README.md

---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: fr
datasets:
- lmqg/qg_frquad
pipeline_tag: text2text-generation
tags:
- question generation
widget:
- text: "Créateur » (Maker), lui aussi au singulier, « <hl> le Suprême Berger <hl> » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc."
  example_title: "Question Generation Example 1" 
- text: "Ce black dog peut être lié à des évènements traumatisants issus du monde extérieur, tels que son renvoi de l'Amirauté après la catastrophe des Dardanelles, lors de la <hl> Grande Guerre <hl> de 14-18, ou son rejet par l'électorat en juillet 1945."
  example_title: "Question Generation Example 2" 
- text: "contre <hl> Normie Smith <hl> et 15 000 dollars le 28 novembre 1938."
  example_title: "Question Generation Example 3" 
model-index:
- name: lmqg/mt5-base-frquad-qg
  results:
  - task:
      name: Text2text Generation
      type: text2text-generation
    dataset:
      name: lmqg/qg_frquad
      type: default
      args: default
    metrics:
    - name: BLEU4 (Question Generation)
      type: bleu4_question_generation
      value: 6.14
    - name: ROUGE-L (Question Generation)
      type: rouge_l_question_generation
      value: 25.88
    - name: METEOR (Question Generation)
      type: meteor_question_generation
      value: 15.55
    - name: BERTScore (Question Generation)
      type: bertscore_question_generation
      value: 77.81
    - name: MoverScore (Question Generation)
      type: moverscore_question_generation
      value: 54.58
    - name: QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]
      type: qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer_gold_answer
      value: 86.41
    - name: QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]
      type: qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer_gold_answer
      value: 86.4
    - name: QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]
      type: qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer_gold_answer
      value: 86.42
    - name: QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]
      type: qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer_gold_answer
      value: 60.19
    - name: QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]
      type: qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer_gold_answer
      value: 60.18
    - name: QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]
      type: qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer_gold_answer
      value: 60.19
    - name: QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold Answer]
      type: qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer
      value: 68.59
    - name: QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold Answer]
      type: qa_aligned_recall_bertscore_question_answer_generation_gold_answer
      value: 69.69
    - name: QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold Answer]
      type: qa_aligned_precision_bertscore_question_answer_generation_gold_answer
      value: 67.59
    - name: QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold Answer]
      type: qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer
      value: 47.87
    - name: QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold Answer]
      type: qa_aligned_recall_moverscore_question_answer_generation_gold_answer
      value: 48.36
    - name: QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold Answer]
      type: qa_aligned_precision_moverscore_question_answer_generation_gold_answer
      value: 47.42
---

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


### Overview
- **Language model:** [google/mt5-base](https://huggingface.co/google/mt5-base)   
- **Language:** fr  
- **Training data:** [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (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="fr", model="lmqg/mt5-base-frquad-qg")

# model prediction
questions = model.generate_q(list_context="Créateur » (Maker), lui aussi au singulier, « le Suprême Berger » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc.", list_answer="le Suprême Berger")

```

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

pipe = pipeline("text2text-generation", "lmqg/mt5-base-frquad-qg")
output = pipe("Créateur » (Maker), lui aussi au singulier, « <hl> le Suprême Berger <hl> » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc.")

```

## Evaluation


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

|            |   Score | Type    | Dataset                                                          |
|:-----------|--------:|:--------|:-----------------------------------------------------------------|
| BERTScore  |   77.81 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| Bleu_1     |   25.06 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| Bleu_2     |   13.73 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| Bleu_3     |    8.93 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| Bleu_4     |    6.14 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| METEOR     |   15.55 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| MoverScore |   54.58 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| ROUGE_L    |   25.88 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |


- ***Metric (Question & Answer Generation, Reference Answer)***: Each question is generated from *the gold answer*. [raw metric file](https://huggingface.co/lmqg/mt5-base-frquad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_frquad.default.json)

|                                 |   Score | Type    | Dataset                                                          |
|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
| QAAlignedF1Score (BERTScore)    |   86.41 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| QAAlignedF1Score (MoverScore)   |   60.19 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| QAAlignedPrecision (BERTScore)  |   86.42 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| QAAlignedPrecision (MoverScore) |   60.19 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| QAAlignedRecall (BERTScore)     |   86.4  | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| QAAlignedRecall (MoverScore)    |   60.18 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |


- ***Metric (Question & Answer Generation, Pipeline Approach)***: Each question is generated on the answer generated by [`lmqg/mt5-base-frquad-ae`](https://huggingface.co/lmqg/mt5-base-frquad-ae). [raw metric file](https://huggingface.co/lmqg/mt5-base-frquad-qg/raw/main/eval_pipeline/metric.first.answer.paragraph.questions_answers.lmqg_qg_frquad.default.lmqg_mt5-base-frquad-ae.json)

|                                 |   Score | Type    | Dataset                                                          |
|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
| QAAlignedF1Score (BERTScore)    |   68.59 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| QAAlignedF1Score (MoverScore)   |   47.87 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| QAAlignedPrecision (BERTScore)  |   67.59 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| QAAlignedPrecision (MoverScore) |   47.42 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| QAAlignedRecall (BERTScore)     |   69.69 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| QAAlignedRecall (MoverScore)    |   48.36 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |



## Training hyperparameters

The following hyperparameters were used during fine-tuning:
 - dataset_path: lmqg/qg_frquad
 - 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: 24
 - batch: 4
 - lr: 0.0001
 - 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-frquad-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",
}

```