diff --git a/training.ipynb b/training.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..73c6b67e47a07a4615fc1110e3bf87e1571b4e72
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+++ b/training.ipynb
@@ -0,0 +1,3693 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "laFgRm6u1O2_"
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "\n",
+ "packages = ['transformers[torch]', 'seqeval', 'evaluate', 'datasets']\n",
+ "\n",
+ "for package in packages:\n",
+ " os.system(f'pip install {package}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "DD7iAWaa1j4u",
+ "outputId": "b221a3ad-a939-46e0-b632-1ba99dda3da6"
+ },
+ "outputs": [],
+ "source": [
+ "from flask import Flask, jsonify, request, render_template\n",
+ "from collections import OrderedDict\n",
+ "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
+ "from seqeval.metrics import classification_report as seq_classification_report, accuracy_score\n",
+ "\n",
+ "import torch\n",
+ "from torchtext.vocab import build_vocab_from_iterator, vocab\n",
+ "\n",
+ "import datasets, evaluate\n",
+ "from datasets import load_dataset, load_metric\n",
+ "from transformers import (\n",
+ " AutoTokenizer,\n",
+ " AutoModelForTokenClassification,\n",
+ " DataCollatorForTokenClassification,\n",
+ " TrainingArguments,\n",
+ " Trainer,\n",
+ " EarlyStoppingCallback\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "MSMkpAYy00Uf"
+ },
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "import numpy as np\n",
+ "import string\n",
+ "import re\n",
+ "from collections import OrderedDict\n",
+ "from torchtext.vocab import build_vocab_from_iterator, vocab\n",
+ "from transformers import DataCollatorForTokenClassification, AutoTokenizer, AutoModelForTokenClassification\n",
+ "from datasets import load_dataset, load_metric\n",
+ "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
+ "import matplotlib.pyplot as plt\n",
+ "from seqeval.metrics import classification_report as seq_classification_report\n",
+ "\n",
+ "from transformers import (AutoTokenizer, AutoModelForTokenClassification,\n",
+ " DataCollatorForTokenClassification, TrainingArguments,\n",
+ " Trainer, EarlyStoppingCallback)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 313,
+ "referenced_widgets": [
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+ },
+ "id": "PTPu_Ewg1JO6",
+ "outputId": "b925fa5d-9dba-4ecc-b4e5-a5ee78860faf"
+ },
+ "outputs": [],
+ "source": [
+ "Large_dataset = load_dataset(\"surrey-nlp/PLOD-filtered\")\n",
+ "\n",
+ "train_Large_dataset = Large_dataset[\"train\"]\n",
+ "validation_Large_dataset = Large_dataset[\"validation\"]\n",
+ "test_Large_dataset = Large_dataset[\"test\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Lfls_SJdckOc",
+ "outputId": "bbf7e342-86fc-468f-df4e-271500bc026b"
+ },
+ "outputs": [],
+ "source": [
+ "def count_label_occurrences(dataset):\n",
+ " label_counts = {}\n",
+ " for labels in dataset[\"ner_tags\"]:\n",
+ " for label in labels:\n",
+ " if label in label_counts:\n",
+ " label_counts[label] += 1\n",
+ " else:\n",
+ " label_counts[label] = 1\n",
+ " return label_counts\n",
+ "count_label_occurrences(train_Large_dataset)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "8XiETPThdB-u"
+ },
+ "outputs": [],
+ "source": [
+ "label_encoding = {\"B-O\": 0, \"B-AC\": 1, \"B-LF\": 3, \"I-LF\": 4}\n",
+ "\n",
+ "reverse_label_encoding = {v: k for k, v in label_encoding.items()}\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Rr_gBW1UCd6u",
+ "outputId": "0d6ae121-c467-4d06-9e40-490b282e0f09"
+ },
+ "outputs": [],
+ "source": [
+ "reverse_label_encoding"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "3r0UhNK4eobk",
