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Various comments #38

@tomaarsen

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@tomaarsen

Hello (again)!

After experimenting with the project some more, I've found a few points of potential improvements. I'll aggregate them here if that's okay:

  • Set device=None as the default in distill, rather than "cpu". This should use Sentence Transformers' functionality to get the "strongest" available device by default (e.g. "cuda" first). P.s.: distilling mxbai-embed-large-v1 takes 00:05 seconds on my GPU vs 01:58 on CPU. (Edit: Might be moot since remove sentence transformers dependency #35)
  • Allow 1) passing an initialized Sentence Transformer model or 2) introduce the trust_remote_code argument. This is required for loading models with custom code, such as https://huggingface.co/jinaai/jina-embeddings-v2-base-en or https://huggingface.co/nomic-ai/nomic-embed-text-v1.5. (Edit: Might be moot since remove sentence transformers dependency #35)
  • Don't be afraid to link to yourself in the README.md, i.e. in the bottom of the model card template you could put a hyperlink over your names to your GitHub or something. I've included this in [enh] Rely on Jinja for model card, use model id/path in snippet #37.
  • In my experience, the private argument in push_to_hub is quite well liked, it allows people to look the model (card) over before making the model public to everyone.
  • The token argument in push_to_hub should be optional. If it's not specified, then the Hugging Face tools will automatically load it from your local filesystem if you ever did huggingface-cli login.
  • "Model Authors" in the model card is a bit of a misnomer: if someone else trained the model then they're the authors, you're the Model2Vec authors.
  • You can add "Model2Vec models on Hugging Face" or something to the Additional Resources, linking to https://huggingface.co/models?library=model2vec, if you want.

  • Tom Aarsen

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