Implementation of this should be generic enough to allow used any other model, for example:

• We'd like to use vectors obtained from a Vector Space model (Raw Counts, Tf-idf, LSA)
• We also need to support using other types of word embeddings, such as GloVe or Word2Vec or embeddings obtained from BERT (for sentences), for example: https://huggingface.co/

Our representation should allow us to implement metrics such as:

• Average sentence similarity (e.g. cosine distance, euclidean distance)
• Other metrics based on sentence similarity (e.g. max distance between two sentences, average distance to the cluster center)
• Givenness using semantic spaces
• etc.

One design idea is having a callable that takes the text and returns de vectors as a numpy array. From the spacy dependency we should already have numpy in our dependencies, so no worries about that.

The, we will need to file issues to implement different metrics.