metadata
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: ko
datasets:
- lmqg/qag_koquad
pipeline_tag: text2text-generation
tags:
- questions and answers generation
widget:
- text: >-
1990년 영화 《 남부군 》에서 단역으로 영화배우 첫 데뷔에 이어 같은 해 KBS 드라마 《지구인》에서 단역으로 출연하였고 이듬해
MBC 《여명의 눈동자》를 통해 단역으로 출연하였다.
example_title: Questions & Answers Generation Example 1
model-index:
- name: lmqg/mt5-small-koquad-qag
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qag_koquad
type: default
args: default
metrics:
- name: QAAlignedF1Score-BERTScore (Question & Answer Generation)
type: qa_aligned_f1_score_bertscore_question_answer_generation
value: 74.23
- name: QAAlignedRecall-BERTScore (Question & Answer Generation)
type: qa_aligned_recall_bertscore_question_answer_generation
value: 74.2
- name: QAAlignedPrecision-BERTScore (Question & Answer Generation)
type: qa_aligned_precision_bertscore_question_answer_generation
value: 74.29
- name: QAAlignedF1Score-MoverScore (Question & Answer Generation)
type: qa_aligned_f1_score_moverscore_question_answer_generation
value: 75.06
- name: QAAlignedRecall-MoverScore (Question & Answer Generation)
type: qa_aligned_recall_moverscore_question_answer_generation
value: 75.04
- name: QAAlignedPrecision-MoverScore (Question & Answer Generation)
type: qa_aligned_precision_moverscore_question_answer_generation
value: 75.14
Model Card of lmqg/mt5-small-koquad-qag
This model is fine-tuned version of google/mt5-small for question & answer pair generation task on the lmqg/qag_koquad (dataset_name: default) via lmqg
.
Overview
- Language model: google/mt5-small
- Language: ko
- Training data: lmqg/qag_koquad (default)
- Online Demo: https://autoqg.net/
- Repository: https://github.com/asahi417/lm-question-generation
- Paper: https://arxiv.org/abs/2210.03992
Usage
- With
lmqg
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="ko", model="lmqg/mt5-small-koquad-qag")
# model prediction
question_answer_pairs = model.generate_qa("1990년 영화 《 남부군 》에서 단역으로 영화배우 첫 데뷔에 이어 같은 해 KBS 드라마 《지구인》에서 단역으로 출연하였고 이듬해 MBC 《여명의 눈동자》를 통해 단역으로 출연하였다.")
- With
transformers
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/mt5-small-koquad-qag")
output = pipe("1990년 영화 《 남부군 》에서 단역으로 영화배우 첫 데뷔에 이어 같은 해 KBS 드라마 《지구인》에서 단역으로 출연하였고 이듬해 MBC 《여명의 눈동자》를 통해 단역으로 출연하였다.")
Evaluation
- Metric (Question & Answer Generation): raw metric file
Score | Type | Dataset | |
---|---|---|---|
QAAlignedF1Score (BERTScore) | 74.23 | default | lmqg/qag_koquad |
QAAlignedF1Score (MoverScore) | 75.06 | default | lmqg/qag_koquad |
QAAlignedPrecision (BERTScore) | 74.29 | default | lmqg/qag_koquad |
QAAlignedPrecision (MoverScore) | 75.14 | default | lmqg/qag_koquad |
QAAlignedRecall (BERTScore) | 74.2 | default | lmqg/qag_koquad |
QAAlignedRecall (MoverScore) | 75.04 | default | lmqg/qag_koquad |
Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qag_koquad
- dataset_name: default
- input_types: ['paragraph']
- output_types: ['questions_answers']
- prefix_types: None
- model: google/mt5-small
- max_length: 512
- max_length_output: 256
- epoch: 13
- batch: 8
- lr: 0.0005
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 16
- label_smoothing: 0.0
The full configuration can be found at fine-tuning config file.
Citation
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}