Model Card of vocabtrimmer/mbart-large-cc25-trimmed-ko-koquad-qg
This model is fine-tuned version of vocabtrimmer/mbart-large-cc25-trimmed-ko for question generation task on the lmqg/qg_koquad (dataset_name: default) via lmqg
.
Overview
- Language model: vocabtrimmer/mbart-large-cc25-trimmed-ko
- 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="vocabtrimmer/mbart-large-cc25-trimmed-ko-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", "vocabtrimmer/mbart-large-cc25-trimmed-ko-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 | 83.8 | default | lmqg/qg_koquad |
Bleu_1 | 27.01 | default | lmqg/qg_koquad |
Bleu_2 | 19.9 | default | lmqg/qg_koquad |
Bleu_3 | 15.07 | default | lmqg/qg_koquad |
Bleu_4 | 11.59 | default | lmqg/qg_koquad |
METEOR | 30.17 | default | lmqg/qg_koquad |
MoverScore | 82.98 | default | lmqg/qg_koquad |
ROUGE_L | 27.96 | 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: vocabtrimmer/mbart-large-cc25-trimmed-ko
- max_length: 512
- max_length_output: 32
- epoch: 6
- batch: 8
- lr: 0.0001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 8
- 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 vocabtrimmer/mbart-large-cc25-trimmed-ko-koquad-qg
Evaluation results
- BLEU4 (Question Generation) on lmqg/qg_koquadself-reported11.590
- ROUGE-L (Question Generation) on lmqg/qg_koquadself-reported27.960
- METEOR (Question Generation) on lmqg/qg_koquadself-reported30.170
- BERTScore (Question Generation) on lmqg/qg_koquadself-reported83.800
- MoverScore (Question Generation) on lmqg/qg_koquadself-reported82.980