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import logging
from typing import Optional, List, Tuple, Set
from presidio_analyzer import (
RecognizerResult,
EntityRecognizer,
AnalysisExplanation,
)
from presidio_analyzer.nlp_engine import NlpArtifacts
logger = logging.getLogger("presidio-analyzer")
try:
from transformers import (
AutoTokenizer,
AutoModelForTokenClassification,
pipeline,
models,
)
from transformers.models.bert.modeling_bert import BertForTokenClassification
except ImportError:
logger.error("transformers is not installed")
class TransformersRecognizer(EntityRecognizer):
"""
Wrapper for a transformers model, if needed to be used within Presidio Analyzer.
:example:
>from presidio_analyzer import AnalyzerEngine, RecognizerRegistry
>transformers_recognizer = TransformersRecognizer()
>registry = RecognizerRegistry()
>registry.add_recognizer(transformers_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",
"ORGANIZATION",
"AGE",
"ID",
"PHONE_NUMBER",
"EMAIL",
"DATE",
]
DEFAULT_EXPLANATION = "Identified as {} by transformers's Named Entity Recognition"
CHECK_LABEL_GROUPS = [
({"LOCATION"}, {"LOC", "HOSP"}),
({"PERSON"}, {"PER", "PERSON", "STAFF","PATIENT"}),
({"ORGANIZATION"}, {"ORGANIZATION", "ORG", "PATORG"}),
({"AGE"}, {"AGE"}),
({"ID"}, {"ID"}),
({"EMAIL"}, {"EMAIL"}),
({"DATE"}, {"DATE"}),
({"PHONE_NUMBER"}, {"PHONE"}),
]
PRESIDIO_EQUIVALENCES = {
"PER": "PERSON",
"LOC": "LOCATION",
"ORG": "ORGANIZATION",
"AGE": "AGE",
"ID": "ID",
"EMAIL": "EMAIL",
"PATIENT": "PERSON",
"STAFF": "PERSON",
"HOSP": "LOCATION",
"PATORG": "ORGANIZATION",
"DATE": "DATE_TIME",
"PHONE": "PHONE_NUMBER",
}
DEFAULT_MODEL_PATH = "obi/deid_roberta_i2b2"
def __init__(
self,
supported_entities: Optional[List[str]] = None,
check_label_groups: Optional[Tuple[Set, Set]] = None,
model: Optional[BertForTokenClassification] = None,
model_path: Optional[str] = None,
):
if not model and not model_path:
model_path = self.DEFAULT_MODEL_PATH
logger.warning(
f"Both 'model' and 'model_path' arguments are None. Using default model_path={model_path}"
)
if model and model_path:
logger.warning(
f"Both 'model' and 'model_path' arguments were provided. Ignoring the model_path"
)
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 pipeline(
"ner",
model=AutoModelForTokenClassification.from_pretrained(model_path),
tokenizer=AutoTokenizer.from_pretrained(model_path),
aggregation_strategy="simple",
)
)
super().__init__(
supported_entities=supported_entities, name="transformers 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 transformers 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.
:return: The list of Presidio RecognizerResult constructed from the recognized
transformers detections.
"""
results = []
ner_results = self.model(text)
# 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 res in ner_results:
if not self.__check_label(
entity, res["entity_group"], self.check_label_groups
):
continue
textual_explanation = self.DEFAULT_EXPLANATION.format(
res["entity_group"]
)
explanation = self.build_transformers_explanation(
round(res["score"], 2), textual_explanation
)
transformers_result = self._convert_to_recognizer_result(
res, explanation
)
results.append(transformers_result)
return results
def _convert_to_recognizer_result(self, res, explanation) -> RecognizerResult:
entity_type = self.PRESIDIO_EQUIVALENCES.get(
res["entity_group"], res["entity_group"]
)
transformers_score = round(res["score"], 2)
transformers_results = RecognizerResult(
entity_type=entity_type,
start=res["start"],
end=res["end"],
score=transformers_score,
analysis_explanation=explanation,
)
return transformers_results
def build_transformers_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
transformers_recognizer = (
TransformersRecognizer()
) # This would download a large (~500Mb) model on the first run
registry = RecognizerRegistry()
registry.add_recognizer(transformers_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)
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