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        "outputId": "fb079af1-91b0-459f-871d-a327dab8af03"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting datasets\n",
            "  Downloading datasets-2.7.1-py3-none-any.whl (451 kB)\n",
            "\u001b[K     |████████████████████████████████| 451 kB 4.9 MB/s \n",
            "\u001b[?25hCollecting transformers\n",
            "  Downloading transformers-4.25.1-py3-none-any.whl (5.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 5.8 MB 69.5 MB/s \n",
            "\u001b[?25hCollecting seqeval\n",
            "  Downloading seqeval-1.2.2.tar.gz (43 kB)\n",
            "\u001b[K     |████████████████████████████████| 43 kB 1.2 MB/s \n",
            "\u001b[?25hCollecting multiprocess\n",
            "  Downloading multiprocess-0.70.14-py38-none-any.whl (132 kB)\n",
            "\u001b[K     |████████████████████████████████| 132 kB 24.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pyarrow>=6.0.0 in /usr/local/lib/python3.8/dist-packages (from datasets) (9.0.0)\n",
            "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.8/dist-packages (from datasets) (4.64.1)\n",
            "Collecting huggingface-hub<1.0.0,>=0.2.0\n",
            "  Downloading huggingface_hub-0.11.1-py3-none-any.whl (182 kB)\n",
            "\u001b[K     |████████████████████████████████| 182 kB 59.6 MB/s \n",
            "\u001b[?25hCollecting responses<0.19\n",
            "  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from datasets) (21.3)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.8/dist-packages (from datasets) (2.23.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from datasets) (6.0)\n",
            "Requirement already satisfied: dill<0.3.7 in /usr/local/lib/python3.8/dist-packages (from datasets) (0.3.6)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from datasets) (1.21.6)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.8/dist-packages (from datasets) (3.8.3)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (from datasets) (1.3.5)\n",
            "Requirement already satisfied: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.8/dist-packages (from datasets) (2022.11.0)\n",
            "Collecting xxhash\n",
            "  Downloading xxhash-3.1.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (212 kB)\n",
            "\u001b[K     |████████████████████████████████| 212 kB 36.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (2.1.1)\n",
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            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.3.1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (22.1.0)\n",
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            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (4.0.2)\n",
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            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub<1.0.0,>=0.2.0->datasets) (4.1.1)\n",
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            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets) (2.10)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets) (2022.9.24)\n",
            "Collecting urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1\n",
            "  Downloading urllib3-1.25.11-py2.py3-none-any.whl (127 kB)\n",
            "\u001b[K     |████████████████████████████████| 127 kB 21.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (2022.6.2)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
            "  Downloading tokenizers-0.13.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 7.6 MB 35.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: scikit-learn>=0.21.3 in /usr/local/lib/python3.8/dist-packages (from seqeval) (1.0.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.21.3->seqeval) (3.1.0)\n",
            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.21.3->seqeval) (1.7.3)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.21.3->seqeval) (1.2.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2022.6)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n",
            "Building wheels for collected packages: seqeval\n",
            "  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16179 sha256=94514ffd8b962f5f98c49cad94fcb6329a7dcf730421523d13ee97e05f356849\n",
            "  Stored in directory: /root/.cache/pip/wheels/ad/5c/ba/05fa33fa5855777b7d686e843ec07452f22a66a138e290e732\n",
            "Successfully built seqeval\n",
