FastClass

Code of paper "FastClass: A Time-Efficient Approach to Weakly-Supervised Text Classification"

We provide run_sst.sh to reproduce the results of FastClass, and take the SST dataset as an example.

bash run_sst.sh

You can also use other datasets for testing, compare with other datasets, SST has a smaller amount of data and a shorter running time.

We used python=3.8, cudatoolkit=11.1. Other packages can be installed via

pip install -r requirements.txt

Citation

If you use this code, please cite this paper:

@inproceedings{xia-etal-2022-fastclass,
    title = "{F}ast{C}lass: A Time-Efficient Approach to Weakly-Supervised Text Classification",
    author = "Xia, Tingyu  and Wang, Yue  and Tian, Yuan  and Chang, Yi",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    year = "2022",
    pages = "4746--4758",
}