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
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "model.MLMTransformer.MLMTransformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_SparseColbertPooling", | |
| "type": "model.pooling.SparseColbertPooling" | |
| } | |
| ] |