---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: es
datasets:
- lmqg/qg_esquad
pipeline_tag: text2text-generation
tags:
- question generation
widget:
- text: "del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India."
  example_title: "Question Generation Example 1" 
- text: "a <hl> noviembre <hl> , que es también la estación lluviosa."
  example_title: "Question Generation Example 2" 
- text: "como <hl> el gobierno de Abbott <hl> que asumió el cargo el 18 de septiembre de 2013."
  example_title: "Question Generation Example 3" 
model-index:
- name: lmqg/mt5-small-esquad
  results:
  - task:
      name: Text2text Generation
      type: text2text-generation
    dataset:
      name: lmqg/qg_esquad
      type: default
      args: default
    metrics:
    - name: BLEU4
      type: bleu4
      value: 0.09610295466326652
    - name: ROUGE-L
      type: rouge-l
      value: 0.2462086653539063
    - name: METEOR
      type: meteor
      value: 0.2271141532255219
    - name: BERTScore
      type: bertscore
      value: 0.8406637574548622
    - name: MoverScore
      type: moverscore
      value: 0.5905647613125026
    - name: QAAlignedF1Score (BERTScore)
      type: qa_aligned_f1_score_bertscore
      value: 0.8942792445970149
    - name: QAAlignedRecall (BERTScore)
      type: qa_aligned_recall_bertscore
      value: 0.8942792441862537
    - name: QAAlignedPrecision (BERTScore)
      type: qa_aligned_precision_bertscore
      value: 0.8942792441862537
    - name: QAAlignedF1Score (MoverScore)
      type: qa_aligned_f1_score_moverscore
      value: 0.6373351335791504
    - name: QAAlignedRecall (MoverScore)
      type: qa_aligned_recall_moverscore
      value: 0.6373352959963351
    - name: QAAlignedPrecision (MoverScore)
      type: qa_aligned_precision_moverscore
      value: 0.6373352959963351
---

# Model Card of `lmqg/mt5-small-esquad`
This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation task on the 
[lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).


Please cite our paper if you use the model ([https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)).

```

@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",
}

```

### Overview
- **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small)   
- **Language:** es  
- **Training data:** [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (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='es', model='lmqg/mt5-small-esquad')
# model prediction
question = model.generate_q(list_context=["a noviembre , que es también la estación lluviosa."], list_answer=["noviembre"])

```

- With `transformers`
```python

from transformers import pipeline
# initialize model
pipe = pipeline("text2text-generation", 'lmqg/mt5-small-esquad')
# question generation
question = pipe('del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India.')

```

## Evaluation Metrics


### Metrics

| Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link |
|:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:|
| [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | default | 0.096 | 0.246 | 0.227 | 0.841 | 0.591 | [link](https://huggingface.co/lmqg/mt5-small-esquad/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_esquad.default.json) | 


### Metrics (QAG)

| Dataset | Type | QA Aligned F1 Score (BERTScore) | QA Aligned F1 Score (MoverScore) | Link |
|:--------|:-----|--------------------------------:|---------------------------------:|-----:|
| [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | default | 0.894 | 0.637 | [link](https://huggingface.co/lmqg/mt5-small-esquad/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_esquad.default.json) | 
    



## Training hyperparameters

The following hyperparameters were used during fine-tuning:
 - dataset_path: lmqg/qg_esquad
 - dataset_name: default
 - input_types: ['paragraph_answer']
 - output_types: ['question']
 - prefix_types: None
 - model: google/mt5-small
 - max_length: 512
 - max_length_output: 32
 - epoch: 16
 - batch: 64
 - lr: 0.0005
 - fp16: False
 - random_seed: 1
 - gradient_accumulation_steps: 1
 - label_smoothing: 0.15

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

```