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-base-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: 12.18
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 28.57
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 29.62
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 84.52
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 83.36
          - 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: 88.8
          - 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: 88.76
          - 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: 88.84
          - 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: 85.93
          - 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: 85.87
          - 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: 86.01
          - name: >-
              QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer
            value: 77.26
          - name: >-
              QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold
              Answer]
            type: qa_aligned_recall_bertscore_question_answer_generation_gold_answer
            value: 78.25
          - name: >-
              QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_precision_bertscore_question_answer_generation_gold_answer
            value: 76.37
          - name: >-
              QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer
            value: 77.51
          - name: >-
              QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_recall_moverscore_question_answer_generation_gold_answer
            value: 78.95
          - name: >-
              QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_precision_moverscore_question_answer_generation_gold_answer
            value: 76.26

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

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

Overview

Language model: google/mt5-base
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-base-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-base-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	84.52	default	lmqg/qg_koquad
Bleu_1	28.54	default	lmqg/qg_koquad
Bleu_2	21.05	default	lmqg/qg_koquad
Bleu_3	15.92	default	lmqg/qg_koquad
Bleu_4	12.18	default	lmqg/qg_koquad
METEOR	29.62	default	lmqg/qg_koquad
MoverScore	83.36	default	lmqg/qg_koquad
ROUGE_L	28.57	default	lmqg/qg_koquad

Metric (Question & Answer Generation, Reference Answer): Each question is generated from the gold answer. raw metric file

	Score	Type	Dataset
QAAlignedF1Score (BERTScore)	88.8	default	lmqg/qg_koquad
QAAlignedF1Score (MoverScore)	85.93	default	lmqg/qg_koquad
QAAlignedPrecision (BERTScore)	88.84	default	lmqg/qg_koquad
QAAlignedPrecision (MoverScore)	86.01	default	lmqg/qg_koquad
QAAlignedRecall (BERTScore)	88.76	default	lmqg/qg_koquad
QAAlignedRecall (MoverScore)	85.87	default	lmqg/qg_koquad

Metric (Question & Answer Generation, Pipeline Approach): Each question is generated on the answer generated by lmqg/mt5-base-koquad-ae. raw metric file

	Score	Type	Dataset
QAAlignedF1Score (BERTScore)	77.26	default	lmqg/qg_koquad
QAAlignedF1Score (MoverScore)	77.51	default	lmqg/qg_koquad
QAAlignedPrecision (BERTScore)	76.37	default	lmqg/qg_koquad
QAAlignedPrecision (MoverScore)	76.26	default	lmqg/qg_koquad
QAAlignedRecall (BERTScore)	78.25	default	lmqg/qg_koquad
QAAlignedRecall (MoverScore)	78.95	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-base
max_length: 512
max_length_output: 32
epoch: 11
batch: 4
lr: 0.0005
fp16: False
random_seed: 1
gradient_accumulation_steps: 16
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",
}