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Alternatives

Products that do what Wikipedia2Vec – A tool for learning embeddings of words and entities does

  1. 1WQ

    2018 · github.com

  2. 2ML

    We’ve recently open-sourced Model2vec, a method to distill sentence transformers into static embeddings that outperform all previous approaches by a large margin on MTEB. Our new models set a new state-of-the-art for static embeddings. Main features: - Our best model (potion-base-8M) has only 8M parameters, which is ~30mb on disk - Inference is ~500x faster than the distilled base model (bge-base), on a CPU - New models can be distilled in 30 seconds on a CPU without requiring a dataset - just a vocabulary - Numpy-only inference: The packaged can be install the package with minimal…

    2024 · github.com

  3. 3RL
  4. 4CY
  5. 5AE
  6. 6AA

    2021 · getmarkup.com

  7. 7RE
  8. 8ET
  9. 9C0
  10. 10CA
  11. 11AN
  12. 123W
  13. 13SA
  14. 14EI
  15. 15MT

    2015 · monkeylearn.com

  16. 16DO
  17. 17CH
  18. 18RE
  19. 19IM

    Just a fun toy I wanted to make. I've been studying and playing around with language models lately and have always been intrigued by how words are processed by these models. Since the vectors generated by embedding models is in very high dimensional space, I thought it would be cool to reduce them to 3D vectors and visualise them myself. This is what I have so far!

    2023 · seesaurus.com

  20. 20NN

    2018 · blog.ayoungprogrammer.com

  21. 21NL
  22. 22RF

    A scalable centralized embeddings platform for efficient embedding and retrieval to build RAG applications faster

    2024 · github.com

  23. 23WA
  24. 24AE

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