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Model Card of lmqg/bart-large-squad-qg

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

Overview

Usage

from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="en", model="lmqg/bart-large-squad-qg")

# model prediction
questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner")
  • With transformers
from transformers import pipeline

pipe = pipeline("text2text-generation", "lmqg/bart-large-squad-qg")
output = pipe("<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")

Evaluation

Score Type Dataset
BERTScore 91 default lmqg/qg_squad
Bleu_1 58.79 default lmqg/qg_squad
Bleu_2 42.79 default lmqg/qg_squad
Bleu_3 33.11 default lmqg/qg_squad
Bleu_4 26.17 default lmqg/qg_squad
METEOR 27.07 default lmqg/qg_squad
MoverScore 64.99 default lmqg/qg_squad
ROUGE_L 53.85 default lmqg/qg_squad
  • Metric (Question & Answer Generation): QAG metrics are computed with the gold answer and generated question on it for this model, as the model cannot provide an answer. raw metric file
Score Type Dataset
QAAlignedF1Score (BERTScore) 95.54 default lmqg/qg_squad
QAAlignedF1Score (MoverScore) 70.82 default lmqg/qg_squad
QAAlignedPrecision (BERTScore) 95.59 default lmqg/qg_squad
QAAlignedPrecision (MoverScore) 71.13 default lmqg/qg_squad
QAAlignedRecall (BERTScore) 95.49 default lmqg/qg_squad
QAAlignedRecall (MoverScore) 70.54 default lmqg/qg_squad
  • Metrics (Question Generation, Out-of-Domain)
Dataset Type BERTScore Bleu_4 METEOR MoverScore ROUGE_L Link
lmqg/qg_squadshifts amazon 90.93 6.53 22.3 60.87 25.03 link
lmqg/qg_squadshifts new_wiki 93.23 11.12 27.32 66.23 29.68 link
lmqg/qg_squadshifts nyt 92.49 8.12 25.25 64.06 25.29 link
lmqg/qg_squadshifts reddit 90.95 5.95 21.5 60.59 22.37 link
lmqg/qg_subjqa books 88.07 0.63 11.58 55.56 12.37 link
lmqg/qg_subjqa electronics 87.83 0.87 15.35 56.35 16.02 link
lmqg/qg_subjqa grocery 87.79 0.53 15.13 57.02 12.34 link
lmqg/qg_subjqa movies 87.49 0.0 11.86 55.29 12.51 link
lmqg/qg_subjqa restaurants 87.98 0.0 12.42 55.43 13.08 link
lmqg/qg_subjqa tripadvisor 88.91 0.0 13.72 56.05 14.03 link

Training hyperparameters

The following hyperparameters were used during fine-tuning:

  • dataset_path: lmqg/qg_squad
  • dataset_name: default
  • input_types: ['paragraph_answer']
  • output_types: ['question']
  • prefix_types: None
  • model: facebook/bart-large
  • max_length: 512
  • max_length_output: 32
  • epoch: 4
  • batch: 32
  • lr: 5e-05
  • fp16: False
  • random_seed: 1
  • gradient_accumulation_steps: 4
  • 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 lmqg/bart-large-squad-qg

Evaluation results

  • BLEU4 (Question Generation) on lmqg/qg_squad
    self-reported
    26.170
  • ROUGE-L (Question Generation) on lmqg/qg_squad
    self-reported
    53.850
  • METEOR (Question Generation) on lmqg/qg_squad
    self-reported
    27.070
  • BERTScore (Question Generation) on lmqg/qg_squad
    self-reported
    91.000
  • MoverScore (Question Generation) on lmqg/qg_squad
    self-reported
    64.990
  • QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold Answer] on lmqg/qg_squad
    self-reported
    95.540
  • QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold Answer] on lmqg/qg_squad
    self-reported
    95.490
  • QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold Answer] on lmqg/qg_squad
    self-reported
    95.590
  • QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold Answer] on lmqg/qg_squad
    self-reported
    70.820
  • QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold Answer] on lmqg/qg_squad
    self-reported
    70.540
  • QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold Answer] on lmqg/qg_squad
    self-reported
    71.130
  • BLEU4 (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.065
  • ROUGE-L (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.250
  • METEOR (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.223
  • BERTScore (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.909
  • MoverScore (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.609
  • BLEU4 (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.111
  • ROUGE-L (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.297
  • METEOR (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.273
  • BERTScore (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.932
  • MoverScore (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.662
  • BLEU4 (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.081
  • ROUGE-L (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.253
  • METEOR (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.253
  • BERTScore (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.925
  • MoverScore (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.641
  • BLEU4 (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.060
  • ROUGE-L (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.224
  • METEOR (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.215
  • BERTScore (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.910
  • MoverScore (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.606
  • BLEU4 (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.006
  • ROUGE-L (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.124
  • METEOR (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.116
  • BERTScore (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.881
  • MoverScore (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.556
  • BLEU4 (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.009
  • ROUGE-L (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.160
  • METEOR (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.153
  • BERTScore (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.878
  • MoverScore (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.563
  • BLEU4 (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.005
  • ROUGE-L (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.123
  • METEOR (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.151
  • BERTScore (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.878
  • MoverScore (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.570
  • BLEU4 (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.000
  • ROUGE-L (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.125
  • METEOR (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.119
  • BERTScore (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.875
  • MoverScore (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.553
  • BLEU4 (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.000
  • ROUGE-L (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.131
  • METEOR (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.124
  • BERTScore (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.880
  • MoverScore (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.554