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mt5-small-trimmed-fr-5000-frquad-qg

Text2Text Generation
Transformers
PyTorch
French
mt5
question generation
Eval Results
Inference Endpoints
text-generation-inference
Model card Files Community
mt5-small-trimmed-fr-5000-frquad-qg
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---
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: vocabtrimmer/mt5-small-trimmed-fr-5000-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: 7.2
    - name: ROUGE-L (Question Generation)
      type: rouge_l_question_generation
      value: 26.89
    - name: METEOR (Question Generation)
      type: meteor_question_generation
      value: 16.13
    - name: BERTScore (Question Generation)
      type: bertscore_question_generation
      value: 79.05
    - name: MoverScore (Question Generation)
      type: moverscore_question_generation
      value: 55.28
---

# Model Card of `vocabtrimmer/mt5-small-trimmed-fr-5000-frquad-qg`
This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-fr-5000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-fr-5000) 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:** [vocabtrimmer/mt5-small-trimmed-fr-5000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-fr-5000)   
- **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="vocabtrimmer/mt5-small-trimmed-fr-5000-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", "vocabtrimmer/mt5-small-trimmed-fr-5000-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/vocabtrimmer/mt5-small-trimmed-fr-5000-frquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_frquad.default.json) 

|            |   Score | Type    | Dataset                                                          |
|:-----------|--------:|:--------|:-----------------------------------------------------------------|
| BERTScore  |   79.05 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| Bleu_1     |   27.12 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| Bleu_2     |   15.65 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| Bleu_3     |   10.42 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| Bleu_4     |    7.2  | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| METEOR     |   16.13 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| MoverScore |   55.28 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| ROUGE_L    |   26.89 | 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: vocabtrimmer/mt5-small-trimmed-fr-5000
 - max_length: 512
 - max_length_output: 32
 - epoch: 15
 - 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-fr-5000-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",
}

```