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handler.py

@@ -8,7 +8,7 @@ class EndpointHandler:
         self.model = AutoModel.from_pretrained(
             path,
             trust_remote_code=True,
-            # do_syntax=True, do_prefix=False, do_morph=False, do_ner=True, do_lex=True
+            do_syntax=True, do_prefix=False, do_morph=False, do_ner=True, do_lex=True
         )
         self.model.eval()
 
@@ -22,19 +22,19 @@ class EndpointHandler:
         """
         # return self.pipeline(data['inputs'])
         outputs = self.model.predict(data['inputs'], self.tokenizer, output_style='json')
-        # for i, output in enumerate(outputs):
-        #     lem = ' '.join([x['lex'] for x in output['tokens']])
-        #     ner = [
-        #         {
-        #             'word': ' '.join([x['lex'] for x in output['tokens'][x['token_start']:x['token_end'] + 1]]),
-        #             'entity_group': x['label'],
-        #             'token_start': x['token_start'],
-        #             'token_end': x['token_end']
-        #         }
-        #         for x in output['ner_entities']
-        #     ]
-        #     outputs[i] = {
-        #         'lex': lem,
-        #         'ner': ner
-        #     }
+        for i, output in enumerate(outputs):
+            lem = ' '.join([x['lex'] for x in output['tokens']])
+            ner = [
+                {
+                    'word': ' '.join([x['lex'] for x in output['tokens'][x['token_start']:x['token_end'] + 1]]),
+                    'entity_group': x['label'],
+                    'token_start': x['token_start'],
+                    'token_end': x['token_end']
+                }
+                for x in output['ner_entities']
+            ]
+            outputs[i] = {
+                'lex': lem,
+                'ner': ner
+            }
         return outputs