Metadata-Version: 2.1
Name: styleclass
Version: 0.0.3
Summary: Citation style classifier
Home-page: https://gitlab.com/crossref/citation_style_classifier
Author: Crossref
Author-email: labs@crossref.org
License: UNKNOWN
Description: # Citation style classifier
        
        Citation 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:
        
          * acm-sig-proceedings
          * american-chemical-society
          * american-chemical-society-with-titles
          * american-institute-of-physics
          * american-sociological-association
          * apa
          * bmc-bioinformatics
          * chicago-author-date
          * elsevier-without-titles
          * elsevier-with-titles
          * harvard3
          * ieee
          * iso690-author-date-en
          * modern-language-association
          * springer-basic-author-date
          * springer-lecture-notes-in-computer-science
          * vancouver
          * unknown
        
        The package contains the training data, the classification model, and the code for feature extraction, selection, training and prediction.
        
        ## Installation
        
                pip3 install styleclass
        
        ## Classification
        
        From command line:
        
                styleclass_classify -r "reference string"
                styleclass_classify -i /file/with/reference/strings/one/per/line -o /output/file
        
        In Python code:
        
                from styleclass.classify import classify
                from styleclass.train import get_default_model
        
                model = get_default_model()
                prediction = classify("reference string", *model)
                prediction = classify(["reference string #1", "reference string #2", "reference string #3"], *model)
        
        ## Data
        
        Styleclass 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.
        
        A new dataset can be generated using the script `styleclass_generate_dataset`.
        
        ## Models
        
        The [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.
        
        Script `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.
        
        ## Evaluation
        
        `styleclass_evaluate` script can be used to evaluate exisitng model on a test set, in terms of accuracy.
        
        The accuracy of the default model estimated on our test set is 95%.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
