Automatic metrics for GEM benchmark tasks. Can also be used standalone for evaluation of various natural language generation tasks.
GEM-metrics require recent Python 3, virtualenv or similar is recommended. To install, simply run:
git clone https://github.com/GEM-benchmark/GEM-metrics
cd GEM-metrics
pip install -r requirements.txt -r requirements-heavy.txt
If you want to just run the metrics from console (and don't need access to the checkout), you can just run:
pip install 'gem-metrics[heavy] @ git+https://github.com/GEM-benchmark/GEM-metrics.git'
Note that some NLTK stuff may be downloaded upon first run into a subdirectory where the code is located, so make sure you have write access when you run this. Also note that all the required Python libraries are around 3 GB in size when installed.
If you don't need trained metrics (BLEURT, BERTScore, NUBIA, QuestEval), you can ignore the “heavy” part,
i.e. only install dependencies from requirements.txt or only use gem-metrics instead of gem-metrics[heavy]
if installing without checkout. That way, your installed libraries will be ~300 MB.
To compute all default metrics for a file, run:
<script> [-r references.json] outputs.json
Where <script> is either ./run_metrics.py (if you created a checkout) or gem_metrics if you installed directly via pip.
See test_data for example JSON file formats.
For calculating basic metrics with the unit test data, run:
./run_metrics.py -s test_data/unit_tests/sources.json -r test_data/unit_tests/references.json test_data/unit_tests/predictions.json
Use ./run_metrics.py -h to see all available options.
By default, the “heavy” metrics (BERTScore, BLEURT, NUBIA and QuestEval) aren't computed. Use --heavy-metrics to compute them.
Licensed under the MIT license.