            "Installing collected packages: urllib3, xxhash, tokenizers, responses, multiprocess, huggingface-hub, transformers, seqeval, datasets\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
            "    Uninstalling urllib3-1.24.3:\n",
            "      Successfully uninstalled urllib3-1.24.3\n",
            "Successfully installed datasets-2.7.1 huggingface-hub-0.11.1 multiprocess-0.70.14 responses-0.18.0 seqeval-1.2.2 tokenizers-0.13.2 transformers-4.25.1 urllib3-1.25.11 xxhash-3.1.0\n"
          ]
        }
      ],
      "source": [
        "! pip install datasets transformers seqeval\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pathlib import Path\n",
        "from argparse import Namespace\n",
        "from typing import Union, List\n",
        "from fastprogress import progress_bar\n",
        "from typing_extensions import TypedDict\n",
        "import torch\n",
        "from datasets import Dataset, DatasetDict, load_metric\n",
        "import numpy as np\n",
        "from transformers import DataCollatorForTokenClassification, AutoTokenizer, AutoModelForTokenClassification, TrainingArguments, Trainer\n",
        "\n"
      ],
      "metadata": {
        "id": "rEtby_xd1wP0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S2QN5BtF1yUE",
        "outputId": "9988ca23-b1ee-4299-9b3a-ab045f8304bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir = Path(\"/content/drive/MyDrive/NLP 2022/nombank_train_dev_test/percentage\")\n",
        "data_dir2 = Path(\"/content/drive/MyDrive/NLP 2022/Results\")\n",
        "\n",
        "args = Namespace(\n",
        "    batch_size=64,\n",
        "    num_workers=4\n",
        ")\n",
        "\n",
        "train_data_path = data_dir / 'train.data'\n",
        "# train_data_path = data_dir / 'corpus'\n",
        "valid_data_path = data_dir / 'dev.data'\n",
        "test_data_path = data_dir / 'test.data'\n"
      ],
      "metadata": {
        "id": "qWLLC0PO2CCK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "SYMBOL_DICT = {\n",
        "    \"COMMA\": \",\",\n",
        "}\n",
        "\n",
        "LABEL_LIST = [\"NONE\", \"ARG1\",\"SUPPORT\",\"PRED\"]\n",
        "LABEL_LIST2 = [\"NONE\", \"ARG1\"]\n",
        "POS_LIST = [\"CC\", \"CD\", \"DT\", \"FW\", \"IN\", \"JJ\", \"JJR\", \"JJS\", \n",
        "            \"LS\", \"MD\", \"NN\", \"NNS\", \"NNP\", \"NNPS\", \"PDT\", \n",
        "            \"POS\", \"PRP\", \"PRP$\", \"RB\", \"RBR\", \"RBS\", \"SYM\", \n",
        "            \"TO\", \"UH\", \"VB\", \"VBD\", \"VBG\", \"VBN\", \"VBP\",\n",
        "            \"VBZ\", \"WDT\", \"WP\", \"WP$\", \"WRB\", \"PU\", \"EX\", \n",
        "            \"RP\"]\n",
        "BIO_TAG_CONVERSION_DICT = {\n",
        "    \".\": \"PU\",\n",
        "    \",\": \"PU\",\n",
        "    \"COMMA\": \"PU\",\n",
        "    \"$\": \"PU\",\n",
        "    \":\": \"PU\",\n",
        "    \"(\": \"PU\",\n",
        "    \")\": \"PU\",\n",
        "    \"``\": \"PU\",\n",
        "    \"''\": \"PU\",\n",
        "    \"#\": \"PU\"\n",
        "}\n",
        "BIO_TAG_LIST = [\"O\", \"B-NP\", \"I-NP\", \"B-VP\", \"I-VP\", \"B-PP\",\n",
        "                \"I-PP\", \"B-ADJP\", \"I-ADJP\", \"B-ADVP\", \"I-ADVP\",\n",
        "                \"B-SBAR\", \"I-SBAR\", \"B-PRT\", \"I-PRT\", \"B-CONJP\",\n",
        "                \"I-CONJP\", \"B-UCP\", \"I-UCP\"]\n"
      ],
      "metadata": {
        "id": "3RkdnDGv2JT8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Word(TypedDict):\n",
        "  word: str\n",
        "  pos: str\n",
        "  biotag: str\n",
        "  label: Union[str, None]\n",
        "\n",
        "def parse_input(input_file: Union[str, Path], drop_label = False) -> List[Union[List[Word], None]]:\n",
        "    \"\"\"\n",
        "    Parses the input file and returns a list of lists of words.\n",
        "    \"\"\"\n",
        "    with open(input_file, \"r\") as f:\n",
        "        lines = f.readlines()\n",
        "    sentences: List[Union[List[Word], None]] = []\n",
        "    last_sentence: List[Word] = []\n",
        "    print(\"Parsing input file lines...\")\n",
        "    line_no = 0\n",
        "    for line in progress_bar(lines):\n",
        "        line_no += 1\n",
        "        line = line.strip()\n",
        "        word_info = line.split(\"\\t\")\n",
        "        if len(word_info) >= 5:\n",
        "            word_str = word_info[0].strip()\n",
        "            if word_str in SYMBOL_DICT:\n",
        "              word_str = SYMBOL_DICT[word_str]\n",
