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Model Card of lmqg/mbart-large-cc25-itquad-qg

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

Overview

Usage

from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="it", model="lmqg/mbart-large-cc25-itquad-qg")

# model prediction
questions = model.generate_q(list_context="Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.", list_answer="Dopo il 1971")
  • With transformers
from transformers import pipeline

pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-itquad-qg")
output = pipe("<hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.")

Evaluation

Score Type Dataset
BERTScore 80.63 default lmqg/qg_itquad
Bleu_1 22.51 default lmqg/qg_itquad
Bleu_2 14.62 default lmqg/qg_itquad
Bleu_3 10.06 default lmqg/qg_itquad
Bleu_4 7.13 default lmqg/qg_itquad
METEOR 17.97 default lmqg/qg_itquad
MoverScore 56.84 default lmqg/qg_itquad
ROUGE_L 21.69 default lmqg/qg_itquad
  • Metric (Question & Answer Generation, Reference Answer): Each question is generated from the gold answer. raw metric file
Score Type Dataset
QAAlignedF1Score (BERTScore) 87.56 default lmqg/qg_itquad
QAAlignedF1Score (MoverScore) 61.71 default lmqg/qg_itquad
QAAlignedPrecision (BERTScore) 87.62 default lmqg/qg_itquad
QAAlignedPrecision (MoverScore) 61.83 default lmqg/qg_itquad
QAAlignedRecall (BERTScore) 87.5 default lmqg/qg_itquad
QAAlignedRecall (MoverScore) 61.59 default lmqg/qg_itquad
Score Type Dataset
QAAlignedF1Score (BERTScore) 40.13 default lmqg/qg_itquad
QAAlignedF1Score (MoverScore) 27.8 default lmqg/qg_itquad
QAAlignedPrecision (BERTScore) 40.43 default lmqg/qg_itquad
QAAlignedPrecision (MoverScore) 28.09 default lmqg/qg_itquad
QAAlignedRecall (BERTScore) 39.88 default lmqg/qg_itquad
QAAlignedRecall (MoverScore) 27.54 default lmqg/qg_itquad

Training hyperparameters

The following hyperparameters were used during fine-tuning:

  • dataset_path: lmqg/qg_itquad
  • 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: 0.0001
  • 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",
}
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Dataset used to train research-backup/mbart-large-cc25-itquad-qg

Evaluation results

  • BLEU4 (Question Generation) on lmqg/qg_itquad
    self-reported
    7.130
  • ROUGE-L (Question Generation) on lmqg/qg_itquad
    self-reported
    21.690
  • METEOR (Question Generation) on lmqg/qg_itquad
    self-reported
    17.970
  • BERTScore (Question Generation) on lmqg/qg_itquad
    self-reported
    80.630
  • MoverScore (Question Generation) on lmqg/qg_itquad
    self-reported
    56.840
  • QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_itquad
    self-reported
    87.560
  • QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_itquad
    self-reported
    87.500
  • QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_itquad
    self-reported
    87.620
  • QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_itquad
    self-reported
    61.710
  • QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_itquad
    self-reported
    61.590
  • QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_itquad
    self-reported
    61.830
  • QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold Answer] on lmqg/qg_itquad
    self-reported
    40.130
  • QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold Answer] on lmqg/qg_itquad
    self-reported
    39.880
  • QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold Answer] on lmqg/qg_itquad
    self-reported
    40.430
  • QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold Answer] on lmqg/qg_itquad
    self-reported
    27.800
  • QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold Answer] on lmqg/qg_itquad
    self-reported
    27.540
  • QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold Answer] on lmqg/qg_itquad
    self-reported
    28.090