from typing import Tuple import spacy from presidio_analyzer import RecognizerRegistry from presidio_analyzer.nlp_engine import NlpEngine, NlpEngineProvider def create_nlp_engine_with_spacy( model_path: str, ) -> Tuple[NlpEngine, RecognizerRegistry]: """ Instantiate an NlpEngine with a spaCy model :param model_path: spaCy model path. """ registry = RecognizerRegistry() registry.load_predefined_recognizers() if not spacy.util.is_package(model_path): spacy.cli.download(model_path) nlp_configuration = { "nlp_engine_name": "spacy", "models": [{"lang_code": "en", "model_name": model_path}], } nlp_engine = NlpEngineProvider(nlp_configuration=nlp_configuration).create_engine() return nlp_engine, registry def create_nlp_engine_with_transformers( model_path: str, ) -> Tuple[NlpEngine, RecognizerRegistry]: """ Instantiate an NlpEngine with a TransformersRecognizer and a small spaCy model. The TransformersRecognizer would return results from Transformers models, the spaCy model would return NlpArtifacts such as POS and lemmas. :param model_path: HuggingFace model path. """ from transformers_rec import ( STANFORD_COFIGURATION, BERT_DEID_CONFIGURATION, TransformersRecognizer, ) registry = RecognizerRegistry() registry.load_predefined_recognizers() if not spacy.util.is_package("en_core_web_sm"): spacy.cli.download("en_core_web_sm") # Using a small spaCy model + a HF NER model transformers_recognizer = TransformersRecognizer(model_path=model_path) if model_path == "StanfordAIMI/stanford-deidentifier-base": transformers_recognizer.load_transformer(**STANFORD_COFIGURATION) elif model_path == "obi/deid_roberta_i2b2": transformers_recognizer.load_transformer(**BERT_DEID_CONFIGURATION) else: print(f"Warning: Model has no configuration, loading default.") transformers_recognizer.load_transformer(**BERT_DEID_CONFIGURATION) # Use small spaCy model, no need for both spacy and HF models # The transformers model is used here as a recognizer, not as an NlpEngine nlp_configuration = { "nlp_engine_name": "spacy", "models": [{"lang_code": "en", "model_name": "en_core_web_sm"}], } registry.add_recognizer(transformers_recognizer) registry.remove_recognizer("SpacyRecognizer") nlp_engine = NlpEngineProvider(nlp_configuration=nlp_configuration).create_engine() return nlp_engine, registry def create_nlp_engine_with_flair( model_path: str, ) -> Tuple[NlpEngine, RecognizerRegistry]: """ Instantiate an NlpEngine with a FlairRecognizer and a small spaCy model. The FlairRecognizer would return results from Flair models, the spaCy model would return NlpArtifacts such as POS and lemmas. :param model_path: Flair model path. """ from flair_recognizer import FlairRecognizer registry = RecognizerRegistry() registry.load_predefined_recognizers() if not spacy.util.is_package("en_core_web_sm"): spacy.cli.download("en_core_web_sm") # Using a small spaCy model + a Flair NER model flair_recognizer = FlairRecognizer(model_path=model_path) nlp_configuration = { "nlp_engine_name": "spacy", "models": [{"lang_code": "en", "model_name": "en_core_web_sm"}], } registry.add_recognizer(flair_recognizer) registry.remove_recognizer("SpacyRecognizer") nlp_engine = NlpEngineProvider(nlp_configuration=nlp_configuration).create_engine() return nlp_engine, registry def create_nlp_engine_with_azure_text_analytics(ta_key: str, ta_endpoint: str): """ Instantiate an NlpEngine with a TextAnalyticsWrapper and a small spaCy model. The TextAnalyticsWrapper would return results from calling Azure Text Analytics PII, the spaCy model would return NlpArtifacts such as POS and lemmas. :param ta_key: Azure Text Analytics key. :param ta_endpoint: Azure Text Analytics endpoint. """ from text_analytics_wrapper import TextAnalyticsWrapper if not ta_key or not ta_endpoint: raise RuntimeError("Please fill in the Text Analytics endpoint details") registry = RecognizerRegistry() registry.load_predefined_recognizers() ta_recognizer = TextAnalyticsWrapper(ta_endpoint=ta_endpoint, ta_key=ta_key) nlp_configuration = { "nlp_engine_name": "spacy", "models": [{"lang_code": "en", "model_name": "en_core_web_sm"}], } nlp_engine = NlpEngineProvider(nlp_configuration=nlp_configuration).create_engine() registry.add_recognizer(ta_recognizer) registry.remove_recognizer("SpacyRecognizer") return nlp_engine, registry