(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.

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 the Grover-Base and Grover-Large models. For Grover-Mega or the RealNews dataset, please submit this form and we will get back to you as soon as possible.

Scroll down 👇 for some easy-to-use instructions for setting up Grover to generate news articles.

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.

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=3.6 to create an environment named grover. To enter the environment use source activate grover. To leave use source deactivate.
• I'm using tensorflow 1.13.1 which requires Cuda 10.0. 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 run export LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64 so tensorflow knows where to find it.
• I always have my pythonpath as the root directory. While in the grover directory, run export PYTHONPATH=$(pwd) to set it.

1. 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
2. Download the model using python download_model.py base
3. 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.

@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},
    journal={arXiv preprint arXiv:1905.12616},
    year={2019}
}