Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
colbert
late-interaction
retrieval
pylate
multi-vector
text-embeddings-inference
Instructions to use chungimungi/GLInt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use chungimungi/GLInt with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("chungimungi/GLInt") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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The corresponding private training artifacts are in
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[GLINT-data](https://huggingface.co/datasets/chungimungi/GLINT-data). It contains the complete
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prepared SFT data, the 1,046,009-row seven-source KD mixture, and Jina teacher-score parquet
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shards. The repository contains no BEIR evaluation corpus or evaluation labels.
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The corresponding private training artifacts are in
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[GLINT-data](https://huggingface.co/datasets/chungimungi/GLINT-data). It contains the complete
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prepared SFT data, the 1,046,009-row seven-source KD mixture, and Jina teacher-score parquet
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shards. The repository contains no BEIR evaluation corpus or evaluation labels.
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## Citation
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```
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@misc{aarush2026glint,
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title={GLInt: Geometry-Matched Hard Negatives for Late-Interaction Retrieval},
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author={Aarush},
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year={2026},
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howpublished={\url{https://huggingface.co/blog/chungimungi/glint}},
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}
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```
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