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:
- 4ad0ba6db8f9a52a165871800ea787c21dcff00e9c479324a0a5907b2ef8fb61
- Size of remote file:
- 16.8 MB
- SHA256:
- 1d433dd2637287d55a4b397285ac7d3fc322b376de9f9dbcf3a5f47c6f8c0124
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