README.md

README.md

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+# Vietnam Tourism Named Entity Recognition
+We fine-tuned BERT to train Vietnam tourism dataset for a question answering system. The model was called NER2QUES because it detected tourism NER in a sentence. From that, the system generated questions corresponding to NER types.
+# How to use
+You can use the model directly with a pipeline
+    from simpletransformers.ner import NERModel, NERArgs
+  
+    line = "King Garden is located in Thanh Thuy, Phu Tho"
+    model_name = '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"
+    ]
+
+    model_args = NERArgs()
+    model = NERModel("bert", model_name, args=model_args, labels=custom_labels)
+    l.append(line)
+    predictions, raw_outputs = model.predict(l)
+    print(predictions)
+  
+# Authors
+
+1. Phuc Do, University of Information Technology, Ho Chi Minh national university, Vietnam
+2. Truong H. V. Phan, Van Lang university, Ho Chi Minh city, Vietnam