diff --git a/presidio_evaluator/models/base_model.py b/presidio_evaluator/models/base_model.py
index ae99780..bd07658 100644
--- a/presidio_evaluator/models/base_model.py
+++ b/presidio_evaluator/models/base_model.py
@@ -33,7 +33,7 @@ def __init__(
         self.verbose = verbose
 
     @abstractmethod
-    def predict(self, sample: InputSample) -> List[str]:
+    def predict(self, sample: InputSample, **kwargs) -> List[str]:
         """
         Abstract. Returns the predicted tokens/spans from the evaluated model
         :param sample: Sample to be evaluated
diff --git a/presidio_evaluator/models/presidio_analyzer_wrapper.py b/presidio_evaluator/models/presidio_analyzer_wrapper.py
index aa85abd..9598b2b 100644
--- a/presidio_evaluator/models/presidio_analyzer_wrapper.py
+++ b/presidio_evaluator/models/presidio_analyzer_wrapper.py
@@ -1,6 +1,6 @@
 from typing import List, Optional, Dict
 
-from presidio_analyzer import AnalyzerEngine
+from presidio_analyzer import AnalyzerEngine, EntityRecognizer
 
 from presidio_evaluator import InputSample, span_to_tag
 from presidio_evaluator.models import BaseModel
@@ -16,6 +16,9 @@ def __init__(
         score_threshold: float = 0.4,
         language: str = "en",
         entity_mapping: Optional[Dict[str, str]] = None,
+        ad_hoc_recognizers: Optional[List[EntityRecognizer]] = None,
+        context: Optional[List[str]] = None,
+        allow_list: Optional[List[str]] = None,
     ):
         """
         Evaluation wrapper for the Presidio Analyzer
@@ -29,25 +32,37 @@ def __init__(
         )
         self.score_threshold = score_threshold
         self.language = language
+        self.ad_hoc_recognizers = ad_hoc_recognizers
+        self.context = context
+        self.allow_list = allow_list
 
         if not analyzer_engine:
             analyzer_engine = AnalyzerEngine()
             self._update_recognizers_based_on_entities_to_keep(analyzer_engine)
         self.analyzer_engine = analyzer_engine
 
-    def predict(self, sample: InputSample) -> List[str]:
+    def predict(self, sample: InputSample, **kwargs) -> List[str]:
+        language = kwargs.get("language", self.language)
+        score_threshold = kwargs.get("score_threshold", self.score_threshold)
+        ad_hoc_recognizers = kwargs.get("ad_hoc_recognizers", self.ad_hoc_recognizers)
+        context = kwargs.get("context", self.context)
+        allow_list = kwargs.get("allow_list", self.allow_list)
 
         results = self.analyzer_engine.analyze(
             text=sample.full_text,
             entities=self.entities,
-            language=self.language,
-            score_threshold=self.score_threshold,
+            language=language,
+            score_threshold=score_threshold,
+            ad_hoc_recognizers=ad_hoc_recognizers,
+            context=context,
+            allow_list=allow_list,
+            **kwargs,
         )
         starts = []
         ends = []
         scores = []
         tags = []
-        #
+
         for res in results:
             starts.append(res.start)
             ends.append(res.end)