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- losses = []
- loss_function = nn.NLLLoss()
- model = LeModel(len(vocab))
- optimizer = optim.SGD(model.parameters(), lr=0.001)
- for epoch in tqdm(range(1), leave=False):
- total_loss = 0
- for context, target in tqdm(data, leave=False):
- model.zero_grad()
- predictions = model(x_train)
- loss = loss_function(predictions, y_train, dtype=torch.long))
- loss.backward()
- optimizer.step()
- total_loss += loss.item()
- print("%s: %s" % (epoch, loss.item()))
- losses.append(total_loss)
- torch.save(model.state_dict(), "model.torch")
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