Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
OpenVINO
English
bert
mteb
Sentence Transformers
Eval Results (legacy)
text-embeddings-inference
Instructions to use intfloat/e5-base-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use intfloat/e5-base-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("intfloat/e5-base-v2") 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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Add exported onnx model 'model_O4.onnx' (#13)
Verified signature- Add exported onnx model 'model_O4.onnx' (74f8dcf42a1b4af2a61fae577ce68230c138f6b7) Co-authored-by: Tom Aarsen <tomaarsen@users.noreply.huggingface.co>6d89b15 -
Add exported onnx model 'model_qint8_avx512_vnni.onnx' (#12)
Verified signature- Add exported onnx model 'model_qint8_avx512_vnni.onnx' (10a40f0fa2f11db176262ac80a75dcda9b57b4e3) Co-authored-by: Tom Aarsen <tomaarsen@users.noreply.huggingface.co>a079a01
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Adding ONNX file of this model (#11)
- Adding ONNX file of this model (3a233be0ddddb328f8b63b9f9b65b3969ba2f504) Co-authored-by: Nayan Shah <nayan06@users.noreply.huggingface.co>1c644c9