Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
Transformers
bert
feature-extraction
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use TaylorAI/bge-micro-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use TaylorAI/bge-micro-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("TaylorAI/bge-micro-v2") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use TaylorAI/bge-micro-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("TaylorAI/bge-micro-v2") model = AutoModel.from_pretrained("TaylorAI/bge-micro-v2") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8f7f133acdbb8a2c2b8caa119b9f026d34180abdd2d1076bde4066dde9b2f0e5
- Size of remote file:
- 34.8 MB
- SHA256:
- 792472b64c3ca1c725e55f6a9d2a58143d4ce6b3ffde05bf55bc8427cb3733c8
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