jury 1.0.1

Evaluation toolkit for neural language generation.

Jury

Simple tool/toolkit for evaluating NLG (Natural Language Generation) offering various automated metrics. Jury offers a smooth and easy-to-use interface. It uses datasets for underlying metric computation, and hence adding custom metric is easy as adopting datasets.Metric.

Main advantages that Jury offers are:

• Easy to use for any NLG system.
• Calculate many metrics at once.
• Metrics calculations are handled concurrently to save processing time.
• It supports evaluating multiple predictions.

To see more, check the official Jury blog post.

Installation

Through pip,

pip install jury

or build from source,

git clone https://github.com/obss/jury.git
cd jury
python setup.py install

Usage

API Usage

It is only two lines of code to evaluate generated outputs.

from jury import Jury

jury = Jury()

# Microsoft translator translation for "Yurtta sulh, cihanda sulh." (16.07.2021)
predictions = ["Peace in the dormitory, peace in the world."]
references = ["Peace at home, peace in the world."]
scores = jury.evaluate(predictions, references)

Specify metrics you want to use on instantiation.

jury = Jury(metrics=["bleu", "meteor"])
scores = jury.evaluate(predictions, references)

Custom Metrics

You can use custom metrics with inheriting jury.metrics.Metric, you can see current metrics on datasets/metrics. The code snippet below gives a brief explanation.

from jury.metrics import Metric

CustomMetric(Metric):
    def compute(self, predictions, references):
        pass

Contributing

PRs are welcomed as always :)

Installation

git clone https://github.com/obss/jury.git
cd jury
pip install -e .[develop]

Tests

To tests simply run.

python tests/run_tests.py

Code Style

To check code style,

python tests/run_code_style.py check

To format codebase,

python tests/run_code_style.py format

License

Licensed under the MIT License.