Update relation_extraction.py

relation_extraction.py

@@ -123,10 +123,24 @@ class relation_extraction(evaluate.Metric):
         # TODO: Download external resources if needed
         pass
 
-    def _compute(self, predictions, references, mode, show=False, relation_types=[]):
-        """Returns the scores"""
-        # TODO: Compute the different scores of the module
+    def _compute(self, predictions, references, mode, detailed_scores=False, relation_types=[]):
+        """
+        This method computes and returns various scoring metrics for the prediction model based on the mode specified, including Precision, Recall, F1-Score and others. It evaluates the model's predictions against the provided reference data. 
         
+        Parameters:
+            predictions: A list of predicted relations from the model.
+            references: A list of ground-truth or reference relations to compare the predictions against.
+            mode: Evaluation mode - 'strict' or 'boundaries'. 'strict' mode takes into account both entities type and their relationships
+                  while 'boundaries' mode only considers the entity spans of the relationships.
+            detailed_scores: Boolean value, if True it returns scores for each relation type specifically, 
+                                      if False it returns the overall scores.
+            relation_types: A list of relation types to consider while evaluating. If not provided, relation types will be constructed 
+                            from the ground truth or reference data.
+        
+        Returns:
+            A dictionary mapping each entity type to its respective scoring metrics such as Precision, Recall, F1 Score.
+        """
+    
         predictions = convert_format(predictions)
         references = convert_format(references)
         
@@ -207,7 +221,7 @@ class relation_extraction(evaluate.Metric):
         scores["ALL"]["Macro_p"] = np.mean([scores[ent_type]["p"] for ent_type in relation_types])
         scores["ALL"]["Macro_r"] = np.mean([scores[ent_type]["r"] for ent_type in relation_types])
 
-        if show:
+        if detailed_scores:
             return scores
             
         return scores["ALL"]