
# Model Card of `lmqg/mt5-small-koquad-qg-ae`
This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation and answer extraction jointly 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-ae")

# model prediction
question_answer_pairs = model.generate_qa("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.")

```

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

pipe = pipeline("text2text-generation", "lmqg/mt5-small-koquad-qg-ae")

# answer extraction
answer = 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.")

# question generation
question = pipe("extract answers: 또한 스피어스는 많은 새로운 여성 아티스트들에게 영향을 끼쳤는데, 대표적으로 데미 로바토, 케이티 페리, 크리스티니아 드바지, 레이디 가가, 리틀 부츠, 셀레나 고메즈 & 더씬, 픽시 로트 이 있다. 2007년 비욘세 놀스는 Total Request Live와의 인터뷰에서 '나는 브리트니를 사랑하고 팬이에요. 특히 새 앨범 Blackout을 좋아해요'라고 말했다. 린제이 로한은 '언제나 브리트니 스피어스에게 영감을 받는다. 학창시절 그녀처럼 타블로이드에 오르기를 꿈꿔왔다'고 말하며 롤 모델로 꼽았다. 스피어스는 현대 음악가들에게 음악적 영감으로 언급되기도 했다. <hl> 마일리 사이러스는 자신의 히트곡 Party in the U.S.A. 가 브리트니에게 영감과 영향을 받은 곡이라고 밝혔다. <hl> 베리 매닐로우의 앨범 15 Minutes 역시 브리트니에게 영감을 얻었다고 언급되었다.")

```

## Evaluation


- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-koquad-qg-ae/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_koquad.default.json) 

|            |   Score | Type    | Dataset                                                          |
|:-----------|--------:|:--------|:-----------------------------------------------------------------|
| BERTScore  |   83.4  | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| Bleu_1     |   25.91 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| Bleu_2     |   19.09 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| Bleu_3     |   14.37 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| Bleu_4     |   10.91 | 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 |   82.54 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| ROUGE_L    |   25.83 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |


- ***Metric (Question & Answer Generation)***:  [raw metric file](https://huggingface.co/lmqg/mt5-small-koquad-qg-ae/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_koquad.default.json)

|                                 |   Score | Type    | Dataset                                                          |
|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
| QAAlignedF1Score (BERTScore)    |   80.36 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| QAAlignedF1Score (MoverScore)   |   82.55 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| QAAlignedPrecision (BERTScore)  |   77.34 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| QAAlignedPrecision (MoverScore) |   78.93 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| QAAlignedRecall (BERTScore)     |   83.72 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| QAAlignedRecall (MoverScore)    |   86.69 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |


- ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-koquad-qg-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_koquad.default.json)

