Infer only returns embedding of one sentence about fast_sentence_embeddings HOT 2 CLOSED

Thank you! As soon as I've got some free time I will deal with it!

from fast_sentence_embeddings.

It's a feature, not a bug :-)

By using the following code you create a 2-to-1 mapping. fse supports n-to-m mappings, so that you sum multiple results onto the same vector. This behavior is dependent on the index you pass.
Passing two sentences with index 0 will result in a 2-to-1 mapping.

2-to-1 mapping -> (1, 100):

tmp = ("Hello my friends".split(), 0)
model.infer([tmp, tmp]).shape

1-to-1 mapping -> (2, 100):

tmp1 = ("Hello my friends".split(), 0)
tmp2 = ("Hello my friends".split(), 1)
model.infer([tmp1, tmp2]).shape

from fast_sentence_embeddings.