From the model card
Fields below are copied from the tags and counters on the HuggingFace repository TaylorAI/bge-micro-v2 at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.
- Publisher (HF namespace)
- TaylorAI
- Pipeline tag
- sentence-similarity
- Library
- Sentence Transformers, Transformers
- Framework tags
- PyTorch
- Weight formats
- ONNX, safetensors
- License tag
mit— read the license file in the repo before relying on it- Downloads (HF counter at last fetch)
- 932,343
- Likes (HF counter at last fetch)
- 65
- Model card
- https://huggingface.co/TaylorAI/bge-micro-v2
Use cases
- CPU-only embedding generation for resource-constrained services
- Mobile semantic search with on-device embedding
- High-throughput embedding where latency is the primary constraint
- Browser-side semantic similarity via transformers.js/ONNX
Pros
- MIT license
- ONNX and sentence-transformers compatible for broad deployment
- Tiny size — very fast CPU inference
- text-embeddings-inference compatible
Cons
- Micro scale sacrifices significant embedding quality vs bge-small-en-v1.5
- English-only
- 512 token input limit
- MTEB scores trail all larger BGE variants — not suitable for quality-sensitive retrieval
Tags
sentence-transformerspytorchonnxsafetensorsbertfeature-extractionsentence-similaritytransformersmteblicense:mitmodel-indextext-embeddings-inferenceendpoints_compatibleregion:usdeploy:azure