
# Model Card of `vocabtrimmer/mt5-small-trimmed-ja-5000-jaquad-qg`
This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-ja-5000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ja-5000) for question generation task 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:** [vocabtrimmer/mt5-small-trimmed-ja-5000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ja-5000)   
- **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="vocabtrimmer/mt5-small-trimmed-ja-5000-jaquad-qg")

# model prediction
questions = model.generate_q(list_context="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。", list_answer="30数点")

```

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

pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-ja-5000-jaquad-qg")
output = pipe("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。")

```

## Evaluation


- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ja-5000-jaquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_jaquad.default.json) 

|            |   Score | Type    | Dataset                                                          |
|:-----------|--------:|:--------|:-----------------------------------------------------------------|
| BERTScore  |   79.76 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| Bleu_1     |   51.21 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| Bleu_2     |   38.74 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| Bleu_3     |   30.76 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| Bleu_4     |   25.03 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| METEOR     |   26.62 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| MoverScore |   57.12 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| ROUGE_L    |   47.09 | 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
 - output_types: question
 - prefix_types: None
 - model: vocabtrimmer/mt5-small-trimmed-ja-5000
 - max_length: 512
 - max_length_output: 32
 - epoch: 16
 - 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/vocabtrimmer/mt5-small-trimmed-ja-5000-jaquad-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": "ja", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_jaquad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "\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": 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"example_title": "Question Generation Example 2"}, {"text": "\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"}], "model-index": [{"name": "vocabtrimmer/mt5-small-trimmed-ja-5000-jaquad-qg", "results": [{"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_jaquad", "type": "default", "args": "default"}, "metrics": [{"type": "bleu4_question_generation", "value": 25.03, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 47.09, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 26.62, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 79.76, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 57.12, "name": "MoverScore (Question Generation)"}]}]}]}
transformers pytorch mt5 text2text-generation question generation ja dataset:lmqg/qg_jaquad 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 #ja #dataset-lmqg/qg_jaquad #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-ja-5000-jaquad-qg'
================================================================


This model is fine-tuned version of vocabtrimmer/mt5-small-trimmed-ja-5000 for question generation task on the lmqg/qg\_jaquad (dataset\_name: default) via 'lmqg'.


### Overview


* Language model: vocabtrimmer/mt5-small-trimmed-ja-5000
* 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



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


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_jaquad
* dataset\_name: default
* input\_types: paragraph\_answer
* output\_types: question
* prefix\_types: None
* model: vocabtrimmer/mt5-small-trimmed-ja-5000
* max\_length: 512
* max\_length\_output: 32
* epoch: 16
* 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: vocabtrimmer/mt5-small-trimmed-ja-5000
* 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



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


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_jaquad
* dataset\_name: default
* input\_types: paragraph\_answer
* output\_types: question
* prefix\_types: None
* model: vocabtrimmer/mt5-small-trimmed-ja-5000
* max\_length: 512
* max\_length\_output: 32
* epoch: 16
* 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 #ja #dataset-lmqg/qg_jaquad #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-ja-5000
* 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



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


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_jaquad
* dataset\_name: default
* input\_types: paragraph\_answer
* output\_types: question
* prefix\_types: None
* model: vocabtrimmer/mt5-small-trimmed-ja-5000
* max\_length: 512
* max\_length\_output: 32
* epoch: 16
* 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 #ja #dataset-lmqg/qg_jaquad #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-ja-5000
* 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



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


The following hyperparameters were used during fine-tuning:


* dataset\_path: lmqg/qg\_jaquad
* dataset\_name: default
* input\_types: paragraph\_answer
* output\_types: question
* prefix\_types: None
* model: vocabtrimmer/mt5-small-trimmed-ja-5000
* max\_length: 512
* max\_length\_output: 32
* epoch: 16
* 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.