n",
        "\n",
        "  return aligned_tok_list, aligned_pred_list\n",
        "\n",
        "\n",
        "predicted_tokens, predicted_tags = predict([\n",
        "         [\"Marie Curie won the Nobel Prize in 1903 and 1911 .\"],\n",
        "         [\"Joe Biden is the current President of the United States .\"]\n",
        "])"
      ],
      "metadata": {
        "id": "ymp6kv9qQMf1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame([predicted_tokens[0], predicted_tags[0]], index=[\"tokens\", \"predicted_tags\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "szXZ0jYmvCma",
        "outputId": "151ddde8-0fba-4721-e943-77aa296493f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   0      1      2    3    4      5      6   7      8      9   \\\n",
              "tokens          [CLS]  Marie  Curie  won  the  Nobel  Prize  in   1903    and   \n",
              "predicted_tags      O  B-per  I-per    O    O  B-org  I-org   O  B-tim  I-tim   \n",
              "\n",
              "                   10 11     12  \n",
              "tokens           1911  .  [SEP]  \n",
              "predicted_tags  I-tim  O      O  "
            ],
            "text/html": [
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              "      <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",
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              "\n",
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              "      <th>tokens</th>\n",
              "      <td>[CLS]</td>\n",
              "      <td>Marie</td>\n",
              "      <td>Curie</td>\n",
              "      <td>won</td>\n",
              "      <td>the</td>\n",
              "      <td>Nobel</td>\n",
              "      <td>Prize</td>\n",
              "      <td>in</td>\n",
              "      <td>1903</td>\n",
              "      <td>and</td>\n",
              "      <td>1911</td>\n",
              "      <td>.</td>\n",
              "      <td>[SEP]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>predicted_tags</th>\n",
              "      <td>O</td>\n",
              "      <td>B-per</td>\n",
              "      <td>I-per</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "      <td>B-org</td>\n",
              "      <td>I-org</td>\n",
              "      <td>O</td>\n",
              "      <td>B-tim</td>\n",
              "      <td>I-tim</td>\n",
              "      <td>I-tim</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-226b00bf-dc10-4872-b7fc-df202c83edba')\"\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",
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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-226b00bf-dc10-4872-b7fc-df202c83edba 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-226b00bf-dc10-4872-b7fc-df202c83edba');\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": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame([predicted_tokens[1], predicted_tags[1]], index=[\"tokens\", \"predicted_tags\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "lXT0H9uRvDem",
        "outputId": "7c12793e-3360-4217-9911-445a91cf2453"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   0      1      2   3    4        5          6   7    8   \\\n",
              "tokens          [CLS]    Joe  Biden  is  the  current  President  of  the   \n",
              "predicted_tags      O  B-per  I-per   O    O        O      B-per   O    O   \n",
              "\n",
              "                    9       10 11     12  \n",
              "tokens          United  States  .  [SEP]  \n",
              "predicted_tags   B-geo   I-geo  O      O  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-79399599-3554-4a88-a5b9-3522476a7f5b\">\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>0</th>\n",
              "      <th>1</th>\n",
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              "      <th>6</th>\n",
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              "      <th>11</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>tokens</th>\n",
              "      <td>[CLS]</td>\n",
              "      <td>Joe</td>\n",
              "      <td>Biden</td>\n",
              "      <td>is</td>\n",
              "      <td>the</td>\n",
              "      <td>current</td>\n",
              "      <td>President</td>\n",
              "      <td>of</td>\n",
              "      <td>the</td>\n",
              "      <td>United</td>\n",
              "      <td>States</td>\n",
              "      <td>.</td>\n",
              "      <td>[SEP]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>predicted_tags</th>\n",
              "      <td>O</td>\n",
              "      <td>B-per</td>\n",
              "      <td>I-per</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "      <td>B-per</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "      <td>B-geo</td>\n",
              "      <td>I-geo</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-79399599-3554-4a88-a5b9-3522476a7f5b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-79399599-3554-4a88-a5b9-3522476a7f5b 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-79399599-3554-4a88-a5b9-3522476a7f5b');\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": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Save Model"
      ],
      "metadata": {
        "id": "jlQkDpddTv6a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!cp -r data/bert-base-cased-gmb-ner/ /content/drive/MyDrive/odsc-2022-data/GMB/"
      ],
      "metadata": {
        "id": "CCC8FZ2KTxif"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!ls /content/drive/MyDrive/odsc-2022-data/GMB/distilbert-base-cased-gmb-ner/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iUoDHkDIUD6s",
        "outputId": "edd09657-f7c6-4719-845d-e9a7fc876d96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ckpt-1\tckpt-2\tckpt-3\thistory.tsv\n"
          ]
        }
      ]
    }
  ]
}