Feature/add support for abbreviation test

nlptest/transform/robustness.py

@@ -6,7 +6,7 @@
 from abc import ABC, abstractmethod
 from typing import Dict, List, Optional
 from nlptest.modelhandler.modelhandler import ModelFactory
-from .utils import (CONTRACTION_MAP, TYPO_FREQUENCY, default_user_prompt ,ocr_typo_dict)
+from .utils import (CONTRACTION_MAP, TYPO_FREQUENCY, default_user_prompt ,ocr_typo_dict, abbreviation_dict)
 from ..utils.custom_types import Sample, Span, Transformation
 from typing import List
 
@@ -835,4 +835,60 @@ def ocr_typo(regex, text):
             else:
                 sample.test_case = ocr_typo(r'[^,\s.!?]+', sample.original)
 
-        return sample_list
+        return sample_list
+
+class AbbreviationInsertion(BaseRobustness):
+    alias_name = "add_abbreviation"
+
+    @staticmethod
+    def transform(sample_list: List[Sample]) -> List[Sample]:
+        """
+        Transforms the given sample list by inserting abbreviations.
+
+        Args:
+            sample_list (List[Sample]): The list of samples to transform.
+
+        Returns:
+            List[Sample]: The transformed list of samples.
+        """
+
+        def insert_abbreviation(text):
+            perturbed_text = text
+            transformations = [] 
+
+            for abbreviation, expansions in abbreviation_dict.items():
+                for expansion in expansions:
+                    pattern = r"(?i)\b" + re.escape(expansion) + r"\b"
+                    corrected_token = abbreviation
+                    perturbed_text = re.sub(pattern, corrected_token, perturbed_text)
+                    matches = re.finditer(pattern, text)
+                    for match in matches:
+                        start = match.start()
+                        end = match.end()
+                        token = text[start:end]
+                        if corrected_token != token:
+                            if sample.task in ("ner", "text-classification"):
+                                transformations.append(
+                                    Transformation(
+                                        original_span=Span(start=start, end=end, word=token),
+                                        new_span=Span(start=start, end=start + len(corrected_token), word=corrected_token),
+                                        ignore=False
+                                    )
+                                ) 
+            sample.category = "robustness"
+            if sample.task in ("ner", "text-classification"):
+                sample.transformations = transformations 
+            return perturbed_text
+
+        for sample in sample_list:
+            if sample.task == 'question-answering':
+                sample.perturbed_question = insert_abbreviation(sample.original_question)
+
+                if "perturbed_context" in sample.__annotations__:
+                    sample.perturbed_context = insert_abbreviation(sample.original_context)
+
+            else:
+                sample.test_case = insert_abbreviation(sample.original)
+            sample.category = "robustness"
+
+        return sample_list