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, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 98ed0c7df50dd0ecc6ef5ae8451d4a200dbda9fe02052ccc7b98833c86807e61
- Size of remote file:
- 17.1 MB
- SHA256:
- 1af481bd08ed9347cf9d3d07c24e5de75a10983819de076436400609e6705686
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