+ "outputId": "3fd054ee-ee94-4d1c-99b2-bc4dcf39b5a6"
+ },
+ "outputs": [],
+ "source": [
+ "print(train_Large_dataset[:5])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "4708akvnZOqj",
+ "outputId": "9d72d1de-8448-4761-a885-861f9ddef98a"
+ },
+ "outputs": [],
+ "source": [
+ "label_encoding = {\"B-O\": 0, \"B-AC\": 1, \"B-LF\": 3, \"I-LF\": 4}\n",
+ "\n",
+ "\n",
+ "label_encoding = {0: 0, 1: 1, 3: 2, 4: 3}\n",
+ "\n",
+ "def create_label_list(dataset, label_encoding):\n",
+ " label_list = []\n",
+ " for sample in dataset[\"ner_tags\"]:\n",
+ " label_list.append([label_encoding[tag] for tag in sample])\n",
+ " return label_list\n",
+ "\n",
+ "def turn_dict_to_list_of_dict(d):\n",
+ " new_list = []\n",
+ " for labels, inputs in zip(d[\"labels\"], d[\"input_ids\"]):\n",
+ " entry = {\"input_ids\": inputs, \"labels\": labels}\n",
+ " new_list.append(entry)\n",
+ " return new_list\n",
+ "\n",
+ "def tokenize_and_align_labels(tokenizer, dataset, label_list, max_length=500, truncation=True, is_split_into_words=True):\n",
+ "\n",
+ " tokenized_inputs = tokenizer(\n",
+ " dataset[\"tokens\"],\n",
+ " max_length=max_length,\n",
+ " truncation=truncation,\n",
+ " is_split_into_words=is_split_into_words)\n",
+ "\n",
+ " labels = []\n",
+ " for i, labels_per_sample in enumerate(label_list):\n",
+ " word_ids = tokenized_inputs.word_ids(batch_index=i)\n",
+ " label_ids, previous_word_idx = [], None\n",
+ "\n",
+ "\n",
+ " for word_idx in word_ids:\n",
+ " if word_idx is None:\n",
+ " label_ids.append(-100) # Label for specials\n",
+ " elif word_idx != previous_word_idx:\n",
+ " label_ids.append(labels_per_sample[word_idx])\n",
+ " else:\n",
+ " label_ids.append(labels_per_sample[word_idx])\n",
+ " previous_word_idx = word_idx\n",
+ "\n",
+ " labels.append(label_ids)\n",
+ "\n",
+ " tokenized_inputs[\"labels\"] = labels\n",
+ " return tokenized_inputs\n",
+ "\n",
+ "metric = load_metric(\"seqeval\")\n",
+ "def compute_metrics(p):\n",
+ " predictions, labels = p\n",
+ " predictions = np.argmax(predictions, axis=2)\n",
+ " true_predictions = [[label_list[p] for (p, l) in zip(prediction, label) if l != -100] for prediction, label in zip(predictions, labels)]\n",
+ " true_labels = [[label_list[l] for (p, l) in zip(prediction, label) if l != -100] for prediction, label in zip(predictions, labels)]\n",
+ " results = metric.compute(predictions=true_predictions, references=true_labels)\n",
+ " return {\"precision\": results[\"overall_precision\"], \"recall\": results[\"overall_recall\"], \"f1\": results[\"overall_f1\"], \"accuracy\": results[\"overall_accuracy\"]}\n",
+ "\n",
+ "def simplify_tags(labels):\n",
+ " tag_mapping = {'B-LF': 'LF', 'I-LF': 'LF', 'B-O': 'O', 'B-AC': 'AC'}\n",
+ " simplified_labels = []\n",
+ " for label_seq in labels:\n",
+ " simplified_seq = [tag_mapping.get(label, label) for label in label_seq]\n",
+ " simplified_labels.append(simplified_seq)\n",
+ " return simplified_labels\n",
+ "\n",
+ "# Decode and Prepare Labels\n",
+ "def decode_and_prepare_labels(predictions, labels, label_encoding):\n",
+ " reversed_label_encoding = {v: k for k, v in label_encoding.items()}\n",
+ " predictions = np.argmax(predictions, axis=2)\n",
+ "\n",
+ " true_predictions = [\n",
+ " [reversed_label_encoding[p] for (p, l) in zip(prediction, label) if l != -100]\n",
+ " for prediction, label in zip(predictions, labels)\n",
+ " ]\n",
+ "\n",
+ " true_labels = [\n",
+ " [reversed_label_encoding[l] for (p, l) in zip(prediction, label) if l != -100]\n",
+ " for prediction, label in zip(predictions, labels)\n",
+ " ]\n",
+ "\n",
