Implement a PreTrained Transformer Embedding Generator for all available architectures.

[dafajon]

• Branching from feature/bert_sentence_embedding branch.
• Via help of sentence-transformers dependency get_pretrained_embeddings method is implemented with the likeness of bert_embeddings. However this is not a property method, it receives architecture type and whether to produce sentence or document embeddings with archtecture and do_sents arguments.
• Necessary checks are done for sentence-transformer dependency and available architectures.
• Implement a sklearn compatible PreTrainedVectorizer that receives same arguments and wraps the Document method access of pretrained model embeddings.
• The vectorizer does not need Text2Doc to be used beforehand on pipeline. This is because transformers based models require minimal preprocessing and the sentence-transformer dependency already handles architecture based tokenization. So the Document to be serialized is initialized as class attribute with default settings of the DocBuilder.
• This may change in the future if preprocessing with filter attributes of Document proves useful.

Future

• Currently architecture selection is restricted to available TR models. Custom fine tuned or pre-trained architecture support for any user's any model on huggingface hub will be available.