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:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SmartComponents/bge-micro-v2") model = AutoModel.from_pretrained("SmartComponents/bge-micro-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Pushing sentencetransformers model
Model distilled from bge-small but ~1/4 the size, improved versione0ba779 -
Pushing sentencetransformers model
Model distilled from bge-small but ~1/4 the size, improved version92652b3 -
Pushing sentencetransformers model
Model distilled from bge-small but ~1/4 the size, improved version304bbd4 -
Pushing sentencetransformers model
Model distilled from bge-small but ~1/4 the size, improved versionbc17654 -
Pushing sentencetransformers model
Model distilled from bge-small but ~1/4 the size, improved version06877be -
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