step2_train_models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Package installation and import"
   ],
   "metadata": {
    "id": "XjobgVOcdtGs"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "M5WPyO1AZX19",
    "outputId": "c5f6d4d7-9923-4155-8c2a-2ac7888597be"
   },
   "source": [
    "!pip install datasets evaluate seqeval\n",
    "!pip install transformers[sentencepiece]\n",
    "!pip install accelerate"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "QxovqvlkZX2D"
   },
   "source": [
    "import os\n",
    "\n",
    "import evaluate\n",
    "import numpy as np\n",
    "from datasets import Dataset, DatasetDict\n",
    "from transformers import BertTokenizerFast, AutoTokenizer, DataCollatorForTokenClassification, \\\n",
    "    AutoModelForTokenClassification, TrainingArguments, Trainer"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Load'n'Import"
   ],
   "metadata": {
    "id": "3Jh3L-yQcBld"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Import _hmBERT_ tokeniser `dbmdz/bert-base-historic-multilingual-cased`"
   ],
   "metadata": {
    "id": "bcgbwIMsbw_l"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "Xg_AIddYZX2E"
   },
   "source": [
    "TOKENISER_CHECKPOINT: str = \"dbmdz/bert-base-historic-multilingual-cased\""
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "PHmkvsUKZX2F"
   },
   "source": [
    "def print_aligned(\n",
    "        list1: list,\n",
    "        list2: list\n",
    "):\n",
    "    line1 = \"\"\n",
    "    line2 = \"\"\n",
    "    for item1, item2 in zip(list1, list2):\n",
    "        max_length = max(len(item1), len(item2))\n",
    "        line1 += item1 + \" \" * (max_length - len(item1) + 1)\n",
    "        line2 += item2 + \" \" * (max_length - len(item2) + 1)\n",
    "    print(line1)\n",
    "    print(line2)"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Define `DATA_DIR`"
   ],
   "metadata": {
    "id": "1hEWmI6Vb3Gd"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "B1eT16z8ZX2F",
    "outputId": "4157eec0-2236-4abf-c6c2-ca0e1e962014"
   },
   "source": [
    "try:\n",
    "    from google.colab import drive\n",
    "\n",
    "    print(\n",
    "        \"You work on Colab. Gentle as we are, we will mount Drive for you. \"\n",
    "        \"It'd help if you allowed this in the popup that opens.\"\n",
    "    )\n",
    "    drive.mount('/content/drive')\n",
    "    DATA_DIR = os.path.join('drive', 'MyDrive', 'KEDiff', 'data')\n",
    "except:\n",
    "    print(\"You do not work on Colab\")\n",
    "    DATA_DIR = os.path.join('data')\n",
    "print(f\"{DATA_DIR=}\", '-->', os.path.abspath(DATA_DIR))"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Import Annotated Datasets"
   ],
   "metadata": {
    "id": "Rzsq6yJ7b9ep"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jPHnt4YWZX2G",
    "outputId": "bff7e9f9-5813-4219-e3db-cb3078be6afd"
   },
   "source": [
    "BILOUs_hug = Dataset.load_from_disk(dataset_path=os.path.join(DATA_DIR, 'BILOUs_hf'))\n",
    "print(\"Dataset:\", BILOUs_hug, sep='\\n')\n",
    "print(\"Features:\", BILOUs_hug.features, sep='\\n')"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Prepare Training"
   ],
   "metadata": {
    "id": "eib9-9qLcE7M"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Split dataset"
   ],
   "metadata": {
    "id": "hO55sI9scMwm"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "*   80% Training Data\n",
    "*   10% Test Data\n",
    "*   10% Evaluation Data"
   ],
   "metadata": {
    "id": "ksF_3AEpdFBQ"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vSetF9QvZX2H",
    "outputId": "a808ef5c-3b1c-471b-8307-2468fa4267a6"
   },
   "source": [
    "train_testvalid = BILOUs_hug.train_test_split(test_size=0.2, seed=42)\n",
    "test_valid = train_testvalid['test'].train_test_split(test_size=0.5, seed=42)\n",
    "\n",
    "# gather everyone if you want to have a single DatasetDict\n",
    "BILOUs_hug = DatasetDict({\n",
    "    'train': train_testvalid['train'],\n",
    "    'test': test_valid['test'],\n",
    "    'validation': test_valid['train']}\n",
    ")\n",
    "del train_testvalid, test_valid\n",
    "print(BILOUs_hug)"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Tokenise text of entire dataset"
   ],
   "metadata": {
    "id": "KB5kFL-OcIC2"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 252,
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    "id": "ukMa28KUIjSU",
    "outputId": "6932e518-cd66-4590-f412-e198e344ccae"
