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
| { | |
| "model_type": "SparseColbertEncoder", | |
| "__version__": { | |
| "sentence_transformers": "5.1.1", | |
| "transformers": "4.57.1", | |
| "pytorch": "2.8.0+cu128" | |
| }, | |
| "prompts": { | |
| "query": "", | |
| "document": "" | |
| }, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": "colbert" | |
| } |