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