+ " return true_predictions, true_labels"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 202,
+ "referenced_widgets": [
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+ ]
+ },
+ "id": "Vi6cYlazARPH",
+ "outputId": "af5e4a68-f31a-4a94-d3ac-0169bdd3a553"
+ },
+ "outputs": [],
+ "source": [
+ "# Initialize Tokenizer and Model for SciBERT\n",
+ "scibert_model_name = \"allenai/scibert_scivocab_uncased\"\n",
+ "SciBERT_tokenizer = AutoTokenizer.from_pretrained(scibert_model_name)\n",
+ "SciBERT_model = AutoModelForTokenClassification.from_pretrained(scibert_model_name, num_labels=4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "hZClcDc6F2OK"
+ },
+ "outputs": [],
+ "source": [
+ "label_list = create_label_list(train_Large_dataset, label_encoding)\n",
+ "val_label_list = create_label_list(validation_Large_dataset, label_encoding)\n",
+ "test_label_list = create_label_list(test_Large_dataset, label_encoding)\n",
+ "\n",
+ "\n",
+ "tokenized_datasets = tokenize_and_align_labels(SciBERT_tokenizer, train_Large_dataset, label_list)\n",
+ "tokenized_val_datasets = tokenize_and_align_labels(SciBERT_tokenizer, validation_Large_dataset, val_label_list)\n",
+ "tokenized_test_datasets = tokenize_and_align_labels(SciBERT_tokenizer, test_Large_dataset, test_label_list)\n",
+ "\n",
+ "tokenised_train = turn_dict_to_list_of_dict(tokenized_datasets)\n",
+ "tokenised_val = turn_dict_to_list_of_dict(tokenized_val_datasets)\n",
+ "tokenised_test = turn_dict_to_list_of_dict(tokenized_test_datasets)\n",
+ "\n",
+ "\n",
+ "data_collator = DataCollatorForTokenClassification(SciBERT_tokenizer)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 332
+ },
+ "id": "VYpcGKFRZvfL",
+ "outputId": "658b2a37-7b2d-4564-9416-595eb0cf1ce5"
+ },
+ "outputs": [],
+ "source": [
+ "args = TrainingArguments(output_dir=\"SciBERT-finetuned-NER\", evaluation_strategy='steps',\n",
+ " eval_steps=5000, learning_rate=2e-5,\n",
+ " per_device_train_batch_size=8, per_device_eval_batch_size=4,\n",
+ " num_train_epochs=1, weight_decay=0.01, save_steps=10000,\n",
+ " metric_for_best_model='f1', load_best_model_at_end=True)\n",
+ "\n",
+ "\n",
+ "SciBERT_trainer = Trainer(SciBERT_model, args, train_dataset=tokenised_train, eval_dataset=tokenised_val,\n",
+ " data_collator=data_collator, tokenizer=SciBERT_tokenizer, compute_metrics=compute_metrics,\n",
+ " callbacks=[EarlyStoppingCallback(early_stopping_patience=3)])\n",
+ "\n",
+ "\n",
+ "SciBERT_trainer.train()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 176
+ },
+ "id": "gpB7g7JCZ0pP",
+ "outputId": "fcbefee1-a691-422b-af45-d79b1513e2b0"
+ },
+ "outputs": [],
+ "source": [
+ "# Make predictions using the trained model\n",
+ "predictions, labels, _ = SciBERT_trainer.predict(tokenised_test)\n",
+ "\n",
+ "# Decode and prepare the predictions and labels\n",
+ "true_predictions, true_labels = decode_and_prepare_labels(predictions, labels, label_encoding)\n",
+ "\n",
+ "# Simplify tags\n",
+ "simplified_true_labels = simplify_tags(true_labels)\n",
+ "simplified_predictions = simplify_tags(true_predictions)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "rUKWY2TbZ6Gj",
+ "outputId": "d56a28c9-dc18-4d7f-8a27-2e86c291a167"
+ },
+ "outputs": [],
+ "source": [
+ "from seqeval.metrics import classification_report\n",
+ "\n",
+ "label_encoding = {\"B-O\": 0, \"B-AC\": 1, \"B-LF\": 3, \"I-LF\": 4}\n",
+ "reverse_label_encoding = {v: k for k, v in label_encoding.items()}\n",
+ "\n",
+ "def decode_tags(encoded_tags, reverse_encoding):\n",
+ " return [reverse_encoding[tag] for tag in encoded_tags]\n",
+ "\n",
+ "decoded_true_labels = [decode_tags(label_seq, reverse_label_encoding) for label_seq in true_labels]\n",
