Sentence Similarity
sentence-transformers
PyTorch
Safetensors
English
bert
mteb
Sentence Transformers
Eval Results (legacy)
text-embeddings-inference
Instructions to use intfloat/e5-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use intfloat/e5-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("intfloat/e5-large") 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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Update README.md
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add sentence similarity tag
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Adding `safetensors` variant of this model (#10)
- Adding `safetensors` variant of this model (4ebfafa1f532cb8d2609229c7e6bb526f04f09e9) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>fbb49ed
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986a6a0
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sentence transformers compatability (#8)
- add sbert config (724bada59cdfb103b9e26342d8ca661fb103b07c)ff45196
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Update README.md
2d870d8
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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) (0d2d3f066422aebfa1dc0f9780b1e02d5cf37338) Co-authored-by: Niklas Muennighoff <Muennighoff@users.noreply.huggingface.co>1d92c8b
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upload models
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initial commit
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