Instructions to use SparseCL/GTE-SparseCL-arguana with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SparseCL/GTE-SparseCL-arguana with Transformers:
# Load model directly from transformers import NewModelForCL model = NewModelForCL.from_pretrained("SparseCL/GTE-SparseCL-arguana", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Create README.md
Browse files
README.md
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---
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license: mit
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---
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# GTE-SparseCL-arguana
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This repository contains a SparseCL model based on `Alibaba-NLP/gte-large-en-v1.5` and trained on the Arguana dataset.
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SparseCL is designed for contradiction retrieval.
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For implementation and usage details, please see the [SparseCL repository](https://github.com/xuhaike/SparseCL).
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## Citation
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Please cite our paper if you use this model:
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```bibtex
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@inproceedings{xu2025contradiction,
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title={Contradiction Retrieval via Contrastive Learning with Sparsity},
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author={Xu, Haike and Lin, Zongyu and Chang, Kai-Wei and Sun, Yizhou and Indyk, Piotr},
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booktitle={International Conference on Machine Learning},
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year={2025}
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}
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```
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---
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license: mit
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---
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