
# Model Card of `vocabtrimmer/mt5-small-trimmed-ko-koquad-qg`
This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-ko](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ko) 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:** [vocabtrimmer/mt5-small-trimmed-ko](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ko)   
- **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="vocabtrimmer/mt5-small-trimmed-ko-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", "vocabtrimmer/mt5-small-trimmed-ko-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/vocabtrimmer/mt5-small-trimmed-ko-koquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_koquad.default.json) 

|            |   Score | Type    | Dataset                                                          |
|:-----------|--------:|:--------|:-----------------------------------------------------------------|
| BERTScore  |   83.69 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| Bleu_1     |   26.6  | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| Bleu_2     |   19.55 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| Bleu_3     |   14.72 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| Bleu_4     |   11.22 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| METEOR     |   27.98 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| MoverScore |   82.62 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| ROUGE_L    |   26.36 | 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: vocabtrimmer/mt5-small-trimmed-ko
 - max_length: 512
 - max_length_output: 32
 - epoch: 12
 - batch: 32
 - lr: 0.001
 - fp16: False
 - random_seed: 1
 - gradient_accumulation_steps: 2
 - label_smoothing: 0.15

The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ko-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",
}

```

{"language": "ko", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_koquad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "1990\ub144 \uc601\ud654 \u300a <hl> \ub0a8\ubd80\uad70 <hl> \u300b\uc5d0\uc11c \ub2e8\uc5ed\uc73c\ub85c \uc601\ud654\ubc30\uc6b0 \uccab \ub370\ubdd4\uc5d0 \uc774\uc5b4 \uac19\uc740 \ud574 KBS \ub4dc\ub77c\ub9c8 \u300a\uc9c0\uad6c\uc778\u300b\uc5d0\uc11c \ub2e8\uc5ed\uc73c\ub85c \ucd9c\uc5f0\ud558\uc600\uace0 \uc774\ub4ec\ud574 MBC \u300a\uc5ec\uba85\uc758 \ub208\ub3d9\uc790\u300b\ub97c \ud1b5\ud574 \ub2e8\uc5ed\uc73c\ub85c \ucd9c\uc5f0\ud558\uc600\ub2e4.", "example_title": "Question Generation Example 1"}, {"text": "\ubc31\uc2e0\uc774 \uc5c6\uae30\ub54c\ubb38\uc5d0 \uc608\ubc29\ucc45\uc740 <hl> \uc0b4\ucda9\uc81c <hl> \ub97c \uc0ac\uc6a9\ud558\uba74\uc11c \uc11c\uc2dd \uc7a5\uc18c(\ucc3b\ucc2c \ubc1b\uce68, \ubc30\uc218\ub85c, \uace0\uc778 \ubb3c\uc758 \uc5f4\ub9b0 \uc800\uc7a5\uc18c, \ubc84\ub824\uc9c4 \ud0c0\uc774\uc5b4 \ub4f1)\uc758 \uc218\ub97c \uc904\uc784\uc73c\ub85c\uc368 \ub9e4\uac1c\uccb4\ub97c \ud1b5\uc81c\ud560 \uc218 \uc788\ub2e4.", "example_title": "Question Generation Example 2"}, {"text": "<hl> \uc6d0\ud14c\uc774\ud06c \ucd2c\uc601 <hl> \uc774\uae30 \ub54c\ubb38\uc5d0 \ud55c \uc0ac\ub78c\uc774 \uc2e4\uc218\ub97c \ud558\uba74 \ucc98\uc74c\ubd80\ud130 \ub2e4\uc2dc \ucc0d\uc5b4\uc57c \ud558\ub294 \uc0c1\ud669\uc774 \ubc1c\uc0dd\ud55c\ub2e4.", "example_title": "Question Generation Example 3"}], "model-index": [{"name": "vocabtrimmer/mt5-small-trimmed-ko-koquad-qg", "results": [{"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_koquad", "type": "default", "args": "default"}, "metrics": [{"type": "bleu4_question_generation", "value": 11.22, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 26.36, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 27.98, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 83.69, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 82.62, "name": "MoverScore (Question Generation)"}]}]}]}
transformers pytorch mt5 text2text-generation question generation ko dataset:lmqg/qg_koquad arxiv:2210.03992 license:cc-by-4.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #mt5 #text2text-generation #question generation #ko #dataset-lmqg/qg_koquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

Model Card of 'vocabtrimmer/mt5-small-trimmed-ko-koquad-qg'
===========================================================


This model is fine-tuned version of vocabtrimmer/mt5-small-trimmed-ko for question generation task on the lmqg/qg\_koquad (dataset\_name: default) via 'lmqg'.


### Overview


* Language model: vocabtrimmer/mt5-small-trimmed-ko
* Language: ko
* Training data: lmqg/qg\_koquad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL


### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


* *Metric (Question Generation)*: raw metric file



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: vocabtrimmer/mt5-small-trimmed-ko
* max\_length: 512
* max\_length\_output: 32
* epoch: 12
* batch: 32
* lr: 0.001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 2
* label\_smoothing: 0.15


The full configuration can be found at fine-tuning config file.



### Overview


* Language model: vocabtrimmer/mt5-small-trimmed-ko
* Language: ko
* Training data: lmqg/qg\_koquad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL ### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


* *Metric (Question Generation)*: raw metric file



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: vocabtrimmer/mt5-small-trimmed-ko
* max\_length: 512
* max\_length\_output: 32
* epoch: 12
* batch: 32
* lr: 0.001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 2
* label\_smoothing: 0.15


The full configuration can be found at fine-tuning config file.
TAGS
#transformers #pytorch #mt5 #text2text-generation #question generation #ko #dataset-lmqg/qg_koquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Overview


* Language model: vocabtrimmer/mt5-small-trimmed-ko
* Language: ko
* Training data: lmqg/qg\_koquad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL ### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


* *Metric (Question Generation)*: raw metric file



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: vocabtrimmer/mt5-small-trimmed-ko
* max\_length: 512
* max\_length\_output: 32
* epoch: 12
* batch: 32
* lr: 0.001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 2
* label\_smoothing: 0.15


The full configuration can be found at fine-tuning config file.
TAGS
#transformers #pytorch #mt5 #text2text-generation #question generation #ko #dataset-lmqg/qg_koquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Overview


* Language model: vocabtrimmer/mt5-small-trimmed-ko
* Language: ko
* Training data: lmqg/qg\_koquad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


* *Metric (Question Generation)*: raw metric file



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: vocabtrimmer/mt5-small-trimmed-ko
* max\_length: 512
* max\_length\_output: 32
* epoch: 12
* batch: 32
* lr: 0.001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 2
* label\_smoothing: 0.15


The full configuration can be found at fine-tuning config file.