Sentence Similarity
Safetensors
sentence-transformers
English
PyLate
modernbert
ColBERT
multi-vector
embeddings
retrieval
feature-extraction
Generated from Trainer
dataset_size:238998494
loss:CachedContrastive
Eval Results (legacy)
text-embeddings-inference
🇪🇺 Region: EU
Instructions to use lightonai/ColBERT-Zero-unsupervised with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use lightonai/ColBERT-Zero-unsupervised 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/ColBERT-Zero-unsupervised") queries_emb = model.encode(queries, is_query=True) docs_emb = model.encode(documents, is_query=False) - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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#### ColBERT-Zero
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```bibtex
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@misc{
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```
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#### Sentence Transformers
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#### ColBERT-Zero
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```bibtex
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@misc{chaffin2026colbertzeropretrainpretraincolbert,
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title = {ColBERT-Zero: To Pre-train Or Not To Pre-train ColBERT models},
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author = {Antoine Chaffin and Luca Arnaboldi and Amélie Chatelain and Florent Krzakala},
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year = {2026},
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eprint = {2602.16609},
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archivePrefix = {arXiv},
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primaryClass = {cs.CL},
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url = {https://arxiv.org/abs/2602.16609},
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}
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```
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#### Sentence Transformers
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