Codestral EmbedMistral AI
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LexVecAlexandre Salle
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About
Codestral Embed is Mistral AI's first embedding model, specialized for code, optimized for high-performance code retrieval and semantic understanding. It significantly outperforms leading code embedders in the market today, such as Voyage Code 3, Cohere Embed v4.0, and OpenAI’s large embedding model. Codestral Embed can output embeddings with different dimensions and precisions; for instance, with a dimension of 256 and int8 precision, it still performs better than any model from competitors. The dimensions of the embeddings are ordered by relevance, allowing users to choose the first n dimensions for a smooth trade-off between quality and cost. It excels in retrieval use cases on real-world code data, particularly in benchmarks like SWE-Bench, which is based on real-world GitHub issues and corresponding fixes, and Text2Code (GitHub), relevant for providing context for code completion or editing.
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About
LexVec is a word embedding model that achieves state-of-the-art results in multiple natural language processing tasks by factorizing the Positive Pointwise Mutual Information (PPMI) matrix using stochastic gradient descent. This approach assigns heavier penalties for errors on frequent co-occurrences while accounting for negative co-occurrences. Pre-trained vectors are available, including a common crawl dataset with 58 billion tokens and 2 million words in 300 dimensions, and an English Wikipedia 2015 + NewsCrawl dataset with 7 billion tokens and 368,999 words in 300 dimensions. Evaluations demonstrate that LexVec matches or outperforms other models like word2vec in terms of word similarity and analogy tasks. The implementation is open source under the MIT License and is available on GitHub.
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Audience
Enterprise software teams needing a tool for semantic code search, retrieval-augmented generation, and code analytics across large-scale codebases
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Audience
Computational linguists and NLP researchers searching for a tool to improve their semantic analysis and language modeling
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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API
Offers API
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API
Offers API
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Pricing
No information available.
Free Version
Free Trial
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Pricing
Free
Free Version
Free Trial
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Reviews/
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Reviews/
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Company InformationMistral AI
Founded: 2023
United States
mistral.ai/news/codestral-embed
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Company InformationAlexandre Salle
Brazil
github.com/alexandres/lexvec
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Integrations
GitHub
Mistral AI
Mistral Code
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