
# Model Card of `lmqg/mbart-large-cc25-jaquad-qg-ae`
This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question generation and answer extraction jointly on the [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).


### Overview
- **Language model:** [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25)   
- **Language:** ja  
- **Training data:** [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) (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="ja", model="lmqg/mbart-large-cc25-jaquad-qg-ae")

# model prediction
question_answer_pairs = model.generate_qa("フェルメールの作品では、17世紀のオランダの画家、ヨハネス・フェルメールの作品について記述する。フェルメールの作品は、疑問作も含め30数点しか現存しない。現存作品はすべて油彩画で、版画、下絵、素描などは残っていない。")

```

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

pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-jaquad-qg-ae")

# answer extraction
answer = pipe("generate question: ゾフィーは貴族出身ではあったが王族出身ではなく、ハプスブルク家の皇位継承者であるフランツ・フェルディナントとの結婚は貴賤結婚となった。皇帝フランツ・ヨーゼフは、2人の間に生まれた子孫が皇位を継がないことを条件として結婚を承認していた。視察が予定されている<hl>6月28日<hl>は2人の14回目の結婚記念日であった。")

# question generation
question = pipe("extract answers: 『クマのプーさん』の物語はまず1925年12月24日、『イヴニング・ニュース』紙のクリスマス特集号に短編作品として掲載された。これは『クマのプーさん』の第一章にあたる作品で、このときだけは挿絵をJ.H.ダウドがつけている。その後作品10話と挿絵が整い、刊行に先駆けて「イーヨーの誕生日」のエピソードが1926年8月に『ロイヤルマガジン』に、同年10月9日に『ニューヨーク・イヴニング・ポスト』紙に掲載されたあと、同年10月14日にロンドンで(メシュエン社)、21日にニューヨークで(ダットン社)『クマのプーさん』が刊行された。<hl>前著『ぼくたちがとてもちいさかったころ』がすでに大きな成功を収めていたこともあり、イギリスでは初版は前著の7倍に当たる3万5000部が刷られた。<hl>他方のアメリカでもその年の終わりまでに15万部を売り上げている。ただし依然として人気のあった前著を売り上げで追い越すには数年の時間を要した。")

```

## Evaluation


- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-jaquad-qg-ae/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_jaquad.default.json) 

|            |   Score | Type    | Dataset                                                          |
|:-----------|--------:|:--------|:-----------------------------------------------------------------|
| BERTScore  |   81.85 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| Bleu_1     |   55.9  | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| Bleu_2     |   43.93 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| Bleu_3     |   36.18 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| Bleu_4     |   30.46 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| METEOR     |   29.21 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| MoverScore |   59.37 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| ROUGE_L    |   51.78 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |


- ***Metric (Question & Answer Generation)***:  [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-jaquad-qg-ae/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_jaquad.default.json)

|                                 |   Score | Type    | Dataset                                                          |
|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
| QAAlignedF1Score (BERTScore)    |   80.55 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| QAAlignedF1Score (MoverScore)   |   55.95 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| QAAlignedPrecision (BERTScore)  |   77.41 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| QAAlignedPrecision (MoverScore) |   53.7  | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| QAAlignedRecall (BERTScore)     |   84.03 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| QAAlignedRecall (MoverScore)    |   58.56 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |


- ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-jaquad-qg-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_jaquad.default.json)

|                  |   Score | Type    | Dataset                                                          |
|:-----------------|--------:|:--------|:-----------------------------------------------------------------|
| AnswerExactMatch |   32.27 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| AnswerF1Score    |   32.27 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| BERTScore        |   78.53 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| Bleu_1           |   36.23 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| Bleu_2           |   33.54 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| Bleu_3           |   31.35 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| Bleu_4           |   29.6  | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| METEOR           |   27.66 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| MoverScore       |   66.82 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| ROUGE_L          |   38.36 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |



## Training hyperparameters

The following hyperparameters were used during fine-tuning:
 - dataset_path: lmqg/qg_jaquad
 - dataset_name: default
 - input_types: ['paragraph_answer', 'paragraph_sentence']
 - output_types: ['question', 'answer']
 - prefix_types: ['qg', 'ae']
 - model: facebook/mbart-large-cc25
 - max_length: 512
 - max_length_output: 32
 - epoch: 7
 - batch: 2
 - lr: 0.0001
 - fp16: False
 - random_seed: 1
 - gradient_accumulation_steps: 32
 - label_smoothing: 0.15

