Sentence Similarity
ONNX
Safetensors
sentence-transformers
English
PyLate
modernbert
ColBERT
multi-vector
feature-extraction
Generated from Trainer
dataset_size:100521
loss:CachedContrastive
Eval Results
text-embeddings-inference
Instructions to use lightonai/Reason-ModernColBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use lightonai/Reason-ModernColBERT with sentence-transformers:
from pylate import models queries = [ "Which planet is known as the Red Planet?", "What is the largest planet in our solar system?", ] documents = [ ["Mars is the Red Planet.", "Venus is Earth's twin."], ["Jupiter is the largest planet.", "Saturn has rings."], ] model = models.ColBERT(model_name_or_path="lightonai/Reason-ModernColBERT") queries_emb = model.encode(queries, is_query=True) docs_emb = model.encode(documents, is_query=False) - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4eb28e514c0432be8d4e6726d09173dcf6b0f1fa4c36aaebed976bdbb14a6542
- Size of remote file:
- 597 MB
- SHA256:
- 2a233f492ea945a03063bf18efb2cadcbf102748d89ac335b4aa325605806f56
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.