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
- answer extraction
widget:
- text: "generate question: <hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento."
  example_title: "Question Generation Example 1" 
- text: "generate question: 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: "generate question: il <hl> Giappone <hl> è stato il paese più dipendente dal petrolio arabo."
  example_title: "Question Generation Example 3" 
- text: "extract answers: <hl> Il 6 ottobre 1973 , la Siria e l' Egitto, con il sostegno di altre nazioni arabe, lanciarono un attacco a sorpresa su Israele, su Yom Kippur. <hl> Questo rinnovo delle ostilità nel conflitto arabo-israeliano ha liberato la pressione economica sottostante sui prezzi del petrolio. All' epoca, l' Iran era il secondo esportatore mondiale di petrolio e un vicino alleato degli Stati Uniti. Settimane più tardi, lo scià d' Iran ha detto in un' intervista: Naturalmente[il prezzo del petrolio] sta andando a salire Certamente! E come! Avete[Paesi occidentali] aumentato il prezzo del grano che ci vendete del 300 per cento, e lo stesso per zucchero e cemento."
  example_title: "Answer Extraction Example 1" 
- text: "extract answers: <hl> Furono introdotti autocarri compatti, come la Toyota Hilux e il Datsun Truck, seguiti dal camion Mazda (venduto come il Ford Courier), e l' Isuzu costruito Chevrolet LUV. <hl> Mitsubishi rebranded il suo Forte come Dodge D-50 pochi anni dopo la crisi petrolifera. Mazda, Mitsubishi e Isuzu avevano partnership congiunte rispettivamente con Ford, Chrysler e GM. In seguito i produttori americani introdussero le loro sostituzioni nazionali (Ford Ranger, Dodge Dakota e la Chevrolet S10/GMC S-15), ponendo fine alla loro politica di importazione vincolata."
  example_title: "Answer Extraction Example 2" 
model-index:
- name: lmqg/mt5-small-itquad-qg-ae
  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: 7.25
    - name: ROUGE-L (Question Generation)
      type: rouge_l_question_generation
      value: 21.84
    - name: METEOR (Question Generation)
      type: meteor_question_generation
      value: 17.5
    - name: BERTScore (Question Generation)
      type: bertscore_question_generation
      value: 80.61
    - name: MoverScore (Question Generation)
      type: moverscore_question_generation
      value: 56.63
    - name: QAAlignedF1Score-BERTScore (Question & Answer Generation)
      type: qa_aligned_f1_score_bertscore_question_answer_generation
      value: 81.81
    - name: QAAlignedRecall-BERTScore (Question & Answer Generation)
      type: qa_aligned_recall_bertscore_question_answer_generation
      value: 82.51
    - name: QAAlignedPrecision-BERTScore (Question & Answer Generation)
      type: qa_aligned_precision_bertscore_question_answer_generation
      value: 81.17
    - name: QAAlignedF1Score-MoverScore (Question & Answer Generation)
      type: qa_aligned_f1_score_moverscore_question_answer_generation
      value: 56.02
    - name: QAAlignedRecall-MoverScore (Question & Answer Generation)
      type: qa_aligned_recall_moverscore_question_answer_generation
      value: 56.32
    - name: QAAlignedPrecision-MoverScore (Question & Answer Generation)
      type: qa_aligned_precision_moverscore_question_answer_generation
      value: 55.76
    - name: BLEU4 (Answer Extraction)
      type: bleu4_answer_extraction
      value: 26.01
    - name: ROUGE-L (Answer Extraction)
      type: rouge_l_answer_extraction
      value: 45.15
    - name: METEOR (Answer Extraction)
      type: meteor_answer_extraction
      value: 42.68
    - name: BERTScore (Answer Extraction)
      type: bertscore_answer_extraction
      value: 90.24
    - name: MoverScore (Answer Extraction)
      type: moverscore_answer_extraction
      value: 81.17
    - name: AnswerF1Score (Answer Extraction)
      type: answer_f1_score__answer_extraction
      value: 72.09
    - name: AnswerExactMatch (Answer Extraction)
      type: answer_exact_match_answer_extraction
      value: 57.85
---

# Model Card of `lmqg/mt5-small-itquad-qg-ae`
This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation and answer extraction jointly 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:** [google/mt5-small](https://huggingface.co/google/mt5-small)   
- **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="lmqg/mt5-small-itquad-qg-ae")

# 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-qg-ae")

# answer extraction
answer = pipe("generate question: <hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.")

# question generation
question = pipe("extract answers: <hl> Il 6 ottobre 1973 , la Siria e l' Egitto, con il sostegno di altre nazioni arabe, lanciarono un attacco a sorpresa su Israele, su Yom Kippur. <hl> Questo rinnovo delle ostilità nel conflitto arabo-israeliano ha liberato la pressione economica sottostante sui prezzi del petrolio. All' epoca, l' Iran era il secondo esportatore mondiale di petrolio e un vicino alleato degli Stati Uniti. Settimane più tardi, lo scià d' Iran ha detto in un' intervista: Naturalmente[il prezzo del petrolio] sta andando a salire Certamente! E come! Avete[Paesi occidentali] aumentato il prezzo del grano che ci vendete del 300 per cento, e lo stesso per zucchero e cemento.")

```

## Evaluation


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

|            |   Score | Type    | Dataset                                                          |
|:-----------|--------:|:--------|:-----------------------------------------------------------------|
| BERTScore  |   80.61 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_1     |   22.53 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_2     |   14.75 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_3     |   10.19 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_4     |    7.25 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| METEOR     |   17.5  | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| MoverScore |   56.63 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| ROUGE_L    |   21.84 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |


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

|                                 |   Score | Type    | Dataset                                                          |
|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
| QAAlignedF1Score (BERTScore)    |   81.81 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedF1Score (MoverScore)   |   56.02 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedPrecision (BERTScore)  |   81.17 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedPrecision (MoverScore) |   55.76 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedRecall (BERTScore)     |   82.51 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedRecall (MoverScore)    |   56.32 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |


- ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-itquad-qg-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_itquad.default.json)

|                  |   Score | Type    | Dataset                                                          |
|:-----------------|--------:|:--------|:-----------------------------------------------------------------|
| AnswerExactMatch |   57.85 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| AnswerF1Score    |   72.09 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| BERTScore        |   90.24 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_1           |   39.33 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_2           |   33.64 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_3           |   29.59 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_4           |   26.01 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| METEOR           |   42.68 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| MoverScore       |   81.17 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| ROUGE_L          |   45.15 | 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', 'paragraph_sentence']
 - output_types: ['question', 'answer']
 - prefix_types: ['qg', 'ae']
 - model: google/mt5-small
 - max_length: 512
 - max_length_output: 32
 - epoch: 13
 - 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/lmqg/mt5-small-itquad-qg-ae/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",
}

```