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-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 for question generation task on the lmqg/qg_frquad (dataset_name: default) via lmqg.

Overview

Language model: google/mt5-base
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-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

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

	Score	Type	Dataset
BERTScore	77.81	default	lmqg/qg_frquad
Bleu_1	25.06	default	lmqg/qg_frquad
Bleu_2	13.73	default	lmqg/qg_frquad
Bleu_3	8.93	default	lmqg/qg_frquad
Bleu_4	6.14	default	lmqg/qg_frquad
METEOR	15.55	default	lmqg/qg_frquad
MoverScore	54.58	default	lmqg/qg_frquad
ROUGE_L	25.88	default	lmqg/qg_frquad

Metric (Question & Answer Generation, Reference Answer): Each question is generated from the gold answer. raw metric file

	Score	Type	Dataset
QAAlignedF1Score (BERTScore)	86.41	default	lmqg/qg_frquad
QAAlignedF1Score (MoverScore)	60.19	default	lmqg/qg_frquad
QAAlignedPrecision (BERTScore)	86.42	default	lmqg/qg_frquad
QAAlignedPrecision (MoverScore)	60.19	default	lmqg/qg_frquad
QAAlignedRecall (BERTScore)	86.4	default	lmqg/qg_frquad
QAAlignedRecall (MoverScore)	60.18	default	lmqg/qg_frquad

Metric (Question & Answer Generation, Pipeline Approach): Each question is generated on the answer generated by lmqg/mt5-base-frquad-ae. raw metric file

	Score	Type	Dataset
QAAlignedF1Score (BERTScore)	68.59	default	lmqg/qg_frquad
QAAlignedF1Score (MoverScore)	47.87	default	lmqg/qg_frquad
QAAlignedPrecision (BERTScore)	67.59	default	lmqg/qg_frquad
QAAlignedPrecision (MoverScore)	47.42	default	lmqg/qg_frquad
QAAlignedRecall (BERTScore)	69.69	default	lmqg/qg_frquad
QAAlignedRecall (MoverScore)	48.36	default	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.

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",
}