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lmqg/
mt5-small-koquad-qag

Name: mt5-small-koquad-qag
Availability: InStock

questions and answers generation

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

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

Overview

Language model: google/mt5-small
Language: ko
Training data: lmqg/qag_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="lmqg/mt5-small-koquad-qag")

# model prediction
question_answer_pairs = model.generate_qa("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.")

With transformers

python

from transformers import pipeline

pipe = pipeline("text2text-generation", "lmqg/mt5-small-koquad-qag")
output = pipe("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.")

Evaluation

Metric (Question & Answer Generation): raw metric file

	Score	Type	Dataset
QAAlignedF1Score (BERTScore)	74.23	default	lmqg/qag_koquad
QAAlignedF1Score (MoverScore)	75.06	default	lmqg/qag_koquad
QAAlignedPrecision (BERTScore)	74.29	default	lmqg/qag_koquad
QAAlignedPrecision (MoverScore)	75.14	default	lmqg/qag_koquad
QAAlignedRecall (BERTScore)	74.2	default	lmqg/qag_koquad
QAAlignedRecall (MoverScore)	75.04	default	lmqg/qag_koquad

Training hyperparameters

The following hyperparameters were used during fine-tuning:

dataset_path: lmqg/qag_koquad
dataset_name: default
input_types: ['paragraph']
output_types: ['questions_answers']
prefix_types: None
model: google/mt5-small
max_length: 512
max_length_output: 256
epoch: 13
batch: 8
lr: 0.0005
fp16: False
random_seed: 1
gradient_accumulation_steps: 16
label_smoothing: 0.0

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",
}

lmqg/mt5-small-koquad-qag

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