Sentence Similarity
Safetensors
sentence-transformers
English
PyLate
modernbert
ColBERT
multi-vector
feature-extraction
Generated from Trainer
dataset_size:640000
loss:Distillation
Eval Results (legacy)
text-embeddings-inference
🇪🇺 Region: EU
Instructions to use lightonai/ColBERT-Zero with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use lightonai/ColBERT-Zero 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") queries_emb = model.encode(queries, is_query=True) docs_emb = model.encode(documents, is_query=False) - Inference
- Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from lightonai/ColBERT-Zero: direct link, hf CLI and curl.
- Browser
- Download file 3.58 MB
-
https://huggingface.co/lightonai/ColBERT-Zero/resolve/refs%2Fpr%2F2/tokenizer.json
- Command line
-
hf download hf://lightonai/ColBERT-Zero@refs/pr/2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/lightonai/ColBERT-Zero/resolve/refs%2Fpr%2F2/tokenizer.json
3.58 MB
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