p208p2002/bart-drcd-qg-hl

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.

Features

• 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

• BART (base on uer/bart-base-chinese-cluecorpussmall)

Expriments

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

   https://pytorch.org/get-started/locally/

2. Install packages pip install -r requirements.txt

3. Setup scorer python setup_scorer.py

4. Download dataset python init_dataset.py

Training

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
  --server
  --run_test
  -fc FROM_CHECKPOINT, --from_checkpoint FROM_CHECKPOINT

Deploy

Start up

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

Request example

curl --location --request POST 'http://127.0.0.1:5000/' \
--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?"}