Sentence Similarity
sentence-transformers
Safetensors
Transformers
French
bilingual
feature-extraction
sentence-embedding
mteb
custom_code
Eval Results (legacy)
Instructions to use Lajavaness/bilingual-document-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Lajavaness/bilingual-document-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Lajavaness/bilingual-document-embedding", trust_remote_code=True) sentences = [ "C'est une personne heureuse", "C'est un chien heureux", "C'est une personne très heureuse", "Aujourd'hui est une journée ensoleillée" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use Lajavaness/bilingual-document-embedding with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Lajavaness/bilingual-document-embedding", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "word_embedding_dimension": 1024, | |
| "pooling_mode_cls_token": true, | |
| "pooling_mode_mean_tokens": false, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |