updated the docs.

langtest/transform/accuracy.py

@@ -2,7 +2,7 @@
 from collections import defaultdict
 import pandas as pd
 from abc import ABC, abstractmethod
-from typing import Any, DefaultDict, Dict, List, Type
+from typing import Any, DefaultDict, Dict, List, Type, Union
 
 from langtest.modelhandler.modelhandler import ModelAPI
 from langtest.transform.base import ITests
@@ -1213,9 +1213,16 @@ async def run(
         return []
 
     @staticmethod
-    def preprocess(y_true, y_pred):
+    def preprocess(y_true: List, y_pred: List):
         """
         Preprocesses the input data for the degradation analysis.
+
+        Args:
+            y_true (List): The true labels.
+            y_pred (List): The predicted labels.
+
+        Returns:
+            Tuple[pd.Series, pd.Series]: The preprocessed true and predicted labels.
         """
 
         if isinstance(y_true, list):

langtest/utils/util_metrics.py

@@ -407,9 +407,19 @@ def calculate_f1_score_multi_label(
 
 def combine_labels(labels: List[str]) -> List[str]:
     """
-    Combines labels for degradation analysis.
-    input labels: ["B-ORG", "I-ORG", "B-PER", "I-PER"]
-    output labels: ["ORG", "PER"]
+    Combine labels by removing the BIO tags and keeping only the entity type.
+
+    Args:
+        labels (List[str]): List of strings or a string. Labels can be in the format of BIO tags.
+                            Example: ["B-ORG", "I-ORG", "B-PER", "I-PER"]
+
+    Raises:
+        ValueError: If the input is not a list or a string.
+
+
+    Returns:
+        labels (List[str]): List of entity types without the BIO tags.
+
     """
     try:
         output_list = []