|                  |   Score | Type    | Dataset                                                          |
|:-----------------|--------:|:--------|:-----------------------------------------------------------------|
| AnswerExactMatch |   80.78 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| AnswerF1Score    |   86.98 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| BERTScore        |   95.65 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| Bleu_1           |   75.14 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| Bleu_2           |   66.16 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| Bleu_3           |   53.61 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| Bleu_4           |   38.2  | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| METEOR           |   59.91 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| MoverScore       |   94.61 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
| ROUGE_L          |   82.32 | 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', 'paragraph_sentence']
 - output_types: ['question', 'answer']
 - prefix_types: ['qg', 'ae']
 - model: google/mt5-small
 - max_length: 512
 - max_length_output: 32
 - epoch: 6
 - 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-ae/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", "answer extraction"], "datasets": ["lmqg/qg_koquad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: 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": "generate question: \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": "generate question: <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"}, {"text": "extract answers: \ub610\ud55c \uc2a4\ud53c\uc5b4\uc2a4\ub294 \ub9ce\uc740 \uc0c8\ub85c\uc6b4 \uc5ec\uc131 \uc544\ud2f0\uc2a4\ud2b8\ub4e4\uc5d0\uac8c \uc601\ud5a5\uc744 \ub07c\ucce4\ub294\ub370, \ub300\ud45c\uc801\uc73c\ub85c \ub370\ubbf8 \ub85c\ubc14\ud1a0, \ucf00\uc774\ud2f0 \ud398\ub9ac, \ud06c\ub9ac\uc2a4\ud2f0\ub2c8\uc544 \ub4dc\ubc14\uc9c0, \ub808\uc774\ub514 \uac00\uac00, \ub9ac\ud2c0 \ubd80\uce20, \uc140\ub808\ub098 \uace0\uba54\uc988 & \ub354\uc52c, \ud53d\uc2dc \ub85c\ud2b8 \uc774 \uc788\ub2e4. 2007\ub144 \ube44\uc698\uc138 \ub180\uc2a4\ub294 Total Request Live\uc640\uc758 \uc778\ud130\ubdf0\uc5d0\uc11c '\ub098\ub294 \ube0c\ub9ac\ud2b8\ub2c8\ub97c \uc0ac\ub791\ud558\uace0 \ud32c\uc774\uc5d0\uc694. \ud2b9\ud788 \uc0c8 \uc568\ubc94 Blackout\uc744 \uc88b\uc544\ud574\uc694'\ub77c\uace0 \ub9d0\ud588\ub2e4. \ub9b0\uc81c\uc774 \ub85c\ud55c\uc740 '\uc5b8\uc81c\ub098 \ube0c\ub9ac\ud2b8\ub2c8 \uc2a4\ud53c\uc5b4\uc2a4\uc5d0\uac8c \uc601\uac10\uc744 \ubc1b\ub294\ub2e4. \ud559\ucc3d\uc2dc\uc808 \uadf8\ub140\ucc98\ub7fc \ud0c0\ube14\ub85c\uc774\ub4dc\uc5d0 \uc624\ub974\uae30\ub97c \uafc8\uafd4\uc654\ub2e4'\uace0 \ub9d0\ud558\uba70 \ub864 \ubaa8\ub378\ub85c \uaf3d\uc558\ub2e4. \uc2a4\ud53c\uc5b4\uc2a4\ub294 \ud604\ub300 \uc74c\uc545\uac00\ub4e4\uc5d0\uac8c \uc74c\uc545\uc801 \uc601\uac10\uc73c\ub85c \uc5b8\uae09\ub418\uae30\ub3c4 \ud588\ub2e4. <hl> \ub9c8\uc77c\ub9ac \uc0ac\uc774\ub7ec\uc2a4\ub294 \uc790\uc2e0\uc758 \ud788\ud2b8\uace1 Party in the U.S.A. \uac00 \ube0c\ub9ac\ud2b8\ub2c8\uc5d0\uac8c \uc601\uac10\uacfc \uc601\ud5a5\uc744 \ubc1b\uc740 \uace1\uc774\ub77c\uace0 \ubc1d\ud614\ub2e4. <hl> \ubca0\ub9ac \ub9e4\ub2d0\ub85c\uc6b0\uc758 \uc568\ubc94 15 Minutes \uc5ed\uc2dc \ube0c\ub9ac\ud2b8\ub2c8\uc5d0\uac8c \uc601\uac10\uc744 \uc5bb\uc5c8\ub2e4\uace0 \uc5b8\uae09\ub418\uc5c8\ub2e4.", "example_title": "Answer Extraction Example 1"}, {"text": "extract answers: \uc9c0\ub09c 22\uc77c \uc544\ud504\ub9ac\uce74TV\ub294 BJ \ucca0\uad6c\uac00 \uc11c\ube44\uc2a4 \uc815\uc9c0 \ucc98\ubd84\uc744 \ubc1b\uc558\uc74c\uc744 \ubc1d\ud614\ub2e4. \uc11c\ube44\uc2a4 \uc815\uc9c0 \ucc98\ubd84\uc744 \uc0ac\uc720\ub294 \ucca0\uad6c\uac00 10\ub300 \uccad\uc18c\ub144\uc5d0\uac8c \uc720\ud574\ud55c \uc7a5\uba74\uc744 \ubc29\uc1a1\uc73c\ub85c \ub0b4\ubcf4\ub0c8\uae30 \ub54c\ubb38\uc774\uc5c8\ub2e4. \ubb38\uc81c\uac00 \ub41c \uc7a5\uba74\uc740 BJ \ucca0\uad6c\uac00 \ubbf8\uc131\ub144\uc790\ub294 \uc2dc\uccad\ud560 \uc218 \uc5c6\uac8c \ud558\ub294 19\uc138 \uc2dc\uccad \uac00\ub2a5 \uc124\uc815\uc744 \ud558\uc9c0 \uc54a\uc740 \ucc44 \ud761\uc5f0\ud558\ub294 \ubaa8\uc2b5\uc744 \uc5ec\uacfc \uc5c6\uc774 \ub4dc\ub7ec\ub0b8 \uc7a5\uba74\uc774\ub2e4. \uc544\ud504\ub9ac\uce74TV\ub294 \uccad\uc18c\ub144 \ubcf4\ud638 \uc815\ucc45\uc758 '\uccad\uc18c\ub144\ub4e4\uc774 \ud574\ub85c\uc6b4 \ud658\uacbd\uc73c\ub85c\ubd80\ud130 \ubcf4\ud638\ubc1b\uc744 \uc218 \uc788\ub3c4\ub85d \uc870\uce58\ud55c\ub2e4'\ub77c\uace0 \uc870\ud56d\uc744 \uadfc\uac70\ub85c \ucca0\uad6c\uc5d0\uac8c \uc11c\ube44\uc2a4 \uc815\uc9c0 \ucc98\ubd84\uc744 \ub0b4\ub838\ub2e4. \ud761\uc5f0 \uc774\uc678\uc5d0 \uc74c\uc8fc \ubc29\uc1a1 \ub4f1\ub3c4 19\uc138 \uc2dc\uccad \uac00\ub2a5 \uc124\uc815\uc744 \ud574\uc57c\ub9cc \ubc29\uc1a1\ud560 \uc218 \uc788\ub2e4. <hl> \uac8c\ub2e4\uac00 \ucca0\uad6c\uc758 \ubc29\uc1a1 \uc815\uc9c0 \ucc98\ubd84\uc740 \uc774\ubc88\uc5d0 \ucc98\uc74c\uc774 \uc544\ub2c8\ub77c 16\ubc88 \uc9f8\uae30 \ub54c\ubb38\uc5d0 \ub354\uc6b1\ub354 \ub17c\ub780\uc774 \ub418\uace0 \uc788\ub2e4. <hl>", "example_title": "Answer Extraction Example 2"}], "model-index": [{"name": "lmqg/mt5-small-koquad-qg-ae", "results": [{"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_koquad", "type": "default", "args": "default"}, "metrics": [{"type": "bleu4_question_generation", "value": 10.91, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 25.83, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 27.52, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 83.4, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 82.54, "name": "MoverScore (Question Generation)"}, {"type": "qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer", "value": 80.36, "name": "QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer))"}, {"type": "qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer", "value": 83.72, "name": "QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer))"}, {"type": "qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer", "value": 77.34, "name": "QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer))"}, {"type": "qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer", "value": 82.55, "name": "QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer))"}, {"type": "qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer", "value": 86.69, "name": "QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer))"}, {"type": "qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer", "value": 78.93, "name": "QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer))"}, {"type": "bleu4_answer_extraction", "value": 38.2, "name": "BLEU4 (Answer Extraction)"}, {"type": "rouge_l_answer_extraction", "value": 82.32, "name": "ROUGE-L (Answer Extraction)"}, {"type": "meteor_answer_extraction", "value": 59.91, "name": "METEOR (Answer Extraction)"}, {"type": "bertscore_answer_extraction", "value": 95.65, "name": "BERTScore (Answer Extraction)"}, {"type": "moverscore_answer_extraction", "value": 94.61, "name": "MoverScore (Answer Extraction)"}, {"type": "answer_f1_score__answer_extraction", "value": 86.98, "name": "AnswerF1Score (Answer Extraction)"}, {"type": "answer_exact_match_answer_extraction", "value": 80.78, "name": "AnswerExactMatch (Answer Extraction)"}]}]}]}
transformers pytorch mt5 text2text-generation question generation answer extraction 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 #answer extraction #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 'lmqg/mt5-small-koquad-qg-ae'
===========================================


