Model Description

This model is intended to be used for QA in the Vietnamese language so the valid set is Vietnamese only (but English works fine). The evaluation result below uses the VLSP MRC 2021 test set. This experiment achieves TOP 1 on the leaderboard.

Model EM F1
large public_test_set 85.847 83.826
large private_test_set 82.072 78.071
Public leaderboard Private leaderboard

MRCQuestionAnswering using XLM-RoBERTa as a pre-trained language model. By default, XLM-RoBERTa will split word in to sub-words. But in my implementation, I re-combine sub-words representation (after encoded by BERT layer) into word representation using sum strategy.

Using pre-trained model

  • Hugging Face pipeline style (NOT using sum features strategy).
from transformers import pipeline
# model_checkpoint = "nguyenvulebinh/vi-mrc-large"
model_checkpoint = "nguyenvulebinh/vi-mrc-base"
nlp = pipeline('question-answering', model=model_checkpoint,
                   tokenizer=model_checkpoint)
QA_input = {
  'question': "Bình là chuyên gia về gì ?",
  'context': "Bình Nguyễn là một người đam mê với lĩnh vực xử lý ngôn ngữ tự nhiên . Anh nhận chứng chỉ Google Developer Expert năm 2020"
}
res = nlp(QA_input)
print('pipeline: {}'.format(res))
#{'score': 0.5782045125961304, 'start': 45, 'end': 68, 'answer': 'xử lý ngôn ngữ tự nhiên'}
from infer import tokenize_function, data_collator, extract_answer
from model.mrc_model import MRCQuestionAnswering
from transformers import AutoTokenizer

# model_checkpoint = "nguyenvulebinh/vi-mrc-large"
model_checkpoint = "nguyenvulebinh/vi-mrc-base"
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
model = MRCQuestionAnswering.from_pretrained(model_checkpoint)

QA_input = {
  'question': "Bình được công nhận với danh hiệu gì ?",
  'context': "Bình Nguyễn là một người đam mê với lĩnh vực xử lý ngôn ngữ tự nhiên . Anh nhận chứng chỉ Google Developer Expert năm 2020"
}

inputs = [tokenize_function(*QA_input)]
inputs_ids = data_collator(inputs)
outputs = model(**inputs_ids)
answer = extract_answer(inputs, outputs, tokenizer)

print(answer)
# answer: Google Developer Expert. Score start: 0.9926977753639221, Score end: 0.9909810423851013

About

Built by Binh Nguyen Follow For more details, visit the project repository. GitHub stars

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