   },
   "source": [
    "tokeniser: BertTokenizerFast = AutoTokenizer.from_pretrained(TOKENISER_CHECKPOINT)\n",
    "print(f\"Is '{TOKENISER_CHECKPOINT}' a fast tokeniser?\", tokeniser.is_fast)"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "Batch embed labels (following several _HuggingFace_ tutorials)"
   ],
   "metadata": {
    "id": "AWBqu1OTcjzU"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "X0I2r1fWIjSV"
   },
   "source": [
    "def batch_embed_labels(batch):\n",
    "    # embed in [CLS] and [SEP] tokens as required by BERT models\n",
    "    for column in ['EVENT-BILOUs', 'LOC-BILOUs', 'MISC-BILOUs', 'ORG-BILOUs', 'PER-BILOUs', 'TIME-BILOUs',\n",
    "                   'EVENT-IOBs',   'LOC-IOBs',   'MISC-IOBs',   'ORG-IOBs',   'PER-IOBs',   'TIME-IOBs']:\n",
    "        all_labels = batch[column]\n",
    "        new_labels = [[-100, *labels[1:-1], -100] for labels in all_labels]\n",
    "        batch[column] = new_labels\n",
    "    return batch\n",
    "BILOUs_hug = BILOUs_hug.map(batch_embed_labels, batched=True)"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 307,
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    "id": "WdAWhOnXIjSV",
    "outputId": "3125dfe5-8332-4356-eb2c-c028c3e7dd52"
   },
   "source": [
    "def batch_tokenise(batch):\n",
    "    tokenised_inputs = tokeniser(batch['Text'], truncation=True)\n",
    "    return tokenised_inputs\n",
    "BILOUs_hug_tokenised = BILOUs_hug.map(batch_tokenise, batched=True)\n",
    "print(BILOUs_hug_tokenised)"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "The column to be trained on must be called `labels`."
   ],
   "metadata": {
    "id": "ZLs3EZNBcrhb"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "BZESTE0qIjSW"
   },
   "source": [
    "def batch_set_labels_column(batch, column_name):\n",
    "    batch[\"labels\"] = batch[column_name]\n",
    "    return batch"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SgvdpuJlIjSW",
    "outputId": "e3266114-8fd5-4b99-8a0e-66082befad22"
   },
   "source": [
    "BILOUs_hug[\"train\"].column_names"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
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    "id": "DkGF65C4ZX2H",
    "outputId": "d269398c-070c-4c6b-d319-50084a6c47a3"
   },
   "source": [
    "# adapt this for each label type\n",
    "BILOUs_hug_tokenised_PER = BILOUs_hug_tokenised.map(\n",
    "    batch_set_labels_column,\n",
    "    fn_kwargs={'column_name': 'PER-IOBs'},\n",
    "    batched=True,\n",
    "    remove_columns=BILOUs_hug[\"train\"].column_names)"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hSwZ5zBMZX2I",
    "outputId": "5a66f8e0-05bb-4d2a-d31e-0aefa78647ef"
   },
   "source": [
    "sample = BILOUs_hug_tokenised_PER[\"train\"][1]\n",
    "print(sample)\n",
    "del sample"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2FB6Pum9ZX2J",
    "outputId": "a35ec6d7-14a8-482f-8b13-9362e62cea17"
   },
   "source": [
    "data_collator = DataCollatorForTokenClassification(tokenizer=tokeniser, padding=True)\n",
    "batch = data_collator([BILOUs_hug_tokenised_PER[\"train\"][i] for i in range(2)])\n",
    "print(batch)\n",
    "print(batch['labels'])\n",
    "\n",
    "for i in range(2):\n",
    "    print(BILOUs_hug_tokenised_PER[\"train\"][i][\"labels\"])\n",
    "del i\n"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Find and define an evaluation metric"
   ],
   "metadata": {
    "id": "6FJQtBnuc3c0"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "id": "GVjzWBMkZX2J",
    "outputId": "fd92e8db-834b-49b9-980e-23dcd940c63d"
   },
   "source": [
    "label_names = BILOUs_hug[\"train\"].features[\"PER-IOBs\"].feature.names\n",
    "\n",
    "batch = {'references': [], 'predictions': []}\n",
    "for i in [0, 1]:\n",
    "    labels = BILOUs_hug[\"train\"][i][\"PER-IOBs\"]\n",
    "    labels = [label_names[i] for i in labels[1:-1]]\n",
    "    # fake predictions\n",
    "    predictions = labels.copy()\n",
    "    predictions[2] = \"B-PER\"\n",
    "    predictions[3] = \"I-PER\"\n",
    "\n",
    "    print_aligned(labels, predictions)\n",
    "\n",
    "    batch['references'] += [labels]\n",
    "    batch['predictions'] += [predictions]\n",
    "del i, labels, predictions\n",
    "\n",
    "# calculate metrics\n",
    "for metric_name in [\"seqeval\", \"poseval\"]:\n",
    "    print(f\"Now evaluating using {metric_name=}\")\n",
    "    metric = evaluate.load(metric_name)\n",
