Embedding model from BAAI mapping texts to vectors.
embedding
335m
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Updated 4 months ago
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b3d71c928059 · 671MB
model
archbert
·
parameters334M
·
quantizationF16
671MB
license
MIT License
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FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search. And it also can be used in vector databases for LLMs.
@misc{bge_embedding,
title={C-Pack: Packaged Resources To Advance General Chinese Embedding},
author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
year={2023},
eprint={2309.07597},
archivePrefix={arXiv},
primaryClass={cs.CL}
}