Upload Bert_Model.py

Bert_Model.py

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+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()