The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mbart-large-cc25-jaquad-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": "ja", "license": "cc-by-4.0", "tags": ["question generation", "answer extraction"], "datasets": ["lmqg/qg_jaquad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: \u30be\u30d5\u30a3\u30fc\u306f\u8cb4\u65cf\u51fa\u8eab\u3067\u306f\u3042\u3063\u305f\u304c\u738b\u65cf\u51fa\u8eab\u3067\u306f\u306a\u304f\u3001\u30cf\u30d7\u30b9\u30d6\u30eb\u30af\u5bb6\u306e\u7687\u4f4d\u7d99\u627f\u8005\u3067\u3042\u308b\u30d5\u30e9\u30f3\u30c4\u30fb\u30d5\u30a7\u30eb\u30c7\u30a3\u30ca\u30f3\u30c8\u3068\u306e\u7d50\u5a5a\u306f\u8cb4\u8ce4\u7d50\u5a5a\u3068\u306a\u3063\u305f\u3002\u7687\u5e1d\u30d5\u30e9\u30f3\u30c4\u30fb\u30e8\u30fc\u30bc\u30d5\u306f\u30012\u4eba\u306e\u9593\u306b\u751f\u307e\u308c\u305f\u5b50\u5b6b\u304c\u7687\u4f4d\u3092\u7d99\u304c\u306a\u3044\u3053\u3068\u3092\u6761\u4ef6\u3068\u3057\u3066\u7d50\u5a5a\u3092\u627f\u8a8d\u3057\u3066\u3044\u305f\u3002\u8996\u5bdf\u304c\u4e88\u5b9a\u3055\u308c\u3066\u3044\u308b<hl>6\u670828\u65e5<hl>\u306f2\u4eba\u306e14\u56de\u76ee\u306e\u7d50\u5a5a\u8a18\u5ff5\u65e5\u3067\u3042\u3063\u305f\u3002", "example_title": "Question Generation Example 1"}, {"text": "generate question: \u300e\u30af\u30de\u306e\u30d7\u30fc\u3055\u3093\u300f\u306e\u7269\u8a9e\u306f\u307e\u305a1925\u5e7412\u670824\u65e5\u3001\u300e\u30a4\u30f4\u30cb\u30f3\u30b0\u30fb\u30cb\u30e5\u30fc\u30b9\u300f\u7d19\u306e\u30af\u30ea\u30b9\u30de\u30b9\u7279\u96c6\u53f7\u306b\u77ed\u7de8\u4f5c\u54c1\u3068\u3057\u3066\u63b2\u8f09\u3055\u308c\u305f\u3002\u3053\u308c\u306f\u300e\u30af\u30de\u306e\u30d7\u30fc\u3055\u3093\u300f\u306e\u7b2c\u4e00\u7ae0\u306b\u3042\u305f\u308b\u4f5c\u54c1\u3067\u3001\u3053\u306e\u3068\u304d\u3060\u3051\u306f\u633f\u7d75\u3092J.H.\u30c0\u30a6\u30c9\u304c\u3064\u3051\u3066\u3044\u308b\u3002\u305d\u306e\u5f8c\u4f5c\u54c110\u8a71\u3068\u633f\u7d75\u304c\u6574\u3044\u3001\u520a\u884c\u306b\u5148\u99c6\u3051\u3066\u300c\u30a4\u30fc\u30e8\u30fc\u306e\u8a95\u751f\u65e5\u300d\u306e\u30a8\u30d4\u30bd\u30fc\u30c9\u304c1926\u5e748\u6708\u306b\u300e\u30ed\u30a4\u30e4\u30eb\u30de\u30ac\u30b8\u30f3\u300f\u306b\u3001\u540c\u5e7410\u67089\u65e5\u306b\u300e\u30cb\u30e5\u30fc\u30e8\u30fc\u30af\u30fb\u30a4\u30f4\u30cb\u30f3\u30b0\u30fb\u30dd\u30b9\u30c8\u300f\u7d19\u306b\u63b2\u8f09\u3055\u308c\u305f\u3042\u3068\u3001\u540c\u5e7410\u670814\u65e5\u306b\u30ed\u30f3\u30c9\u30f3\u3067(\u30e1\u30b7\u30e5\u30a8\u30f3\u793e)\u300121\u65e5\u306b\u30cb\u30e5\u30fc\u30e8\u30fc\u30af\u3067(\u30c0\u30c3\u30c8\u30f3\u793e)\u300e\u30af\u30de\u306e\u30d7\u30fc\u3055\u3093\u300f\u304c\u520a\u884c\u3055\u308c\u305f\u3002\u524d\u8457\u300e\u307c\u304f\u305f\u3061\u304c\u3068\u3066\u3082\u3061\u3044\u3055\u304b\u3063\u305f\u3053\u308d\u300f\u304c\u3059\u3067\u306b\u5927\u304d\u306a\u6210\u529f\u3092\u53ce\u3081\u3066\u3044\u305f\u3053\u3068\u3082\u3042\u308a\u3001\u30a4\u30ae\u30ea\u30b9\u3067\u306f\u521d\u7248\u306f\u524d\u8457\u306e7\u500d\u306b\u5f53\u305f\u308b<hl>3\u4e075000\u90e8<hl>\u304c\u5237\u3089\u308c\u305f\u3002\u4ed6\u65b9\u306e\u30a2\u30e1\u30ea\u30ab\u3067\u3082\u305d\u306e\u5e74\u306e\u7d42\u308f\u308a\u307e\u3067\u306b15\u4e07\u90e8\u3092\u58f2\u308a\u4e0a\u3052\u3066\u3044\u308b\u3002\u305f\u3060\u3057\u4f9d\u7136\u3068\u3057\u3066\u4eba\u6c17\u306e\u3042\u3063\u305f\u524d\u8457\u3092\u58f2\u308a\u4e0a\u3052\u3067\u8ffd\u3044\u8d8a\u3059\u306b\u306f\u6570\u5e74\u306e\u6642\u9593\u3092\u8981\u3057\u305f\u3002", "example_title": "Question