What does this PR do?

Add Model2Vec as an incredibly fast but still quite accurate embedding backend.

Usage is straightforward and you first need to install model2vec:

pip install model2vec

Then, you can load in any of their models and pass it to BERTopic like so:

from model2vec import StaticModel
embedding_model = StaticModel.from_pretrained("minishlab/potion-base-8M")

topic_model = BERTopic(embedding_model=embedding_model)

Distillation

These models are extremely versatile and can be distilled from existing embedding model (like those compatible with sentence-transformers). This distillation process doesn't require a vocabulary (as it uses the tokenizer's vocabulary) but can benefit from having one. Fortunately, this allows you to use the vocabulary from your input documents to distill a model yourself.

Doing so requires you to install some additional dependencies of model2vec like so:

pip install model2vec[distill]

To then distill common embedding models, you need to import the Model2VecBackend from BERTopic:

from bertopic.backend import Model2VecBackend

# Choose a model to distill (a non-Model2Vec model)
embedding_model = Model2VecBackend(
    "sentence-transformers/all-MiniLM-L6-v2", 
    distill=True
)

topic_model = BERTopic(embedding_model=embedding_model)

You can also choose a custom vectorizer for creating the vocabulary and define custom arguments for the distillatio process:

from bertopic.backend import Model2VecBackend
from sklearn.feature_extraction.text import CountVectorizer

# Choose a model to distill (a non-Model2Vec model)
embedding_model = Model2VecBackend(
    "sentence-transformers/all-MiniLM-L6-v2", 
    distill=True,
    distill_kwargs={"pca_dims": 256, "apply_zipf": True, "use_subword": True},
    distill_vectorizer=CountVectorizer(ngram_range=(1, 3))
)

topic_model = BERTopic(embedding_model=embedding_model)

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• This PR fixes a typo or improves the docs (if yes, ignore all other checks!).
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