retrain.py

import torch
import pandas as pd
from transformers import AutoTokenizer
from torch.utils.data import Dataset, DataLoader
from evo_model import EvoTransformerV22
import torch.nn as nn
import torch.optim as optim

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

class FeedbackDataset(Dataset):
    def __init__(self, csv_file):
        self.data = pd.read_csv(csv_file).dropna()

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        x = self.data.iloc[idx]
        combined = x['query'] + " " + x['context']
        enc = tokenizer(combined, padding="max_length", truncation=True, max_length=128, return_tensors="pt")
        label = torch.tensor(float(x['label'])).unsqueeze(0)  # Single logit
        return enc['input_ids'].squeeze(0), label

def fine_tune_on_feedback(model_path="trained_model_evo_hellaswag.pt", feedback_file="feedback_log.csv"):
    model = EvoTransformerV22()
    model.load_state_dict(torch.load(model_path))
    model.train()

    dataset = FeedbackDataset(feedback_file)
    dataloader = DataLoader(dataset, batch_size=4, shuffle=True)

    model.to("cpu")
    optimizer = optim.Adam(model.parameters(), lr=1e-5)
    loss_fn = nn.BCEWithLogitsLoss()

    for epoch in range(2):  # Light touch-up
        total_loss = 0
        for input_ids, labels in dataloader:
            optimizer.zero_grad()
            outputs = model(input_ids)
            loss = loss_fn(outputs.view(-1), labels.view(-1))
            loss.backward()
            optimizer.step()
            total_loss += loss.item()
        print(f"Epoch {epoch + 1} Loss: {total_loss:.4f}")

    torch.save(model.state_dict(), model_path)
    print("✅ Evo updated from feedback.")