Vietnam Tourism Named Entity Recognition (English version)

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 in Transformers

from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("truongphan/vntourismNER")

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 province"

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'])


  1. Phuc Do, University of Information Technology, Ho Chi Minh national university, Vietnam


Link Google scholar

  1. Truong H. V. Phan, Van Lang university, Ho Chi Minh city, Vietnam


Link Google scholar


If you use the model in your work, please cite our paper

Phan, T.H.V., Do, P. NER2QUES: combining named entity recognition and sequence to sequence to automatically generating Vietnamese questions. Neural Comput & Applic (2021).

Downloads last month
Hosted inference API
Token Classification
This model can be loaded on the Inference API on-demand.