
# Model Card of `vocabtrimmer/mt5-small-trimmed-ko-koquad-qa`
This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-ko](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ko) for question answering 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-qa")

# model prediction
answers = model.answer_q(list_question="매드 클라운이 참가해 큰 화제를 모았던 프로그램은?", list_context=" 과거 소울 컴퍼니 소속으로 소울 컴퍼니 해체 후 현재의 소속사는 스타쉽 엑스이다. Mad Clown vs Crucial Star (매드 클라운 vs 크루셜 스타)라는 프로젝트 그룹으로 크루셜 스타와 함께 활동하기도 하였으며, 2013년부터는 MC인 저스디스와 팀을 이루어 랩 듀오 커먼콜드로 활동하고 있다. 또한 Mnet 《쇼미더머니 2》에서 참가자로 참가하여 큰 화제를 모았으며, 《쇼미더머니 5》에서는 길 & 매드 클라운 팀으로 프로듀서로 출연하였다., 재발매 물량도 완판되어 추가 제작에 들어갔다. 2016년 4월, 소속사와 자신의 SNS를 통해 2016년 5월 15일 현재 교제 중인 일반인 여자친구와의 결혼을 공식발표하였다.")

```

- With `transformers`
```python
from transformers import pipeline

pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-ko-koquad-qa")
output = pipe("question: 매드 클라운이 참가해 큰 화제를 모았던 프로그램은?, context: 과거 소울 컴퍼니 소속으로 소울 컴퍼니 해체 후 현재의 소속사는 스타쉽 엑스이다. Mad Clown vs Crucial Star (매드 클라운 vs 크루셜 스타)라는 프로젝트 그룹으로 크루셜 스타와 함께 활동하기도 하였으며, 2013년부터는 MC인 저스디스와 팀을 이루어 랩 듀오 커먼콜드로 활동하고 있다. 또한 Mnet 《쇼미더머니 2》에서 참가자로 참가하여 큰 화제를 모았으며, 《쇼미더머니 5》에서는 길 & 매드 클라운 팀으로 프로듀서로 출연하였다., 재발매 물량도 완판되어 추가 제작에 들어갔다. 2016년 4월, 소속사와 자신의 SNS를 통해 2016년 5월 15일 현재 교제 중인 일반인 여자친구와의 결혼을 공식발표하였다.")

```

## Evaluation


- ***Metric (Question Answering)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ko-koquad-qa/raw/main/eval/metric.first.answer.paragraph_question.answer.lmqg_qg_koquad.default.json) 

|                  |   Score | Type    | Dataset                                                          |
|:-----------------|--------:|:--------|:-----------------------------------------------------------------|
| AnswerExactMatch |   67.31 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| AnswerF1Score    |   74.45 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| BERTScore        |   96.08 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| Bleu_1           |   63.81 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| Bleu_2           |   55.08 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| Bleu_3           |   44.98 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| Bleu_4           |   31.5  | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| METEOR           |   50.8  | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| MoverScore       |   89.75 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| ROUGE_L          |   70.34 | 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_question']
 - output_types: ['answer']
 - prefix_types: None
 - model: vocabtrimmer/mt5-small-trimmed-ko
 - max_length: 512
 - max_length_output: 32
 - epoch: 4
 - 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-qa/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 answering"], "datasets": ["lmqg/qg_koquad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "question: \ub9e4\ub4dc \ud074\ub77c\uc6b4\uc774 \ucc38\uac00\ud574 \ud070 \ud654\uc81c\ub97c \ubaa8\uc558\ub358 \ud504\ub85c\uadf8\ub7a8\uc740?, context: \uacfc\uac70 \uc18c\uc6b8 \ucef4\ud37c\ub2c8 \uc18c\uc18d\uc73c\ub85c \uc18c\uc6b8 \ucef4\ud37c\ub2c8 \ud574\uccb4 \ud6c4 \ud604\uc7ac\uc758 \uc18c\uc18d\uc0ac\ub294 \uc2a4\ud0c0\uc27d \uc5d1\uc2a4\uc774\ub2e4. Mad Clown vs Crucial Star (\ub9e4\ub4dc \ud074\ub77c\uc6b4 vs \ud06c\ub8e8\uc15c \uc2a4\ud0c0)\ub77c\ub294 \ud504\ub85c\uc81d\ud2b8 \uadf8\ub8f9\uc73c\ub85c \ud06c\ub8e8\uc15c \uc2a4\ud0c0\uc640 \ud568\uaed8 \ud65c\ub3d9\ud558\uae30\ub3c4 \ud558\uc600\uc73c\uba70, 