Machine Learning, Sentiment Analysis, Vertex AI, Programming, Bert.
max_steps: return {'loss': loss.item(), 'acc': total_acc / total_count} return {'loss': loss.item(), 'acc': total_acc / total_count} def evaluate(model, dataloader, loss_fn): model.eval() total_acc, total_count = 0, 0 with torch.no_grad(): for idx, (inputs, label) in enumerate(dataloader):