|
|
|
--- |
|
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-small-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: 10.57 |
|
- name: ROUGE-L (Question Generation) |
|
type: rouge_l_question_generation |
|
value: 25.64 |
|
- name: METEOR (Question Generation) |
|
type: meteor_question_generation |
|
value: 27.52 |
|
- name: BERTScore (Question Generation) |
|
type: bertscore_question_generation |
|
value: 82.89 |
|
- name: MoverScore (Question Generation) |
|
type: moverscore_question_generation |
|
value: 82.49 |
|
- name: BLEU4 (Question & Answer Generation (with Gold Answer)) |
|
type: bleu4_question_answer_generation_with_gold_answer |
|
value: 10.86 |
|
- name: ROUGE-L (Question & Answer Generation (with Gold Answer)) |
|
type: rouge_l_question_answer_generation_with_gold_answer |
|
value: 46.66 |
|
- name: METEOR (Question & Answer Generation (with Gold Answer)) |
|
type: meteor_question_answer_generation_with_gold_answer |
|
value: 36.66 |
|
- name: BERTScore (Question & Answer Generation (with Gold Answer)) |
|
type: bertscore_question_answer_generation_with_gold_answer |
|
value: 87.21 |
|
- name: MoverScore (Question & Answer Generation (with Gold Answer)) |
|
type: moverscore_question_answer_generation_with_gold_answer |
|
value: 84.46 |
|
- name: QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] |
|
type: qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer_gold_answer |
|
value: 87.52 |
|
- name: QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] |
|
type: qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer_gold_answer |
|
value: 87.49 |
|
- name: QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] |
|
type: qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer_gold_answer |
|
value: 87.57 |
|
- name: QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] |
|
type: qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer_gold_answer |
|
value: 85.15 |
|
- name: QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] |
|
type: qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer_gold_answer |
|
value: 85.09 |
|
- name: QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] |
|
type: qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer_gold_answer |
|
value: 85.23 |
|
--- |
|
|
|
# Model Card of `lmqg/mt5-small-koquad-qg` |
|
This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) 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-small](https://huggingface.co/google/mt5-small) |
|
- **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-small-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-small-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-small-koquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_koquad.default.json) |
|
|
|
| | Score | Type | Dataset | |
|
|:-----------|--------:|:--------|:-----------------------------------------------------------------| |
|
| BERTScore | 82.89 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
|
| Bleu_1 | 25.31 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
|
| Bleu_2 | 18.59 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
|
| Bleu_3 | 13.98 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
|
| Bleu_4 | 10.57 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
|
| METEOR | 27.52 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
|
| MoverScore | 82.49 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
|
| ROUGE_L | 25.64 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
|
|
|
|
|
- ***Metric (Question & Answer Generation, Reference Answer)***: Each question is generated from *the gold answer*. [raw metric file](https://huggingface.co/lmqg/mt5-small-koquad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_koquad.default.json) |
|
|
|
| | Score | Type | Dataset | |
|
|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| |
|
| BERTScore | 87.21 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
|
| Bleu_1 | 41.86 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
|
| Bleu_2 | 30.67 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
|
| Bleu_3 | 18.85 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
|
| Bleu_4 | 10.86 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
|
| METEOR | 36.66 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
|
| MoverScore | 84.46 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
|
| QAAlignedF1Score (BERTScore) | 87.52 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
|
| QAAlignedF1Score (MoverScore) | 85.15 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
|
| QAAlignedPrecision (BERTScore) | 87.57 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
|
| QAAlignedPrecision (MoverScore) | 85.23 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
|
| QAAlignedRecall (BERTScore) | 87.49 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
|
| QAAlignedRecall (MoverScore) | 85.09 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
|
| ROUGE_L | 46.66 | 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-small |
|
- max_length: 512 |
|
- max_length_output: 32 |
|
- epoch: 7 |
|
- 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](https://huggingface.co/lmqg/mt5-small-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", |
|
} |
|
|
|
``` |
|
|
