Metadata-Version: 2.1
Name: kindred
Version: 2.7.4
Summary: A relation extraction toolkit for biomedical text mining
Home-page: http://github.com/jakelever/kindred
Author: Jake Lever
Author-email: jake.lever@gmail.com
License: MIT
Description: # Kindred
        
        <p>
        <a href="https://pypi.python.org/pypi/kindred">
           <img src="https://img.shields.io/pypi/v/kindred.svg" />
        </a>
        <a href="https://travis-ci.org/jakelever/kindred">
           <img src="https://travis-ci.org/jakelever/kindred.svg?branch=master" />
        </a>
        <a href="https://coveralls.io/github/jakelever/kindred?branch=master">
           <img src="https://coveralls.io/repos/github/jakelever/kindred/badge.svg?branch=master" />
        </a>
        <a href="http://kindred.readthedocs.io/en/stable/">
           <img src="https://readthedocs.org/projects/kindred/badge/?version=stable" />
        </a>
        <a href="https://opensource.org/licenses/MIT">
           <img src="https://img.shields.io/badge/License-MIT-blue.svg" />
        </a>
        </p>
        
        Kindred is a Python3 package for relation extraction in biomedical texts. Given some training data, it can build a model to identify relations between entities (e.g. drugs, genes, etc) in a sentence.
        
        ## Installation
        
        You can install "kindred" via [pip](https://pypi.python.org/pypi/pip/) from [PyPI](https://pypi.org/project/kindred/)
        
        ```bash
        pip install kindred
        ```
        
        As of v2, Kindred relies on the [Spacy](https://spacy.io) toolkit for parsing. After installing kindred (which also installs spacy), you will need to install a Spacy language model. For instance, the command below installs the English language model::
        
        ```bash
        python -m spacy download en
        ```
        
        ## Usage
        
        Check out the [tutorial](https://github.com/jakelever/kindred/tree/master/tutorial) that goes through a simple use case of extracting capital cities from text. More details and the full documentation can be found at [readthedocs](http://kindred.readthedocs.io/).
        
        ### BioNLP Shared Task Example
        
        ```python
        import kindred
        trainCorpus = kindred.bionlpst.load('2016-BB3-event-train')
        devCorpus = kindred.bionlpst.load('2016-BB3-event-dev')
        predictionCorpus = devCorpus.clone()
        predictionCorpus.removeRelations()
        classifier = kindred.RelationClassifier()
        classifier.train(trainCorpus)
        classifier.predict(predictionCorpus)
        f1score = kindred.evaluate(devCorpus, predictionCorpus, metric='f1score')
        ```
        
        ### PubAnnotation Example
        
        ```python
        corpus = kindred.pubannotation.load('bionlp-st-gro-2013-development')
        ```
        
        ### PubTator Example
        
        ```python
        corpus = kindred.pubtator.load([19894120,19894121])
        ```
        
        ### Input Formats
        
        Kindred can load several formats, including BioNLP Shared Task, JSON, BioC XML and a simple tag format. Check out the [file format documentation](https://kindred.readthedocs.io/en/stable/fileformats.html) for example data and code.
        
        ### Citing
        
        It would be wonderful if you could cite the [associated paper](http://aclweb.org/anthology/W17-2322) for this package if used in any academic research.
        
        ```bibtex
        @article{lever2017painless,
           title={Painless {R}elation {E}xtraction with {K}indred},
           author={Lever, Jake and Jones, Steven},
           journal={BioNLP 2017},
           pages={176--183},
           year={2017}
        }
        ```
        
        ## Contributing
        
        Contributions are very welcome.
        
        ## License
        
        Distributed under the terms of the [MIT](http://opensource.org/licenses/MIT) license, "kindred" is free and open source software
        
        ## Issues
        
        If you encounter any problems, please [file an issue](https://github.com/jakelever/kindred/issues) along with a detailed description.
        
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Human Machine Interfaces
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Text Processing
Classifier: Topic :: Text Processing :: General
Classifier: Topic :: Text Processing :: Indexing
Classifier: Topic :: Text Processing :: Linguistic
Description-Content-Type: text/markdown
