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vocabtrimmer/
mt5-small-trimmed-ja-120000-jaquad-qg

Name: mt5-small-trimmed-ja-120000-jaquad-qg
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Model Card of `vocabtrimmer/mt5-small-trimmed-ja-120000-jaquad-qg`

This model is fine-tuned version of vocabtrimmer/mt5-small-trimmed-ja-120000 for question generation task on the lmqg/qg_jaquad (dataset_name: default) via lmqg.

Overview

Language model: vocabtrimmer/mt5-small-trimmed-ja-120000
Language: ja
Training data: lmqg/qg_jaquad (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="ja", model="vocabtrimmer/mt5-small-trimmed-ja-120000-jaquad-qg")

# model prediction
questions = model.generate_q(list_context="フェルメールの作品では、17世紀のオランダの画家、ヨハネス・フェルメールの作品について記述する。フェルメールの作品は、疑問作も含め30数点しか現存しない。現存作品はすべて油彩画で、版画、下絵、素描などは残っていない。", list_answer="30数点")

With transformers

python

from transformers import pipeline

pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-ja-120000-jaquad-qg")
output = pipe("ゾフィーは貴族出身ではあったが王族出身ではなく、ハプスブルク家の皇位継承者であるフランツ・フェルディナントとの結婚は貴賤結婚となった。皇帝フランツ・ヨーゼフは、2人の間に生まれた子孫が皇位を継がないことを条件として結婚を承認していた。視察が予定されている<hl>6月28日<hl>は2人の14回目の結婚記念日であった。")

Evaluation

Metric (Question Generation): raw metric file

	Score	Type	Dataset
BERTScore	80.2	default	lmqg/qg_jaquad
Bleu_1	54.72	default	lmqg/qg_jaquad
Bleu_2	42.45	default	lmqg/qg_jaquad
Bleu_3	34.51	default	lmqg/qg_jaquad
Bleu_4	28.73	default	lmqg/qg_jaquad
METEOR	28.18	default	lmqg/qg_jaquad
MoverScore	58.09	default	lmqg/qg_jaquad
ROUGE_L	49.22	default	lmqg/qg_jaquad

Training hyperparameters

The following hyperparameters were used during fine-tuning:

dataset_path: lmqg/qg_jaquad
dataset_name: default
input_types: paragraph_answer
output_types: question
prefix_types: None
model: vocabtrimmer/mt5-small-trimmed-ja-120000
max_length: 512
max_length_output: 32
epoch: 17
batch: 16
lr: 0.0005
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-ja-120000-jaquad-qg

Model Card of vocabtrimmer/mt5-small-trimmed-ja-120000-jaquad-qg