Update presidio_nlp_engine_config.py

presidio_nlp_engine_config.py

@@ -48,32 +48,7 @@ def create_nlp_engine_with_spacy(
     return nlp_engine, registry
 
 
-def create_nlp_engine_with_stanza(
-    model_path: str,
-) -> Tuple[NlpEngine, RecognizerRegistry]:
-    """
-    Instantiate an NlpEngine with a stanza model
-    :param model_path: path to model / model name.
-    """
-    nlp_configuration = {
-        "nlp_engine_name": "stanza",
-        "models": [{"lang_code": "en", "model_name": model_path}],
-        "ner_model_configuration": {
-            "model_to_presidio_entity_mapping": {
-                "PER": "PERSON",
-                "PERSON": "PERSON",
-                "NORP": "NRP",
-                "FAC": "FACILITY",
-                "LOC": "LOCATION",
-                "GPE": "LOCATION",
-                "LOCATION": "LOCATION",
-                "ORG": "ORGANIZATION",
-                "ORGANIZATION": "ORGANIZATION",
-                "DATE": "DATE_TIME",
-                "TIME": "DATE_TIME",
-            }
-        },
-    }
+
 
     nlp_engine = NlpEngineProvider(nlp_configuration=nlp_configuration).create_engine()
 
@@ -150,66 +125,3 @@ def create_nlp_engine_with_transformers(
 
     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()
-
-    # there is no official Flair NlpEngine, hence we load it as an additional recognizer
-
-    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_ai_language(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 azure_ai_language_wrapper import AzureAIServiceWrapper
-
-    if not ta_key or not ta_endpoint:
-        raise RuntimeError("Please fill in the Text Analytics endpoint details")
-
-    registry = RecognizerRegistry()
-    registry.load_predefined_recognizers()
-
-    azure_ai_language_recognizer = AzureAIServiceWrapper(
-        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(azure_ai_language_recognizer)
-    registry.remove_recognizer("SpacyRecognizer")
-
-    return nlp_engine, registry