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lmqg
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mt5-small-itquad-qag

Text2Text Generation
Transformers
PyTorch
Italian
mt5
questions and answers generation
Eval Results
Inference Endpoints
Model card Files Community
mt5-small-itquad-qag
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---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: it
datasets:
- lmqg/qag_itquad
pipeline_tag: text2text-generation
tags:
- questions and answers generation
widget:
- text: "Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento."
  example_title: "Questions & Answers Generation Example 1" 
model-index:
- name: lmqg/mt5-small-itquad-qag
  results:
  - task:
      name: Text2text Generation
      type: text2text-generation
    dataset:
      name: lmqg/qag_itquad
      type: default
      args: default
    metrics:
    - name: QAAlignedF1Score-BERTScore (Question & Answer Generation)
      type: qa_aligned_f1_score_bertscore_question_answer_generation
      value: 79.41
    - name: QAAlignedRecall-BERTScore (Question & Answer Generation)
      type: qa_aligned_recall_bertscore_question_answer_generation
      value: 77.79
    - name: QAAlignedPrecision-BERTScore (Question & Answer Generation)
      type: qa_aligned_precision_bertscore_question_answer_generation
      value: 81.16
    - name: QAAlignedF1Score-MoverScore (Question & Answer Generation)
      type: qa_aligned_f1_score_moverscore_question_answer_generation
      value: 54.15
    - name: QAAlignedRecall-MoverScore (Question & Answer Generation)
      type: qa_aligned_recall_moverscore_question_answer_generation
      value: 52.94
    - name: QAAlignedPrecision-MoverScore (Question & Answer Generation)
      type: qa_aligned_precision_moverscore_question_answer_generation
      value: 55.49
---

# Model Card of `lmqg/mt5-small-itquad-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_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) (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:** it  
- **Training data:** [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) (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="it", model="lmqg/mt5-small-itquad-qag")

# model prediction
question_answer_pairs = model.generate_qa("Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.")

```

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

pipe = pipeline("text2text-generation", "lmqg/mt5-small-itquad-qag")
output = pipe("Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.")

```

## Evaluation


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

|                                 |   Score | Type    | Dataset                                                            |
|:--------------------------------|--------:|:--------|:-------------------------------------------------------------------|
| QAAlignedF1Score (BERTScore)    |   79.41 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
| QAAlignedF1Score (MoverScore)   |   54.15 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
| QAAlignedPrecision (BERTScore)  |   81.16 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
| QAAlignedPrecision (MoverScore) |   55.49 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
| QAAlignedRecall (BERTScore)     |   77.79 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
| QAAlignedRecall (MoverScore)    |   52.94 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |



## Training hyperparameters

The following hyperparameters were used during fine-tuning:
 - dataset_path: lmqg/qag_itquad
 - 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: 17
 - 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-itquad-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",
}

```