SMAL

Single Model Active Learning

Instructions

1. Setup a new conda environment with Python 3.6 and pytorch 1.3.1: conda create -y -q -n smal_env python=3.6
2. Run conda install pytorch==1.5.0 torchvision cudatoolkit=10.1 -c pytorch
3. Install the requirements: pip: -r requirements.txt
4. Process your datasets (Eg: refer the data/ directory for NER and the albert.data_process sub-module for classification and dependency parsing)
5. Run bash get_embeddings.sh --type=mbert. Note that for some embeddings like fasttext, you might have to also specify the language as --lang=en (with the appropriate language(s) to download the embeddings)
6. Run python3.6 -m albert.baseline_active_learning_main --config_file path/to/config/

References

If you found the resources in this paper or repository useful, please cite On Efficiently Acquiring Annotations for Multilingual Models:

@inproceedings{moniz2022efficiently,
  title={On Efficiently Acquiring Annotations for Multilingual Models},
  author={Moniz, Joel and Patra, Barun and Gormley, Matthew R},
  booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  pages={69--85},
  year={2022}
}