This is a modified work of GPT-2, updated to work with tensorflow v2.

Checked Python Version: 3.8.5 and 3.8.7.

License of GPT-2 is as follows:

Modified MIT License

Software Copyright (c) 2019 OpenAI

We don’t claim ownership of the content you create with GPT-2, so it is yours to do with as you please.
We only ask that you use GPT-2 responsibly and clearly indicate your content was created using GPT-2.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
associated documentation files (the "Software"), to deal in the Software without restriction,
including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so,
subject to the following conditions:

The above copyright notice and this permission notice shall be included
in all copies or substantial portions of the Software.
The above copyright notice and this permission notice need not be included
with content created by the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS
BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE
OR OTHER DEALINGS IN THE SOFTWARE.

Status: Archive (code is provided as-is, no 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 post, 6 month follow-up post, and 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 information, see our model card.

• 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.

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={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
  year={2019}
}

We may release code for evaluating the models on various benchmarks.

We are still considering release of the larger models.

Modified MIT