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-testing/inference-free-splade-bert-tiny-nq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sparse-encoder-testing/inference-free-splade-bert-tiny-nq with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sparse-encoder-testing/inference-free-splade-bert-tiny-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
- Xet hash:
- b01cc23f9c8385dd6f3b437ce53227e9ac69f5a3995174e8e2e63e5341ecfa77
- Size of remote file:
- 122 kB
- SHA256:
- 82ca927cba4916c595053947bfea48b28692538d894ab9f94cceca63f57a845a
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