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reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bionlp","python","relation-extraction","text-mining"],"created_at":"2026-01-16T10:51:38.326Z","updated_at":"2026-01-16T10:51:39.022Z","avatar_url":"https://github.com/jakelever.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Kindred\n\n\u003cp\u003e\n\u003ca href=\"https://pypi.python.org/pypi/kindred\"\u003e\n   \u003cimg src=\"https://img.shields.io/pypi/v/kindred.svg\" /\u003e\n\u003c/a\u003e\n\u003ca href=\"https://github.com/jakelever/kindred/actions/workflows/tests.yml\"\u003e\n   \u003cimg src=\"https://github.com/jakelever/kindred/actions/workflows/tests.yml/badge.svg\" /\u003e\n\u003c/a\u003e\n\u003ca href=\"https://coveralls.io/github/jakelever/kindred?branch=master\"\u003e\n   \u003cimg src=\"https://coveralls.io/repos/github/jakelever/kindred/badge.svg?branch=master\" /\u003e\n\u003c/a\u003e\n\u003ca href=\"http://kindred.readthedocs.io/en/stable/\"\u003e\n   \u003cimg src=\"https://readthedocs.org/projects/kindred/badge/?version=stable\" /\u003e\n\u003c/a\u003e\n\u003ca href=\"https://opensource.org/licenses/MIT\"\u003e\n   \u003cimg src=\"https://img.shields.io/badge/License-MIT-blue.svg\" /\u003e\n\u003c/a\u003e\n\u003c/p\u003e\n\nKindred 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.\n\n## Installation\n\nYou can install \"kindred\" via [pip](https://pypi.python.org/pypi/pip/) from [PyPI](https://pypi.org/project/kindred/)\n\n```bash\npip install kindred\n```\n\nKindred 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::\n\n```bash\npython -m spacy download en_core_web_sm\n```\n\n## Usage\n\nCheck 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/).\n\n### BioNLP Shared Task Example\n\n```python\nimport kindred\n\n# Load the SeeDev corpus\ntrainCorpus = kindred.bionlpst.load('2016-SeeDev-binary-train')\ndevCorpus = kindred.bionlpst.load('2016-SeeDev-binary-dev')\n\n# Create a copy of the dev corpus to make predictions on\npredictionCorpus = devCorpus.clone()\npredictionCorpus.removeRelations()\n\n# Create a relation classifier, train it and make predictions\nclassifier = kindred.RelationClassifier()\nclassifier.train(trainCorpus)\nclassifier.predict(predictionCorpus)\n\n# Get the F1 score of the predicted relations\nf1score = kindred.evaluate(devCorpus, predictionCorpus, metric='f1score')\n```\n\n### PubAnnotation Example\n\n```python\ncorpus = kindred.pubannotation.load('bionlp-st-gro-2013-development')\n```\n\n### PubTator Example\n\n```python\ncorpus = kindred.pubtator.load([19894120,19894121])\n```\n\n### Input Formats\n\nKindred 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.\n\n### Citing\n\nIt 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.\n\n```bibtex\n@article{lever2017painless,\n   title={Painless {R}elation {E}xtraction with {K}indred},\n   author={Lever, Jake and Jones, Steven},\n   journal={BioNLP 2017},\n   pages={176--183},\n   year={2017}\n}\n```\n\n## Contributing\n\nContributions are very welcome.\n\n## License\n\nDistributed under the terms of the [MIT](http://opensource.org/licenses/MIT) license, \"kindred\" is free and open source software\n\n## Issues\n\nIf you encounter any problems, please [file an issue](https://github.com/jakelever/kindred/issues) along with a detailed description.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjakelever%2Fkindred","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjakelever%2Fkindred","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjakelever%2Fkindred/lists"}