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
Quick Links
GTE-SparseCL-arguana
This repository contains a SparseCL model based on Alibaba-NLP/gte-large-en-v1.5 and trained on the Arguana dataset.
SparseCL is designed for contradiction retrieval.
For implementation and usage details, please see the SparseCL repository.
Citation
Please cite our paper if you use this model:
@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
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# Load model directly from transformers import NewModelForCL model = NewModelForCL.from_pretrained("SparseCL/GTE-SparseCL-arguana", trust_remote_code=True, device_map="auto")