| license: mit | |
| See our [Example.ipynb](./Example.ipynb) | |
| ## Model Overview | |
| Trained and evaluated on mixed (noun-phrase and claim) targets from subtask A of EZStance: | |
| ``` | |
| @inproceedings{zhao-caragea-2024-ez, | |
| title = "{EZ}-{STANCE}: A Large Dataset for {E}nglish Zero-Shot Stance Detection", | |
| author = "Zhao, Chenye and | |
| Caragea, Cornelia", | |
| editor = "Ku, Lun-Wei and | |
| Martins, Andre and | |
| Srikumar, Vivek", | |
| booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", | |
| month = aug, | |
| year = "2024", | |
| address = "Bangkok, Thailand", | |
| publisher = "Association for Computational Linguistics", | |
| url = "https://aclanthology.org/2024.acl-long.838/", | |
| doi = "10.18653/v1/2024.acl-long.838", | |
| pages = "15697--15714", | |
| } | |
| ``` | |
| Used the `BART-MNLI-e` architecture from the same paper. | |
| Weights were initialized from [facebook/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli): | |
| Obtained macro F1-score of 0.82 (see [exp\_results/metrics.csv](exp_results/metrics.csv)) on the test data. | |
| ## Dependencies | |
| - `python>=3.9.22` | |
| - `transformers>=4.51.0` | |
| - `accelerate>=0.26.0` | |
| - `torch>=2.7.0` | |