        "            pos = word_info[1].strip()\n",
        "            if pos in BIO_TAG_CONVERSION_DICT:\n",
        "              pos = BIO_TAG_CONVERSION_DICT[pos]\n",
        "            if pos not in POS_LIST:\n",
        "              print(f\"Warning: invalid POS on line {line_no} \\\"{pos}\\\", treated as PU.\")\n",
        "              pos = \"PU\"\n",
        "            biotag = word_info[2].strip()\n",
        "            if biotag not in BIO_TAG_LIST:\n",
        "              print(f\"Warning: invalid bio tag on line {line_no} \\\"{biotag}\\\", treated as O.\")\n",
        "              biotag = \"O\"\n",
        "            if len(word_info) >= 6:\n",
        "                label = word_info[5].strip()\n",
        "            else:\n",
        "                label = \"NONE\"\n",
        "            if label not in LABEL_LIST:\n",
        "              print(f\"Warning: invalid label on line {line_no} \\\"{label}\\\", treated as NONE.\")\n",
        "              label = \"NONE\"\n",
        "            if drop_label:\n",
        "              label = None\n",
        "            word = Word(word=word_str, pos=pos, biotag=biotag, label=label)\n",
        "            last_sentence.append(word)\n",
        "        else:\n",
        "            if len(last_sentence) > 0:\n",
        "                sentences.append(last_sentence)\n",
        "            last_sentence = []\n",
        "    if len(last_sentence) > 0:\n",
        "        sentences.append(last_sentence)\n",
        "    return sentences\n"
      ],
      "metadata": {
        "id": "pG2QkMxI2OvW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n",
        "model = AutoModelForTokenClassification.from_pretrained(\"bert-base-uncased\", num_labels=len(LABEL_LIST))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForTokenClassification: ['cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight']\n",
            "- This IS expected if you are initializing BertForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def build_dataset_from_sentences(sentences, drop_label = False):\n",
        "  dataset_tokens = []\n",
        "  dataset_partitive_roles = []\n",
        "  dataset_pos_tags = []\n",
        "  dataset_bio_tags = []\n",
        "  for sentence in sentences:\n",
        "    tokens = [word['word'] for word in sentence]\n",
        "    if not drop_label:\n",
        "      partitive_roles = [LABEL_LIST.index(word['label']) for word in sentence]\n",
        "    else:\n",
        "      partitive_roles = None\n",
        "    pos_tags = [POS_LIST.index(word['pos']) for word in sentence]\n",
        "    bio_tags = [BIO_TAG_LIST.index(word['biotag']) for word in sentence]\n",
        "    dataset_tokens.append(tokens)\n",
        "    if not drop_label:\n",
        "      dataset_partitive_roles.append(partitive_roles)\n",
        "    dataset_pos_tags.append(pos_tags)\n",
        "    dataset_bio_tags.append(bio_tags)\n",
        "  if not drop_label:\n",
        "    dataset_dict = {\n",
        "        \"tokens\": dataset_tokens,\n",
        "        \"partitive_roles\": dataset_partitive_roles,\n",
        "        \"pos_tags\": dataset_pos_tags,\n",
        "        \"bio_tags\": dataset_bio_tags\n",
        "    }\n",
        "  else:\n",
        "    dataset_dict = {\n",
        "        \"tokens\": dataset_tokens,\n",
        "        \"pos_tags\": dataset_pos_tags,\n",
        "        \"bio_tags\": dataset_bio_tags\n",
        "    }\n",
        "  return Dataset.from_dict(dataset_dict)"
      ],
      "metadata": {
        "id": "u8x5YwTb2Uad"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_sentences = parse_input(train_data_path)\n",
        "valid_sentences = parse_input(valid_data_path)\n",
        "test_sentences = parse_input(test_data_path, drop_label=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        },
        "id": "CGgdATG_2YI-",
        "outputId": "ca8d30b7-8233-482d-ca54-74912004f163"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Parsing input file lines...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
              "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ]
          },
          "metadata": {}
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          "output_type": "display_data",
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              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      <progress value='63939' class='' max='63939' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      100.00% [63939/63939 00:00&lt;00:00]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: invalid POS on line 14996 \"/\", treated as PU.\n",
