metadata
datasets:
- drcd
tags:
- question-generation
widget:
- text: 哈利·波特是英國作家[HL]羅琳[HL]撰寫的七部幻想小說系列
Transformer QG on DRCD
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
If you don't have pytorch 1.6+ please install or update first
Install packages
pip install -r requirements.txt
Setup scorer
python setup_scorer.py
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=哈利·波特是英國作家[HL]羅琳[HL]撰寫的七部幻想小說系列'
{"predict": "誰撰寫哈利·波特?"}