Feature Extraction
sentence-transformers
PyTorch
ONNX
Safetensors
OpenVINO
xlm-roberta
mteb
Sentence Transformers
sentence-similarity
Eval Results (legacy)
Eval Results
text-embeddings-inference
Instructions to use intfloat/multilingual-e5-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use intfloat/multilingual-e5-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("intfloat/multilingual-e5-large") 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
Download openvino/openvino_model.xml from intfloat/multilingual-e5-large: direct link, hf CLI and curl.
- Browser
- Download file 713 kB
-
https://huggingface.co/intfloat/multilingual-e5-large/resolve/refs%2Fpr%2F58/openvino/openvino_model.xml
- Command line
-
hf download hf://intfloat/multilingual-e5-large@refs/pr/58/openvino/openvino_model.xml
-
curl -L -o openvino_model.xml https://huggingface.co/intfloat/multilingual-e5-large/resolve/refs%2Fpr%2F58/openvino/openvino_model.xml
713 kB
File too large to display, you can check the raw version instead.