            "Warning: invalid POS on line 23018 \"-\", treated as PU.\n",
            "Warning: invalid POS on line 23020 \"-\", treated as PU.\n",
            "Warning: invalid POS on line 23036 \"/\", treated as PU.\n",
            "Warning: invalid POS on line 24114 \"/\", treated as PU.\n",
            "Warning: invalid POS on line 24124 \"/\", treated as PU.\n",
            "Warning: invalid POS on line 31275 \"/\", treated as PU.\n",
            "Warning: invalid POS on line 35203 \"-\", treated as PU.\n",
            "Warning: invalid POS on line 35319 \"/\", treated as PU.\n",
            "Warning: invalid POS on line 42913 \"-\", treated as PU.\n",
            "Warning: invalid POS on line 45420 \"/\", treated as PU.\n",
            "Warning: invalid POS on line 56824 \"-\", treated as PU.\n",
            "Warning: invalid POS on line 56826 \"-\", treated as PU.\n",
            "Warning: invalid POS on line 57778 \"-\", treated as PU.\n",
            "Warning: invalid POS on line 57913 \"-\", treated as PU.\n",
            "Warning: invalid POS on line 57915 \"-\", treated as PU.\n",
            "Warning: invalid POS on line 57953 \"-\", treated as PU.\n",
            "Warning: invalid POS on line 57955 \"-\", treated as PU.\n",
            "Warning: invalid POS on line 57969 \"-\", treated as PU.\n",
            "Warning: invalid POS on line 57971 \"-\", treated as PU.\n",
            "Warning: invalid POS on line 57995 \"-\", treated as PU.\n",
            "Warning: invalid POS on line 57997 \"-\", treated as PU.\n",
            "Warning: invalid POS on line 58006 \"-\", treated as PU.\n",
            "Warning: invalid POS on line 58008 \"-\", treated as PU.\n",
            "Warning: invalid POS on line 61709 \"-\", treated as PU.\n",
            "Parsing input file lines...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
              "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      <progress value='2310' class='' max='2310' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      100.00% [2310/2310 00:00&lt;00:00]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: invalid POS on line 929 \"/\", treated as PU.\n",
            "Parsing input file lines...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
              "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      <progress value='4428' class='' max='4428' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      100.00% [4428/4428 00:00&lt;00:00]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label_all_tokens = True\n",
        "\n",
        "def tokenize_and_align_labels(examples):\n",
        "    tokenized_inputs = tokenizer(examples[\"tokens\"], truncation=True, is_split_into_words=True)\n",
        "\n",
        "    if \"partitive_roles\" in examples:\n",
        "      labels = []\n",
        "      for i, label in enumerate(examples[\"partitive_roles\"]):\n",
        "          word_ids = tokenized_inputs.word_ids(batch_index=i)\n",
        "          previous_word_idx = None\n",
        "          label_ids = []\n",
        "          for word_idx in word_ids:\n",
        "              # Special tokens have a word id that is None. We set the label to -100 so they are automatically\n",
        "              # ignored in the loss function.\n",
        "              if word_idx is None:\n",
        "                  label_ids.append(-100)\n",
        "              # We set the label for the first token of each word.\n",
        "              elif word_idx != previous_word_idx:\n",
        "                  label_ids.append(label[word_idx])\n",
        "              # For the other tokens in a word, we set the label to either the current label or -100, depending on\n",
        "              # the label_all_tokens flag.\n",
        "              else:\n",
        "                  label_ids.append(label[word_idx] if label_all_tokens else -100)\n",
        "              previous_word_idx = word_idx\n",
        "\n",
        "          labels.append(label_ids)\n",
        "\n",
        "      tokenized_inputs[\"labels\"] = labels\n",
        "    return tokenized_inputs"
      ],
      "metadata": {
        "id": "NcqmPXT92a7w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_raw_dataset = build_dataset_from_sentences(train_sentences)\n",
        "valid_raw_dataset = build_dataset_from_sentences(valid_sentences)\n",
