Model Card of lmqg/mt5-small-koquad-qg-ae
This model is fine-tuned version of google/mt5-small for question generation and answer extraction jointly on the lmqg/qg_koquad (dataset_name: default) via lmqg
.
Overview
- Language model: google/mt5-small
- 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="lmqg/mt5-small-koquad-qg-ae")
# 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
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/mt5-small-koquad-qg-ae")
# answer extraction
answer = pipe("generate question: 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.")
# question generation
question = pipe("extract answers: 또한 스피어스는 많은 새로운 여성 아티스트들에게 영향을 끼쳤는데, 대표적으로 데미 로바토, 케이티 페리, 크리스티니아 드바지, 레이디 가가, 리틀 부츠, 셀레나 고메즈 & 더씬, 픽시 로트 이 있다. 2007년 비욘세 놀스는 Total Request Live와의 인터뷰에서 '나는 브리트니를 사랑하고 팬이에요. 특히 새 앨범 Blackout을 좋아해요'라고 말했다. 린제이 로한은 '언제나 브리트니 스피어스에게 영감을 받는다. 학창시절 그녀처럼 타블로이드에 오르기를 꿈꿔왔다'고 말하며 롤 모델로 꼽았다. 스피어스는 현대 음악가들에게 음악적 영감으로 언급되기도 했다. <hl> 마일리 사이러스는 자신의 히트곡 Party in the U.S.A. 가 브리트니에게 영감과 영향을 받은 곡이라고 밝혔다. <hl> 베리 매닐로우의 앨범 15 Minutes 역시 브리트니에게 영감을 얻었다고 언급되었다.")
Evaluation
- Metric (Question Generation): raw metric file
Score | Type | Dataset | |
---|---|---|---|
BERTScore | 83.4 | default | lmqg/qg_koquad |
Bleu_1 | 25.91 | default | lmqg/qg_koquad |
Bleu_2 | 19.09 | default | lmqg/qg_koquad |
Bleu_3 | 14.37 | default | lmqg/qg_koquad |
Bleu_4 | 10.91 | default | lmqg/qg_koquad |
METEOR | 27.52 | default | lmqg/qg_koquad |
MoverScore | 82.54 | default | lmqg/qg_koquad |
ROUGE_L | 25.83 | default | lmqg/qg_koquad |
- Metric (Question & Answer Generation): raw metric file
Score | Type | Dataset | |
---|---|---|---|
QAAlignedF1Score (BERTScore) | 80.36 | default | lmqg/qg_koquad |
QAAlignedF1Score (MoverScore) | 82.55 | default | lmqg/qg_koquad |
QAAlignedPrecision (BERTScore) | 77.34 | default | lmqg/qg_koquad |
QAAlignedPrecision (MoverScore) | 78.93 | default | lmqg/qg_koquad |
QAAlignedRecall (BERTScore) | 83.72 | default | lmqg/qg_koquad |
QAAlignedRecall (MoverScore) | 86.69 | default | lmqg/qg_koquad |
- Metric (Answer Extraction): raw metric file
Score | Type | Dataset | |
---|---|---|---|
AnswerExactMatch | 80.78 | default | lmqg/qg_koquad |
AnswerF1Score | 86.98 | default | lmqg/qg_koquad |
BERTScore | 95.65 | default | lmqg/qg_koquad |
Bleu_1 | 75.14 | default | lmqg/qg_koquad |
Bleu_2 | 66.16 | default | lmqg/qg_koquad |
Bleu_3 | 53.61 | default | lmqg/qg_koquad |
Bleu_4 | 38.2 | default | lmqg/qg_koquad |
METEOR | 59.91 | default | lmqg/qg_koquad |
MoverScore | 94.61 | default | lmqg/qg_koquad |
ROUGE_L | 82.32 | 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', 'paragraph_sentence']
- output_types: ['question', 'answer']
- prefix_types: ['qg', 'ae']
- model: google/mt5-small
- max_length: 512
- max_length_output: 32
- epoch: 6
- 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 lmqg/mt5-small-koquad-qg-ae
Evaluation results
- BLEU4 (Question Generation) on lmqg/qg_koquadself-reported10.910
- ROUGE-L (Question Generation) on lmqg/qg_koquadself-reported25.830
- METEOR (Question Generation) on lmqg/qg_koquadself-reported27.520
- BERTScore (Question Generation) on lmqg/qg_koquadself-reported83.400
- MoverScore (Question Generation) on lmqg/qg_koquadself-reported82.540
- QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer)) on lmqg/qg_koquadself-reported80.360
- QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer)) on lmqg/qg_koquadself-reported83.720
- QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer)) on lmqg/qg_koquadself-reported77.340
- QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer)) on lmqg/qg_koquadself-reported82.550
- QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer)) on lmqg/qg_koquadself-reported86.690