    "    metric_result = metric.compute(predictions=batch['predictions'], references=batch['references'])\n",
    "    print(metric_result)\n",
    "# del batch, metric, metric_name, metric_result\n"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "CfAZYlpzZX2K"
   },
   "source": [
    "metric = evaluate.load('poseval')\n"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "tFWF1awLZX2L"
   },
   "source": [
    "def compute_metrics(eval_preds):\n",
    "    logits, labels = eval_preds\n",
    "    predictions = np.argmax(logits, axis=-1)\n",
    "\n",
    "    # Remove ignored index (special tokens) and convert to labels\n",
    "    true_labels = [[label_names[l] for l in label if l != -100] for label in labels]\n",
    "    true_predictions = [\n",
    "        [label_names[p] for (p, l) in zip(prediction, label) if l != -100]\n",
    "        for prediction, label in zip(predictions, labels)\n",
    "    ]\n",
    "    metric_result = metric.compute(predictions=true_predictions, references=true_labels)\n",
    "    return {\n",
    "        \"macro precision\": metric_result[\"macro avg\"][\"precision\"],\n",
    "        \"macro recall\": metric_result[\"macro avg\"][\"recall\"],\n",
    "        \"macro f1\": metric_result[\"macro avg\"][\"f1-score\"],\n",
    "        \"macro support\": metric_result[\"macro avg\"][\"support\"],\n",
    "\n",
    "        \"weighted precision\": metric_result[\"weighted avg\"][\"precision\"],\n",
    "        \"weighted recall\": metric_result[\"weighted avg\"][\"recall\"],\n",
    "        \"weighted f1\": metric_result[\"weighted avg\"][\"f1-score\"],\n",
    "        \"weighted support\": metric_result[\"weighted avg\"][\"support\"],\n",
    "\n",
    "        \"accuracy\": metric_result[\"accuracy\"],\n",
    "    }\n",
    "\n"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Define dictionaries to convert `int` representation to labels' names"
   ],
   "metadata": {
    "id": "gu6FYz1ndKec"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "6ObuFG0wZX2L"
   },
   "source": [
    "id2label = {i: label for i, label in enumerate(label_names)}\n",
    "label2id = {v: k for k, v in id2label.items()}\n",
    "\n"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Perform the training and materialise results"
   ],
   "metadata": {
    "id": "rkD2_oFQdUIR"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Warning: evaluation metrics are not materialised except for the tables in the output. Trained models are only stored once training of a `label_type` model has finished. ⚠️"
   ],
   "metadata": {
    "id": "N4NAzny4dbni"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
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   "source": [
    "for label_type in ['EVENT', 'LOC', 'MISC', 'ORG', 'PER', 'TIME']:\n",
    "    trained_model_name = f\"oalz-1788-q1-ner-{label_type}\"\n",
    "\n",
    "    print(f\"Now training '{trained_model_name}'\")\n",
    "\n",
    "    print(\"Compose the relevant tokenised and annotated dataset\")\n",
    "    label_ds = BILOUs_hug_tokenised.map(\n",
    "        batch_set_labels_column,\n",
    "        fn_kwargs={'column_name': f'{label_type}-IOBs'},\n",
    "        batched=True,\n",
    "        remove_columns=BILOUs_hug[\"train\"].column_names)\n",
    "\n",
    "    model = AutoModelForTokenClassification.from_pretrained(\n",
    "        TOKENISER_CHECKPOINT,\n",
    "        id2label=id2label,\n",
    "        label2id=label2id,\n",
    "    )\n",
    "    model.config.num_labels\n",
    "\n",
    "    args = TrainingArguments(\n",
    "        output_dir = os.path.join(DATA_DIR, trained_model_name),\n",
    "        evaluation_strategy=\"epoch\",\n",
    "        save_strategy=\"epoch\",\n",
    "        learning_rate=2e-5,\n",
    "        num_train_epochs=5,\n",
    "        weight_decay=0.01\n",
    "    )\n",
    "\n",
    "    trainer = Trainer(\n",
    "        model=model,\n",
    "        args=args,\n",
    "        train_dataset=label_ds[\"train\"],\n",
    "        eval_dataset=label_ds[\"validation\"],\n",
    "        data_collator=data_collator,\n",
    "        compute_metrics=compute_metrics,\n",
    "        tokenizer=tokeniser,\n",
    "    )\n",
    "    trainer.train()\n",
    "    trainer.save_model(os.path.join(DATA_DIR, trained_model_name))\n"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "",
   "outputs": [],
   "execution_count": null
  }
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