        "test_raw_dataset = build_dataset_from_sentences(test_sentences, drop_label=True)\n",
        "\n",
        "raw_datasets = DatasetDict(train=train_raw_dataset, valid=valid_raw_dataset, test=test_raw_dataset)"
      ],
      "metadata": {
        "id": "44boWR7w2dnT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size=64\n",
        "\n",
        "train_args = TrainingArguments(\n",
        "    \"bert_partitive_roles\",\n",
        "    evaluation_strategy = \"epoch\",\n",
        "    learning_rate=4e-6,\n",
        "    per_device_train_batch_size=batch_size,\n",
        "    per_device_eval_batch_size=batch_size,\n",
        "    num_train_epochs=10,\n",
        "    weight_decay=0.01\n",
        ")\n",
        "\n",
        "data_collator = DataCollatorForTokenClassification(tokenizer)\n",
        "\n",
        "metric = load_metric(\"seqeval\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105,
          "referenced_widgets": [
            "6a851b919ad54de5aa7310a3d188447d",
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        "id": "T3NorDlp2f8Y",
        "outputId": "3ccbbf7a-2fde-4830-f70d-0532a7598ad3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-12-75d0bd284b10>:15: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
            "  metric = load_metric(\"seqeval\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.47k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def metricsOnTraining(p):\n",
        "    predictions, labels = p\n",
        "    predictions = np.argmax(predictions, axis=2)\n",
        "\n",
        "    # Remove ignored index (special tokens)\n",
        "    true_predictions = [\n",
        "        [LABEL_LIST[p] for (p, l) in zip(prediction, label) if l != -100]\n",
        "        for prediction, label in zip(predictions, labels)\n",
        "    ]\n",
        "    true_labels = [\n",
        "        [LABEL_LIST[l] for (p, l) in zip(prediction, label) if l != -100]\n",
        "        for prediction, label in zip(predictions, labels)\n",
        "    ]\n",
        "\n",
        "    results = metric.compute(predictions=true_predictions, references=true_labels)\n",
        "    return {\n",
        "        \"precision\": results[\"overall_precision\"],\n",
        "        \"recall\": results[\"overall_recall\"],\n",
        "        \"f1\": results[\"overall_f1\"],\n",
        "        \"accuracy\": results[\"overall_accuracy\"],\n",
        "    }"
      ],
      "metadata": {
        "id": "0zQIcxTL2kxH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets = raw_datasets.map(tokenize_and_align_labels, batched=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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            "1a5b2c24b124498ca1e31844bf7a7429",
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        "outputId": "dd9fdd41-6f86-492b-d2ac-e0f22be39dee"
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      "execution_count": null,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?ba/s]"
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        {
          "output_type": "display_data",
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              "  0%|          | 0/1 [00:00<?, ?ba/s]"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = Trainer(\n",
        "    model,\n",
        "    train_args,\n",
        "    train_dataset=tokenized_datasets[\"train\"],\n",
        "    eval_dataset=tokenized_datasets[\"valid\"],\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=metricsOnTraining\n",
        ")"
      ],
      "metadata": {
        "id": "kyUl46p33Afq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size=64\n",
        "\n",
        "train_args = TrainingArguments(\n",
        "    \"bert_partitive_roles\",\n",
        "    evaluation_strategy = \"epoch\",\n",
        "    learning_rate=2e-5,\n",
        "    per_device_train_batch_size=batch_size,\n",
        "    per_device_eval_batch_size=batch_size,\n",
        "    num_train_epochs=10,\n",
        "    weight_decay=0.01\n",
        ")\n",
        "\n",
        "data_collator = DataCollatorForTokenClassification(tokenizer)\n",
        "\n",
        "metric = load_metric(\"seqeval\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l53C-jjA3Ey1",
        "outputId": "97cc1c03-24ef-4b1d-a252-57a2c0f79eb7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "PyTorch: setting up devices\n",
            "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_tIorbyD3Agr",
