Grover
UPDATE, Sept 17 2019. We got into NeurIPS (camera ready coming soon!) and we've made Grover-Mega publicly available without you needing to fill out the form. You can download it using download_model.py.
(aka, code for Defending Against Neural Fake News)
Grover is a model for Neural Fake News -- both generation and detection. However, it probably can also be used for other generation tasks.
Visit our project page at rowanzellers.com/grover, the AI2 online demo, or read the full paper at arxiv.org/abs/1905.12616.
What's in this repo?
We are releasing the following:
- Code for the Grover generator (in lm/). This involves training the model as a language model across fields.
- Code for the Grover discriminator in discrimination/. Without much changing, you can run Grover as a discriminator to detect Neural Fake News.
- Code for generating from a Grover model, in sample/.
- Code for making your own RealNews dataset in realnews/.
- Model checkpoints freely available online for all of the Grover models. For using the RealNews dataset for research, please submit this form and message me on contact me on Twitter or through email. You will need to use a valid account that has google cloud enabled, otherwise, I won't be able to give you access
😢
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Setting up your environment
NOTE: If you just care about making your own RealNews dataset, you will need to set up your environment separately just for that, using an AWS machine (see realnews/.)
There are a few ways you can run Grover:
- Generation mode (inference). This requires a GPU because I wasn't able to get top-p sampling, or caching of transformer hidden states, to work on a TPU.
- LM Validation mode (perplexity). This could be run on a GPU or a TPU, but I've only tested this with TPU inference.
- LM Training mode. This requires a large TPU pod.
- Discrimination mode (training). This requires a TPU pod.
- Discrimination mode (inference). This could be run on a GPU or a TPU, but I've only tested this with TPU inference.
NOTE: You might be able to get things to work using different hardware. However, it might be a lot of work engineering wise and I don't recommend it if possible. Please don't contact me with requests like this, as there's not much help I can give you.
I used Python3.6 for everything. Usually I set it up using the following commands:
curl -o ~/miniconda.sh -O https://repo.continuum.io/miniconda/Miniconda3-4.5.4-Linux-x86_64.sh && \
chmod +x ~/miniconda.sh && \
~/miniconda.sh -b -p ~/conda && \
rm ~/miniconda.sh && \
~/conda/bin/conda install -y python=3.6
Then pip install -r requirements-gpu.txt if you're installing on a GPU, or pip install requirements-tpu.txt for TPU.
Misc notes/tips:
- If you have a lot of projects on your machine, you might want to use an anaconda environment to handle them all. Use
conda create -n grover python=3.6to create an environment namedgrover. To enter the environment usesource activate grover. To leave usesource deactivate. - I'm using tensorflow
1.13.1which requires Cuda10.0. You'll need to install that from the nvidia website. I usually install it into/usr/local/cuda-10.0/, so you will need to runexport LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64so tensorflow knows where to find it. - I always have my pythonpath as the root directory. While in the
groverdirectory, runexport PYTHONPATH=$(pwd)to set it.
Quickstart: setting up Grover for generation!
- Set up your environment. Here's the easy way, assuming anaconda is installed:
conda create -y -n grover python=3.6 && source activate grover && pip install -r requirements-gpu.txt - Download the model using
python download_model.py base - Now generate:
PYTHONPATH=$(pwd) python sample/contextual_generate.py -model_config_fn lm/configs/base.json -model_ckpt models/base/model.ckpt -metadata_fn sample/april2019_set_mini.jsonl -out_fn april2019_set_mini_out.jsonl
Congrats! You can view the generations, conditioned on the domain/headline/date/authors, in april2019_set_mini_out.jsonl.
FAQ: What's the deal with the release of Grover?
Our core position is that it is important to release possibly-dangerous models to researchers. At the same time, we believe Grover-Mega isn't particularly useful to anyone who isn't doing research in this area, particularly as we have an online web demo available and the model is computationally expensive. We previously were a bit stricter and limited initial use of Grover-Mega to researchers. Now that several months have passed since we put the paper on arxiv, and since several other large-scale language models have been publicly released, we figured that there is little harm in fully releasing Grover-Mega.
Bibtex
@inproceedings{zellers2019grover,
title={Defending Against Neural Fake News},
author={Zellers, Rowan and Holtzman, Ari and Rashkin, Hannah and Bisk, Yonatan and Farhadi, Ali and Roesner, Franziska and Choi, Yejin},
booktitle={Advances in Neural Information Processing Systems 32},
year={2019}
}
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