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Relevant code for the "Show Your Work" paper, EMNLP 2019.
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Code for the "Show Your Work" paper, EMNLP 2019.


If you use this repository for your research, please cite:

 author = {Jesse Dodge and Suchin Gururangan and Dallas Card and Roy Schwartz and Noah A. Smith},
 title = {Show Your Work: Improved Reporting of Experimental Results},
 year = {2019},
 booktitle = {Proceedings of EMNLP},


First, clone this repository.

Then, clone the allentune repository and install it on your system:

git clone
cd allentune/
pip install --editable .

Run example

cd show-your-work/
allentune search --experiment-name cnn_search \
                 --search_space ./search_spaces/cnn_sst5.json \
                 --base-config ./training_config/cnn_classifier.jsonnet \
                 --cpus-per-trial 1 \
                 --num-gpus 0 \
                 --gpus-per-trial 0  \
                 --num-samples 1
                 --include-package show-your-work

If you have GPUs, set the --num-gpus flag and --gpus-per-trial flag appropriately.

Note: To run DGEM search on Scitail, you must use the implementation in, which notably depends on a previous version of allennlp. Clone that repository, copy the search_spaces/dgem.json and training_config/dgem.jsonnet files over, and run

allentune search --experiment-name dgem_search \
                 --search_space ./search_spaces/dgem.json \
                 --base-config ./training_config/dgem.jsonnet \
                 --cpus-per-trial 1 \
                 --num-gpus 0 \
                 --gpus-per-trial 0  \
                 --num-samples 1
                 --include-package scitail
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