This model is fine-tuned version of google/mt5-small for question generation and answer extraction jointly on the lmqg/qg\_koquad (dataset\_name: default) via 'lmqg'.


### Overview


* Language model: google/mt5-small
* 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



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



* *Metric (Answer Extraction)*: 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', 'paragraph\_sentence']
* output\_types: ['question', 'answer']
* prefix\_types: ['qg', 'ae']
* model: google/mt5-small
* max\_length: 512
* max\_length\_output: 32
* epoch: 6
* 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.



### Overview


* Language model: google/mt5-small
* 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



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



* *Metric (Answer Extraction)*: 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', 'paragraph\_sentence']
* output\_types: ['question', 'answer']
* prefix\_types: ['qg', 'ae']
* model: google/mt5-small
* max\_length: 512
* max\_length\_output: 32
* epoch: 6
* 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.
TAGS
#transformers #pytorch #mt5 #text2text-generation #question generation #answer extraction #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: google/mt5-small
* 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



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



* *Metric (Answer Extraction)*: 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', 'paragraph\_sentence']
* output\_types: ['question', 'answer']
* prefix\_types: ['qg', 'ae']
* model: google/mt5-small
* max\_length: 512
* max\_length\_output: 32
* epoch: 6
* 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.
passage: TAGS
#transformers #pytorch #mt5 #text2text-generation #question generation #answer extraction #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: google/mt5-small
* 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



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



* *Metric (Answer Extraction)*: 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', 'paragraph\_sentence']
* output\_types: ['question', 'answer']
* prefix\_types: ['qg', 'ae']
* model: google/mt5-small
* max\_length: 512
* max\_length\_output: 32
* epoch: 6
* 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.