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: BLEU4 (Question & Answer Generation (with Gold Answer))
            type: bleu4_question_answer_generation_with_gold_answer
            value: 10.86
          - name: ROUGE-L (Question & Answer Generation (with Gold Answer))
            type: rouge_l_question_answer_generation_with_gold_answer
            value: 46.66
          - name: METEOR (Question & Answer Generation (with Gold Answer))
            type: meteor_question_answer_generation_with_gold_answer
            value: 36.66
          - name: BERTScore (Question & Answer Generation (with Gold Answer))
            type: bertscore_question_answer_generation_with_gold_answer
            value: 87.21
          - name: MoverScore (Question & Answer Generation (with Gold Answer))
            type: moverscore_question_answer_generation_with_gold_answer
            value: 84.46
          - name: >-
              QAAlignedF1Score-BERTScore (Question & Answer Generation (with
              Gold Answer)) [Gold Answer]
            type: >-
              qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer_gold_answer
            value: 87.52
          - name: >-
              QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold
              Answer)) [Gold Answer]
            type: >-
              qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer_gold_answer
            value: 87.49
          - name: >-
              QAAlignedPrecision-BERTScore (Question & Answer Generation (with
              Gold Answer)) [Gold Answer]
            type: >-
              qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer_gold_answer
            value: 87.57
          - name: >-
              QAAlignedF1Score-MoverScore (Question & Answer Generation (with
              Gold Answer)) [Gold Answer]
            type: >-
              qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer_gold_answer
            value: 85.15
          - name: >-
              QAAlignedRecall-MoverScore (Question & Answer Generation (with
              Gold Answer)) [Gold Answer]
            type: >-
              qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer_gold_answer
            value: 85.09
          - name: >-
              QAAlignedPrecision-MoverScore (Question & Answer Generation (with
              Gold Answer)) [Gold Answer]
            type: >-
              qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer_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, Reference Answer): Each question is generated from the gold answer. raw metric file

	Score	Type	Dataset
BERTScore	87.21	default	lmqg/qg_koquad
Bleu_1	41.86	default	lmqg/qg_koquad
Bleu_2	30.67	default	lmqg/qg_koquad
Bleu_3	18.85	default	lmqg/qg_koquad
Bleu_4	10.86	default	lmqg/qg_koquad
METEOR	36.66	default	lmqg/qg_koquad
MoverScore	84.46	default	lmqg/qg_koquad
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
ROUGE_L	46.66	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",
}