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

def GenerateNgrams(words, ngrams):
    nglist = []
    for ng in ngrams:
        for word in words:
            nglist.extend([word[n:n+ng] for n in range(len(word)-ng+1)])
    return ngli

maybe it should like following

def GenerateNgrams(words, ngrams):
    nglist = []
    for ng in ngrams:
        nglist.extend(''.join([words[n:n+ng]) for n in range(len(words)-ng+1)])
 

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

fastText supervised model does not take into account of the document and words representation, it just learns bag of words and labels.
embeddings are computed only on the relation word->label. it would be interesting to learn jointly the semantic relation label<->document<->word<->context.
for now it is only possible to pre-train word embeddings and then use them as initial vectors for the clas