README.md

---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: it
datasets:
- lmqg/qg_itquad
pipeline_tag: text2text-generation
tags:
- question generation
widget:
- text: "<hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento."
  example_title: "Question Generation Example 1" 
- text: "L' individuazione del petrolio e lo sviluppo di nuovi giacimenti richiedeva in genere <hl> da cinque a dieci anni <hl> prima di una produzione significativa."
  example_title: "Question Generation Example 2" 
- text: "il <hl> Giappone <hl> è stato il paese più dipendente dal petrolio arabo."
  example_title: "Question Generation Example 3" 
model-index:
- name: vocabtrimmer/mt5-small-trimmed-it-60000-itquad-qg
  results:
  - task:
      name: Text2text Generation
      type: text2text-generation
    dataset:
      name: lmqg/qg_itquad
      type: default
      args: default
    metrics:
    - name: BLEU4 (Question Generation)
      type: bleu4_question_generation
      value: 7.43
    - name: ROUGE-L (Question Generation)
      type: rouge_l_question_generation
      value: 21.95
    - name: METEOR (Question Generation)
      type: meteor_question_generation
      value: 17.36
    - name: BERTScore (Question Generation)
      type: bertscore_question_generation
      value: 80.89
    - name: MoverScore (Question Generation)
      type: moverscore_question_generation
      value: 56.76
---

# Model Card of `vocabtrimmer/mt5-small-trimmed-it-60000-itquad-qg`
This model is fine-tuned version of [ckpts/mt5-small-trimmed-it-60000](https://huggingface.co./ckpts/mt5-small-trimmed-it-60000) for question generation task on the [lmqg/qg_itquad](https://huggingface.co./datasets/lmqg/qg_itquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).


### Overview
- **Language model:** [ckpts/mt5-small-trimmed-it-60000](https://huggingface.co./ckpts/mt5-small-trimmed-it-60000)   
- **Language:** it  
- **Training data:** [lmqg/qg_itquad](https://huggingface.co./datasets/lmqg/qg_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="vocabtrimmer/mt5-small-trimmed-it-60000-itquad-qg")

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

```

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

pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-it-60000-itquad-qg")
output = pipe("<hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.")

```

## Evaluation


- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co./vocabtrimmer/mt5-small-trimmed-it-60000-itquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_itquad.default.json) 

|            |   Score | Type    | Dataset                                                          |
|:-----------|--------:|:--------|:-----------------------------------------------------------------|
| BERTScore  |   80.89 | default | [lmqg/qg_itquad](https://huggingface.co./datasets/lmqg/qg_itquad) |
| Bleu_1     |   22.98 | default | [lmqg/qg_itquad](https://huggingface.co./datasets/lmqg/qg_itquad) |
| Bleu_2     |   15.09 | default | [lmqg/qg_itquad](https://huggingface.co./datasets/lmqg/qg_itquad) |
| Bleu_3     |   10.44 | default | [lmqg/qg_itquad](https://huggingface.co./datasets/lmqg/qg_itquad) |
| Bleu_4     |    7.43 | default | [lmqg/qg_itquad](https://huggingface.co./datasets/lmqg/qg_itquad) |
| METEOR     |   17.36 | default | [lmqg/qg_itquad](https://huggingface.co./datasets/lmqg/qg_itquad) |
| MoverScore |   56.76 | default | [lmqg/qg_itquad](https://huggingface.co./datasets/lmqg/qg_itquad) |
| ROUGE_L    |   21.95 | default | [lmqg/qg_itquad](https://huggingface.co./datasets/lmqg/qg_itquad) |



## Training hyperparameters

The following hyperparameters were used during fine-tuning:
 - dataset_path: lmqg/qg_itquad
 - dataset_name: default
 - input_types: paragraph_answer
 - output_types: question
 - prefix_types: None
 - model: ckpts/mt5-small-trimmed-it-60000
 - 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-it-60000-itquad-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",
}

```