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lmqg
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mt5-small-zhquad-qag

Text2Text Generation
Transformers
PyTorch
Chinese
mt5
questions and answers generation
Eval Results
Inference Endpoints
Model card Files Community
2
mt5-small-zhquad-qag
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---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: zh
datasets:
- lmqg/qag_zhquad
pipeline_tag: text2text-generation
tags:
- questions and answers generation
widget:
- text: "南安普敦的警察服务由汉普郡警察提供。南安普敦行动的主要基地是一座新的八层专用建筑，造价3000万英镑。该建筑位于南路，2011年启用，靠近 南安普敦中央 火车站。此前，南安普顿市中心的行动位于市民中心西翼，但由于设施老化，加上计划在旧警察局和地方法院建造一座新博物馆，因此必须搬迁。在Portswood、Banister Park、Hille和Shirley还有其他警察局，在南安普顿中央火车站还有一个英国交通警察局。"
  example_title: "Questions & Answers Generation Example 1" 
model-index:
- name: lmqg/mt5-small-zhquad-qag
  results:
  - task:
      name: Text2text Generation
      type: text2text-generation
    dataset:
      name: lmqg/qag_zhquad
      type: default
      args: default
    metrics:
    - name: QAAlignedF1Score-BERTScore (Question & Answer Generation)
      type: qa_aligned_f1_score_bertscore_question_answer_generation
      value: 75.47
    - name: QAAlignedRecall-BERTScore (Question & Answer Generation)
      type: qa_aligned_recall_bertscore_question_answer_generation
      value: 75.41
    - name: QAAlignedPrecision-BERTScore (Question & Answer Generation)
      type: qa_aligned_precision_bertscore_question_answer_generation
      value: 75.56
    - name: QAAlignedF1Score-MoverScore (Question & Answer Generation)
      type: qa_aligned_f1_score_moverscore_question_answer_generation
      value: 52.42
    - name: QAAlignedRecall-MoverScore (Question & Answer Generation)
      type: qa_aligned_recall_moverscore_question_answer_generation
      value: 52.33
    - name: QAAlignedPrecision-MoverScore (Question & Answer Generation)
      type: qa_aligned_precision_moverscore_question_answer_generation
      value: 52.53
---

# Model Card of `lmqg/mt5-small-zhquad-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_zhquad](https://huggingface.co/datasets/lmqg/qag_zhquad) (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:** zh  
- **Training data:** [lmqg/qag_zhquad](https://huggingface.co/datasets/lmqg/qag_zhquad) (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="zh", model="lmqg/mt5-small-zhquad-qag")

# model prediction
question_answer_pairs = model.generate_qa("南安普敦的警察服务由汉普郡警察提供。南安普敦行动的主要基地是一座新的八层专用建筑，造价3000万英镑。该建筑位于南路，2011年启用，靠近南安普敦中央火车站。此前，南安普顿市中心的行动位于市民中心西翼，但由于设施老化，加上计划在旧警察局和地方法院建造一座新博物馆，因此必须搬迁。在Portswood、Banister Park、Hille和Shirley还有其他警察局，在南安普顿中央火车站还有一个英国交通警察局。")

```

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

pipe = pipeline("text2text-generation", "lmqg/mt5-small-zhquad-qag")
output = pipe("南安普敦的警察服务由汉普郡警察提供。南安普敦行动的主要基地是一座新的八层专用建筑，造价3000万英镑。该建筑位于南路，2011年启用，靠近 南安普敦中央 火车站。此前，南安普顿市中心的行动位于市民中心西翼，但由于设施老化，加上计划在旧警察局和地方法院建造一座新博物馆，因此必须搬迁。在Portswood、Banister Park、Hille和Shirley还有其他警察局，在南安普顿中央火车站还有一个英国交通警察局。")

```

## Evaluation


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

|                                 |   Score | Type    | Dataset                                                            |
|:--------------------------------|--------:|:--------|:-------------------------------------------------------------------|
| QAAlignedF1Score (BERTScore)    |   75.47 | default | [lmqg/qag_zhquad](https://huggingface.co/datasets/lmqg/qag_zhquad) |
| QAAlignedF1Score (MoverScore)   |   52.42 | default | [lmqg/qag_zhquad](https://huggingface.co/datasets/lmqg/qag_zhquad) |
| QAAlignedPrecision (BERTScore)  |   75.56 | default | [lmqg/qag_zhquad](https://huggingface.co/datasets/lmqg/qag_zhquad) |
| QAAlignedPrecision (MoverScore) |   52.53 | default | [lmqg/qag_zhquad](https://huggingface.co/datasets/lmqg/qag_zhquad) |
| QAAlignedRecall (BERTScore)     |   75.41 | default | [lmqg/qag_zhquad](https://huggingface.co/datasets/lmqg/qag_zhquad) |
| QAAlignedRecall (MoverScore)    |   52.33 | default | [lmqg/qag_zhquad](https://huggingface.co/datasets/lmqg/qag_zhquad) |



## Training hyperparameters

The following hyperparameters were used during fine-tuning:
 - dataset_path: lmqg/qag_zhquad
 - 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: 12
 - batch: 8
 - lr: 0.001
 - fp16: False
 - random_seed: 1
 - gradient_accumulation_steps: 8
 - label_smoothing: 0.15

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

```