Update DiaBLa.py

DiaBLa.py

@@ -86,20 +86,20 @@ class Diabla(datasets.GeneratorBasedBuilder):
                             'translation_model': datasets.Value('string'),
                             'final_evaluation_user1': datasets.features.Sequence(
                                 {
-                                    'style': ClassLabel(num_classes=3, names=['poor', 'average', 'good', 'excellent']),
-                                    'coherence': ClassLabel(num_classes=3, names=['poor', 'average', 'good', 'excellent']),
-                                    'grammaticality': ClassLabel(num_classes=3, names=['poor', 'average', 'good', 'excellent']),
-                                    'meaning': ClassLabel(num_classes=3, names=['poor', 'average', 'good', 'excellent']),
-                                    'word_choice': ClassLabel(num_classes=3, names=['poor', 'average', 'good', 'excellent'])
+                                    'style': ClassLabel(num_classes=4, names=['poor', 'average', 'good', 'excellent']),
+                                    'coherence': ClassLabel(num_classes=4, names=['poor', 'average', 'good', 'excellent']),
+                                    'grammaticality': ClassLabel(num_classes=4, names=['poor', 'average', 'good', 'excellent']),
+                                    'meaning': ClassLabel(num_classes=4, names=['poor', 'average', 'good', 'excellent']),
+                                    'word_choice': ClassLabel(num_classes=4, names=['poor', 'average', 'good', 'excellent'])
                                  }
                              ),
                             'final_evaluation_user2': datasets.features.Sequence(
                                 {
-                                    'style': ClassLabel(num_classes=3, names=['poor', 'average', 'good', 'excellent']),
-                                    'coherence': ClassLabel(num_classes=3, names=['poor', 'average', 'good', 'excellent']),
-                                    'grammaticality': ClassLabel(num_classes=3, names=['poor', 'average', 'good', 'excellent']),
-                                    'meaning': ClassLabel(num_classes=3, names=['poor', 'average', 'good', 'excellent']),
-                                    'word_choice': ClassLabel(num_classes=3, names=['poor', 'average', 'good', 'excellent'])
+                                    'style': ClassLabel(num_classes=4, names=['poor', 'average', 'good', 'excellent']),
+                                    'coherence': ClassLabel(num_classes=4, names=['poor', 'average', 'good', 'excellent']),
+                                    'grammaticality': ClassLabel(num_classes=4, names=['poor', 'average', 'good', 'excellent']),
+                                    'meaning': ClassLabel(num_classes=4, names=['poor', 'average', 'good', 'excellent']),
+                                    'word_choice': ClassLabel(num_classes=4, names=['poor', 'average', 'good', 'excellent'])
                                  }
                              ),
                             'scenario': datasets.features.Sequence(