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
File size: 235 Bytes
93f7525 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | [
{
"idx": 0,
"name": "0",
"path": "",
"type": "model.MLMTransformer.MLMTransformer"
},
{
"idx": 1,
"name": "1",
"path": "1_SparseColbertPooling",
"type": "model.pooling.SparseColbertPooling"
}
] |