Fix tags

utilities_language_bert/esp_sentence_bert.py

@@ -65,7 +65,7 @@ class SENTENCE:
                     tags['Number'] = original_token1_tags.get('Number')
                     tags['Gender'] = None
                 else:
-                    tags = original_token1_tags | original_token2_tags
+                    tags = {**original_token1_tags, **original_token2_tags}
                 not_ner = True if (original_token1.ent_type == 0 and original_token2.ent_type == 0) else False
                 target_word = {
                     'masked_sentence': self.original.replace(f'{original_token1.text} {original_token2.text}',
@@ -116,7 +116,7 @@ class SENTENCE:
                         tags['Number'] = parse_utw[0].morph.to_dict().get('Number')
                         tags['Gender'] = None
                     else:
-                        tags = parse_utw[0].morph.to_dict() | parse_utw[1].morph.to_dict()
+                        tags = {**parse_utw[0].morph.to_dict(), **parse_utw[1].morph.to_dict()}
                     user_target_word_lemma = '_'.join([f'{token.lemma_}_{token.pos_}' for token in parse_utw])
                     user_target_word_pos = 'phrase'
                     user_target_word_tags = tags

utilities_language_w2v/esp_sentence_w2v.py

@@ -64,7 +64,7 @@ class SENTENCE:
                     tags['Number'] = original_token1_tags.get('Number')
                     tags['Gender'] = None
                 else:
-                    tags = original_token1_tags | original_token2_tags
+                    tags = {**original_token1_tags, **original_token2_tags}
                 not_ner = True if (original_token1.ent_type == 0 and original_token2.ent_type == 0) else False
                 target_word = {
                     'sentence_number': self.n_sentence,
@@ -112,7 +112,7 @@ class SENTENCE:
                         tags['Number'] = parse_utw[0].morph.to_dict().get('Number')
                         tags['Gender'] = None
                     else:
-                        tags = parse_utw[0].morph.to_dict() | parse_utw[1].morph.to_dict()
+                        tags = {**parse_utw[0].morph.to_dict(), **parse_utw[1].morph.to_dict()}
                     user_target_word_lemma = '_'.join([f'{token.lemma_}_{token.pos_}' for token in parse_utw])
                     user_target_word_pos = 'phrase'
                     user_target_word_tags = tags