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Upload transformers_recognizer.py
Browse files- transformers_recognizer.py +252 -0
transformers_recognizer.py
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1 |
+
import logging
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2 |
+
from typing import Optional, List, Tuple, Set
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+
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+
from presidio_analyzer import (
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+
RecognizerResult,
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+
EntityRecognizer,
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+
AnalysisExplanation,
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)
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+
from presidio_analyzer.nlp_engine import NlpArtifacts
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+
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logger = logging.getLogger("presidio-analyzer")
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+
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+
try:
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+
from transformers import (
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AutoTokenizer,
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+
AutoModelForTokenClassification,
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+
pipeline,
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+
models,
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+
)
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+
from transformers.models.bert.modeling_bert import BertForTokenClassification
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except ImportError:
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+
logger.error("transformers is not installed")
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+
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+
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+
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+
class TransformersRecognizer(EntityRecognizer):
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+
"""
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+
Wrapper for a transformers model, if needed to be used within Presidio Analyzer.
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+
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+
:example:
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+
>from presidio_analyzer import AnalyzerEngine, RecognizerRegistry
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+
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+
>transformers_recognizer = TransformersRecognizer()
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+
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>registry = RecognizerRegistry()
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>registry.add_recognizer(transformers_recognizer)
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+
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>analyzer = AnalyzerEngine(registry=registry)
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+
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>results = analyzer.analyze(
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+
> "My name is Christopher and I live in Irbid.",
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> language="en",
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+
> return_decision_process=True,
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+
>)
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+
>for result in results:
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+
> print(result)
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+
> print(result.analysis_explanation)
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+
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+
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+
"""
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+
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+
ENTITIES = [
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"LOCATION",
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+
"PERSON",
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+
"ORGANIZATION",
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+
"AGE",
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+
"ID",
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+
"PHONE_NUMBER",
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+
"EMAIL",
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+
"DATE",
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+
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+
]
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+
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+
DEFAULT_EXPLANATION = "Identified as {} by transformers's Named Entity Recognition"
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+
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+
CHECK_LABEL_GROUPS = [
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+
({"LOCATION"}, {"LOC", "HOSP"}),
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+
({"PERSON"}, {"PER", "PERSON", "STAFF","PATIENT"}),
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({"ORGANIZATION"}, {"ORGANIZATION", "ORG", "PATORG"}),
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+
({"AGE"}, {"AGE"}),
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({"ID"}, {"ID"}),
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+
({"EMAIL"}, {"EMAIL"}),
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+
({"DATE"}, {"DATE"}),
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+
({"PHONE_NUMBER"}, {"PHONE"}),
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+
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+
]
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+
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+
PRESIDIO_EQUIVALENCES = {
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"PER": "PERSON",
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"LOC": "LOCATION",
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"ORG": "ORGANIZATION",
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+
"AGE": "AGE",
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+
"ID": "ID",
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+
"EMAIL": "EMAIL",
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+
"PATIENT": "PERSON",
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+
"STAFF": "PERSON",
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+
"HOSP": "LOCATION",
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+
"PATORG": "ORGANIZATION",
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+
"DATE": "DATE_TIME",
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+
"PHONE": "PHONE_NUMBER",
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+
}
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+
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+
DEFAULT_MODEL_PATH = "obi/deid_roberta_i2b2"
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+
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+
def __init__(
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+
self,
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+
supported_entities: Optional[List[str]] = None,
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+
check_label_groups: Optional[Tuple[Set, Set]] = None,
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+
model: Optional[BertForTokenClassification] = None,
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+
model_path: Optional[str] = None,
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+
):
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+
if not model and not model_path:
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+
model_path = self.DEFAULT_MODEL_PATH
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+
logger.warning(
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+
f"Both 'model' and 'model_path' arguments are None. Using default model_path={model_path}"
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+
)
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+
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+
if model and model_path:
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+
logger.warning(
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+
f"Both 'model' and 'model_path' arguments were provided. Ignoring the model_path"
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+
)
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+
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+
self.check_label_groups = (
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+
check_label_groups if check_label_groups else self.CHECK_LABEL_GROUPS
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+
)
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+
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+
supported_entities = supported_entities if supported_entities else self.ENTITIES
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+
self.model = (
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+
model
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+
if model
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+
else pipeline(
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+
"ner",
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+
model=AutoModelForTokenClassification.from_pretrained(model_path),
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+
tokenizer=AutoTokenizer.from_pretrained(model_path),
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+
aggregation_strategy="simple",
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+
)
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+
)
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+
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+
super().__init__(
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+
supported_entities=supported_entities, name="transformers Analytics",
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+
)
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+
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+
def load(self) -> None:
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+
"""Load the model, not used. Model is loaded during initialization."""
