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lmqg
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mt5-small-jaquad-qag

Text2Text Generation
Transformers
PyTorch
Japanese
mt5
questions and answers generation
Eval Results
Inference Endpoints
Model card Files Community
mt5-small-jaquad-qag
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---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: ja
datasets:
- lmqg/qag_jaquad
pipeline_tag: text2text-generation
tags:
- questions and answers generation
widget:
- text: "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6月28日は2人の14回目の結婚記念日であった。"
  example_title: "Questions & Answers Generation Example 1" 
model-index:
- name: lmqg/mt5-small-jaquad-qag
  results:
  - task:
      name: Text2text Generation
      type: text2text-generation
    dataset:
      name: lmqg/qag_jaquad
      type: default
      args: default
    metrics:
    - name: QAAlignedF1Score-BERTScore (Question & Answer Generation)
      type: qa_aligned_f1_score_bertscore_question_answer_generation
      value: 58.35
    - name: QAAlignedRecall-BERTScore (Question & Answer Generation)
      type: qa_aligned_recall_bertscore_question_answer_generation
      value: 58.38
    - name: QAAlignedPrecision-BERTScore (Question & Answer Generation)
      type: qa_aligned_precision_bertscore_question_answer_generation
      value: 58.34
    - name: QAAlignedF1Score-MoverScore (Question & Answer Generation)
      type: qa_aligned_f1_score_moverscore_question_answer_generation
      value: 39.19
    - name: QAAlignedRecall-MoverScore (Question & Answer Generation)
      type: qa_aligned_recall_moverscore_question_answer_generation
      value: 39.17
    - name: QAAlignedPrecision-MoverScore (Question & Answer Generation)
      type: qa_aligned_precision_moverscore_question_answer_generation
      value: 39.21
---

# Model Card of `lmqg/mt5-small-jaquad-qag`
This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question & answer pair generation task on the [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) (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:** ja  
- **Training data:** [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_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/mt5-small-jaquad-qag")

# 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/mt5-small-jaquad-qag")
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6月28日は2人の14回目の結婚記念日であった。")

```

## Evaluation


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

|                                 |   Score | Type    | Dataset                                                            |
|:--------------------------------|--------:|:--------|:-------------------------------------------------------------------|
| QAAlignedF1Score (BERTScore)    |   58.35 | default | [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) |
| QAAlignedF1Score (MoverScore)   |   39.19 | default | [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) |
| QAAlignedPrecision (BERTScore)  |   58.34 | default | [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) |
| QAAlignedPrecision (MoverScore) |   39.21 | default | [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) |
| QAAlignedRecall (BERTScore)     |   58.38 | default | [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) |
| QAAlignedRecall (MoverScore)    |   39.17 | default | [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) |



## Training hyperparameters

The following hyperparameters were used during fine-tuning:
 - dataset_path: lmqg/qag_jaquad
 - dataset_name: default
 - input_types: ['paragraph']
 - output_types: ['questions_answers']
 - prefix_types: None
 - model: google/mt5-small
 - max_length: 512
 - max_length_output: 256
 - epoch: 18
 - batch: 8
 - lr: 0.001
 - fp16: False
 - random_seed: 1
 - gradient_accumulation_steps: 8
 - label_smoothing: 0.0

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

```