Generation Example 2"}, {"text": "generate question: \u30d5\u30a7\u30eb\u30e1\u30fc\u30eb\u306e\u4f5c\u54c1\u3067\u306f\u300117\u4e16\u7d00\u306e\u30aa\u30e9\u30f3\u30c0\u306e\u753b\u5bb6\u3001\u30e8\u30cf\u30cd\u30b9\u30fb\u30d5\u30a7\u30eb\u30e1\u30fc\u30eb\u306e\u4f5c\u54c1\u306b\u3064\u3044\u3066\u8a18\u8ff0\u3059\u308b\u3002\u30d5\u30a7\u30eb\u30e1\u30fc\u30eb\u306e\u4f5c\u54c1\u306f\u3001\u7591\u554f\u4f5c\u3082\u542b\u3081<hl>30\u6570\u70b9<hl>\u3057\u304b\u73fe\u5b58\u3057\u306a\u3044\u3002\u73fe\u5b58\u4f5c\u54c1\u306f\u3059\u3079\u3066\u6cb9\u5f69\u753b\u3067\u3001\u7248\u753b\u3001\u4e0b\u7d75\u3001\u7d20\u63cf\u306a\u3069\u306f\u6b8b\u3063\u3066\u3044\u306a\u3044\u3002\u4ee5\u4e0b\u306b\u306f\u82e5\u5e72\u306e\u7591\u554f\u4f5c\u3082\u542b\u3081\u300137\u70b9\u306e\u57fa\u672c\u60c5\u5831\u3092\u8a18\u8f09\u3057\u3001\u5404\u4f5c\u54c1\u306b\u3064\u3044\u3066\u7565\u8aac\u3059\u308b\u3002\u53ce\u9332\u9806\u5e8f\u3001\u63a8\u5b9a\u5236\u4f5c\u5e74\u4ee3\u306f\u300e\u300c\u30d5\u30a7\u30eb\u30e1\u30fc\u30eb\u3068\u305d\u306e\u6642\u4ee3\u5c55\u300d\u56f3\u9332\u300f\u306b\u3088\u308b\u3002\u65e5\u672c\u8a9e\u306e\u4f5c\u54c1\u30bf\u30a4\u30c8\u30eb\u306b\u3064\u3044\u3066\u306f\u3001\u4e0a\u63b2\u56f3\u9332\u306e\u307b\u304b\u3001\u300e\u300c\u30d5\u30a7\u30eb\u30e1\u30fc\u30eb\u5c55\u300d\u56f3\u9332\u300f\u3001\u300e\u30d5\u30a7\u30eb\u30e1\u30fc\u30eb\u751f\u6daf\u3068\u4f5c\u54c1\u300f\u306b\u3088\u308b\u3002\u4fbf\u5b9c\u4e0a\u300c1650\u5e74\u4ee3\u306e\u4f5c\u54c1\u300d\u300c1660\u5e74\u4ee3\u306e\u4f5c\u54c1\u300d\u300c1670\u5e74\u4ee3\u306e\u4f5c\u54c1\u300d\u306e3\u3064\u306e\u7bc0\u3092\u8a2d\u3051\u305f\u304c\u3001\u30d5\u30a7\u30eb\u30e1\u30fc\u30eb\u306e\u4f5c\u54c1\u306b\u306f\u5236\u4f5c\u5e74\u4ee3\u4e0d\u660e\u306e\u3082\u306e\u304c\u591a\u304f\u3001\u63a8\u5b9a\u5236\u4f5c\u5e74\u4ee3\u306b\u3064\u3044\u3066\u306f\u7814\u7a76\u8005\u3084\u6587\u732e\u306b\u3088\u3063\u3066\u82e5\u5e72\u306e\u5dee\u304c\u3042\u308b\u3002", "example_title": "Question Generation Example 3"}, {"text": "extract answers: \u300e\u30af\u30de\u306e\u30d7\u30fc\u3055\u3093\u300f\u306e\u7269\u8a9e\u306f\u307e\u305a1925\u5e7412\u670824\u65e5\u3001\u300e\u30a4\u30f4\u30cb\u30f3\u30b0\u30fb\u30cb\u30e5\u30fc\u30b9\u300f\u7d19\u306e\u30af\u30ea\u30b9\u30de\u30b9\u7279\u96c6\u53f7\u306b\u77ed\u7de8\u4f5c\u54c1\u3068\u3057\u3066\u63b2\u8f09\u3055\u308c\u305f\u3002\u3053\u308c\u306f\u300e\u30af\u30de\u306e\u30d7\u30fc\u3055\u3093\u300f\u306e\u7b2c\u4e00\u7ae0\u306b\u3042\u305f\u308b\u4f5c\u54c1\u3067\u3001\u3053\u306e\u3068\u304d\u3060\u3051\u306f\u633f\u7d75\u3092J.H.