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
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Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences.
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## Citation
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If you find our paper or models helpful, please consider cite as follows:
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Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences.
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**3. Why does the cosine similarity scores distribute around 0.7 to 1.0?**
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This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss.
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For text embedding tasks like text retrieval or semantic similarity,
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what matters is the relative order of the scores instead of the absolute values,
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so this should not be an issue.
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## Citation
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If you find our paper or models helpful, please consider cite as follows:
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