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/mbart-large-cc25-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: 0.72
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 16.4
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 7.78
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 71.48
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 50.35
          - name: BLEU4 (Question & Answer Generation (with Gold Answer))
            type: bleu4_question_answer_generation_with_gold_answer
            value: 9.7
          - name: ROUGE-L (Question & Answer Generation (with Gold Answer))
            type: rouge_l_question_answer_generation_with_gold_answer
            value: 33.61
          - name: METEOR (Question & Answer Generation (with Gold Answer))
            type: meteor_question_answer_generation_with_gold_answer
            value: 26.31
          - name: BERTScore (Question & Answer Generation (with Gold Answer))
            type: bertscore_question_answer_generation_with_gold_answer
            value: 80.27
          - name: MoverScore (Question & Answer Generation (with Gold Answer))
            type: moverscore_question_answer_generation_with_gold_answer
            value: 55.65
          - 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: 81.27
          - 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: 81.25
          - 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: 81.29
          - 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: 55.61
          - 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: 55.6
          - 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: 55.61

Model Card of `lmqg/mbart-large-cc25-frquad-qg`

This model is fine-tuned version of facebook/mbart-large-cc25 for question generation task on the lmqg/qg_frquad (dataset_name: default) via lmqg.

Overview

Language model: facebook/mbart-large-cc25
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/mbart-large-cc25-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/mbart-large-cc25-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	71.48	default	lmqg/qg_frquad
Bleu_1	14.36	default	lmqg/qg_frquad
Bleu_2	3.58	default	lmqg/qg_frquad
Bleu_3	1.45	default	lmqg/qg_frquad
Bleu_4	0.72	default	lmqg/qg_frquad
METEOR	7.78	default	lmqg/qg_frquad
MoverScore	50.35	default	lmqg/qg_frquad
ROUGE_L	16.4	default	lmqg/qg_frquad

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

	Score	Type	Dataset
BERTScore	80.27	default	lmqg/qg_frquad
Bleu_1	29.47	default	lmqg/qg_frquad
Bleu_2	19.07	default	lmqg/qg_frquad
Bleu_3	13.39	default	lmqg/qg_frquad
Bleu_4	9.7	default	lmqg/qg_frquad
METEOR	26.31	default	lmqg/qg_frquad
MoverScore	55.65	default	lmqg/qg_frquad
QAAlignedF1Score (BERTScore)	81.27	default	lmqg/qg_frquad
QAAlignedF1Score (MoverScore)	55.61	default	lmqg/qg_frquad
QAAlignedPrecision (BERTScore)	81.29	default	lmqg/qg_frquad
QAAlignedPrecision (MoverScore)	55.61	default	lmqg/qg_frquad
QAAlignedRecall (BERTScore)	81.25	default	lmqg/qg_frquad
QAAlignedRecall (MoverScore)	55.6	default	lmqg/qg_frquad
ROUGE_L	33.61	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: facebook/mbart-large-cc25
max_length: 512
max_length_output: 32
epoch: 8
batch: 4
lr: 0.001
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",
}