update demo.

examples/macbert/model_correction_pipeline_demo.py

@@ -30,19 +30,17 @@
         '因为爸爸在看录音机，所以我没得看',
         '不过在许多传统国家，女人向未得到平等',
         '我想喝小明同学。',  # 漏召回
-        '直接采用君主常用名，如赵昺。',  # 人名误杀
     ]
 
     model1 = MacBertCorrector()
     # add confusion corrector for post process
-    confusion_dict = {"喝小明同学": "喝小茗同学", "老人让坐": "老人让座", "平净": "平静", "分知": "分支",
-                      "赵昺": "赵昺"}
+    confusion_dict = {"喝小明同学": "喝小茗同学", "老人让坐": "老人让座", "平净": "平静", "分知": "分支"}
     model2 = ConfusionCorrector(custom_confusion_path_or_dict=confusion_dict)
     for line in error_sentences:
         r1 = model1.correct(line)
         correct_sent = r1['target']
         print("query:{} => {} err:{}".format(line, correct_sent, r1['errors']))
-        r2 = model2.correct(line)
+        r2 = model2.correct(correct_sent)
         corrected_sent2 = r2['target']
         if corrected_sent2 != correct_sent:
-            print("update, query:{} => {} err:{}".format(line, corrected_sent2, r2['errors']))
+            print("update, query:{} => {} err:{}".format(correct_sent, corrected_sent2, r2['errors']))

pycorrector/macbert/macbert_corrector.py

@@ -30,7 +30,7 @@ def __init__(self, model_name_or_path="shibing624/macbert4csc-base-chinese"):
         logger.debug("Use device: {}".format(device))
         logger.debug('Loaded macbert4csc model: %s, spend: %.3f s.' % (model_name_or_path, time.time() - t1))
 
-    def _predict(self, sentences, threshold=0.75, batch_size=32, silent=True):
+    def _predict(self, sentences, threshold=0.7, batch_size=32, silent=True):
         """Predict sentences with macbert4csc model"""
         corrected_sents = []
         for batch in tqdm(