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 original blog post, 6 month follow-up post, and final post.
They have also released a dataset for researchers to study their behaviors.
This repository is meant to be a starting point for researchers and engineers to experiment with GPT-2. This repository adds compatibility for GPT-2 for tensorflow 2.0 and above.
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.
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git clone https://github.com/namelessCrusader/Gpt-2-compat-tf2/edit/master/
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python/python3 download_model.py 117M/335M/etc.
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pip install tensorflow(if you don't have V2.0, this package add compatibility for Tensorflow 2.0)
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pip install -r requirements.txt
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cd src
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python/python3 generate_unconditional_samples.py/interactive_conditional_samples.py
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}
}