2013\ub144\ubd80\ud130\ub294 MC\uc778 \uc800\uc2a4\ub514\uc2a4\uc640 \ud300\uc744 \uc774\ub8e8\uc5b4 \ub7a9 \ub4c0\uc624 \ucee4\uba3c\ucf5c\ub4dc\ub85c \ud65c\ub3d9\ud558\uace0 \uc788\ub2e4. \ub610\ud55c Mnet \u300a\uc1fc\ubbf8\ub354\uba38\ub2c8 2\u300b\uc5d0\uc11c \ucc38\uac00\uc790\ub85c \ucc38\uac00\ud558\uc5ec \ud070 \ud654\uc81c\ub97c \ubaa8\uc558\uc73c\uba70, \u300a\uc1fc\ubbf8\ub354\uba38\ub2c8 5\u300b\uc5d0\uc11c\ub294 \uae38 & \ub9e4\ub4dc \ud074\ub77c\uc6b4 \ud300\uc73c\ub85c \ud504\ub85c\ub4c0\uc11c\ub85c \ucd9c\uc5f0\ud558\uc600\ub2e4., \uc7ac\ubc1c\ub9e4 \ubb3c\ub7c9\ub3c4 \uc644\ud310\ub418\uc5b4 \ucd94\uac00 \uc81c\uc791\uc5d0 \ub4e4\uc5b4\uac14\ub2e4. 2016\ub144 4\uc6d4, \uc18c\uc18d\uc0ac\uc640 \uc790\uc2e0\uc758 SNS\ub97c \ud1b5\ud574 2016\ub144 5\uc6d4 15\uc77c \ud604\uc7ac \uad50\uc81c \uc911\uc778 \uc77c\ubc18\uc778 \uc5ec\uc790\uce5c\uad6c\uc640\uc758 \uacb0\ud63c\uc744 \uacf5\uc2dd\ubc1c\ud45c\ud558\uc600\ub2e4.", "example_title": "Question Answering Example 1"}, {"text": "question: 1913\ub144 \ud544\ub77c\ub378\ud53c\uc544 \uc560\uc2ac\ub808\ud2f1\uc2a4\uc758 \uac1c\ub9c9\uc804 \uc0c1\ub300\ub294?, context: 1913\ub144 \uc2dc\uc98c\uc744 \uc55e\ub450\uace0 \uc2a4\ud504\ub9c1 \ud2b8\ub808\uc774\ub2dd\uc5d0\uc11c \uc7ad \ucff0\uc2a4\ub294 \uc568\ub77c\ubc30\ub9c8 \uc8fc \ubabd\uace0\uba54\ub9ac\uc5d0\uc11c \uace0\uc5f4\ub85c \ud798\ub4e4\uc5b4\ud588\ub294\ub370, \ub2f9\uc2dc\uc5d0\ub294 \uc2dd\uc911\ub3c5 \ubc0f \ub291\ub9c9\uc5fc \uc9c4\ub2e8\uc744 \ubc1b\uace0 \ud734\uc2dd\uc744 \ucde8\ud588\ub2e4. 4\uc6d4 10\uc77c, \ubcf4\uc2a4\ud134 \ub808\ub4dc\uc0ad\uc2a4\ub97c \uc0c1\ub300\ub85c \uce58\ub7ec\uc9c4 \uac1c\ub9c9\uc804\uc5d0\uc11c \uc7ad \ucff0\uc2a4\ub294 \uc120\ubc1c\ud22c\uc218\ub85c \ub0b4\uc815\ub418\uc5c8\ub2e4. \uadf8\ub294 3\uc774\ub2dd\uc744 \ub178\ud788\ud2b8\ub85c \ub9c9\uace0 6\ud68c \uce58\ud504 \ubca4\ub354\uc640 \uad50\uccb4\ub418\uc5c8\uc73c\uba70, \uacbd\uae30\ub294 10-5\ub85c \uc560\uc2ac\ub808\ud2f1\uc2a4\uac00 \uc2b9\ub9ac\ud588\ub2e4. \uc774\ud2c0 \ub4a4\uc5d0 \ub2e4\uc2dc \uc120\ubc1c \ub4f1\ud310\uc5d0 \ub098\uc130\uc73c\ub098 \u20443\uc774\ub2dd \ub3d9\uc548 2\ud53c\uc548\ud0c0 1\ubcfc\ub137, 4\uc2e4\uc810\ub9cc\uc744 \uae30\ub85d\ud558\uace0 \uac15\ud310\ub418\uc5c8\ub2e4. \ucff0\uc2a4\ub294 \ubcf4\uc2a4\ud134\uc5d0\uc11c\uc758 \uc2dc\ub9ac\uc988\ub97c \ub05d\ub0b4\uace0 \ud300 \ub3d9\ub8cc\ub4e4\uacfc \ud568\uaed8 \uc6cc\uc2f1\ud134\uc73c\ub85c \ud5a5\ud588\uc9c0\ub9cc, \uace0\ud1b5\uc774 \uc2ec\ud574\uc9c0\uc790 \uad6c\ub2e8\uc740 \uadf8\ub97c \ud544\ub77c\ub378\ud53c\uc544\ub85c \ub3cc\ub824\ubcf4\ub0c8\ub2e4. \uadf8\uacf3\uc5d0\uc11c \uadf8\ub294 \uc7a5\ud2f0\ud478\uc2a4 \uc9c4\ub2e8\uc744 \ubc1b\uace0 \ud734\uc2dd\uc744 \ucde8\ud588\uc73c\uba70, 8\uc6d4\uc5d0 \ub2e4\uc2dc \ud300\uc5d0 \ubcf5\uadc0\ud558\ub824\uace0 \ud588\uc9c0\ub9cc \uc815\uc0c1\uc801\uc778 \ud68c\ubcf5\uc744 \uc704\ud574\uc11c \ub2e4\uc2dc \ubcd1\uc6d0\uc5d0 \ub4e4\uc5b4\uac14\ub2e4. \uc774 \uae30\uac04 \ubab8\ubb34\uac8c\uac00 25 kg \uac00\ub7c9\uc774\ub098 \uac10\uc18c\ud588\ub2e4. \uc774 \ud574 \ud544\ub77c\ub378\ud53c\uc544 \uc560\uc2ac\ub808\ud2f1\uc2a4\ub294 \uc6d4\ub4dc \uc2dc\ub9ac\uc988\uc5d0\uc11c 2\ub144\ub9cc\uc5d0 \ub2e4\uc2dc \ub274\uc695 \uc790\uc774\uc5b8\uce20\uc640 \ub9de\ubd99\uc5c8\uace0, \uc6b0\uc2b9\uc744 \ucc28\uc9c0\ud588\ub2e4. \ucff0\uc2a4\uc758 \uacf5\ubc31\uae30\ub294 \ub2e4\uc74c\ud574\uc778 1914\ub144 \uc2dc\uc98c\uae4c\uc9c0 \uae38\uc5b4\uc84c\ub2e4. \uc774 \ud574 \uc2dc\uc98c\uc5d0\ub294 \ud300 \uc21c\uc704\uac00 \uc815\ud574\uc9c4 \uc2dc\uc98c \ub9c9\ud310\uc5d0\uc57c \ub450 \uacbd\uae30\uc5d0 \uc120\ubc1c \ucd9c\uc804\ud574\uc11c, \ub3c4\ud569 8\uc774\ub2dd 8\ud53c\uc548\ud0c0 4\uc2e4\uc810, 4.50\uc758 \ud3c9\uade0\uc790\ucc45\uc810\uc744 \uae30\ub85d\ud588\ub2e4. \uc2dc\uc98c \ud6c4\uc778 12\uc6d4 9\uc77c, \uc560\uc2ac\ub808\ud2f1\uc2a4\uc5d0\uc11c \ubc29\ucd9c\ub418\uc5c8\ub2e4.", "example_title": "Question Answering Example 2"}], "model-index": [{"name": "vocabtrimmer/mt5-small-trimmed-ko-koquad-qa", "results": [{"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_koquad", "type": "default", "args": "default"}, "metrics": [{"type": "bleu4_question_answering", "value": 31.5, "name": "BLEU4 (Question Answering)"}, {"type": "rouge_l_question_answering", "value": 70.34, "name": "ROUGE-L (Question Answering)"}, {"type": "meteor_question_answering", "value": 50.8, "name": "METEOR (Question Answering)"}, {"type": "bertscore_question_answering", "value": 96.08, "name": "BERTScore (Question Answering)"}, {"type": "moverscore_question_answering", "value": 89.75, "name": "MoverScore (Question Answering)"}, {"type": "answer_f1_score__question_answering", "value": 74.45, "name": "AnswerF1Score (Question Answering)"}, {"type": "answer_exact_match_question_answering", "value": 67.31, "name": "AnswerExactMatch (Question Answering)"}]}]}]}
transformers pytorch mt5 text2text-generation question answering 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 answering #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-qa'
===========================================================


This model is fine-tuned version of vocabtrimmer/mt5-small-trimmed-ko for question answering 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 Answering)*: raw metric file



Training hyperparameters
------------------------


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_koquad
* dataset\_name: default
* input\_types: ['paragraph\_question']
* output\_types: ['answer']
* prefix\_types: None
* model: vocabtrimmer/mt5-small-trimmed-ko
* max\_length: 512
* max\_length\_output: 32
* epoch: 4
* 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 Answering)*: raw metric file



Training hyperparameters
------------------------


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_koquad
* dataset\_name: default
* input\_types: ['paragraph\_question']
* output\_types: ['answer']
* prefix\_types: None
* model: vocabtrimmer/mt5-small-trimmed-ko
* max\_length: 512
* max\_length\_output: 32
* epoch: 4
* 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 answering #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 Answering)*: raw metric file



Training hyperparameters
------------------------


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_koquad
* dataset\_name: default
* input\_types: ['paragraph\_question']
* output\_types: ['answer']
* prefix\_types: None
* model: vocabtrimmer/mt5-small-trimmed-ko
* max\_length: 512
* max\_length\_output: 32
* epoch: 4
* 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 answering #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 Answering)*: raw metric file



Training hyperparameters
------------------------


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_koquad
* dataset\_name: default
* input\_types: ['paragraph\_question']
* output\_types: ['answer']
* prefix\_types: None
* model: vocabtrimmer/mt5-small-trimmed-ko
* max\_length: 512
* max\_length\_output: 32
* epoch: 4
* 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.