Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
OpenVINO
xlm-roberta
mteb
Sentence Transformers
Eval Results (legacy)
Eval Results
text-embeddings-inference
Instructions to use intfloat/multilingual-e5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use intfloat/multilingual-e5-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("intfloat/multilingual-e5-base") 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 1_Pooling/config.json from intfloat/multilingual-e5-base: direct link, hf CLI and curl.
- Browser
- Download file 200 Bytes
-
https://huggingface.co/intfloat/multilingual-e5-base/resolve/refs%2Fpr%2F31/1_Pooling/config.json
- Command line
-
hf download hf://intfloat/multilingual-e5-base@refs/pr/31/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/intfloat/multilingual-e5-base/resolve/refs%2Fpr%2F31/1_Pooling/config.json
200 Bytes
| { | |
| "word_embedding_dimension": 768, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false | |
| } |