Status: Archive (code is provided as-isno updates expected)
Code and models from the paper "Language Models are Unsupervised Multitask Learners".
You can read about GPT-2 and its staged release in our original blog post6 month follow-up postand final post.
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.
This repository is meant to be a starting point for researchers and engineers to experiment with GPT-2.
For basic informationsee our model card.
- GPT-2 models' robustness and worst case behaviors are not well-understood. As with any machine-learned modelcarefully evaluate GPT-2 for your use caseespecially 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 inaccuraciesand thus GPT-2 models are likely to be biased and inaccurate as well.
- To avoid having samples mistaken as human-writtenwe recommend clearly labeling samples as synthetic before wide dissemination. Our models are often incoherent or inaccurate in subtle wayswhich takes more than a quick read for a human to notice.
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
See DEVELOPERS.md
See CONTRIBUTORS.md
Please use the following bibtex entry:
@article{radford2019language,
title={Language Models are Unsupervised Multitask Learners},
author={RadfordAlec and WuJeff and ChildRewon and LuanDavid and AmodeiDario and SutskeverIlya},
year={2019}
}
We may release code for evaluating the models on various benchmarks.
We are still considering release of the larger models.