We have also released a dataset for researchers to study their behaviors.
*Note that our original parameter counts were wrong
due to an error (in our previous blog posts and paper). Thus you may
have seen small referred to as 117M and medium referred to as 345M.
Usage
This repository is meant to be a starting point for researchers and engineers to experiment with GPT-2.
GPT-2 models' robustness and worst case behaviors are not
well-understood. As with any machine-learned model, carefully evaluate
GPT-2 for your use case, especially if used without fine-tuning or in
safety-critical applications where reliability is important.
The dataset our GPT-2 models were trained on contains many texts with biases and factual inaccuracies, and thus GPT-2 models are likely to be biased and inaccurate as well.
To avoid having samples mistaken as human-written, we recommend
clearly labeling samples as synthetic before wide dissemination. Our
models are often incoherent or inaccurate in subtle ways, which takes
more than a quick read for a human to notice.
Work with us
Please let us know if you’re doing interesting research with or working on applications of
GPT-2! We’re especially interested in hearing from and potentially
working with those who are studying
Potential malicious use cases and defenses against them (e.g. the detectability of synthetic text)
The extent of problematic content (e.g. bias) being baked into the models and effective mitigations
@article{radford2019language,
title={Language Models are Unsupervised Multitask Learners},
author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
year={2019}
}
Future work
We may release code for evaluating the models on various benchmarks.
We are still considering release of the larger models.