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 | [ | |
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| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
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| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
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| ] |