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lmqg
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mt5-small-koquad-qg

Text2Text Generation
Transformers
PyTorch
Korean
mt5
question generation
Eval Results
Inference Endpoints
Model card Files Community
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mt5-small-koquad-qg
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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-small-koquad
  results:
  - task:
      name: Text2text Generation
      type: text2text-generation
    dataset:
      name: lmqg/qg_koquad
      type: default
      args: default
    metrics:
    - name: BLEU4
      type: bleu4
      value: 0.10570915349557093
    - name: ROUGE-L
      type: rouge-l
      value: 0.2564353531078813
    - name: METEOR
      type: meteor
      value: 0.2752329744142515
    - name: BERTScore
      type: bertscore
      value: 0.8288608218241639
    - name: MoverScore
      type: moverscore
      value: 0.8249013345139385
---

# Language Models Fine-tuning on Question Generation: `lmqg/mt5-small-koquad`
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).


### 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:** [TBA](TBA)

### Usage
```python

from transformers import pipeline

model_path = 'lmqg/mt5-small-koquad'
pipe = pipeline("text2text-generation", model_path)

# Question Generation
input_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.'
question = pipe(input_text)
```

## Evaluation Metrics


### Metrics

| Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link |
|:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:|
| [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) | default | 0.10570915349557093 | 0.2564353531078813 | 0.2752329744142515 | 0.8288608218241639 | 0.8249013345139385 | [link](https://huggingface.co./lmqg/mt5-small-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-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/raw/main/trainer_config.json).

## Citation
TBA