Update README.md

README.md

@@ -9,11 +9,21 @@ We fine-tuned BERT to train Vietnam tourism dataset for a question answering sys
 
     model = AutoModelForTokenClassification.from_pretrained("truongphan/vntourismNER")
     
+    custom_labels = [
+    "O", "B-TA", "I-TA", "B-PRO", "I-PRO", "B-TEM", "I-TEM", "B-COM", "I-COM", "B-PAR", "I-PAR", "B-CIT", "I-CIT",
+    "B-MOU", "I-MOU", "B-HAM", "I-HAM", "B-AWA", "I-AWA", "B-VIS", "I-VIS", "B-FES", "I-FES", "B-ISL", "I-ISL",
+    "B-TOW", "I-TOW", "B-VIL", "I-VIL", "B-CHU", "I-CHU", "B-PAG", "I-PAG", "B-BEA", "I-BEA", "B-WAR", "I-WAR",
+    "B-WAT", "I-WAT", "B-SA", "I-SA", "B-SER", "I-SER", "B-STR", "I-STR", "B-NUN", "I-NUN", "B-PAL", "I-PAL",
+    "B-VOL", "I-VOL", "B-HIL", "I-HIL", "B-MAR", "I-MAR", "B-VAL", "I-VAL", "B-PROD", "I-PROD", "B-DIS", "I-DIS",
+    "B-FOO", "I-FOO", "B-DISH", "I-DISH", "B-DRI", "I-DRI"
+    ]
     line = "King Garden is located in Thanh Thuy, Phu Tho"
-    l.append(line)
-    
-    predictions, raw_outputs = model.predict(l)
-    print(predictions)
+
+    nlp = pipeline('ner', model=model, tokenizer=tokenizer)
+
+    ner_rs = nlp(line)
+    for k in ner_rs:
+      print(custom_labels[int(str(k['entity']).replace('LABEL_',''))], '-', k['word'])
     
 
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