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"""
Training script for NLP models.
This module contains the main training loop and model training functions.
"""
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
import argparse
import logging
from pathlib import Path
# Import your custom modules here
# from models.model import YourModel
# from preprocessing.data_loader import YourDataLoader
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def train_model(model, train_loader, val_loader, epochs=10, lr=0.001):
"""
Train the NLP model.
Args:
model: The neural network model
train_loader: Training data loader
val_loader: Validation data loader
epochs: Number of training epochs
lr: Learning rate
"""
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=lr)
for epoch in range(epochs):
model.train()
train_loss = 0.0
for batch_idx, (data, target) in enumerate(train_loader):
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
train_loss += loss.item()
if batch_idx % 100 == 0:
logger.info(f'Epoch: {epoch}, Batch: {batch_idx}, Loss: {loss.item():.4f}')
# Validation
model.eval()
val_loss = 0.0
correct = 0
with torch.no_grad():
for data, target in val_loader:
output = model(data)
val_loss += criterion(output, target).item()
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
val_accuracy = correct / len(val_loader.dataset)
logger.info(f'Epoch {epoch}: Val Loss: {val_loss:.4f}, Val Accuracy: {val_accuracy:.4f}')
def main():
"""Main training function."""
parser = argparse.ArgumentParser(description='Train NLP Model')
parser.add_argument('--epochs', type=int, default=10, help='Number of epochs')
parser.add_argument('--lr', type=float, default=0.001, help='Learning rate')
parser.add_argument('--batch_size', type=int, default=32, help='Batch size')
args = parser.parse_args()
# Initialize your model, data loaders here
# model = YourModel()
# train_loader = YourDataLoader(batch_size=args.batch_size, split='train')
# val_loader = YourDataLoader(batch_size=args.batch_size, split='val')
logger.info("Starting training...")
# train_model(model, train_loader, val_loader, args.epochs, args.lr)
logger.info("Training completed!")
if __name__ == "__main__":
main() |