Package: zshot
ZshotZero and Few shot named entity & relationships recognitionDocumentation:https://ibm.github.io/zshotSource Code:https://github.com/IBM/zshotZshot is a highly customisable framework for performing Zero and Few shot named entity recognition.Can be used to perform:Mentions extraction: Identify globally relevant mentions or mentions relevant for a given domainWikification: The task of linking textual mentions to entities in WikipediaZero and Few Shot named entity recognition: using language description perform NER to generalize to unseen domainsZero and Few Shot named relationship recognitionVisualization: Zero-shot NER and RE extractionRequirementsPython 3.6+spacy- Zshot rely onSpacyfor pipelining and visualizationtorch- PyTorch is required to run pytorch models.transformers- Required for pre-trained language models.evaluate- Required for evaluation.datasets- Required to evaluate over datasets (e.g.: OntoNotes).Optional Dependenciesflair- Required if you want to use Flair mentions extractor and for TARS linker.blink- Required if you want to use Blink for linking to Wikipedia pages.Installation$pipinstallzshot---> 100%ExamplesExampleNotebookInstallation and VisualizationZshot ApproachZShot contains two different components, thementions extractorand thelinker.Mentions ExtractorThementions extractorwill detect the possible entities (a.k.a. mentions), that will be then linked to a data source (e.g.: Wikidata) by thelinker.Currently, there are 4 differentmentions extractorssupported, 2 of them are based onSpaCy, and 2 of them are based onFlair. The two different versions for each library are similar, one is based on Named Entity Recognition and Classification (NERC) and the other one is based on the linguistics (i.e.: using Part Of the Speech tagging (PoS) and Dependency Parsing(DP)).The NERC approach will use NERC models to detect all the entities that have to be linked. This approach depends on the model that is being used, and the entities the model has been trained on, so depending on the use case and the target entities it may be not the best approach, as the entities may be not recognized by the NERC model and thus won't be linked.The linguistic approach relies on the idea that mentions will usually be a syntagma or a noun. Therefore, this approach detects nouns that are included in a syntagma and that act like objects, subjects, etc. This approach do not depend on the model (although the performance does), but a noun in a text should be always a noun, it doesn't depend on the dataset the model has been trained on.LinkerThelinkerwill link the detected entities to a existing set of labels. Some of thelinkers, however, areend-to-end, i.e. they don't need thementions extractor, as they detect and link the entities at the same time.Again, there are 4linkersavailable currently, 2 of them areend-to-endand 2 are not. Let's start with those thar are notend-to-end:Linker Nameend-to-endSource CodePaperBlinkXSource CodePaperGENREXSource CodePaperSMXM✓Source CodePaperTARS✓Source CodePaperExample: Zero-Shot Entity RecognitionHow to use itInstall requirements:pip install -r requirements.txtInstall a spacy pipeline to use it for mentions extraction:python -m spacy download en_core_web_smCreate a filemain.pywith the pipeline configuration and entities definition (Wikipedia abstract are usually a good starting point for descriptions):importspacyfromzshotimportPipelineConfig,displacyfromzshot.linkerimportLinkerRegenfromzshot.mentions_extractorimportMentionsExtractorSpacyfromzshot.utils.data_modelsimportEntitynlp=spacy.load("en_core_web_sm")nlp_config=PipelineConfig(mentions_extractor=MentionsExtractorSpacy(),linker=LinkerRegen(),entities=[Entity(name="Paris",description="Paris is located in northern central France, in a north-bending arc of the river Seine"),Entity(name="IBM",description="International Business Machines Corporation (IBM) is an American multinational technology corporation headquartered in Armonk, New York"),Entity(name="New York",description="New York is a city in U.S. state"),Entity(name="Florida",description="southeasternmost U.S. state"),Entity(name="American",description="American, something of, from, or related to the United States of America, commonly