Sentence Similarity
Safetensors
sentence-transformers
English
PyLate
modernbert
ColBERT
embeddings
retrieval
feature-extraction
Generated from Trainer
dataset_size:1695819
loss:Contrastive
Eval Results (legacy)
text-embeddings-inference
🇪🇺 Region: EU
Instructions to use lightonai/ModernColBERT-embed-base-supervised with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use lightonai/ModernColBERT-embed-base-supervised 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/ModernColBERT-embed-base-supervised") queries_emb = model.encode(queries, is_query=True) docs_emb = model.encode(documents, is_query=False) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b17a39143f8b2f8c5ef092a87421fef0581bfb0903a2d16ba18e72622d998b89
- Size of remote file:
- 596 MB
- SHA256:
- 1636013dde532e6672783409cd9ff3580415eed66e7b1186f530c09b9b2cb724
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