Sentence Similarity
sentence-transformers
PyTorch
Safetensors
English
bert
mteb
Sentence Transformers
Eval Results (legacy)
text-embeddings-inference
Instructions to use intfloat/e5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use intfloat/e5-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("intfloat/e5-base") 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 (#6)
- Adding `safetensors` variant of this model (e5a920ec4c97225645a99a67c8ba546cb8041957) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>7de30b1
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sentence transformers (#4)
- adding st config (35b5119c8f50724c100b099e6e25b30c4bb1a207)d945d70
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Fix SummEval scores (https://github.com/embeddings-benchmark/mteb/pull/99) (#2)
- Fix SummEval scores (https://github.com/embeddings-benchmark/mteb/pull/990) (e18e2eb5aff0261fdfa5d6e9a0192a5ce4348835) Co-authored-by: Niklas Muennighoff <Muennighoff@users.noreply.huggingface.co>c31dd12
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initial commit
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