Code for our paper GPT3Mix and conducting classification experiments using GPT-3 prompt-based data augmentation.
The main depedencies can be installed via pip install -r requirements.txt.
The main code is run through main.py. Check out --help for full list of commands.
python main.py --help
The code will automatically use the first GPU device, if detected.
A typical command to run BERT-base 10 times on the 1% subsample set of the SST-2 dataset and computing the average of all run is as follows.
python main.py --datasets sst2 \
--train-subsample 0.01f \
--classifier transformers \
--model-name bert-base-uncased \
--num-trials 1 \
--augmenter none \
--save-dir out
The script will create a directory named out in the current working directory and save the script log
as out/run.log. It will also save any augmentations created during the experiments (if any augmentation is enabled).
To test GPT3Mix, prepare an OpenAI API key as described at the bottom of this README file, then use the following command:
python main.py --datasets sst2 \
--train-subsample 0.01f \
--classifier transformers \
--model-name bert-base-uncased \
--num-trials 1 \
--augmenter gpt3-mix \
--save-dir out
In the command above, the script will automatically generate seeds for sampling data and optimizing models.
The seed used to generate each individual seed is called "master seed" and can be set using --master-data-seed
and --master-exp-seed options. As evident from the option names, they are responsible for sampling data and
optimizing a freshly initialized models respectively.
Sometimes, we need to manually set the seeds and not rely on automatically generated seeds from the master seeds.
Manually seeding can be achieved via --data-seeds option. If this option is given, the master data seed will
be ignored. We only support manualy data seeding for now.
Store OpenAI API Key under the current working directory as a file named openai-key.
When running the main script, it will automatically detect the api key.
API keys can be provided to the script by --api-key option (not recommended) or from a file named openai-key in the current working directory.
At the moment we only support data augmentation leveraging OpenAI GPT-3 (GPT3Mix), but we will release an update that supports HyperCLOVA as soon as it becomes available to the public (HyperMix).
To cite our code or work, please use the following bibtex:
@inproceedings{yoo2021gpt3mix,
title = "GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation",
author = "Yoo, Kang Min and
Park, Dongju and
Kang, Jaewook and
Lee, Sang-Woo and
Park, Woomyoung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.192",
pages = "2225--2239",
}
HyperMix
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