        "outputId": "b8a6fddd-c892-456d-85b0-c6bb230616c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the training set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: partitive_roles, pos_tags, tokens, bio_tags. If partitive_roles, pos_tags, tokens, bio_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "***** Running training *****\n",
            "  Num examples = 2192\n",
            "  Num Epochs = 10\n",
            "  Instantaneous batch size per device = 64\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 64\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 350\n",
            "  Number of trainable parameters = 108894724\n",
            "You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='350' max='350' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [350/350 04:25, Epoch 10/10]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.413910</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.907592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.252579</td>\n",
              "      <td>0.480519</td>\n",
              "      <td>0.294821</td>\n",
              "      <td>0.365432</td>\n",
              "      <td>0.927914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.180472</td>\n",
              "      <td>0.583942</td>\n",
              "      <td>0.478088</td>\n",
              "      <td>0.525739</td>\n",
              "      <td>0.940184</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.148950</td>\n",
              "      <td>0.609127</td>\n",
              "      <td>0.611554</td>\n",
              "      <td>0.610338</td>\n",
              "      <td>0.945552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.133248</td>\n",
              "      <td>0.634058</td>\n",
              "      <td>0.697211</td>\n",
              "      <td>0.664137</td>\n",
              "      <td>0.951304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.122722</td>\n",
              "      <td>0.648464</td>\n",
              "      <td>0.756972</td>\n",
              "      <td>0.698529</td>\n",
              "      <td>0.954755</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.114973</td>\n",
              "      <td>0.678756</td>\n",
              "      <td>0.782869</td>\n",
              "      <td>0.727105</td>\n",
              "      <td>0.958206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.108483</td>\n",
              "      <td>0.711504</td>\n",
              "      <td>0.800797</td>\n",
              "      <td>0.753515</td>\n",
              "      <td>0.962807</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.106479</td>\n",
              "      <td>0.706927</td>\n",
              "      <td>0.792829</td>\n",
              "      <td>0.747418</td>\n",
              "      <td>0.962807</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.105918</td>\n",
              "      <td>0.706927</td>\n",
              "      <td>0.792829</td>\n",
              "      <td>0.747418</td>\n",
              "      <td>0.962807</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: partitive_roles, pos_tags, tokens, bio_tags. If partitive_roles, pos_tags, tokens, bio_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 83\n",
            "  Batch size = 64\n",
            "/usr/local/lib/python3.8/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: NONE seems not to be NE tag.\n",
            "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
            "/usr/local/lib/python3.8/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PRED seems not to be NE tag.\n",
            "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
            "/usr/local/lib/python3.8/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: SUPPORT seems not to be NE tag.\n",
            "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
            "/usr/local/lib/python3.8/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARG1 seems not to be NE tag.\n",
            "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
            "/usr/local/lib/python3.8/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: partitive_roles, pos_tags, tokens, bio_tags. If partitive_roles, pos_tags, tokens, bio_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 83\n",
            "  Batch size = 64\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: partitive_roles, pos_tags, tokens, bio_tags. If partitive_roles, pos_tags, tokens, bio_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 83\n",
