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
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_SparseAutoEncoder", | |
| "type": "sentence_transformers.sparse_encoder.models.SparseAutoEncoder" | |
| } | |
| ] |