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---
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: "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."
example_title: "Question Generation Example 1"
- text: "generate question: 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: "generate question: <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
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_koquad
type: default
args: default
metrics:
- name: BLEU4
type: bleu4
value: 0.12184665382055122
- name: ROUGE-L
type: rouge-l
value: 0.2856948017709817
- name: METEOR
type: meteor
value: 0.29623847263524816
- name: BERTScore
type: bertscore
value: 0.8451586993172961
- name: MoverScore
type: moverscore
value: 0.8335888774638588
---
# Language Models Fine-tuning on Question Generation: `lmqg/mt5-base-koquad`
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).
### 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:** [TBA](TBA)
### Usage
```python
from transformers import pipeline
model_path = 'lmqg/mt5-base-koquad'
pipe = pipeline("text2text-generation", model_path)
# Question Generation
question = 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.')
```
## Evaluation Metrics
### Metrics
| Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link |
|:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:|
| [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | default | 0.12184665382055122 | 0.2856948017709817 | 0.29623847263524816 | 0.8451586993172961 | 0.8335888774638588 | [link](https://huggingface.co/lmqg/mt5-base-koquad/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_koquad.default.json) |
## 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/raw/main/trainer_config.json).
## Citation
TBA
|