Bert_Model.py

import pandas as pd
import numpy as np
from datasets import Dataset
from transformers import AutoTokenizer, AutoModelForTokenClassification, TrainingArguments, Trainer
from seqeval.metrics import classification_report
import torch


def main():
    # Verify GPU availability
    device = "cuda" if torch.cuda.is_available() else "cpu"
    print(f"Using device: {device}")

    # Load dataset from CSV
    df = pd.read_excel('Augmented_Dataset.xlsx')

    # Clean the data
    df = df.dropna(subset=['Word', 'Tag'])
    df['Word'] = df['Word'].astype(str)
    df['Tag'] = df['Tag'].astype(str)

    # Group sentences
    grouped_data = df.groupby("Sentence").apply(lambda s: {
        'words': s['Word'].tolist(),
        'labels': s['Tag'].tolist()
    }).tolist()

    # Convert grouped data to Dataset
    dataset = Dataset.from_list(grouped_data)
    print(f"Total dataset size: {len(dataset)}")

    # Split into train and test sets
    train_test_split = dataset.train_test_split(test_size=0.2)
    train_dataset = train_test_split['train']
    test_dataset = train_test_split['test']

    # Load the tokenizer
    tokenizer = AutoTokenizer.from_pretrained("abdulhade/RoBERTa-large-SizeCorpus_1B")

    # Map labels to unique IDs
    unique_labels = list(set(df['Tag']))
    label2id = {label: i for i, label in enumerate(unique_labels)}
    id2label = {i: label for label, i in label2id.items()}

    # Tokenize and align labels
    def tokenize_and_align_labels(examples):
        tokenized_inputs = tokenizer(
            examples['words'],
            truncation=True,
            is_split_into_words=True,
            padding='max_length',
            max_length=128
        )
        labels = []
        for i, label in enumerate(examples['labels']):
            word_ids = tokenized_inputs.word_ids(batch_index=i)
            label_ids = [-100 if word_id is None else label2id[label[word_id]] for word_id in word_ids]
            labels.append(label_ids)

        tokenized_inputs["labels"] = labels
        return tokenized_inputs

    # Apply tokenization to datasets without parallel processing
    train_dataset = train_dataset.map(tokenize_and_align_labels, batched=True)
    test_dataset = test_dataset.map(tokenize_and_align_labels, batched=True)

    # Load the model
    model = AutoModelForTokenClassification.from_pretrained(
        "abdulhade/RoBERTa-large-SizeCorpus_1B",
        num_labels=len(unique_labels),
        id2label=id2label,
        label2id=label2id
    ).to(device)

    # Set up training arguments
    training_args = TrainingArguments(
        output_dir='results',
        evaluation_strategy="epoch",
        learning_rate=2e-5,
        per_device_train_batch_size=64,  # Adjusted for 8GB VRAM
        per_device_eval_batch_size=64,
        num_train_epochs=50,  # Increased to 50
        weight_decay=0.01,
        save_steps=5000,
        save_total_limit=2,
        logging_dir='./logs',
        fp16=True,  # Use mixed precision for faster computation
    )

    # Initialize Trainer
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=test_dataset,
        tokenizer=tokenizer,
    )

    # Train the model
    trainer.train()

    # Save the trained model and tokenizer
    output_dir = 'NER_RoBERTa_fineTuning'
    model.save_pretrained(output_dir)
    tokenizer.save_pretrained(output_dir)

    print(f"Model and tokenizer saved to {output_dir}")

    # Evaluate the model
    predictions, labels, _ = trainer.predict(test_dataset)
    predictions = np.argmax(predictions, axis=2)

    true_labels = [[id2label[label] for label in label_set if label != -100] for label_set in labels]
    true_predictions = [[id2label[pred] for pred, label in zip(pred_set, label_set) if label != -100]
                        for pred_set, label_set in zip(predictions, labels)]

    # Print classification report
    print(classification_report(true_labels, true_predictions))


if __name__ == "__main__":
    main()