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
add sentence similarity tag
Browse files
README.md
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tags:
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- mteb
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- Sentence Transformers
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model-index:
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- name: multilingual-e5-large
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results:
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## Limitations
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Long texts will be truncated to at most 512 tokens.
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tags:
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- mteb
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- Sentence Transformers
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- sentence-similarity
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- sentence-transformers
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model-index:
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- name: multilingual-e5-large
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results:
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## Limitations
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Long texts will be truncated to at most 512 tokens.
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