metadata

license: cc-by-4.0
metrics:
  - bleu4
  - meteor
  - rouge-l
  - bertscore
  - moverscore
language: ko
datasets:
  - lmqg/qg_koquad
pipeline_tag: text2text-generation
tags:
  - question generation
widget:
  - text: >-
      1990년 영화 《 <hl> 남부군 <hl> 》에서 단역으로 영화배우 첫 데뷔에 이어 같은 해 KBS 드라마 《지구인》에서 단역으로
      출연하였고 이듬해 MBC 《여명의 눈동자》를 통해 단역으로 출연하였다.
    example_title: Question Generation Example 1
  - text: >-
      백신이 없기때문에 예방책은 <hl> 살충제 <hl> 를 사용하면서 서식 장소(찻찬 받침, 배수로, 고인 물의 열린 저장소, 버려진
      타이어 등)의 수를 줄임으로써 매개체를 통제할 수 있다.
    example_title: Question Generation Example 2
  - text: <hl> 원테이크 촬영 <hl> 이기 때문에 한 사람이 실수를 하면 처음부터 다시 찍어야 하는 상황이 발생한다.
    example_title: Question Generation Example 3
model-index:
  - name: lmqg/mt5-small-koquad-qg
    results:
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_koquad
          type: default
          args: default
        metrics:
          - name: BLEU4 (Question Generation)
            type: bleu4_question_generation
            value: 10.57
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 25.64
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 27.52
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 82.89
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 82.49
          - name: QAAlignedF1Score-BERTScore (Gold Answer)
            type: qa_aligned_f1_score_bertscore_gold_answer
            value: 87.52
          - name: QAAlignedRecall-BERTScore (Gold Answer)
            type: qa_aligned_recall_bertscore_gold_answer
            value: 87.49
          - name: QAAlignedPrecision-BERTScore (Gold Answer)
            type: qa_aligned_precision_bertscore_gold_answer
            value: 87.57
          - name: QAAlignedF1Score-MoverScore (Gold Answer)
            type: qa_aligned_f1_score_moverscore_gold_answer
            value: 85.15
          - name: QAAlignedRecall-MoverScore (Gold Answer)
            type: qa_aligned_recall_moverscore_gold_answer
            value: 85.09
          - name: QAAlignedPrecision-MoverScore (Gold Answer)
            type: qa_aligned_precision_moverscore_gold_answer
            value: 85.23

Model Card of `lmqg/mt5-small-koquad-qg`

This model is fine-tuned version of google/mt5-small for question generation task on the lmqg/qg_koquad (dataset_name: default) via lmqg.

Overview

Language model: google/mt5-small
Language: ko
Training data: lmqg/qg_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-qg")

# model prediction
questions = model.generate_q(list_context="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.", list_answer="남부군")

With transformers

from transformers import pipeline

pipe = pipeline("text2text-generation", "lmqg/mt5-small-koquad-qg")
output = pipe("1990년 영화 《 <hl> 남부군 <hl> 》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 Generation): raw metric file

	Score	Type	Dataset
BERTScore	82.89	default	lmqg/qg_koquad
Bleu_1	25.31	default	lmqg/qg_koquad
Bleu_2	18.59	default	lmqg/qg_koquad
Bleu_3	13.98	default	lmqg/qg_koquad
Bleu_4	10.57	default	lmqg/qg_koquad
METEOR	27.52	default	lmqg/qg_koquad
MoverScore	82.49	default	lmqg/qg_koquad
ROUGE_L	25.64	default	lmqg/qg_koquad

Metric (Question & Answer Generation): QAG metrics are computed with the gold answer and generated question on it for this model, as the model cannot provide an answer. raw metric file

	Score	Type	Dataset
QAAlignedF1Score (BERTScore)	87.52	default	lmqg/qg_koquad
QAAlignedF1Score (MoverScore)	85.15	default	lmqg/qg_koquad
QAAlignedPrecision (BERTScore)	87.57	default	lmqg/qg_koquad
QAAlignedPrecision (MoverScore)	85.23	default	lmqg/qg_koquad
QAAlignedRecall (BERTScore)	87.49	default	lmqg/qg_koquad
QAAlignedRecall (MoverScore)	85.09	default	lmqg/qg_koquad

Training hyperparameters

The following hyperparameters were used during fine-tuning:

dataset_path: lmqg/qg_koquad
dataset_name: default
input_types: ['paragraph_answer']
output_types: ['question']
prefix_types: None
model: google/mt5-small
max_length: 512
max_length_output: 32
epoch: 7
batch: 16
lr: 0.001
fp16: False
random_seed: 1
gradient_accumulation_steps: 4
label_smoothing: 0.15

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",
}