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Transformer QG on DRCD

請參閱 https://github.com/p208p2002/Transformer-QG-on-DRCD 獲得更多細節

The inputs of the model refers to

we integrate C and A into a new C' in the following form.
C' = [c1, c2, ..., [HL], a1, ..., a|A|, [HL], ..., c|C|]

Proposed by Ying-Hong Chan & Yao-Chung Fan. (2019). A Re-current BERT-based Model for Question Generation.


  • Fully pipline from fine-tune to evaluation
  • Support most of state of the art models
  • Fast deploy as a API server

DRCD dataset

台達閱讀理解資料集 Delta Reading Comprehension Dataset (DRCD) 屬於通用領域繁體中文機器閱讀理解資料集。 DRCD資料集從2,108篇維基條目中整理出10,014篇段落,並從段落中標註出30,000多個問題。

Available models


Model Bleu 1 Bleu 2 Bleu 3 Bleu 4 METEOR ROUGE-L
BART-HLSQG 34.25 27.70 22.43 18.13 23.58 36.88

Environment requirements

The hole development is based on Ubuntu system

  1. If you don't have pytorch 1.6+ please install or update first


  2. Install packages pip install -r requirements.txt

  3. Setup scorer python setup_scorer.py

  4. Download dataset python init_dataset.py


Seq2Seq LM

usage: train_seq2seq_lm.py [-h]
                           [--base_model {facebook/bart-base,facebook/bart-large,t5-small,t5-base,t5-large}]
                           [-d {squad,squad-nqg}] [--epoch EPOCH] [--lr LR]
                           [--dev DEV] [--server] [--run_test]
                           [-fc FROM_CHECKPOINT]

optional arguments:
  -h, --help            show this help message and exit
  --base_model {facebook/bart-base,facebook/bart-large,t5-small,t5-base,t5-large}
  -d {squad,squad-nqg}, --dataset {squad,squad-nqg}
  --epoch EPOCH
  --lr LR
  --dev DEV
  -fc FROM_CHECKPOINT, --from_checkpoint FROM_CHECKPOINT


Start up

python train_seq2seq_lm.py --server --base_model YOUR_BASE_MODEL --from_checkpoint FROM_CHECKPOINT

Request example

curl --location --request POST '' \
--header 'Content-Type: application/x-www-form-urlencoded' \
--data-urlencode 'context=[HL]伊隆·里夫·馬斯克[HL]是一名企業家和商業大亨'
{"predict": "哪一個人是一名企業家和商業大亨?"}
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