Showing 7 open source projects for "face"

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  • 1
    CocoIndex

    CocoIndex

    ETL framework to index data for AI, such as RAG

    ...It lets users index and retrieve content based on meaning rather than keywords, making it ideal for modern AI-based search applications. CocoIndex leverages vector embeddings and integrates with various models and frameworks, including OpenAI and Hugging Face, to provide high-quality semantic understanding. It’s built for transparency, ease of use, and local control over your search data, distinguishing itself from closed, black-box systems. The tool is suitable for developers working on personal knowledge bases, AI search interfaces, or private LLM applications.
    Downloads: 2 This Week
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  • 2
    FlagEmbedding

    FlagEmbedding

    Retrieval and Retrieval-augmented LLMs

    FlagEmbedding is an open-source toolkit for building and deploying high-performance text embedding models used in information retrieval and retrieval-augmented generation systems. The project is part of the BAAI FlagOpen ecosystem and focuses on creating embedding models that transform text into dense vector representations suitable for semantic search and large language model pipelines. FlagEmbedding includes a family of models known as BGE (BAAI General Embedding), which are designed to...
    Downloads: 0 This Week
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  • 3
    Hugging Face Transformer

    Hugging Face Transformer

    CPU/GPU inference server for Hugging Face transformer models

    Optimize and deploy in production Hugging Face Transformer models in a single command line. At Lefebvre Dalloz we run in-production semantic search engines in the legal domain, in the non-marketing language it's a re-ranker, and we based ours on Transformer. In that setup, latency is key to providing a good user experience, and relevancy inference is done online for hundreds of snippets per user query.
    Downloads: 0 This Week
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  • 4
    LibrePhotos

    LibrePhotos

    A self-hosted open source photo management service

    LibrePhotos is an open-source self-hosted photo management platform designed to organize, browse, and analyze personal media libraries while preserving user privacy. The system allows individuals to store and manage their photos and videos locally rather than relying on commercial cloud services. It provides features similar to services like Google Photos but runs on a private server controlled by the user. The application includes AI-powered tools that automatically analyze images to detect...
    Downloads: 3 This Week
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  • 5
    bge-large-en-v1.5

    bge-large-en-v1.5

    BGE-Large v1.5: High-accuracy English embedding model for retrieval

    ...It is recommended for use in document retrieval tasks, semantic search, and passage reranking, particularly when paired with a reranker like BGE-Reranker. The model supports inference through multiple frameworks, including FlagEmbedding, Sentence-Transformers, LangChain, and Hugging Face Transformers. It accepts English text as input and returns normalized 1024-dimensional embeddings suitable for cosine similarity comparisons.
    Downloads: 0 This Week
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  • 6
    bge-base-en-v1.5

    bge-base-en-v1.5

    Efficient English embedding model for semantic search and retrieval

    ...With 768 embedding dimensions and a maximum sequence length of 512 tokens, it achieves strong performance across multiple MTEB benchmarks, nearly matching larger models while maintaining efficiency. It supports use via SentenceTransformers, Hugging Face Transformers, FlagEmbedding, and ONNX for various deployment scenarios. Typical usage includes normalizing output embeddings and calculating cosine similarity via dot product for ranking.
    Downloads: 0 This Week
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  • 7
    bge-small-en-v1.5

    bge-small-en-v1.5

    Compact English sentence embedding model for semantic search tasks

    ...The model is optimized for speed and efficiency, making it suitable for resource-constrained environments. It is compatible with popular libraries such as FlagEmbedding, Sentence-Transformers, and Hugging Face Transformers. The model achieves competitive results on the MTEB benchmark, especially in retrieval and classification tasks. With only 33.4M parameters, it provides a strong balance of accuracy and performance for English-only use cases.
    Downloads: 0 This Week
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