\u30c0\u30a6\u30c9\u304c\u3064\u3051\u3066\u3044\u308b\u3002\u305d\u306e\u5f8c\u4f5c\u54c110\u8a71\u3068\u633f\u7d75\u304c\u6574\u3044\u3001\u520a\u884c\u306b\u5148\u99c6\u3051\u3066\u300c\u30a4\u30fc\u30e8\u30fc\u306e\u8a95\u751f\u65e5\u300d\u306e\u30a8\u30d4\u30bd\u30fc\u30c9\u304c1926\u5e748\u6708\u306b\u300e\u30ed\u30a4\u30e4\u30eb\u30de\u30ac\u30b8\u30f3\u300f\u306b\u3001\u540c\u5e7410\u67089\u65e5\u306b\u300e\u30cb\u30e5\u30fc\u30e8\u30fc\u30af\u30fb\u30a4\u30f4\u30cb\u30f3\u30b0\u30fb\u30dd\u30b9\u30c8\u300f\u7d19\u306b\u63b2\u8f09\u3055\u308c\u305f\u3042\u3068\u3001\u540c\u5e7410\u670814\u65e5\u306b\u30ed\u30f3\u30c9\u30f3\u3067(\u30e1\u30b7\u30e5\u30a8\u30f3\u793e)\u300121\u65e5\u306b\u30cb\u30e5\u30fc\u30e8\u30fc\u30af\u3067(\u30c0\u30c3\u30c8\u30f3\u793e)\u300e\u30af\u30de\u306e\u30d7\u30fc\u3055\u3093\u300f\u304c\u520a\u884c\u3055\u308c\u305f\u3002<hl>\u524d\u8457\u300e\u307c\u304f\u305f\u3061\u304c\u3068\u3066\u3082\u3061\u3044\u3055\u304b\u3063\u305f\u3053\u308d\u300f\u304c\u3059\u3067\u306b\u5927\u304d\u306a\u6210\u529f\u3092\u53ce\u3081\u3066\u3044\u305f\u3053\u3068\u3082\u3042\u308a\u3001\u30a4\u30ae\u30ea\u30b9\u3067\u306f\u521d\u7248\u306f\u524d\u8457\u306e7\u500d\u306b\u5f53\u305f\u308b3\u4e075000\u90e8\u304c\u5237\u3089\u308c\u305f\u3002<hl>\u4ed6\u65b9\u306e\u30a2\u30e1\u30ea\u30ab\u3067\u3082\u305d\u306e\u5e74\u306e\u7d42\u308f\u308a\u307e\u3067\u306b15\u4e07\u90e8\u3092\u58f2\u308a\u4e0a\u3052\u3066\u3044\u308b\u3002\u305f\u3060\u3057\u4f9d\u7136\u3068\u3057\u3066\u4eba\u6c17\u306e\u3042\u3063\u305f\u524d\u8457\u3092\u58f2\u308a\u4e0a\u3052\u3067\u8ffd\u3044\u8d8a\u3059\u306b\u306f\u6570\u5e74\u306e\u6642\u9593\u3092\u8981\u3057\u305f\u3002", "example_title": "Answer Extraction Example 1"}, {"text": "extract answers: \u30d5\u30a7\u30eb\u30e1\u30fc\u30eb\u306e\u4f5c\u54c1\u3067\u306f\u300117\u4e16\u7d00\u306e\u30aa\u30e9\u30f3\u30c0\u306e\u753b\u5bb6\u3001\u30e8\u30cf\u30cd\u30b9\u30fb\u30d5\u30a7\u30eb\u30e1\u30fc\u30eb\u306e\u4f5c\u54c1\u306b\u3064\u3044\u3066\u8a18\u8ff0\u3059\u308b\u3002\u30d5\u30a7\u30eb\u30e1\u30fc\u30eb\u306e\u4f5c\u54c1\u306f\u3001\u7591\u554f\u4f5c\u3082\u542b\u308130\u6570\u70b9\u3057\u304b\u73fe\u5b58\u3057\u306a\u3044\u3002<hl>\u73fe\u5b58\u4f5c\u54c1\u306f\u3059\u3079\u3066\u6cb9\u5f69\u753b\u3067\u3001\u7248\u753b\u3001\u4e0b\u7d75\u3001\u7d20\u63cf\u306a\u3069\u306f\u6b8b\u3063\u3066\u3044\u306a\u3044\u3002\u4ee5\u4e0b\u306b\u306f\u82e5\u5e72\u306e\u7591\u554f\u4f5c\u3082\u542b\u3081\u300137\u70b9\u306e\u57fa\u672c\u60c5\u5831\u3092\u8a18\u8f09\u3057\u3001\u5404\u4f5c\u54c1\u306b\u3064\u3044\u3066\u7565\u8aac\u3059\u308b\u3002<hl>\u53ce\u9332\u9806\u5e8f\u3001\u63a8\u5b9a\u5236\u4f5c\u5e74\u4ee3\u306f\u300e\u300c\u30d5\u30a7\u30eb\u30e1\u30fc\u30eb\u3068\u305d\u306e\u6642\u4ee3\u5c55\u300d\u56f3\u9332\u300f\u306b\u3088\u308b\u3002\u65e5\u672c\u8a9e\u306e\u4f5c\u54c1\u30bf\u30a4\u30c8\u30eb\u306b\u3064\u3044\u3066\u306f\u3001\u4e0a\u63b2\u56f3\u9332\u306e\u307b\u304b\u3001\u300e\u300c\u30d5\u30a7\u30eb\u30e1\u30fc\u30eb\u5c55\u300d\u56f3\u9332\u300f\u3001\u300e\u30d5\u30a7\u30eb\u30e1\u30fc\u30eb\u751f\u6daf\u3068\u4f5c\u54c1\u300f\u306b\u3088\u308b\u3002