Feature Extraction
ONNX
sentence-transformers
multilingual
text-embeddings-inference
xlm-roberta
embeddings
Instructions to use newtechstudio/bge-m3-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use newtechstudio/bge-m3-onnx with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("newtechstudio/bge-m3-onnx") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 610d14d3c1491ced8dc6919faea023c53e696a97633eb11d09cc249e74b9eda6
- Size of remote file:
- 17.1 MB
- SHA256:
- 249df0778f236f6ece390de0de746838ef25b9d6954b68c2ee71249e0a9d8fd4
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