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
-
Add evaluation results for model intfloat/multilingual-e5-base revision d13f1b27baf31030b7fd040960d60d909913633f (#31)
- Add evaluation results for model intfloat/multilingual-e5-base revision d13f1b27baf31030b7fd040960d60d909913633f (ea6fe3baa7f6bfe27fbc99fde19d01b3e2bca610) Co-authored-by: Solomatin Roman <Samoed@users.noreply.huggingface.co>d128750