import logging from typing import Optional, List, Tuple, Set from presidio_analyzer import ( RecognizerResult, EntityRecognizer, AnalysisExplanation, ) from presidio_analyzer.nlp_engine import NlpArtifacts try: from flair.data import Sentence from flair.models import SequenceTagger except ImportError: print("Flair is not installed") logger = logging.getLogger("presidio-analyzer") class FlairRecognizer(EntityRecognizer): """ Wrapper for a flair model, if needed to be used within Presidio Analyzer. :example: >from presidio_analyzer import AnalyzerEngine, RecognizerRegistry >flair_recognizer = FlairRecognizer() >registry = RecognizerRegistry() >registry.add_recognizer(flair_recognizer) >analyzer = AnalyzerEngine(registry=registry) >results = analyzer.analyze( > "My name is Christopher and I live in Irbid.", > language="en", > return_decision_process=True, >) >for result in results: > print(result) > print(result.analysis_explanation) """ ENTITIES = [ "LOCATION", "PERSON", "NRP", "GPE", "ORGANIZATION", "MAC_ADDRESS", "US_BANK_NUMBER", "IMEI", "TITLE", "LICENSE_PLATE", "US_PASSPORT", "CURRENCY", "ROUTING_NUMBER", "US_ITIN", "US_BANK_NUMBER", "US_DRIVER_LICENSE", "AGE", "PASSWORD", "SWIFT_CODE", ] DEFAULT_EXPLANATION = "Identified as {} by Flair's Named Entity Recognition" CHECK_LABEL_GROUPS = [ ({"LOCATION"}, {"LOC", "LOCATION", "STREET_ADDRESS", "COORDINATE"}), ({"PERSON"}, {"PER", "PERSON"}), ({"NRP"}, {"NORP", "NRP"}), ({"GPE"}, {"GPE"}), ({"ORGANIZATION"}, {"ORG"}), ({"MAC_ADDRESS"}, {"MAC_ADDRESS"}), ({"US_BANK_NUMBER"}, {"US_BANK_NUMBER"}), ({"IMEI"}, {"IMEI"}), ({"TITLE"}, {"TITLE"}), ({"LICENSE_PLATE"}, {"LICENSE_PLATE"}), ({"US_PASSPORT"}, {"US_PASSPORT"}), ({"CURRENCY"}, {"CURRENCY"}), ({"ROUTING_NUMBER"}, {"ROUTING_NUMBER"}), ({"AGE"}, {"AGE"}), ({"CURRENCY"}, {"CURRENCY"}), ({"SWIFT_CODE"}, {"SWIFT_CODE"}), ({"US_ITIN"}, {"US_ITIN"}), ({"US_BANK_NUMBER"}, {"US_BANK_NUMBER"}), ({"US_DRIVER_LICENSE"}, {"US_DRIVER_LICENSE"}), ] MODEL_LANGUAGES = { "en":"beki/flair-pii-english-large", # "en":"flair-trf.pt", } PRESIDIO_EQUIVALENCES = { "PER": "PERSON", "LOC": "LOCATION", "ORG": "ORGANIZATION", "NROP": "NRP", "URL": "URL", "US_ITIN": "US_ITIN", "US_PASSPORT": "US_PASSPORT", "IBAN_CODE": "IBAN_CODE", "IP_ADDRESS": "IP_ADDRESS", "EMAIL_ADDRESS": "EMAIL", "US_DRIVER_LICENSE": "US_DRIVER_LICENSE", "US_BANK_NUMBER": "US_BANK_NUMBER", } def __init__( self, supported_language: str = "en", supported_entities: Optional[List[str]] = None, check_label_groups: Optional[Tuple[Set, Set]] = None, model: SequenceTagger = None, ): self.check_label_groups = ( check_label_groups if check_label_groups else self.CHECK_LABEL_GROUPS ) supported_entities = supported_entities if supported_entities else self.ENTITIES self.model = ( model if model else SequenceTagger.load(self.MODEL_LANGUAGES.get(supported_language)) ) super().__init__( supported_entities=supported_entities, supported_language=supported_language, name="Flair Analytics", ) def load(self) -> None: """Load the model, not used. Model is loaded during initialization.""" pass def get_supported_entities(self) -> List[str]: """ Return supported entities by this model. :return: List of the supported entities. """ return self.supported_entities # Class to use Flair with Presidio as an external recognizer. def analyze( self, text: str, entities: List[str], nlp_artifacts: NlpArtifacts = None ) -> List[RecognizerResult]: """ Analyze text using Text Analytics. :param text: The text for analysis. :param entities: Not working properly for this recognizer. :param nlp_artifacts: Not used by this recognizer. :param language: Text language. Supported languages in MODEL_LANGUAGES :return: The list of Presidio RecognizerResult constructed from the recognized Flair detections. """ results = [] sentences = Sentence(text) self.model.predict(sentences) # If there are no specific list of entities, we will look for all of it. if not entities: entities = self.supported_entities for entity in entities: if entity not in self.supported_entities: continue for ent in sentences.get_spans("ner"): if not self.__check_label( entity, ent.labels[0].value, self.check_label_groups ): continue textual_explanation = self.DEFAULT_EXPLANATION.format( ent.labels[0].value ) explanation = self.build_flair_explanation( round(ent.score, 2), textual_explanation ) flair_result = self._convert_to_recognizer_result(ent, explanation) results.append(flair_result) return results def _convert_to_recognizer_result(self, entity, explanation) -> RecognizerResult: entity_type = self.PRESIDIO_EQUIVALENCES.get(entity.tag, entity.tag) flair_score = round(entity.score, 2) flair_results = RecognizerResult( entity_type=entity_type, start=entity.start_position, end=entity.end_position, score=flair_score, analysis_explanation=explanation, ) return flair_results def build_flair_explanation( self, original_score: float, explanation: str ) -> AnalysisExplanation: """ Create explanation for why this result was detected. :param original_score: Score given by this recognizer :param explanation: Explanation string :return: """ explanation = AnalysisExplanation( recognizer=self.__class__.__name__, original_score=original_score, textual_explanation=explanation, ) return explanation @staticmethod def __check_label( entity: str, label: str, check_label_groups: Tuple[Set, Set] ) -> bool: return any( [entity in egrp and label in lgrp for egrp, lgrp in check_label_groups] ) if __name__ == "__main__": from presidio_analyzer import AnalyzerEngine, RecognizerRegistry flair_recognizer = ( FlairRecognizer() ) # This would download a very large (+2GB) model on the first run registry = RecognizerRegistry() registry.add_recognizer(flair_recognizer) analyzer = AnalyzerEngine(registry=registry) results = analyzer.analyze( "{first_name: Moustafa, sale_id: 235234}", language="en", return_decision_process=True, ) for result in results: print(result) print(result.analysis_explanation)