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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

Usage

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

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
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
Score Type Dataset
QAAlignedF1Score (BERTScore) 80.52 default lmqg/qg_koquad
QAAlignedF1Score (MoverScore) 82.95 default lmqg/qg_koquad
QAAlignedPrecision (BERTScore) 77.56 default lmqg/qg_koquad
QAAlignedPrecision (MoverScore) 79.39 default lmqg/qg_koquad
QAAlignedRecall (BERTScore) 83.8 default lmqg/qg_koquad
QAAlignedRecall (MoverScore) 87.02 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",
}
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Dataset used to train lmqg/mt5-small-koquad-qg

Evaluation results

  • BLEU4 (Question Generation) on lmqg/qg_koquad
    self-reported
    10.570
  • ROUGE-L (Question Generation) on lmqg/qg_koquad
    self-reported
    25.640
  • METEOR (Question Generation) on lmqg/qg_koquad
    self-reported
    27.520
  • BERTScore (Question Generation) on lmqg/qg_koquad
    self-reported
    82.890
  • MoverScore (Question Generation) on lmqg/qg_koquad
    self-reported
    82.490
  • QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_koquad
    self-reported
    87.520
  • QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_koquad
    self-reported
    87.490
  • QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_koquad
    self-reported
    87.570
  • QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_koquad
    self-reported
    85.150
  • QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_koquad
    self-reported
    85.090
  • QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_koquad
    self-reported
    85.230
  • QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold Answer] on lmqg/qg_koquad
    self-reported
    80.520
  • QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold Answer] on lmqg/qg_koquad
    self-reported
    83.800
  • QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold Answer] on lmqg/qg_koquad
    self-reported
    77.560
  • QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold Answer] on lmqg/qg_koquad
    self-reported
    82.950
  • QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold Answer] on lmqg/qg_koquad
    self-reported
    87.020
  • QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold Answer] on lmqg/qg_koquad
    self-reported
    79.390