Vi Mrc Base

🚀 Question Answering Model

This is a question answering model supporting Vietnamese and English, fine - tuned on multiple datasets for extractive QA tasks.

🚀 Quick Start

You can quickly start using this pre - trained model through the following ways:

Using Hugging Face Pipeline

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'}

More Accurate Infer Process

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

✨ Features

• Multi - language Support: Supports both Vietnamese and English for question - answering tasks.
• Fine - tuned on Multiple Datasets: Fine - tuned on a combination of English and Vietnamese datasets, including Squad 2.0, mailong25, UIT - ViQuAD, and MultiLingual Question Answering.
• High - performance: Achieves good evaluation results on the Vietnamese dataset.

📚 Documentation

Model Description

• Language model: XLM - RoBERTa
• Fine - tune: MRCQuestionAnswering
• Language: Vietnamese, English
• Downstream - task: Extractive QA
• Dataset (combine English and Vietnamese):
  • Squad 2.0
  • mailong25
  • UIT - ViQuAD
  • MultiLingual Question Answering

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 using 10% of the Vietnamese dataset.

Model Evaluation

The evaluation results are based on 10% of the Vietnamese dataset, showing the model's performance in terms of Exact Match (EM) and F1 score.

📄 License

This model is released under the cc - by - nc - 4.0 license.

About

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