Alternatives
Products that do what Wikipedia2Vec – A tool for learning embeddings of words and entities does
- 1WQ
2018 · github.com
- 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
- 3RL
2011 · diffbot.com
- 4CY
2018 · robotmindmeld.com
- 5AE
2014 · aylien.com
- 6AA
2021 · getmarkup.com
- 7RE
2016 · github.com
- 8ET
2023 · github.com
- 9C0
2019 · github.com
- 10CA
2018 · github.com
- 11AN
2018 · github.com
- 123W
2013 · wordcloud.ersatz1.com
- 13SA
2023 · github.com
- 14EI
2018 · github.com
- 15MT
2015 · monkeylearn.com
- 16DO
2018 · dataturks.com
- 17CH
2016 · smmc.pythonanywhere.com
- 18RE
2015 · cosmiclattes.github.io
- 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
- 20NN
2018 · blog.ayoungprogrammer.com
- 21NL
2016 · esapi.intellexer.com
- 22RF
A scalable centralized embeddings platform for efficient embedding and retrieval to build RAG applications faster
2024 · github.com
- 23WA
2020 · wikimap.wiki
- 24AE
2015 · apiembed.com
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