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 sparse-encoder/example-splade-distilbert-base-uncased-gooaq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sparse-encoder/example-splade-distilbert-base-uncased-gooaq with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sparse-encoder/example-splade-distilbert-base-uncased-gooaq") 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:
- 4d364c9badb2527de76edcd3660be1c85226c507f6153fb3e6ee3b46ed2459f1
- Size of remote file:
- 268 MB
- SHA256:
- de857b559488d5e8c5619291c5dd41f7feadffa0b6091cd263820a3c344c4f17
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