metadata

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-small-frquad
    results:
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_frquad
          type: default
          args: default
        metrics:
          - name: BLEU4
            type: bleu4
            value: 0.0855433375613263
          - name: ROUGE-L
            type: rouge-l
            value: 0.28563221971096636
          - name: METEOR
            type: meteor
            value: 0.17511468784257161
          - name: BERTScore
            type: bertscore
            value: 0.8070819788573244
          - name: MoverScore
            type: moverscore
            value: 0.5650286067741268

Model Card of `lmqg/mt5-small-frquad`

This model is fine-tuned version of google/mt5-small for question generation task on the lmqg/qg_frquad (dataset_name: default) via lmqg.

Please cite our paper if you use the model (https://arxiv.org/abs/2210.03992).


@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",
}

Overview

Language model: google/mt5-small
Language: fr
Training data: lmqg/qg_frquad (default)
Online Demo: https://autoqg.net/
Repository: https://github.com/asahi417/lm-question-generation
Paper: https://arxiv.org/abs/2210.03992

Usage

With lmqg


from lmqg import TransformersQG
# initialize model
model = TransformersQG(language='fr', model='lmqg/mt5-small-frquad')
# model prediction
question = 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


from transformers import pipeline
# initialize model
pipe = pipeline("text2text-generation", 'lmqg/mt5-small-frquad')
# question generation
question = 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 Metrics

Metrics

Dataset	Type	BLEU4	ROUGE-L	METEOR	BERTScore	MoverScore	Link
lmqg/qg_frquad	default	0.086	0.286	0.175	0.807	0.565	link

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-small
max_length: 512
max_length_output: 32
epoch: 14
batch: 64
lr: 0.001
fp16: False
random_seed: 1
gradient_accumulation_steps: 1
label_smoothing: 0.15

The full configuration can be found at fine-tuning config file.

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",
}

Model Card of lmqg/mt5-small-frquad