Hi, is there any plan to provide a tutorial of showing an example of employing the Transformer as an alternative of RNN for seq2seq task such as machine translation?

I tried some RNN regression learning based on the code in the "PyTorch-Tutorial/tutorial-contents/403_RNN_regressor.py" file, which did not work for me at all.

According to an accepted answer on stack-overflow (https://stackoverflow.com/questions/52857213/recurrent-network-rnn-wont-learn-a-very-simple-function-plots-shown-in-the-q?noredirect=1#comment92916825_52857213), it turns out that the li

Thanks for your tutorial from scratch. It helps me a lot.
In article part 3, there are some codes you wrote. I copy that codes but some error for me.

model = Darknet("cfg/yolov3.cfg")
inp = get_test_input()
pred = model(inp)
print (pred)

TypeError: forward() missing 1 required positional argument: 'CUDA'

and I want to ask you that training module is the only left work

I add a function(according the code in the tutorial4) to calculate the bleu score, but i get the vey low score(0.09), could you tell me why?
This is code to calculate bleu:

def translate_sentence(sentence, src_field, trg_field, model, device, max_len = 50):
    
    model.eval()
        
    if isinstance(sentence, str):
        nlp = spacy.load('de')
        tokens = [token.te

e.g. based on the google doc https://docs.google.com/document/d/1ZI1V_2I3tETAeGnAwYZnrYTdeiOfdtWCL4l4DmRimFc/

p.s. I think that using docker for the course is an overhead which is not justified. As people create a designated VM for the course it would have been better to share a VM image so installation is one click and forget about docker. Docker is great when you need multiple environment or

I'm studying 5_3_Document_Classification_with_CNN.

The make_embedding_matrix helper docs say that it should be fed in a list of words in the dataset. However, for the embedding matrix to return the correct embedding of a word from pretrained embeddings, the word list should be fed in the same order as in the vocabulary. Furthermore, there should be no gaps in the word indices in the vocabulary

    assert len(references) == len(hypotheses)

# Calculate BLEU-4 scores
bleu4 = corpus_bleu(references, hypotheses)