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BAAI/

bge-m3

BGE-M3 is a versatile text embedding model that supports multi-functionality, multi-linguality, and multi-granularity, allowing it to perform dense retrieval, multi-vector retrieval, and sparse retrieval in over 100 languages and with input sizes up to 8192 tokens. The model can be used in a retrieval pipeline with hybrid retrieval and re-ranking to achieve higher accuracy and stronger generalization capabilities. BGE-M3 has shown state-of-the-art performance on several benchmarks, including MKQA, MLDR, and NarritiveQA, and can be used as a drop-in replacement for other embedding models like DPR and BGE-v1.5.

Public
$0.010 / Mtoken
fp32
8,192
ProjectPaperLicense
BAAI/bge-m3 cover image

Input

inputs
You can add more items with the button on the right

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Settings

The service tier used for processing the request. When set to 'priority', the request will be processed with higher priority. 3

whether to normalize the computed embeddings 2

The number of dimensions in the embedding. If not provided, the model's default will be used.If provided bigger than model's default, the embedding will be padded with zeros. (Default: empty, 32 ≤ dimensions ≤ 8192)

Output

[
  [
    0,
    0.5,
    1
  ],
  [
    1,
    0.5,
    0
  ]
]
Model Information