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 model.safetensors from intfloat/multilingual-e5-large: direct link, hf CLI and curl.
- Browser
- Download file 2.24 GB
-
https://huggingface.co/intfloat/multilingual-e5-large/resolve/main/model.safetensors
- Command line
-
hf download hf://intfloat/multilingual-e5-large/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/intfloat/multilingual-e5-large/resolve/main/model.safetensors
2.24 GB
- Xet hash:
- f28c1cf3dd20605d66fa5bff84ffebfb2955c2f76821bb44245fc7cfbe9e3b0f
- Size of remote file:
- 2.24 GB
- SHA256:
- 020afdebf2762b29fcaf286629a96c3b3b65af241f6a08226b1cfee60a21def6
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