known as the United States or America"),Entity(name="Chemical formula",description="In chemistry, a chemical formula is a way of presenting information about the chemical proportions of atoms that constitute a particular chemical compound or molecule"),Entity(name="Acetamide",description="Acetamide (systematic name: ethanamide) is an organic compound with the formula CH3CONH2. It is the simplest amide derived from acetic acid. It finds some use as a plasticizer and as an industrial solvent."),Entity(name="Armonk",description="Armonk is a hamlet and census-designated place (CDP) in the town of North Castle, located in Westchester County, New York, United States."),Entity(name="Acetic Acid",description="Acetic acid, systematically named ethanoic acid, is an acidic, colourless liquid and organic compound with the chemical formula CH3COOH"),Entity(name="Industrial solvent",description="Acetamide (systematic name: ethanamide) is an organic compound with the formula CH3CONH2. It is the simplest amide derived from acetic acid. It finds some use as a plasticizer and as an industrial solvent."),])nlp.add_pipe("zshot",config=nlp_config,last=True)text="International Business Machines Corporation (IBM) is an American multinational technology corporation"\" headquartered in Armonk, New York, with operations in over 171 countries."doc=nlp(text)displacy.serve(doc,style="ent")Run itRun with$pythonmain.pyUsing the 'ent' visualizerServing on http://0.0.0.0:5000 ...The script will annotate the text using Zshot and use Displacy for visualising the annotationsCheck itOpen your browser athttp://127.0.0.1:5000.You will see the annotated sentence:How to create a custom componentIf you want to implement your own mentions_extractor or linker and use it with ZShot you can do it. To make it easier for the user to implement a new component, some base classes are provided that you have to extend with your code.It is as simple as create a new class extending the base class (MentionsExtractororLinker). You will have to implement the predict method, which will receive the SpaCy Documents and will return a list ofzshot.utils.data_models.Spanfor each document.This is a simple mentions_extractor that will extract as mentions all words that contain the letter s:fromtypingimportIterableimportspacyfromspacy.tokensimportDocfromzshotimportPipelineConfigfromzshot.utils.data_modelsimportSpanfromzshot.mentions_extractorimportMentionsExtractorclassSimpleMentionExtractor(MentionsExtractor):defpredict(self,docs:Iterable[Doc],batch_size=None):spans=[[Span(tok.idx,tok.idx+len(tok))fortokindocif"s"intok.text]fordocindocs]returnspansnew_nlp=spacy.load("en_core_web_sm")config=PipelineConfig(mentions_extractor=SimpleMentionExtractor())new_nlp.add_pipe("zshot",config=config,last=True)text_acetamide="CH2O2 is a chemical compound similar to Acetamide used in International Business "\"Machines Corporation (IBM)."doc=new_nlp(text_acetamide)print(doc._.mentions)>>>[is,similar,used,Business,Machines,materials]How to evaluate ZShotEvaluation is an important process to keep improving the performance of the models, that's why ZShot allows to evaluate the component with two predefined datasets: OntoNotes and MedMentions, in a Zero-Shot version in which the entities of the test and validation splits don't appear in the train set.The packageevaluationcontains all the functionalities to evaluate the ZShot components. The main function iszshot.evaluation.zshot_evaluate.evaluate, that will take as input the SpaCynlpmodel and the dataset to evaluate. It will return astrcontaining a table with the results of the evaluation. For instance the evaluation of the TARS linker in ZShot for theOntonotes validationset would be:importspacyfromzshotimportPipelineConfigfromzshot.linkerimportLinkerTARSfromzshot.evaluation.datasetimportload_ontonotes_zsfromzshot.evaluation.zshot_evaluateimportevaluate,prettify_evaluate_reportfromzshot.evaluation.metrics.seqeval.seqevalimportSeqevalontonotes_zs=load_ontonotes_zs('validation')nlp=spacy.blank("en")nlp_config=PipelineConfig(linker=LinkerTARS(),entities=ontonotes_zs.entities)nlp.add_pipe("zshot",config=nlp_config,last=True)evaluation=evaluate(nlp,ontonotes_zs,metric=Seqeval())prettify_evaluate_report(evaluation)