\u4fbf\u5b9c\u4e0a\u300c1650\u5e74\u4ee3\u306e\u4f5c\u54c1\u300d\u300c1660\u5e74\u4ee3\u306e\u4f5c\u54c1\u300d\u300c1670\u5e74\u4ee3\u306e\u4f5c\u54c1\u300d\u306e3\u3064\u306e\u7bc0\u3092\u8a2d\u3051\u305f\u304c\u3001\u30d5\u30a7\u30eb\u30e1\u30fc\u30eb\u306e\u4f5c\u54c1\u306b\u306f\u5236\u4f5c\u5e74\u4ee3\u4e0d\u660e\u306e\u3082\u306e\u304c\u591a\u304f\u3001\u63a8\u5b9a\u5236\u4f5c\u5e74\u4ee3\u306b\u3064\u3044\u3066\u306f\u7814\u7a76\u8005\u3084\u6587\u732e\u306b\u3088\u3063\u3066\u82e5\u5e72\u306e\u5dee\u304c\u3042\u308b\u3002", "example_title": "Answer Extraction Example 2"}], "model-index": [{"name": "lmqg/mbart-large-cc25-jaquad-qg-ae", "results": [{"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_jaquad", "type": "default", "args": "default"}, "metrics": [{"type": "bleu4_question_generation", "value": 30.46, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 51.78, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 29.21, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 81.85, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 59.37, "name": "MoverScore (Question Generation)"}, {"type": "qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer", "value": 80.55, "name": "QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer))"}, {"type": "qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer", "value": 84.03, "name": "QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer))"}, {"type": "qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer", "value": 77.41, "name": "QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer))"}, {"type": "qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer", "value": 55.95, "name": "QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer))"}, {"type": "qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer", "value": 58.56, "name": "QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer))"}, {"type": "qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer", "value": 53.7, "name": "QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer))"}, {"type": "bleu4_answer_extraction", "value": 29.6, "name": "BLEU4 (Answer Extraction)"}, {"type": "rouge_l_answer_extraction", "value": 38.36, "name": "ROUGE-L (Answer Extraction)"}, {"type": "meteor_answer_extraction", "value": 27.66, "name": "METEOR (Answer Extraction)"}, {"type": "bertscore_answer_extraction", "value": 78.53, "name": "BERTScore (Answer Extraction)"}, {"type": "moverscore_answer_extraction", "value": 66.82, "name": "MoverScore (Answer Extraction)"}, {"type": "answer_f1_score__answer_extraction", "value": 32.27, "name": "AnswerF1Score (Answer Extraction)"}, {"type": "answer_exact_match_answer_extraction", "value": 32.27, "name": "AnswerExactMatch (Answer Extraction)"}]}]}]}
transformers pytorch mbart text2text-generation question generation answer extraction ja dataset:lmqg/qg_jaquad arxiv:2210.03992 license:cc-by-4.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #mbart #text2text-generation #question generation #answer extraction #ja #dataset-lmqg/qg_jaquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

