Feature Extraction
sentence-transformers
Safetensors
modernbert
sparse-encoder
sparse
Generated from Trainer
dataset_size:202427
loss:SpladeColbertTopKLoss
loss:FlopsLoss
text-embeddings-inference
Instructions to use UBC-SLIME/sparcol-large-k512-no-cls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use UBC-SLIME/sparcol-large-k512-no-cls with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("UBC-SLIME/sparcol-large-k512-no-cls") 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:
- 68caf315ae75385149003d27b06979ce3894174cf73ac3ae4a2254f8df87d3f4
- Size of remote file:
- 6.23 kB
- SHA256:
- 3d6f02ebe6d12e1ae28c28351e104ec0a0acb978c635d998d5bf14486939094d
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