Model Card of vocabtrimmer/mt5-small-trimmed-ja-60000-jaquad-qg
This model is fine-tuned version of vocabtrimmer/mt5-small-trimmed-ja-60000 for question generation task on the lmqg/qg_jaquad (dataset_name: default) via lmqg
.
Overview
- Language model: vocabtrimmer/mt5-small-trimmed-ja-60000
- 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
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="ja", model="vocabtrimmer/mt5-small-trimmed-ja-60000-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
from transformers import pipeline
pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-ja-60000-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 | 80.3 | default | lmqg/qg_jaquad |
Bleu_1 | 53.81 | default | lmqg/qg_jaquad |
Bleu_2 | 41.82 | default | lmqg/qg_jaquad |
Bleu_3 | 34.02 | default | lmqg/qg_jaquad |
Bleu_4 | 28.35 | default | lmqg/qg_jaquad |
METEOR | 27.72 | default | lmqg/qg_jaquad |
MoverScore | 58.1 | default | lmqg/qg_jaquad |
ROUGE_L | 48.9 | 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-60000
- max_length: 512
- max_length_output: 32
- epoch: 14
- 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 vocabtrimmer/mt5-small-trimmed-ja-60000-jaquad-qg
Evaluation results
- BLEU4 (Question Generation) on lmqg/qg_jaquadself-reported28.350
- ROUGE-L (Question Generation) on lmqg/qg_jaquadself-reported48.900
- METEOR (Question Generation) on lmqg/qg_jaquadself-reported27.720
- BERTScore (Question Generation) on lmqg/qg_jaquadself-reported80.300
- MoverScore (Question Generation) on lmqg/qg_jaquadself-reported58.100