Set a LRU cache for word embeddings for a decrease of 20% of inference time

[pommedeterresautee]

Set an embedding LRU cache of Tensor of word embeddings.
This approach avoid to convert most used Gensim embeddings to Pytorch Tensor and even more important to avoid to transfer from computer RAM to GPU Ram (this is a slow operation).
Because of zipf law, the effect of such caching approach are magnified.
LRU cache is set to 10000 because it s very small and still provide most of the performance boost compared to loading all embeddings in GPU Ram. A 1000 embedding LRU cache is slightly less performant on my own dataset.

A second little optimization is to replace a call of unsqueeze on each token followed by a cat by a single call of stack.

Time to process 100 French documents decreased from 33s to 26s with this PR.
For what it worths, Spacy takes exactly the same time (26s) on my dataset with much lower accuracy on some tricky entities (and same accuracy on easiest to recognize entities).
Another change is on Ner HTML viewer with the introduction of a HTML title parameter.

[alanakbik]

This is really great, including this PR the combined improvements of your recent PRs take inference time from ~95 seconds on CoNLL-03 down to ~33 seconds - Thanks for all your great work!

[alanakbik]

👍

[kashif]

👍