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
Add exported onnx model 'model_qint8_avx512_vnni.onnx' (#12)
Browse files- Add exported onnx model 'model_qint8_avx512_vnni.onnx' (10a40f0fa2f11db176262ac80a75dcda9b57b4e3)
Co-authored-by: Tom Aarsen <tomaarsen@users.noreply.huggingface.co>
onnx/model_qint8_avx512_vnni.onnx
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