Feature Extraction
sentence-transformers
Safetensors
English
bert
sparse-encoder
sparse
splade
Generated from Trainer
dataset_size:99000
loss:SpladeLoss
loss:SparseDistillKLDivLoss
loss:FlopsLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use tomaarsen/splade-cocondenser-msmarco-kldiv-minilm-temp-4-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use tomaarsen/splade-cocondenser-msmarco-kldiv-minilm-temp-4-4 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tomaarsen/splade-cocondenser-msmarco-kldiv-minilm-temp-4-4") 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:
- 26c2f58d42aa72dffff247d12121d2cf337b2839a2aeb6b4543dc1d999c791b9
- Size of remote file:
- 438 MB
- SHA256:
- 8d402126a91e36a643f570ebd0e1926569091db09c27ef226c91c2437dc22843
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