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        "author_email": "labs@crossref.org",
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        "description": "# Citation style classifier\n\nCitation style classifier can automatically infer citation style from a reference string. The classifier is a Logistic Regression model trained on 90,000 reference strings. The following citation styles are supported by default:\n\n  * acm-sig-proceedings\n  * american-chemical-society\n  * american-chemical-society-with-titles\n  * american-institute-of-physics\n  * american-sociological-association\n  * apa\n  * bmc-bioinformatics\n  * chicago-author-date\n  * elsevier-without-titles\n  * elsevier-with-titles\n  * harvard3\n  * ieee\n  * iso690-author-date-en\n  * modern-language-association\n  * springer-basic-author-date\n  * springer-lecture-notes-in-computer-science\n  * vancouver\n  * unknown\n\nThe package contains the training data, the classification model, and the code for feature extraction, selection, training and prediction.\n\n## Installation\n\n        pip3 install styleclass\n\n## Classification\n\nFrom command line:\n\n        styleclass_classify -r \"reference string\"\n        styleclass_classify -i /file/with/reference/strings/one/per/line -o /output/file\n\nIn Python code:\n\n        from styleclass.classify import classify\n        from styleclass.train import get_default_model\n\n        model = get_default_model()\n        prediction = classify(\"reference string\", *model)\n        prediction = classify([\"reference string #1\", \"reference string #2\", \"reference string #3\"], *model)\n\n## Data\n\nStyleclass package contains [two datasets](https://gitlab.com/crossref/citation_style_classifier/tree/master/styleclass/datasets): training set and test set. Each of them contains a sample of 5,000 DOIs formatted in 17 citation styles (listed above), which gives 85,000 reference strings. Both datasets were generated automatically using Crossref REST API.\n\nA new dataset can be generated using the script `styleclass_generate_dataset`.\n\n## Models\n\nThe [default model](https://gitlab.com/crossref/citation_style_classifier/tree/master/styleclass/models) was trained on the training dataset. Before the training, the dataset was cleaned and enriched with random noise. 5,000 strings with \"unknown\" style were also generated and added to the dataset.\n\nScript `styleclass_train_model` can be used to train a new model. This is useful especially when you need to operate of a different set of citation styles than our default. The script prepares the data for training in the same was as was done for training of the default model.\n\n## Evaluation\n\n`styleclass_evaluate` script can be used to evaluate exisitng model on a test set, in terms of accuracy.\n\nThe accuracy of the default model estimated on our test set is 95%.\n\n\n",
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