            "  Batch size = 64\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: partitive_roles, pos_tags, tokens, bio_tags. If partitive_roles, pos_tags, tokens, bio_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 83\n",
            "  Batch size = 64\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: partitive_roles, pos_tags, tokens, bio_tags. If partitive_roles, pos_tags, tokens, bio_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 83\n",
            "  Batch size = 64\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: partitive_roles, pos_tags, tokens, bio_tags. If partitive_roles, pos_tags, tokens, bio_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 83\n",
            "  Batch size = 64\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: partitive_roles, pos_tags, tokens, bio_tags. If partitive_roles, pos_tags, tokens, bio_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 83\n",
            "  Batch size = 64\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: partitive_roles, pos_tags, tokens, bio_tags. If partitive_roles, pos_tags, tokens, bio_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 83\n",
            "  Batch size = 64\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: partitive_roles, pos_tags, tokens, bio_tags. If partitive_roles, pos_tags, tokens, bio_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 83\n",
            "  Batch size = 64\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: partitive_roles, pos_tags, tokens, bio_tags. If partitive_roles, pos_tags, tokens, bio_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 83\n",
            "  Batch size = 64\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=350, training_loss=0.21682386125837053, metrics={'train_runtime': 268.3472, 'train_samples_per_second': 81.685, 'train_steps_per_second': 1.304, 'total_flos': 793972717123968.0, 'train_loss': 0.21682386125837053, 'epoch': 10.0})"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_results = trainer.predict(tokenized_datasets['test'])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "id": "Zn18k8Kg3VnK",
        "outputId": "9de1d374-d90d-41ce-9e34-7c38d936decc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the test set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: pos_tags, tokens, bio_tags. If pos_tags, tokens, bio_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Prediction *****\n",
            "  Num examples = 150\n",
            "  Batch size = 64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "out = []\n",
        "\n",
        "for i in range(len(tokenized_datasets['test'])):\n",
        "  sentence = tokenized_datasets['test'][i]\n",
        "  tokenized_input = tokenizer(sentence[\"tokens\"], truncation=True, is_split_into_words=True)\n",
        "  predictions = test_results.predictions[i]\n",
        "  label_ids = []\n",
        "  for prediction in predictions:\n",
        "    label_ids.append(np.argmax(prediction))\n",
        "  word_id_to_label_idx = {}\n",
        "  for j, word_id in enumerate(tokenized_input.word_ids()):\n",
        "    if word_id in word_id_to_label_idx or word_id is None:\n",
        "      continue\n",
        "    word_id_to_label_idx[word_id] = j\n",
        "  labelings = []\n",
        "  for j, token in enumerate(sentence[\"tokens\"]):\n",
        "    label_idx = word_id_to_label_idx[j]\n",
        "    label_id = label_ids[label_idx]\n",
        "    label = LABEL_LIST[label_id] if label_id == 1 else None\n",
        "    labelings.append((token, label))\n",
        "  out.append(labelings)"
      ],
      "metadata": {
        "id": "79bCaqoI3i-h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "outputFilePath = data_dir2 / 'BERT_BASE_PBP'\n"
      ],
      "metadata": {
        "id": "MYtWBjte3ntx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open(outputFilePath, 'w') as f:\n",
        "  for line in out:\n",
        "    for labling in line:\n",
        "      if labling[1]:\n",
        "        f.write(f\"{labling[0]}\\t{labling[1]}\\n\")\n",
        "      else:\n",
        "        f.write(f\"{labling[0]}\\n\")\n",
        "    f.write(\"\\n\")"
      ],
      "metadata": {
        "id": "LpYE_Wch4nBS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def test_sentence_string(s: str):\n",
        "  tokenized_input = tokenizer(s, truncation=True)\n",
        "  model(tokenized_input)"
      ],
      "metadata": {
        "id": "MCPWgxOC4pgm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ILghxHyZ4ph0"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}