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+
pass
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+
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+
def get_supported_entities(self) -> List[str]:
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+
"""
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+
Return supported entities by this model.
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+
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+
:return: List of the supported entities.
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+
"""
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+
return self.supported_entities
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+
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+
# Class to use transformers with Presidio as an external recognizer.
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146 |
+
def analyze(
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+
self, text: str, entities: List[str], nlp_artifacts: NlpArtifacts = None
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148 |
+
) -> List[RecognizerResult]:
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149 |
+
"""
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150 |
+
Analyze text using Text Analytics.
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+
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+
:param text: The text for analysis.
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+
:param entities: Not working properly for this recognizer.
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+
:param nlp_artifacts: Not used by this recognizer.
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+
:return: The list of Presidio RecognizerResult constructed from the recognized
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156 |
+
transformers detections.
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+
"""
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+
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+
results = []
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+
ner_results = self.model(text)
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161 |
+
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162 |
+
# If there are no specific list of entities, we will look for all of it.
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163 |
+
if not entities:
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+
entities = self.supported_entities
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165 |
+
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166 |
+
for entity in entities:
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167 |
+
if entity not in self.supported_entities:
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168 |
+
continue
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169 |
+
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170 |
+
for res in ner_results:
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171 |
+
if not self.__check_label(
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172 |
+
entity, res["entity_group"], self.check_label_groups
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173 |
+
):
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174 |
+
continue
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175 |
+
textual_explanation = self.DEFAULT_EXPLANATION.format(
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176 |
+
res["entity_group"]
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177 |
+
)
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178 |
+
explanation = self.build_transformers_explanation(
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179 |
+
round(res["score"], 2), textual_explanation
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180 |
+
)
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181 |
+
transformers_result = self._convert_to_recognizer_result(
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182 |
+
res, explanation
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183 |
+
)
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184 |
+
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185 |
+
results.append(transformers_result)
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186 |
+
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187 |
+
return results
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188 |
+
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189 |
+
def _convert_to_recognizer_result(self, res, explanation) -> RecognizerResult:
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190 |
+
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191 |
+
entity_type = self.PRESIDIO_EQUIVALENCES.get(
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192 |
+
res["entity_group"], res["entity_group"]
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193 |
+
)
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194 |
+
transformers_score = round(res["score"], 2)
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195 |
+
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196 |
+
transformers_results = RecognizerResult(
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+
entity_type=entity_type,
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198 |
+
start=res["start"],
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199 |
+
end=res["end"],
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+
score=transformers_score,
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201 |
+
analysis_explanation=explanation,
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202 |
+
)
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203 |
+
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204 |
+
return transformers_results
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205 |
+
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206 |
+
def build_transformers_explanation(
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207 |
+
self, original_score: float, explanation: str
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208 |
+
) -> AnalysisExplanation:
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209 |
+
"""
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210 |
+
Create explanation for why this result was detected.
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211 |
+
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212 |
+
:param original_score: Score given by this recognizer
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213 |
+
:param explanation: Explanation string
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214 |
+
:return:
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215 |
+
"""
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216 |
+
explanation = AnalysisExplanation(
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217 |
+
recognizer=self.__class__.__name__,
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218 |
+
original_score=original_score,
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219 |
+
textual_explanation=explanation,
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220 |
+
)
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221 |
+
return explanation
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222 |
+
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223 |
+
@staticmethod
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224 |
+
def __check_label(
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225 |
+
entity: str, label: str, check_label_groups: Tuple[Set, Set]
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226 |
+
) -> bool:
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227 |
+
return any(
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228 |
+
[entity in egrp and label in lgrp for egrp, lgrp in check_label_groups]
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229 |
+
)
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230 |
+
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231 |
+
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232 |
+
if __name__ == "__main__":
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233 |
+
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234 |
+
from presidio_analyzer import AnalyzerEngine, RecognizerRegistry
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235 |
+
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236 |
+
transformers_recognizer = (
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237 |
+
TransformersRecognizer()
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238 |
+
) # This would download a large (~500Mb) model on the first run
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239 |
+
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240 |
+
registry = RecognizerRegistry()
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241 |
+
registry.add_recognizer(transformers_recognizer)
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242 |
+
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243 |
+
analyzer = AnalyzerEngine(registry=registry)
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244 |
+
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245 |
+
results = analyzer.analyze(
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246 |
+
"My name is Christopher and I live in Irbid.",
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247 |
+
language="en",
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248 |
+
return_decision_process=True,
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249 |
+
)
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250 |
+
for result in results:
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251 |
+
print(result)
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252 |
+
print(result.analysis_explanation)
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