Example (from TfidfTransformer)

if isinstance(docs[0], tuple):
    docs = [docs]
return [self.gensim_model[doc] for doc in docs]

This method expects a list of tuples, instead of an iterable. This means that the entire corpus has to be stored as a lis

If I have a word, how do i get top k words closest to that given word. As far as i understand, there is a way to get it from cpp code but I can't find anything in the python library.
Something similar to what gensim word2vec implementation has:

model.most_similar(positive=[your_word_vector], topn=1))

The documentation in DJL was originally written with the expectation that users are reasonably familiar with deep learning. So, it does not go out of the way to define and explain some of the key concepts. To help users who are newer to deep learning, we created a [documentation convention](https://github.com/awslabs/djl/blob/master/docs/development/development_guideline.md#documentation-conventio

official Python binding from the fastText repository: https://github.com/facebookresearch/fastText/tree/master/python , open this website , only little example , compare pyfasttext document , I cannot understand official document , have anybody know how to understand official fasttext example , In my mind pyfasttext document better than official fasttext document