ALBERT_BASE_PRED.ipynb

{
  "cells": [
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "WOz8KRM6VsbR",
        "outputId": "5a77e24c-a171-49c6-eb0a-1b14377db10e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Building wheels for collected packages: seqeval\n",
            "  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16179 sha256=1b79dabc5a9ec130f9c4047bed4d4c6fe7a7f98a3aa7d30d140f8c696e036503\n",
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            "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",
      "execution_count": null,
      "metadata": {
        "id": "RxAcpp1DV9h3"
      },
      "outputs": [],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_BoIyDLoV_dr",
        "outputId": "aed614c0-d1f9-432b-cc2f-983261a8cf83"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6V_kqbwqWFda"
      },
      "outputs": [],
      "source": [
        "data_dir = Path(\"/content/drive/MyDrive/NLP 2022/nombank_train_dev_test/percentage\")\n",
        "\n",
        "args = Namespace(\n",
        "    batch_size=64,\n",
        "    num_workers=4\n",
        ")\n",
        "\n",
        "train_data_path = data_dir / '%-train'\n",
        "# train_data_path = data_dir / 'corpus'\n",
        "valid_data_path = data_dir / 'dev.data'\n",
        "test_data_path = data_dir / 'test.data'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "clR-9WRMWPuo"
      },
      "outputs": [],
      "source": [
        "SYMBOL_DICT = {\n",
        "    \"COMMA\": \",\",\n",
        "}\n",
        "\n",
        "LABEL_LIST = [\"NONE\", \"PRED\", \"ARG1\",\"SUPPORT\"]\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\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5QM-XZsLWSwF"
      },
      "outputs": [],
      "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"
      ]
    },
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        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at albert-base-v2 were not used when initializing AlbertForTokenClassification: ['predictions.LayerNorm.weight', 'predictions.dense.bias', 'predictions.dense.weight', 'predictions.decoder.bias', 'predictions.LayerNorm.bias', 'predictions.bias', 'predictions.decoder.weight']\n",
            "- This IS expected if you are initializing AlbertForTokenClassification 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 AlbertForTokenClassification 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 AlbertForTokenClassification were not initialized from the model checkpoint at albert-base-v2 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"
          ]
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(\"albert-base-v2\")\n",
        "model = AutoModelForTokenClassification.from_pretrained(\"albert-base-v2\", num_labels=len(LABEL_LIST))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ge_zV_LmWaLZ"
      },
      "outputs": [],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        },
        "id": "1PYAvKeCWdQS",
        "outputId": "d5462918-1024-4e3d-b24d-ea374d7834e8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Parsing input file lines...\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
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              "        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",
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              "\n",
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              "      100.00% [63939/63939 00:00&lt;00:00]\n",
              "    </div>\n",
              "    "
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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          "metadata": {},
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        {
          "name": "stdout",
          "output_type": "stream",
          "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"
          ]
        },
        {
          "data": {
            "text/html": [
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              "        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",
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              "</style>\n"
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        },
        {
          "data": {
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              "\n",
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              "      100.00% [2310/2310 00:00&lt;00:00]\n",
              "    </div>\n",
              "    "
            ],
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              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Warning: invalid POS on line 929 \"/\", treated as PU.\n",
            "Parsing input file lines...\n"
          ]
        },
        {
          "data": {
            "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",
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              "        background: #F44336;\n",
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              "</style>\n"
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              "<IPython.core.display.HTML object>"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
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              "      100.00% [4428/4428 00:00&lt;00:00]\n",
              "    </div>\n",
              "    "
            ],
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              "<IPython.core.display.HTML object>"
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      ],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RzYw1WcSWgfF"
      },
      "outputs": [],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XgfCiO4eWjMW"
      },
      "outputs": [],
      "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)"
      ]
    },
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      "cell_type": "code",
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      "outputs": [
        {
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              "  0%|          | 0/3 [00:00<?, ?ba/s]"
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          },
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        {
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          },
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      ],
      "source": [
        "tokenized_datasets = raw_datasets.map(tokenize_and_align_labels, batched=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jymE-8mMWno1"
      },
      "outputs": [],
      "source": [
        "def compute_metrics(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",
        "    }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105,
          "referenced_widgets": [
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      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-14-6d23ca99e727>: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"
          ]
        },
        {
          "data": {
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              "model_id": "6b574ea35e5e4f4c88c471034dd77a6a",
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              "version_minor": 0
            },
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.47k [00:00<?, ?B/s]"
            ]
          },
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        }
      ],
      "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\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ws6xyUcfW15Y"
      },
      "outputs": [],
      "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=compute_metrics\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "blhXf5L0W5Bb",
        "outputId": "b05e9db3-5e7a-4328-d9aa-3469c9f64eb0"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The following columns in the training set don't have a corresponding argument in `AlbertForTokenClassification.forward` and have been ignored: tokens, bio_tags, pos_tags, partitive_roles. If tokens, bio_tags, pos_tags, partitive_roles are not expected by `AlbertForTokenClassification.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 = 11096068\n",
