Sentence Similarity
sentence-transformers
Safetensors
camembert
feature-extraction
dense
Generated from Trainer
dataset_size:2544
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use S13v3n-2/scoring-camembert-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use S13v3n-2/scoring-camembert-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("S13v3n-2/scoring-camembert-v2") sentences = [ "Implémenter des pipelines DevOps avec CI/CD (Jenkins, GitLab CI)", "Analyser les performances avec Meta Business Suite et Google Analytics", "Prospecter de nouveaux clients et développer un portefeuille commercial", "Maîtriser la suite Adobe (Photoshop, Illustrator, InDesign)" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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