Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
SMVE
ColBERT
multi-vector
PyLate
feature-extraction
text-embeddings-inference
Instructions to use topk-io/Iso-ModernColBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use topk-io/Iso-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="topk-io/Iso-ModernColBERT") queries_emb = model.encode(queries, is_query=True) docs_emb = model.encode(documents, is_query=False) - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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language:
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---
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# Iso-ModernColBERT
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This model is an isotropically corrected version of [GTE-ModernColBERT-v1](https://huggingface.co/lightonai/GTE-ModernColBERT-v1).
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It's built for production use cases where retrieval speed and quality matter. Compared to the original model, this version delivers
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language:
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---
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<p align="center">
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<sup>Looking for production ready multi-vector search? Check out <a href="https://topk.io">TopK</a>, hybrid retrieval engine build on object storage.</sup>
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</p>
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+
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# Iso-ModernColBERT
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This model is an isotropically corrected version of [GTE-ModernColBERT-v1](https://huggingface.co/lightonai/GTE-ModernColBERT-v1).
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It's built for production use cases where retrieval speed and quality matter. Compared to the original model, this version delivers
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