---
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: lmqg/mt5-base-itquad
  results:
  - task:
      name: Text2text Generation
      type: text2text-generation
    dataset:
      name: lmqg/qg_itquad
      type: default
      args: default
    metrics:
    - name: BLEU4
      type: bleu4
      value: 0.07701528877424803
    - name: ROUGE-L
      type: rouge-l
      value: 0.22511430414292052
    - name: METEOR
      type: meteor
      value: 0.17997929735967222
    - name: BERTScore
      type: bertscore
      value: 0.8116317604756539
    - name: MoverScore
      type: moverscore
      value: 0.5711254911196173
---

# Language Models Fine-tuning on Question Generation: `lmqg/mt5-base-itquad`
This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for question generation task on the 
[lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (dataset_name: default).


### Overview
- **Language model:** [google/mt5-base](https://huggingface.co/google/mt5-base)   
- **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:** [TBA](TBA)

### Usage
```python

from transformers import pipeline

model_path = 'lmqg/mt5-base-itquad'
pipe = pipeline("text2text-generation", model_path)

# Question Generation
question = pipe('<hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.')
```

## Evaluation Metrics


### Metrics

| Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link |
|:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:|
| [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | default | 0.077 | 0.225 | 0.18 | 0.812 | 0.571 | [link](https://huggingface.co/lmqg/mt5-base-itquad/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_itquad.default.json) | 




## 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: google/mt5-base
 - max_length: 512
 - max_length_output: 32
 - epoch: 11
 - batch: 4
 - lr: 0.001
 - fp16: False
 - random_seed: 1
 - gradient_accumulation_steps: 16
 - label_smoothing: 0.15

The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-base-itquad/raw/main/trainer_config.json).

## Citation
TBA