Sentence Similarity
sentence-transformers
Safetensors
camembert
feature-extraction
dense
Generated from Trainer
dataset_size:14481
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use RavenAgent/devis-matcher with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use RavenAgent/devis-matcher with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("RavenAgent/devis-matcher") sentences = [ "Plomberie sanitaire", "Semis manuel de pelouses à gazon, mauresques et ordinaires", "interne", "Installation sanitaire" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Initial upload: camembert-large fine-tune for French construction matching (v2, 14k pairs)
01590b8 verified | { | |
| "word_embedding_dimension": 1024, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |