Tasks and inputs
BAAI/bge-m3 is listed for Sentence similarity. It accepts text and produces score. Use the publisher’s input format and preprocessing for your chosen deployment.
BAAI/bge-m3 is a source-linked Hugging Face repository assigned to Sentence similarity. Its recorded task interface accepts text and produces score.
The specification score summarizes recorded features and limits. It is not a benchmark result.
Recorded modality support shows what this model can accept and produce. An undocumented modality is shown as unknown rather than assumed to be unsupported.
Accepts
Produces
BAAI/bge-m3 is a Hugging Face repository returned by the public Sentence similarity task feed. Its recorded download count is 35,237,628 and it has 3,304 likes when the repository snapshot was collected. Popularity is useful for discovery, but it is not a performance ranking or a guarantee of suitability.
This record is categorized for Sentence similarity. A task category describes the intended machine-learning problem; it does not prove that every version, checkpoint, quantization, or deployment method produces the same quality. Teams should read the model card and test representative inputs before choosing it.
The public repository lists sentence-transformers as its library metadata. Its recorded tags include sentence-transformers, pytorch, onnx, xlm-roberta, feature-extraction, sentence-similarity, arxiv:2402.03216, arxiv:2004.04906. These source details are retained so readers can trace this directory entry back to the repository instead of relying on copied descriptions.
Build queries with known relevant passages, then measure recall at k and ranking quality. Test short queries, long passages, languages and required query/document prefixes before indexing the full corpus.
The Hugging Face task assignment records text as input and score as output for BAAI/bge-m3. This interface summary comes from the repository’s recorded Sentence similarity task classification. It helps readers distinguish a text, image, audio, video, document, embedding, or prediction workflow without inferring undocumented API behavior. Model wrappers can expose different preprocessing and response formats, so verify the upstream model card and the exact runtime before deployment.
Explore BAAI models and the sentence similarity collection. Use the comparison tool to inspect a second candidate.
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BAAI/bge-m3 is listed for Sentence similarity. It accepts text and produces score. Use the publisher’s input format and preprocessing for your chosen deployment.
Documented limits: 8.19K context and 1.02K maximum output. The listed route charges $0.02 input and $0.00 output per million tokens. Measure a completed task using the prompts, documents and tools your application will use. Include reasoning and retries in the result.
The weights are downloadable under the publisher’s model licence. Check runtime support and size the complete model and working context for your expected concurrency.
These records share a directory category. Check the source card, output type and deployment requirements before treating a model as a replacement.
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