Feature Extraction
sentence-transformers
Safetensors
English
distilbert
sparse-encoder
sparse
splade
Generated from Trainer
dataset_size:99000
loss:SpladeLoss
loss:SparseMultipleNegativesRankingLoss
loss:FlopsLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use tomaarsen/splade-distilbert-base-uncased-nq-updated-sparsity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use tomaarsen/splade-distilbert-base-uncased-nq-updated-sparsity with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tomaarsen/splade-distilbert-base-uncased-nq-updated-sparsity") 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:
- abe47d4c497b7882d06bb470ed46551a9f7d0f31efdc67c66587130ee746df87
- Size of remote file:
- 268 MB
- SHA256:
- 6bd3f05c693a68febfc7144f1400ab02e93993e240ebd30f1bfa3ad8550611b5
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