Phạm Văn Lĩnh phamvanlinh143

*2vec papers

	model.zero_grad() # Reset gradients tensors
	for i, (inputs, labels) in enumerate(training_set):
	predictions = model(inputs) # Forward pass
	loss = loss_function(predictions, labels) # Compute loss function
	loss = loss / accumulation_steps # Normalize our loss (if averaged)
	loss.backward() # Backward pass
	if (i+1) % accumulation_steps == 0: # Wait for several backward steps
	optimizer.step() # Now we can do an optimizer step
	model.zero_grad() # Reset gradients tensors
	if (i+1) % evaluation_steps == 0: # Evaluate the model when we...