train_finetune.py

# train_finetune.py
# Script to fine-tune a SentenceTransformer model on pre-generated ESG triplets.

import pandas as pd
from sentence_transformers import SentenceTransformer, InputExample, losses
from torch.utils.data import DataLoader
from create_triplets import create_triplets # Import the function

# --- File Paths ---
TRIPLETS_PATH = "data/esg_triplets.csv"
OUTPUT_MODEL_PATH = "./fine_tuned_esg_model"

# --- Training Parameters ---
BASE_MODEL = "all-MiniLM-L6-v2"
TRAIN_BATCH_SIZE = 16
NUM_EPOCHS = 4
LEARNING_RATE = 2e-5

def fine_tune_model():
    """
    Main function to fine-tune the model using pre-generated triplets.
    """
    print("--- Starting ESG Fine-Tuning ---")

    # Load a pre-trained SentenceTransformer model
    model = SentenceTransformer(BASE_MODEL)
    
    try:
        triplets_df = pd.read_csv(TRIPLETS_PATH)
        train_examples = [InputExample(texts=[row['anchor'], row['positive'], row['negative']]) for _, row in triplets_df.iterrows()]
    except FileNotFoundError:
        print(f"Info: Triplets file not found at {TRIPLETS_PATH}. Generating it now...")
        create_triplets() # Generate the triplets
        try:
            triplets_df = pd.read_csv(TRIPLETS_PATH)
            train_examples = [InputExample(texts=[row['anchor'], row['positive'], row['negative']]) for _, row in triplets_df.iterrows()]
        except FileNotFoundError:
            print(f"Error: Failed to generate or find triplets file at {TRIPLETS_PATH}. Aborting.")
            return
        
    if not train_examples:
        print("No training examples found. Aborting fine-tuning.")
        return

    # Create a DataLoader
    train_dataloader = DataLoader(train_examples, shuffle=True, batch_size=TRAIN_BATCH_SIZE)
    
    # Define the loss function. MultipleNegativesRankingLoss is great for triplets.
    train_loss = losses.MultipleNegativesRankingLoss(model)
    
    # Fine-tune the model
    print(f"Starting training for {NUM_EPOCHS} epochs...")
    model.fit(
        train_objectives=[(train_dataloader, train_loss)],
        epochs=NUM_EPOCHS,
        warmup_steps=100,
        optimizer_params={'lr': LEARNING_RATE},
        output_path=OUTPUT_MODEL_PATH,
        show_progress_bar=True
    )
    
    print(f"--- Fine-tuning complete. Model saved to {OUTPUT_MODEL_PATH} ---")
    print(f"To use this model, update FINE_TUNED_MODEL_PATH in app.py to '{OUTPUT_MODEL_PATH}'.")

if __name__ == "__main__":
    fine_tune_model()