This a GPT-2 fork. The idea for this repo is to be able to update and experinment with GPT-2 in a new way. For example, this repo offers a reengineered version of the orginal GPT (tensorflow) in PyTorch (pytorch).
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.
This dataset contains:
- 250K documents from the WebText test set
- For each GPT-2 model (trained on the WebText training set), 250K random samples (temperature 1, no truncation) and 250K samples generated with Top-K 40 truncation
We look forward to the research produced using this data!
For each model, we have a training split of 250K generated examples, as well as validation and test splits of 5K examples.
All data is located in Google Cloud Storage, under the directory gs://gpt-2/output-dataset/v1. (NOTE: everything has been migrated to Azure https://openaipublic.blob.core.windows.net/gpt-2/output-dataset/v1/)
- 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.
- PyTorch version of GPT-2 is not fully completed
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}
}