+ "decoded_true_predictions = [decode_tags(pred_seq, reverse_label_encoding) for pred_seq in true_predictions]\n",
+ "\n",
+ "print(\"Classification Report:\")\n",
+ "print(classification_report(decoded_true_labels, decoded_true_predictions))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 807
+ },
+ "id": "j5ZpGeY_as_r",
+ "outputId": "5e014926-cd10-4a2d-ece7-786832bcab31"
+ },
+ "outputs": [],
+ "source": [
+ "label_encoding = {\"O\": 0, \"AC\": 1, \"LF\": 3}\n",
+ "reverse_label_encoding = {0: 'O', 1: 'AC', 3: 'LF', 4: 'LF'}\n",
+ "\n",
+ "\n",
+ "def decode_labels(labels, reverse_encoding):\n",
+ " decoded_labels = []\n",
+ " for label_seq in labels:\n",
+ " decoded_seq = [reverse_encoding[label] for label in label_seq]\n",
+ " decoded_labels.append(decoded_seq)\n",
+ " return decoded_labels\n",
+ "\n",
+ "decoded_true_labels = decode_labels(true_labels, reverse_label_encoding)\n",
+ "decoded_true_predictions = decode_labels(true_predictions, reverse_label_encoding)\n",
+ "\n",
+ "flat_true_labels = [label for sublist in decoded_true_labels for label in sublist]\n",
+ "flat_predictions = [label for sublist in decoded_true_predictions for label in sublist]\n",
+ "\n",
+ "unique_labels = sorted(set(flat_true_labels + flat_predictions))\n",
+ "\n",
+ "cm_normalized = confusion_matrix(flat_true_labels, flat_predictions, labels=unique_labels, normalize='true')\n",
+ "cm_counts = confusion_matrix(flat_true_labels, flat_predictions, labels=unique_labels)\n",
+ "\n",
+ "fig, ax = plt.subplots(figsize=(10, 10))\n",
+ "disp = ConfusionMatrixDisplay(confusion_matrix=cm_normalized, display_labels=unique_labels)\n",
+ "disp.plot(cmap=plt.cm.Blues, ax=ax, values_format=\".2f\")\n",
+ "\n",
+ "for i in range(cm_counts.shape[0]):\n",
+ " for j in range(cm_counts.shape[1]):\n",
+ " count = cm_counts[i, j]\n",
+ " ax.text(j, i + 0.2, f\"({count})\", ha='center', va='center', color='black', fontsize=12)\n",
+ "\n",
+ "ax.set_title('Confusion Matrix')\n",
+ "plt.ylabel('True Labels')\n",
+ "plt.xlabel('Predicted Labels')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "4cXBNskUfetU",
+ "outputId": "d4a67cc8-8ae4-4153-a441-9132d32cc7cb"
+ },
+ "outputs": [],
+ "source": [
+ "import json\n",
+ "import os\n",
+ "\n",
+ "# Save the trained model and tokenizer\n",
+ "output_dir = \"SciBERT-finetuned-NER\"\n",
+ "\n",
+ "# Create the output directory if it does not exist\n",
+ "os.makedirs(output_dir, exist_ok=True)\n",
+ "\n",
+ "# Save the model\n",
+ "SciBERT_trainer.save_model(output_dir)\n",
+ "\n",
+ "# Save the tokenizer\n",
+ "SciBERT_tokenizer.save_pretrained(output_dir)\n",
+ "\n",
+ "# Save the training arguments\n",
+ "training_args = args.to_dict()\n",
+ "with open(os.path.join(output_dir, \"training_args.json\"), \"w\") as f:\n",
+ " json.dump(training_args, f)\n",
+ "\n",
+ "print(f\"Model, tokenizer, and training arguments saved to {output_dir}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
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+ },
+ "id": "GcnDVcV_ffQE",
+ "outputId": "5c5ff3ea-ed93-4671-8d77-e27e2dae955a"
+ },
+ "outputs": [],
+ "source": [
+ "import shutil\n",
+ "from google.colab import files\n",
+ "\n",
+ "# Define the output directory\n",
+ "output_dir = \"SciBERT-finetuned-NER\"\n",
+ "\n",
+ "# Zip the folder\n",
+ "shutil.make_archive(output_dir, 'zip', output_dir)\n",
+ "\n",
+ "# Download the zip file\n",
+ "files.download(f\"{output_dir}.zip\")"
+ ]
+ }
+ ],
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+ "accelerator": "GPU",
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