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) - Notebooks
- Google Colab
- Kaggle
Commit History
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Add Sentence Transformers usage
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Upload onnx_config.json with huggingface_hub
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Upload tokenizer.json with huggingface_hub
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Upload model.onnx with huggingface_hub
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Add evaluation results for model lightonai/Reason-ModernColBERT revision a1f75a3d01084caf9d4cea3500a498533352f001 (#4)
- Add evaluation results for model lightonai/Reason-ModernColBERT revision a1f75a3d01084caf9d4cea3500a498533352f001 (688e31f1a4364371fbb00fbec444a6f6b93c0027) Co-authored-by: Solomatin Roman <Samoed@users.noreply.huggingface.co>b0e5f1a
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Add do_query_expansion parameter
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Add GPT4 reasoning trace results
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Add new SentenceTransformer model
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initial commit
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