Model Card of 'lmqg/mbart-large-cc25-jaquad-qg-ae'
==================================================


This model is fine-tuned version of facebook/mbart-large-cc25 for question generation and answer extraction jointly on the lmqg/qg\_jaquad (dataset\_name: default) via 'lmqg'.


### Overview


* Language model: facebook/mbart-large-cc25
* Language: ja
* Training data: lmqg/qg\_jaquad (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\_jaquad
* dataset\_name: default
* input\_types: ['paragraph\_answer', 'paragraph\_sentence']
* output\_types: ['question', 'answer']
* prefix\_types: ['qg', 'ae']
* model: facebook/mbart-large-cc25
* max\_length: 512
* max\_length\_output: 32
* epoch: 7
* batch: 2
* lr: 0.0001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 32
* label\_smoothing: 0.15


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



### Overview


* Language model: facebook/mbart-large-cc25
* Language: ja
* Training data: lmqg/qg\_jaquad (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\_jaquad
* dataset\_name: default
* input\_types: ['paragraph\_answer', 'paragraph\_sentence']
* output\_types: ['question', 'answer']
* prefix\_types: ['qg', 'ae']
* model: facebook/mbart-large-cc25
* max\_length: 512
* max\_length\_output: 32
* epoch: 7
* batch: 2
* lr: 0.0001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 32
* label\_smoothing: 0.15


The full configuration can be found at fine-tuning config file.
TAGS
#transformers #pytorch #mbart #text2text-generation #question generation #answer extraction #ja #dataset-lmqg/qg_jaquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Overview


* Language model: facebook/mbart-large-cc25
* Language: ja
* Training data: lmqg/qg\_jaquad (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\_jaquad
* dataset\_name: default
* input\_types: ['paragraph\_answer', 'paragraph\_sentence']
* output\_types: ['question', 'answer']
* prefix\_types: ['qg', 'ae']
* model: facebook/mbart-large-cc25
* max\_length: 512
* max\_length\_output: 32
* epoch: 7
* batch: 2
* lr: 0.0001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 32
* label\_smoothing: 0.15


The full configuration can be found at fine-tuning config file.
TAGS
#transformers #pytorch #mbart #text2text-generation #question generation #answer extraction #ja #dataset-lmqg/qg_jaquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Overview


* Language model: facebook/mbart-large-cc25
* Language: ja
* Training data: lmqg/qg\_jaquad (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\_jaquad
* dataset\_name: default
* input\_types: ['paragraph\_answer', 'paragraph\_sentence']
* output\_types: ['question', 'answer']
* prefix\_types: ['qg', 'ae']
* model: facebook/mbart-large-cc25
* max\_length: 512
* max\_length\_output: 32
* epoch: 7
* batch: 2
* lr: 0.0001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 32
* label\_smoothing: 0.15


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