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"""
Training Script for Fine-tuning IndicBERT on Misinformation Detection
Combines the data loader and enhanced IndicBERT processor for end-to-end training.
"""
import sys
import os
import logging
from pathlib import Path
# Add backend to path
sys.path.insert(0, str(Path(__file__).parent))
from data_loader import DataLoader
from enhanced_indicbert_processor import EnhancedIndicBERTProcessor
from advanced_ml_classifier import create_comprehensive_training_data
import pandas as pd
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def train_indicbert_model(
use_existing_dataset: bool = False,
dataset_filename: str = "indian_misinformation.csv",
epochs: int = 3,
batch_size: int = 16,
learning_rate: float = 2e-5,
test_size: float = 0.2,
val_size: float = 0.1,
output_dir: str = "models/finetuned_indicbert",
apply_quantization: bool = False
):
"""
Complete training pipeline for IndicBERT fine-tuning
Args:
use_existing_dataset: Whether to use existing dataset file or create new one
dataset_filename: Name of dataset file in datasets/ directory
epochs: Number of training epochs
batch_size: Batch size for training
learning_rate: Learning rate for optimizer
test_size: Fraction of data for test set
val_size: Fraction of data for validation set
output_dir: Directory to save fine-tuned model
apply_quantization: Whether to apply quantization after training
"""
logger.info("🚀 Starting IndicBERT Fine-tuning Pipeline")
logger.info("=" * 70)
# Step 1: Load Data
logger.info("\n📊 Step 1: Loading Data")
data_loader = DataLoader(datasets_dir='datasets')
if use_existing_dataset and os.path.exists(f'datasets/{dataset_filename}'):
logger.info(f"Loading existing dataset: {dataset_filename}")
df = data_loader.load_csv(dataset_filename)
else:
logger.info("Creating new Indian context dataset...")
# Combine synthetic dataset from advanced_ml_classifier
synthetic_df = create_comprehensive_training_data()
# Add Indian-specific examples
indian_df = data_loader.create_indian_context_dataset()
# Combine datasets
df = pd.concat([synthetic_df, indian_df], ignore_index=True)
# Save combined dataset
data_loader.save_processed_dataset(df, "combined_training_data.csv")
logger.info(f"Created and saved combined dataset with {len(df)} examples")
# Step 2: Preprocess Data
logger.info("\n🔧 Step 2: Preprocessing Data")
df = data_loader.preprocess(
df,
clean_text=True,
remove_duplicates=True,
balance_classes=True # Balance for better training
)
# Step 3: Split Data
logger.info("\n✂️ Step 3: Splitting Data")
train_df, val_df, test_df = data_loader.split_data(
df,
test_size=test_size,
val_size=val_size
)
# Step 4: Initialize IndicBERT Processor
logger.info("\n🧠 Step 4: Initializing IndicBERT Processor")
processor = EnhancedIndicBERTProcessor(
model_name="ai4bharat/indic-bert",
num_labels=2
)
# Step 5: Fine-tune Model
logger.info("\n🎓 Step 5: Fine-tuning IndicBERT")
training_results = processor.fine_tune(
train_texts=train_df['text'].tolist(),
train_labels=train_df['label'].tolist(),
val_texts=val_df['text'].tolist() if len(val_df) > 0 else None,
val_labels=val_df['label'].tolist() if len(val_df) > 0 else None,
epochs=epochs,
batch_size=batch_size,
learning_rate=learning_rate,
output_dir=output_dir
)
logger.info(f"\n📊 Training Results:")
logger.info(f" Final Training Accuracy: {training_results['final_train_accuracy']:.4f}")
if training_results['best_val_accuracy']:
logger.info(f" Best Validation Accuracy: {training_results['best_val_accuracy']:.4f}")
# Step 6: Evaluate on Test Set
if len(test_df) > 0:
logger.info("\n📈 Step 6: Evaluating on Test Set")
test_predictions = processor.predict_batch(test_df['text'].tolist())
# Calculate test accuracy
correct = sum(
1 for i, pred in enumerate(test_predictions)
if (pred['prediction'] == 'fake' and test_df.iloc[i]['label'] == 1) or
(pred['prediction'] == 'real' and test_df.iloc[i]['label'] == 0)
)
test_accuracy = correct / len(test_df)
logger.info(f" Test Set Accuracy: {test_accuracy:.4f}")
# Show some sample predictions
logger.info("\n🔍 Sample Predictions:")
for i in range(min(5, len(test_df))):
text = test_df.iloc[i]['text']
true_label = 'fake' if test_df.iloc[i]['label'] == 1 else 'real'
pred = test_predictions[i]
logger.info(f"\n Text: {text[:80]}...")
logger.info(f" True: {true_label}, Predicted: {pred['prediction']}, Confidence: {pred['confidence']:.4f}")
# Step 7: Apply Quantization (Optional)
if apply_quantization:
logger.info("\n⚡ Step 7: Applying Quantization")
processor.quantize_model()
# Save quantized model
quantized_output_dir = output_dir + "_quantized"
processor.save_model(quantized_output_dir)
logger.info(f"Quantized model saved to {quantized_output_dir}")
logger.info("\n" + "=" * 70)
logger.info("✅ Training Pipeline Completed Successfully!")
logger.info(f"📁 Model saved to: {output_dir}")
return processor, training_results
def test_trained_model(model_dir: str = "models/finetuned_indicbert"):
"""Test the trained model with sample inputs"""
logger.info("🧪 Testing Trained Model")
logger.info("=" * 70)
# Load model
processor = EnhancedIndicBERTProcessor()
processor.load_model(model_dir)
# Test samples
test_samples = [
"BREAKING: Modi government secretly selling India to China, conspiracy exposed!",
"Government of India announces new infrastructure development plan",
"EXPOSED: Vaccines contain microchips to control population",
"According to Ministry of Health, COVID-19 cases declining nationwide",
"URGENT: This miracle cure will shock you - doctors hate it!",
"Supreme Court delivers verdict on constitutional matter",
]
logger.info("\n📝 Test Predictions:\n")
predictions = processor.predict_batch(test_samples)
for text, pred in zip(test_samples, predictions):
logger.info(f"Text: {text}")
logger.info(f"Prediction: {pred['prediction'].upper()} (confidence: {pred['confidence']:.4f})")
logger.info(f"Probabilities: Real={pred['probabilities']['real']:.4f}, Fake={pred['probabilities']['fake']:.4f}")
logger.info("-" * 70)
logger.info("\n✅ Testing Complete!")
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Train IndicBERT for misinformation detection")
parser.add_argument("--mode", type=str, default="train", choices=["train", "test"],
help="Mode: train or test")
parser.add_argument("--epochs", type=int, default=3, help="Number of training epochs")
parser.add_argument("--batch_size", type=int, default=16, help="Batch size")
parser.add_argument("--learning_rate", type=float, default=2e-5, help="Learning rate")
parser.add_argument("--output_dir", type=str, default="models/finetuned_indicbert",
help="Output directory for model")
parser.add_argument("--quantize", action="store_true", help="Apply quantization after training")
parser.add_argument("--use_existing", action="store_true",
help="Use existing dataset file")
parser.add_argument("--dataset", type=str, default="indian_misinformation.csv",
help="Dataset filename")
args = parser.parse_args()
if args.mode == "train":
processor, results = train_indicbert_model(
use_existing_dataset=args.use_existing,
dataset_filename=args.dataset,
epochs=args.epochs,
batch_size=args.batch_size,
learning_rate=args.learning_rate,
output_dir=args.output_dir,
apply_quantization=args.quantize
)
else:
test_trained_model(model_dir=args.output_dir)
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