            "You're using a AlbertTokenizerFast 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"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='350' max='350' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [350/350 05:00, 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>0.290940</td>\n",
              "      <td>0.643979</td>\n",
              "      <td>0.491018</td>\n",
              "      <td>0.557191</td>\n",
              "      <td>0.944574</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.289423</td>\n",
              "      <td>0.678404</td>\n",
              "      <td>0.576846</td>\n",
              "      <td>0.623517</td>\n",
              "      <td>0.952207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.280438</td>\n",
              "      <td>0.688095</td>\n",
              "      <td>0.576846</td>\n",
              "      <td>0.627579</td>\n",
              "      <td>0.953203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.279245</td>\n",
              "      <td>0.698113</td>\n",
              "      <td>0.590818</td>\n",
              "      <td>0.640000</td>\n",
              "      <td>0.953535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.282339</td>\n",
              "      <td>0.721939</td>\n",
              "      <td>0.564870</td>\n",
              "      <td>0.633819</td>\n",
              "      <td>0.954198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.280334</td>\n",
              "      <td>0.725641</td>\n",
              "      <td>0.564870</td>\n",
              "      <td>0.635241</td>\n",
              "      <td>0.954198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.281626</td>\n",
              "      <td>0.704651</td>\n",
              "      <td>0.604790</td>\n",
              "      <td>0.650913</td>\n",
              "      <td>0.954530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.284320</td>\n",
              "      <td>0.691943</td>\n",
              "      <td>0.582834</td>\n",
              "      <td>0.632719</td>\n",
              "      <td>0.953867</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.285559</td>\n",
              "      <td>0.710526</td>\n",
              "      <td>0.592814</td>\n",
              "      <td>0.646355</td>\n",
              "      <td>0.954530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.286984</td>\n",
              "      <td>0.708531</td>\n",
              "      <td>0.596806</td>\n",
              "      <td>0.647887</td>\n",
              "      <td>0.954530</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
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          },
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        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The following columns in the evaluation set don't have a corresponding argument in `AlbertForTokenClassification.forward` and have been ignored: tokens, bio_tags, pos_tags, partitive_roles. If tokens, bio_tags, pos_tags, partitive_roles are not expected by `AlbertForTokenClassification.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 `AlbertForTokenClassification.forward` and have been ignored: tokens, bio_tags, pos_tags, partitive_roles. If tokens, bio_tags, pos_tags, partitive_roles are not expected by `AlbertForTokenClassification.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 `AlbertForTokenClassification.forward` and have been ignored: tokens, bio_tags, pos_tags, partitive_roles. If tokens, bio_tags, pos_tags, partitive_roles are not expected by `AlbertForTokenClassification.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 `AlbertForTokenClassification.forward` and have been ignored: tokens, bio_tags, pos_tags, partitive_roles. If tokens, bio_tags, pos_tags, partitive_roles are not expected by `AlbertForTokenClassification.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 `AlbertForTokenClassification.forward` and have been ignored: tokens, bio_tags, pos_tags, partitive_roles. If tokens, bio_tags, pos_tags, partitive_roles are not expected by `AlbertForTokenClassification.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 `AlbertForTokenClassification.forward` and have been ignored: tokens, bio_tags, pos_tags, partitive_roles. If tokens, bio_tags, pos_tags, partitive_roles are not expected by `AlbertForTokenClassification.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 `AlbertForTokenClassification.forward` and have been ignored: tokens, bio_tags, pos_tags, partitive_roles. If tokens, bio_tags, pos_tags, partitive_roles are not expected by `AlbertForTokenClassification.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 `AlbertForTokenClassification.forward` and have been ignored: tokens, bio_tags, pos_tags, partitive_roles. If tokens, bio_tags, pos_tags, partitive_roles are not expected by `AlbertForTokenClassification.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 `AlbertForTokenClassification.forward` and have been ignored: tokens, bio_tags, pos_tags, partitive_roles. If tokens, bio_tags, pos_tags, partitive_roles are not expected by `AlbertForTokenClassification.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 `AlbertForTokenClassification.forward` and have been ignored: tokens, bio_tags, pos_tags, partitive_roles. If tokens, bio_tags, pos_tags, partitive_roles are not expected by `AlbertForTokenClassification.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"
          ]
        },
        {
          "data": {
            "text/plain": [
              "TrainOutput(global_step=350, training_loss=0.059860730852399555, metrics={'train_runtime': 304.3563, 'train_samples_per_second': 72.021, 'train_steps_per_second': 1.15, 'total_flos': 74713755260544.0, 'train_loss': 0.059860730852399555, 'epoch': 10.0})"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "id": "PYFe4ASbW70W",
        "outputId": "08ba12ca-e3ae-41c9-a336-dbc572f698d0"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The following columns in the test set don't have a corresponding argument in `AlbertForTokenClassification.forward` and have been ignored: tokens, bio_tags, pos_tags. If tokens, bio_tags, pos_tags are not expected by `AlbertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Prediction *****\n",
            "  Num examples = 150\n",
            "  Batch size = 64\n"
          ]
        },
        {
          "data": {
            "text/html": [],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          "metadata": {},
          "output_type": "display_data"
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      ],
      "source": [
        "test_results = trainer.predict(tokenized_datasets['test'])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b6t4VInAXEfk"
      },
      "outputs": [],
      "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 ==2 else None\n",
        "    labelings.append((token, label))\n",
        "  out.append(labelings)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nuOcdgSNXOlR"
      },
      "outputs": [],
      "source": [
        "dir = Path(\"/content/drive/MyDrive/NLP 2022/Results\")\n",
        "out_path = dir / 'Albert_PB'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W5GqVn0zXypw"
      },
      "outputs": [],
      "source": [
        "with open(out_path, '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\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZtrMA4wHX06W"
      },
      "outputs": [],
      "source": [
        "\n",
        "def test_sentence_string(s: str):\n",
        "  tokenized_input = tokenizer(s, truncation=True)\n",
        "  model(tokenized_input)\n"
      ]
    }
  ],
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