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 evaluation results for model intfloat/multilingual-e5-large revision ab10c1a7f42e74530fe7ae5be82e6d4f11a719eb (#59)
3d7cfbd | - dataset: | |
| id: mteb/BRIGHT | |
| task_id: BrightStackoverflowLongRetrieval_default_long | |
| revision: c26703e6600d97c579ee2985f16cf307db13ed85 | |
| value: 15.812 | |
| date: '2026-03-31' | |
| notes: Obtained using MTEB v2.10.12 | |
| source: | |
| url: https://github.com/embeddings-benchmark/mteb/ | |
| name: Obtained using MTEB v2.10.12 | |
| user: mteb | |
| - dataset: | |
| id: mteb/BRIGHT | |
| task_id: BrightStackoverflowLongRetrieval | |
| revision: c26703e6600d97c579ee2985f16cf307db13ed85 | |
| value: 15.812 | |
| date: '2026-03-31' | |
| notes: Obtained using MTEB v2.10.12 | |
| source: | |
| url: https://github.com/embeddings-benchmark/mteb/ | |
| name: Obtained using MTEB v2.10.12 | |
| user: mteb | |