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vocabtrimmer/
mt5-small-trimmed-ko-60000-koquad-qg

Name: mt5-small-trimmed-ko-60000-koquad-qg
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Model Card of `vocabtrimmer/mt5-small-trimmed-ko-60000-koquad-qg`

This model is fine-tuned version of ckpts/mt5-small-trimmed-ko-60000 for question generation task on the lmqg/qg_koquad (dataset_name: default) via lmqg.

Overview

Language model: ckpts/mt5-small-trimmed-ko-60000
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

python

from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="ko", model="vocabtrimmer/mt5-small-trimmed-ko-60000-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

python

from transformers import pipeline

pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-ko-60000-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	83.43	default	lmqg/qg_koquad
Bleu_1	26.36	default	lmqg/qg_koquad
Bleu_2	19.38	default	lmqg/qg_koquad
Bleu_3	14.59	default	lmqg/qg_koquad
Bleu_4	11.1	default	lmqg/qg_koquad
METEOR	28.4	default	lmqg/qg_koquad
MoverScore	82.96	default	lmqg/qg_koquad
ROUGE_L	26.7	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: ckpts/mt5-small-trimmed-ko-60000
max_length: 512
max_length_output: 32
epoch: 12
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",
}

vocabtrimmer/mt5-small-trimmed-ko-60000-koquad-qg

Model Card of vocabtrimmer/mt5-small-trimmed-ko-60000-koquad-qg