This directory contains utility functions used by AutoNLP. Currently we support four NLP tasks: sequence classification, sequence regression, multiple choice and summarization.

Please refer to this link for examples.

Troubleshooting fine-tuning HPO for pre-trained language models

The frequent updates of transformers may lead to fluctuations in the results of tuning. To help users quickly troubleshoot the result of AutoNLP when a tuning failure occurs (e.g., failing to reproduce previous results), we have provided the following jupyter notebook:

Troubleshooting HPO for fine-tuning pre-trained language models

Our findings on troubleshooting fine-tuning the Electra and RoBERTa model for the GLUE dataset can be seen in the following paper published in ACL 2021:

An Empirical Study on Hyperparameter Optimization for Fine-Tuning Pre-trained Language Models. Xueqing Liu, Chi Wang. ACL-IJCNLP 2021.

@inproceedings{liu2021hpo,
    title={An Empirical Study on Hyperparameter Optimization for Fine-Tuning Pre-trained Language Models},
    author={Xueqing Liu and Chi Wang},
    year={2021},
    booktitle={ACL-IJCNLP},
}