Sentence Similarity
sentence-transformers
PyTorch
ONNX
xlm-roberta
feature-extraction
Eval Results
text-embeddings-inference
Instructions to use BAAI/bge-m3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BAAI/bge-m3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BAAI/bge-m3") 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] - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
Browse filesChange pipeline to feature extraction since it's more useful to get the embeddings for a sentence than to compare similarity.
Also we are able to use the serverless endpoint to get the embeddings
README.md
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pipeline_tag:
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tags:
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- sentence-transformers
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- feature-extraction
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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```
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pipeline_tag: feature-extraction
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tags:
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- sentence-transformers
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- feature-extraction
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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```
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