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
- Xet hash:
- 81d63204a496678121b465cb8323ee875d6c0b54230f5281edd6a8607030b925
- Size of remote file:
- 1.11 GB
- SHA256:
- f061cb7641880f52895cbacab7c4ab39b0844e2e6b73794f2798de460d9fa418
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.