Metadata-Version: 2.1
Name: jury
Version: 1.0.0
Summary: Evaluation toolkit for neural language generation.
Home-page: https://github.com/obss/jury
Author: 
License: MIT
Description: <h1 align="center">Jury</h1>
        
        <p align="center">
        <a href="https://pypi.org/project/jury"><img src="https://img.shields.io/pypi/pyversions/jury" alt="Python versions"></a>
        <a href="https://pepy.tech/project/jury"><img src="https://pepy.tech/badge/jury" alt="downloads"></a>
        <a href="https://pypi.org/project/jury"><img src="https://img.shields.io/pypi/v/jury?color=blue" alt="PyPI version"></a>
        <a href="https://github.com/obss/jury/releases/latest"><img alt="Latest Release" src="https://img.shields.io/github/release-date/obss/jury"></a>
        <br>
        <a href="https://github.com/obss/jury/actions"><img alt="Build status" src="https://github.com/obss/jury/actions/workflows/ci.yml/badge.svg"></a>
        <a href="https://libraries.io/pypi/jury"><img alt="Dependencies" src="https://img.shields.io/librariesio/github/obss/jury"></a>
        <a href="https://github.com/psf/black"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg"></a>
        <a href="https://github.com/obss/jury/blob/main/LICENSE"><img alt="License: MIT" src="https://img.shields.io/pypi/l/jury"></a>
        </p>
        
        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](https://github.com/huggingface/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](https://medium.com/codable/jury-evaluating-performance-of-nlg-models-730eb9c9999f).
        
        ## <div align="center"> Installation </div>
        
        Through pip,
        
            pip install jury
        
        or build from source,
        
            git clone https://github.com/obss/jury.git
            cd jury
            python setup.py install
        
        ## <div align="center"> Usage </div>
        
        ### 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](https://github.com/huggingface/datasets/tree/master/metrics). The code snippet below gives a brief explanation.
        
            from jury.metrics import Metric
        
            CustomMetric(Metric):
                def compute(self, predictions, references):
                    pass
        
        ## <div align="center"> Contributing </div>
        
        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
        
        
        ## <div align="center"> License </div>
        
        Licensed under the [MIT](LICENSE) License.
        
Keywords: machine-learning,deep-learning,ml,pytorch,NLP,evaluation,question-answering,question-generation
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Education
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: tests
Provides-Extra: metrics
Provides-Extra: develop
