model2vec

urious to hear your feedback and whether there are any other features you’d like to see! Our benchmark results are documented here: https://github.com/MinishLab/model2vec/tree/main/results#tra...

mbedding models out there, while still being performant enough to power usecases such as RAG, or semantic search. We are curious to hear your feedback on this and whether there’s any usecases you can think of that we have not explored yet! Link to the code and results: https://github.com/MinishLab/model2vec

ec/tree/main?tab=readme-... for results. Fitting a Model2Vec does not require any data, just a sentence transformer and, possibly, a frequency-sorted vocabulary, making it an easy solution to implement in whatever workflow you have lying around. We wrote this library because we separately got a bit frustrated with the lack of options if you need extremely fast CPU inference that still works well. If MiniLM isn’t fast enough and you don’t have access to a GPU, you’re often resigned to using BPemb, which is not flexible, or training your own GLoVe/word2vec models, which requires lots of data. Model2Vec solves all of these problems, and works better than specialized static embeddings trained on huge corpora. We spent a lot of time thinking about how the library could be easy to use and integrate into common workflows. Besides tight integration with huggingface, we also have an upcoming sentence transformers integration, releasing this week. This will allow you to directly integrate and distill model2vec models in whatever libraries support sentence transformers. This means out of the box support for llama-index, scikit-learn via embetter, langchain, and many other frameworks. Please let us know what you think. We’re very interested in getting feedback from you. We’re already using this in our own projects, and ultimately built this because we kind of needed it, but we’d be happy to hear from you if you have interesting use-cases or questions. Have a nice day!

equire any data, just a sentence transformer and, possibly, a frequency-sorted vocabulary, making it an easy solution to implement in whatever workflow you have lying around. We wrote this library because we separately got a bit frustrated with the lack of options if you need extremely fast CPU inference that still works well. If MiniLM isn’t fast enough and you don’t have access to a GPU, you’re often resigned to using BPemb, which is not flexible, or training your own GLoVe/word2vec models, which requires lots of data. Model2Vec solves all of these problems, and works better than specialized static embeddings trained on huge corpora. We spent a lot of time thinking about how the library could be easy to use and integrate into common workflows. It’s a tiny thing: we’d rather make only a few generic functions that work well instead of having a ton of integrations. Please let us know what you think. We’re very interested in getting feedback from you. We’re already using this in our own projects, and ultimately built this because we kind of needed it, but we’d be happy to hear from you if you have interesting use-cases or questions. Finally, If you think this sounds a lot like WordLlama, which was featured last week. It is! We were working on this in “stealth mode” for a while, since May, so I guess we and the WordLlama authors came up with the same idea at about the same time. We directly compare our models to WordLlama in our experiments. In short: WordLlama does a little bit worse, and is not unsupervised or multilingual, so it’s more difficult to adapt to new domains than Model2Vec. Have a nice day!