Thank you so much. Yes, these are indices of unique words in a corpus. Embedding works very well.
These are the losses:
tensor(39.4518)
tensor(0.7318)
tensor(0.0179)
tensor(0.0121)
tensor(0.0125)
tensor(0.0324)
tensor(0.0114)
tensor(0.0113)
tensor(0.0112)
tensor(0.0111)

I think if we don’t use embedding, the solution is logically wrong. Because if just pass the indices to the neural network (as I was doing before) we say the only thing that matters is the order of a word in a content, which is not sufficient. In my case where I want to classify Yelp reviews into two classes (positive and negative) the position of a word does not really matter. If I am not wrong, embedding (after training) is aimed to represent the word semantically, which is good for my task.
Thank you once again for your help.