Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
OpenVINO
xlm-roberta
mteb
Sentence Transformers
Eval Results (legacy)
Eval Results
text-embeddings-inference
Instructions to use intfloat/multilingual-e5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use intfloat/multilingual-e5-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("intfloat/multilingual-e5-base") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
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Add exported onnx model 'model_O4.onnx' (#23)
Verified signature- Add exported onnx model 'model_O4.onnx' (eb5fe5cb36952a96d77748af375b070639a346c3) Co-authored-by: Tom Aarsen <tomaarsen@users.noreply.huggingface.co>f5bd48c -
Add exported onnx model 'model_qint8_avx512_vnni.onnx' (#22)
Verified signature- Add exported onnx model 'model_qint8_avx512_vnni.onnx' (bb5ff77a5ed5e44b1931dc7d6decde62d114dd37) Co-authored-by: Tom Aarsen <tomaarsen@users.noreply.huggingface.co>e0d62dd
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Adding ONNX file of this model (#11)
- Adding ONNX file of this model (7d8760377f73aadb2f2a4e31fff2e48d2cbe68bf) Co-authored-by: Romain <romaindeveaud@users.noreply.huggingface.co>167cf6a