File size: 8,306 Bytes
4df4f54 6825032 4df4f54 6825032 4df4f54 6825032 4df4f54 330f02d 4df4f54 6ef64f5 0d04487 6ef64f5 0d04487 6ef64f5 0d04487 6ef64f5 0d04487 6ef64f5 0d04487 f6149b4 4df4f54 330f02d 0d04487 4df4f54 41c00bf 4df4f54 41c00bf 4df4f54 41c00bf 0d04487 41c00bf 330f02d 0d04487 41c00bf 0d04487 41c00bf 4df4f54 41c00bf 8e48314 330f02d 0d04487 4df4f54 0d04487 4df4f54 0d04487 4df4f54 330f02d 4df4f54 0d04487 4df4f54 f6149b4 4df4f54 330f02d 4df4f54 41c00bf 8e48314 41c00bf 8e48314 41c00bf 8e48314 41c00bf |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: ko
datasets:
- lmqg/qg_koquad
pipeline_tag: text2text-generation
tags:
- question generation
widget:
- text: "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."
example_title: "Question Generation Example 1"
- text: "백신이 없기때문에 예방책은 <hl> 살충제 <hl> 를 사용하면서 서식 장소(찻찬 받침, 배수로, 고인 물의 열린 저장소, 버려진 타이어 등)의 수를 줄임으로써 매개체를 통제할 수 있다."
example_title: "Question Generation Example 2"
- text: "<hl> 원테이크 촬영 <hl> 이기 때문에 한 사람이 실수를 하면 처음부터 다시 찍어야 하는 상황이 발생한다."
example_title: "Question Generation Example 3"
model-index:
- name: lmqg/mt5-base-koquad-qg
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_koquad
type: default
args: default
metrics:
- name: BLEU4 (Question Generation)
type: bleu4_question_generation
value: 12.18
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 28.57
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 29.62
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 84.52
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 83.36
- name: QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold Answer]
type: qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer
value: 88.8
- name: QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold Answer]
type: qa_aligned_recall_bertscore_question_answer_generation_gold_answer
value: 88.76
- name: QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold Answer]
type: qa_aligned_precision_bertscore_question_answer_generation_gold_answer
value: 88.84
- name: QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold Answer]
type: qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer
value: 85.93
- name: QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold Answer]
type: qa_aligned_recall_moverscore_question_answer_generation_gold_answer
value: 85.87
- name: QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold Answer]
type: qa_aligned_precision_moverscore_question_answer_generation_gold_answer
value: 86.01
---
# Model Card of `lmqg/mt5-base-koquad-qg`
This model is fine-tuned version of [google/mt5-base](https://huggingface.co./google/mt5-base) for question generation task on the [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [google/mt5-base](https://huggingface.co./google/mt5-base)
- **Language:** ko
- **Training data:** [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="ko", model="lmqg/mt5-base-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", "lmqg/mt5-base-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](https://huggingface.co./lmqg/mt5-base-koquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_koquad.default.json)
| | Score | Type | Dataset |
|:-----------|--------:|:--------|:-----------------------------------------------------------------|
| BERTScore | 84.52 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) |
| Bleu_1 | 28.54 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) |
| Bleu_2 | 21.05 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) |
| Bleu_3 | 15.92 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) |
| Bleu_4 | 12.18 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) |
| METEOR | 29.62 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) |
| MoverScore | 83.36 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) |
| ROUGE_L | 28.57 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) |
- ***Metric (Question & Answer Generation)***: QAG metrics are computed with *the gold answer* and generated question on it for this model, as the model cannot provide an answer. [raw metric file](https://huggingface.co./lmqg/mt5-base-koquad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_koquad.default.json)
| | Score | Type | Dataset |
|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
| QAAlignedF1Score (BERTScore) | 88.8 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) |
| QAAlignedF1Score (MoverScore) | 85.93 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) |
| QAAlignedPrecision (BERTScore) | 88.84 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) |
| QAAlignedPrecision (MoverScore) | 86.01 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) |
| QAAlignedRecall (BERTScore) | 88.76 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) |
| QAAlignedRecall (MoverScore) | 85.87 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/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: google/mt5-base
- max_length: 512
- max_length_output: 32
- epoch: 11
- batch: 4
- lr: 0.0005
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 16
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co./lmqg/mt5-base-koquad-qg/raw/main/trainer_config.json).
## 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",
}
```
|