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
    results:
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_koquad
          type: default
          args: default
        metrics:
          - name: BLEU4
            type: bleu4
            value: 0.10570915349557093
          - name: ROUGE-L
            type: rouge-l
            value: 0.2564353531078813
          - name: METEOR
            type: meteor
            value: 0.2752329744142515
          - name: BERTScore
            type: bertscore
            value: 0.8288608218241639
          - name: MoverScore
            type: moverscore
            value: 0.8249013345139385
          - name: QAAlignedF1Score (BERTScore)
            type: qa_aligned_f1_score_bertscore
            value: 0.8752356041211194
          - name: QAAlignedRecall (BERTScore)
            type: qa_aligned_recall_bertscore
            value: 0.8748548507424297
          - name: QAAlignedPrecision (BERTScore)
            type: qa_aligned_precision_bertscore
            value: 0.8756506176752925
          - name: QAAlignedF1Score (MoverScore)
            type: qa_aligned_f1_score_moverscore
            value: 0.851506510139283
          - name: QAAlignedRecall (MoverScore)
            type: qa_aligned_recall_moverscore
            value: 0.8508582613719472
          - name: QAAlignedPrecision (MoverScore)
            type: qa_aligned_precision_moverscore
            value: 0.8523224343163479

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

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

Please cite our paper if you use the model (https://arxiv.org/abs/2210.03992).


@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",
}

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')
# model prediction
question = 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
# initialize model
pipe = pipeline("text2text-generation", 'lmqg/mt5-small-koquad')
# question generation
question = 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 Metrics

Metrics

Dataset	Type	BLEU4	ROUGE-L	METEOR	BERTScore	MoverScore	Link
lmqg/qg_koquad	default	0.106	0.256	0.275	0.829	0.825	link

Metrics (QAG)

Dataset	Type	QA Aligned F1 Score (BERTScore)	QA Aligned F1 Score (MoverScore)	Link
lmqg/qg_koquad	default	0.875	0.852	link

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",
}

Model Card of lmqg/mt5-small-koquad