Feature Extraction
sentence-transformers
Safetensors
French
camembert
sparse-encoder
sparse
csr
Generated from Trainer
dataset_size:12227
loss:SpladeLoss
loss:SparseCosineSimilarityLoss
loss:FlopsLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use CATIE-AQ/CSR_Sparse_Encoder_camembert-large_STS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use CATIE-AQ/CSR_Sparse_Encoder_camembert-large_STS with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("CATIE-AQ/CSR_Sparse_Encoder_camembert-large_STS") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 06cdd66b27f6875fa8ed6468d3fe7b3d533f0f9c98b0919b517ece26373a8b08
- Size of remote file:
- 1.35 GB
- SHA256:
- a7aa1500445a9c7c972f6c1603db7b7b4c70ec499b3653d4e2a023c76559808a
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