app.py

import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration, AdamW
from torch.utils.data import DataLoader, Dataset
from tqdm import tqdm

# Define your dataset class
class SpiderDataset(Dataset):
    def __init__(self, encodings, labels):
        self.encodings = encodings
        self.labels = labels

    def __getitem__(self, idx):
        return {'input_ids': self.encodings[idx], 'labels': self.labels[idx]}

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

# Load your preprocessed Spider dataset
train_encodings = # Your preprocessed input encodings for training (e.g., a list of input IDs)
train_labels = # Your preprocessed labels for training (e.g., a list of label IDs)

# Create a PyTorch dataset and dataloader
train_dataset = SpiderDataset(train_encodings, train_labels)
train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True)

# Load the pre-trained T5 model
model = T5ForConditionalGeneration.from_pretrained('t5-base')
tokenizer = T5Tokenizer.from_pretrained('t5-base')

# Move the model to the GPU if available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)

# Set up the optimizer
optimizer = AdamW(model.parameters(), lr=5e-5)

# Fine-tune the model
model.train()
for epoch in range(3):  # Number of epochs
    for batch in tqdm(train_loader):
        optimizer.zero_grad()
        input_ids = batch['input_ids'].to(device)
        labels = batch['labels'].to(device)
        outputs = model(input_ids=input_ids, labels=labels)
        loss = outputs.loss
        loss.backward()
        optimizer.step()

# Save the fine-tuned model
model.save_pretrained('your_model_directory')
tokenizer.save_pretrained('your_model_directory')