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        "author": "Anders Johannsen",
        "author_email": "ajohannsen@hum.ku.dk",
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        "description": "[![Build Status](https://travis-ci.org/coastalcph/rungsted.svg?branch=master)](https://travis-ci.org/coastalcph/rungsted)\n\n\n## Rungsted structured perceptron sequential tagger\n\n### Install\n\nThe software is installable via PyPI, e.g. do \n\n```\npip install rungsted\n```\n\n\n\n### Demo\n\nThe repository contains a subset of the part-of-speech tagged Brown corpus. To run the structured perceptron labeler on this dataset, execute:\n\n``python src/labeler.py --train data/brown.train --test data/brown.test.vw``\n\nRungsted's input format is closely modeled on the powerful and flexible format of [Vowpal Wabbit](https://github.com/JohnLangford/vowpal_wabbit/wiki/Input-format),\nwith the exception that Rungsted is perfectly fine with labels that are not integers.\n\n### Datasets\n\nProvided you have a working installation of NLTK, you can recreate the Brown dataset with this command. \n\n``python rungsted/datasets/cr_brown_pos_data.py data/brown.train.vw data/brown.test.vw``\n\nThere is also a script `rungsted/datasets/conll_to_vw.py` to convert from CONLL-formatted input to Rungsted \n\n\n### Building and uploading to PyPI\n\nFirst, run `python setup.py sdist` to generate a source distribution. \nThen upload the distribution files to PyPI with twine: `twine upload dist/*`.\n\nTo develop locally, use `python setup.py develop`.",
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        "summary": "Rungsted. An efficient HMM-based structured prediction model for sequential labeling tasks, with extras.",
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