Feature Extraction
sentence-transformers
Safetensors
English
sparse-encoder
sparse
asymmetric
inference-free
splade
Generated from Trainer
dataset_size:99000
loss:SpladeLoss
loss:SparseMultipleNegativesRankingLoss
loss:FlopsLoss
Eval Results (legacy)
Instructions to use sparse-encoder/example-inference-free-splade-distilbert-base-uncased-nq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sparse-encoder/example-inference-free-splade-distilbert-base-uncased-nq with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sparse-encoder/example-inference-free-splade-distilbert-base-uncased-nq") 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
example-inference-free-splade-distilbert-base-uncased-nq / query_0_SparseStaticEmbedding /model.safetensors
- Xet hash:
- e18e73d1dd094f97fce147f4019c4c44f1e09055039c8e4b5ea757600e399a3e
- Size of remote file:
- 122 kB
- SHA256:
- d935d0b630320c301459390294d086b1d50583953fb9d57353b5d8ede163010d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.