included examples in description

absa_evaluator.py

@@ -14,7 +14,7 @@ _CITATION = """
 _DESCRIPTION = """
 This module provides evaluation metrics for Aspect-Based Sentiment Analysis (ABSA). 
 The metrics include precision, recall, and F1 score for both aspect terms and category detection.
-Additionally it calculates de accuracy for polarities from aspect terms and category detection.
+Additionally it calculates the accuracy for polarities from aspect terms and category detection.
 ABSA evaluates the capability of a model to identify and correctly classify the sentiment of specific aspects within a text.
 """
 
@@ -30,6 +30,21 @@ Args:
             - 'category': Category
             - 'polarity': polarity of the category
     references: List of ABSA references with the same structure as predictions.
+
+Examples for predictions:
+        [
+            {
+                "aspects": [
+                    {"term": "battery life", "polarity": "positive"},
+                    {"term": "camera", "polarity": "negative"}
+                ],
+                "category": [
+                    {"category": "Battery", "polarity": "positive"},
+                    {"category": "Camera", "polarity": "negative"}
+                ]
+            }
+        ]
+
 Returns:
     term_extraction_results: f1 score, precision and recall for aspect terms
     term_polarity_results_accuracy: accuracy for polarities on aspect terms
@@ -144,7 +159,7 @@ class AbsaEvaluator(evaluate.Metric):
         Returns:
         - A dictionary containing the precision, recall, F1 score, and counts of common, retrieved, and relevant.
 
-        link for code: link for this code: https://github.com/davidsbatista/Aspect-Based-Sentiment-Analysis/blob/1d9c8ec1131993d924e96676fa212db6b53cb870/libraries/baselines.py#L387
+        link for this code: https://github.com/davidsbatista/Aspect-Based-Sentiment-Analysis/blob/1d9c8ec1131993d924e96676fa212db6b53cb870/libraries/baselines.py#L387
         """
         b = 1
         common, relevant, retrieved = 0.0, 0.0, 0.0