Package: pybart-nlp
A Python converter from Universal-Dependencies trees toBARTrepresentation.Try out our UD-BART comparisonDemoBART (Bar-Ilan &AI2RepresentationTransformation) is our new and cool enhanced-syntatic-representation specialized to improve Relation Extraction, but suitable for any NLP down-stream task.See ourpyBART: Evidence-based Syntactic Transformations for IEfor detailed description of BART's creation/linguisical-verification/evaluation processes, and list of conversions.This project is part of a wider project series, related to BART:Converter:The current project.Model:UD basedspaCymodel (pip installthe_large_model). This model is needed when using the converter as a spaCy pipeline component (as spaCy doesn't provide UD-format based models).Demo:Web-demo making use of the converter, to compare between UD and BART representations.Table of contentsConverter descriptionInstallationUsagespaCy pipeline componentCoNLL-U formatConfigurationCitingTeamTable of contents generated with markdown-tocConverter descriptionConverts UD (supports both versions 1 and 2) to BART.Supports Conll-U format, spaCy docs, and spaCy pipeline component (seeUsage).Highly configurable (seeConfiguration).Note:The BART representation subsumes Stanford's EnhancedUD conversions, these conversions are describedhere, and were already implemented bycore-NLP Java converter. As such they were not avialable to python users and thus we have ported them to pyBART and tried to maintain their behavior as much as reasonable.InstallationpyBART requires Python 3.7 or later (yes including up to 3.11). The preferred way to install pyBART is viapip. Just runpip install pybart-nlpin your Python environment and you're good to go!
If you want to use pyBART as a spaCy pipeline component, then you should install as well: (1) the spaCy package and (2) a spaCy-model based on UD-format (which we happen to provide (details arehere)# if you want to use pyBART as a spaCy pipeline component, well,#   you need spaCy installed and a transformer-based spaCy model (based on UD-format):pipinstallspacy
pipinstallhttps://storage.googleapis.com/en_ud_model/en_ud_model_trf-2.0.0.tar.gz# or if you want non-trandformer-based smaller models:#   large: https://storage.googleapis.com/en_ud_model/en_ud_model_lg-2.0.0.tar.gz#   medium: https://storage.googleapis.com/en_ud_model/en_ud_model_md-2.0.0.tar.gz#   small: https://storage.googleapis.com/en_ud_model/en_ud_model_sm-2.0.0.tar.gz# and this is us. please don't confuse with pybart/bart-py/bartpipinstallpybart-nlpUsageOnce you've installed pyBART, you can use the package in one of the following ways.
Notice, in the spacy mode we assign a method in the doc context named "get_pybart" which returns a list of lists. Each list corresponds to a sentence in doc.sents, and contains a list of edge dictionaries. Each edge contains the following fields: "head", "tail", and "label". "head" and "tail" can be either a reference to the corresponding spacy Token or a string representing an added node (and as such can't have a spacy Token reference).Notice that for both methods the API calls can be called with a list of optional parameters to configure the conversion process. We will elaborate about them next.spaCy pipeline componentimportspacyfrompybart.apiimport*# Load a UD-based english modelnlp=spacy.load("en_ud_model_sm")# here you can change it to md/sm/lg as you preffer# Add BART converter to spaCy's pipelinenlp.add_pipe("pybart_spacy_pipe",last="True",config={'remove_extra_info':True})# you can pass an empty config for default behavior, this is just an example# Test the new converter componentdoc=nlp("He saw me while driving")fori,sentinenumerate(doc._.get_pybart()):print(f"Sentence{i}")foredgeinsent:print(f"{edge['head']}--{edge['label']}-->{edge['tail']}")# Output:# Sentence 0:# saw --root--> saw# saw --nsubj--> He# saw --dobj--> me# saw --advcl:while--> driving# driving --mark--> while# driving --nsubj--> HeCoNLL-U formatfrompybart.apiimportconvert_bart_conllu# read a CoNLL-U formatted filewithopen(conllu_formatted_file_in)asf:sents=f.read()# convertconverted=convert_bart_conllu(sents)# use it, probably wanting to write the textual output to a new filewithopen(conllu_formatted_file_out,"w")asf:f.write(converted)ConfigurationEach of our API calls can get the following optional parameters:NameTypeDefaultExplanationenhance_udbooleanTrueInclude Stanford's EnhancedUD conversions.enhanced_plus_plusbooleanTrueInclude Stanford's EnhancedUD++ conversions.enhanced_extrabooleanTrueInclude BART's unique conversions.conv_iterationsintinfStop the (defaultive) behaivor of iterating on the list of conversions afterconv_iterationsiterations, though before reaching convergance (that is, no change in graph when conversion-list is applied).remove_eud_infobooleanFalseDo not include Stanford's EnhancedUD&EnhancedUD++'s extra label information.remove_extra_infobooleanFalseDo not include BART's extra label information.remove_node_adding_conversionsbooleanFalseDo not include conversions that might add nodes to the given graph.remove_uncbooleanFalseDo not include conversions that might containuncertainty(see paper for detailed explanation).query_modebooleanFalseDo not include conversions that add arcs rather than reorder arcs.funcs_to_cancelList[str]NoneA list of conversions to prevent from occuring by their names. Useget_conversion_namesfor the full conversion name listud_versionint1Which UD version to expect as input and to set the converter to. Currently we support 1 and 2.CitingIf you use pyBART or BART in your research, please citepyBART: Evidence-based Syntactic Transformations for IE.@inproceedings{Tiktinsky2020pyBARTES,title={pyBART: Evidence-based Syntactic Transformations for IE},author={Aryeh Tiktinsky and Yoav Goldberg and Reut Tsarfaty},booktitle={ACL},year={2020}}TeampyBART is an open-source project backed bythe Allen Institute for Artificial Intelligence (AI2), and by Bar-Ilan University as being part ofmythesis under the supervision of Yoav Goldberg.
AI2 is a non-profit institute with the mission to contribute to humanity through high-impact AI research and engineering.
Our team consists of Yoav Goldberg, Reut Tsarfaty and myself. Currently we are the contributors to this project but we will be more than happy for anyone who wants to help, via Issues and PR's.