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* support 'ts_forecast_classification' task to forecast discrete values * update test_forecast.py - add test for forecasting discrete values * update test_model.py * pre-commit changes
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AutoML for NLP
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:
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},
}