Sentence Similarity
sentence-transformers
PyTorch
ONNX
Transformers
bert
feature-extraction
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use SmartComponents/bge-micro-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use SmartComponents/bge-micro-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("SmartComponents/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 SmartComponents/bge-micro-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SmartComponents/bge-micro-v2") model = AutoModel.from_pretrained("SmartComponents/bge-micro-v2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8933e559845238b6a87209823e5a3b45083a6a4e41715049924e837166adf555
- Size of remote file:
- 69 MB
- SHA256:
- 9f705befe60d00ca3d8d14c9dd61a3ecfca9f1920a39fbc4a5b056c0ccd977d4
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