Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
English
bert
mteb
Sentence Transformers
Eval Results (legacy)
text-embeddings-inference
Instructions to use intfloat/e5-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use intfloat/e5-small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("intfloat/e5-small") 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
Commit History
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compatibility_sentence_transformers (#6)
- add configs (31c4c82d6bf234d289db231795da28d9b1790e66) - sentence_bert_config.json (23fef9b590d3cf49ed732683a1d950cfbfbfd201)c8dae5e
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Fix SummEval scores (https://github.com/embeddings-benchmark/mteb/pull/99) (#4)
- Fix SummEval scores (https://github.com/embeddings-benchmark/mteb/pull/990) (896066f44115c70dcb90b6ab0c5e7e8ead6bf41a) Co-authored-by: Niklas Muennighoff <Muennighoff@users.noreply.huggingface.co>a634d9b
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Adding ONNX file of this model (#2)
- Adding ONNX file of this model (75655f7e205aa649e3f3681c85728d316d8c977f) Co-authored-by: AJEGHRIR mustapha <mustapha@users.noreply.huggingface.co>64747c1
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