Contextualized-topic-models

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* Introduced a method to predict the topics for a set of documents (supports multiple sampling to reduce variation)
* Adding some features to bert embeddings creation like increased batch size and progress bar
* Supporting training directly from lists without the need to deal with files
* Adding a simple quick preprocessing pipeline

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* Updating sentence-transformers package to avoid errors

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* Changed the encoding on file load for the SBERT embedding function

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* Fixed bug over sparse matrices

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* New feature handling sparse bow for optimized processing
* New method to return topic distributions for words

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* Released models with the main features implemented