Tasks and inputs
BAAI/bge-multilingual-gemma2 is listed for Feature extraction. It accepts text and produces embeddings. Use the publisher’s input format and preprocessing for your chosen deployment.
SyncDev model directory · Profile updated Oct 7, 2026
BAAI/bge-multilingual-gemma2 is a source-linked Hugging Face repository assigned to Feature extraction. Its recorded task interface accepts text and produces embeddings.
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-multilingual-gemma2 is a Hugging Face repository returned by the public Feature extraction task feed. Its recorded download count is 1,094,391 and it has 203 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 Feature extraction. 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, safetensors, gemma2, feature-extraction, sentence-similarity, transformers, mteb, arxiv:2402.03216. These source details are retained so readers can trace this directory entry back to the repository instead of relying on copied descriptions.
Confirm tensor shape, pooling and normalization in the runtime you will use. Evaluate the downstream task on held-out data and compare memory and throughput at realistic batch sizes.
The Hugging Face task assignment records text as input and embeddings as output for BAAI/bge-multilingual-gemma2. This interface summary comes from the repository’s recorded Feature extraction 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.
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BAAI/bge-multilingual-gemma2 is listed for Feature extraction. It accepts text and produces embeddings. Use the publisher’s input format and preprocessing for your chosen deployment.
Documented limits: 8K context and 3.58K maximum output. The listed route charges $0.08 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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