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Model Card of lmqg/t5-base-squad-qg

This model is fine-tuned version of t5-base 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/t5-base-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/t5-base-squad-qg")
output = pipe("generate question: <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 90.6 default lmqg/qg_squad
Bleu_1 58.69 default lmqg/qg_squad
Bleu_2 42.66 default lmqg/qg_squad
Bleu_3 32.99 default lmqg/qg_squad
Bleu_4 26.13 default lmqg/qg_squad
METEOR 26.97 default lmqg/qg_squad
MoverScore 64.74 default lmqg/qg_squad
ROUGE_L 53.33 default lmqg/qg_squad
  • Metric (Question & Answer Generation, Reference Answer): Each question is generated from the gold answer. raw metric file
Score Type Dataset
QAAlignedF1Score (BERTScore) 95.42 default lmqg/qg_squad
QAAlignedF1Score (MoverScore) 70.63 default lmqg/qg_squad
QAAlignedPrecision (BERTScore) 95.48 default lmqg/qg_squad
QAAlignedPrecision (MoverScore) 70.92 default lmqg/qg_squad
QAAlignedRecall (BERTScore) 95.37 default lmqg/qg_squad
QAAlignedRecall (MoverScore) 70.34 default lmqg/qg_squad
Score Type Dataset
QAAlignedF1Score (BERTScore) 92.75 default lmqg/qg_squad
QAAlignedF1Score (MoverScore) 64.36 default lmqg/qg_squad
QAAlignedPrecision (BERTScore) 92.59 default lmqg/qg_squad
QAAlignedPrecision (MoverScore) 64.45 default lmqg/qg_squad
QAAlignedRecall (BERTScore) 92.93 default lmqg/qg_squad
QAAlignedRecall (MoverScore) 64.35 default lmqg/qg_squad
  • Metrics (Question Generation, Out-of-Domain)
Dataset Type BERTScore Bleu_4 METEOR MoverScore ROUGE_L Link
lmqg/qg_squadshifts amazon 90.75 6.57 22.37 60.8 24.81 link
lmqg/qg_squadshifts new_wiki 93.02 11.09 27.23 65.97 29.59 link
lmqg/qg_squadshifts nyt 92.2 7.77 25.16 63.83 24.56 link
lmqg/qg_squadshifts reddit 90.59 5.68 21.3 60.23 21.96 link
lmqg/qg_subjqa books 88.14 0.49 13.51 55.65 9.44 link
lmqg/qg_subjqa electronics 87.71 0.0 16.53 55.77 13.48 link
lmqg/qg_subjqa grocery 87.46 0.0 16.24 56.59 10.26 link
lmqg/qg_subjqa movies 87.66 0.72 13.06 55.45 11.89 link
lmqg/qg_subjqa restaurants 87.83 0.0 13.3 55.45 10.7 link
lmqg/qg_subjqa tripadvisor 89.23 0.93 16.51 56.67 13.51 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: ['qg']
  • model: t5-base
  • max_length: 512
  • max_length_output: 32
  • epoch: 5
  • batch: 16
  • lr: 0.0001
  • 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/t5-base-squad-qg

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Evaluation results

  • BLEU4 (Question Generation) on lmqg/qg_squad
    self-reported
    26.130
  • ROUGE-L (Question Generation) on lmqg/qg_squad
    self-reported
    53.330
  • METEOR (Question Generation) on lmqg/qg_squad
    self-reported
    26.970
  • BERTScore (Question Generation) on lmqg/qg_squad
    self-reported
    90.600
  • MoverScore (Question Generation) on lmqg/qg_squad
    self-reported
    64.740
  • QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_squad
    self-reported
    95.420
  • QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_squad
    self-reported
    95.370
  • QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_squad
    self-reported
    95.480
  • QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_squad
    self-reported
    70.630
  • QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_squad
    self-reported
    70.340
  • QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_squad
    self-reported
    70.920
  • QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold Answer] on lmqg/qg_squad
    self-reported
    92.750
  • QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold Answer] on lmqg/qg_squad
    self-reported
    92.930
  • QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold Answer] on lmqg/qg_squad
    self-reported
    92.590
  • QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold Answer] on lmqg/qg_squad
    self-reported
    64.360
  • QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold Answer] on lmqg/qg_squad
    self-reported
    64.350
  • QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold Answer] on lmqg/qg_squad
    self-reported
    64.450
  • BLEU4 (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.066
  • ROUGE-L (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.248
  • METEOR (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.224
  • BERTScore (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.908
  • MoverScore (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.608
  • BLEU4 (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.111
  • ROUGE-L (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.296
  • METEOR (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.272
  • BERTScore (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.930
  • MoverScore (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.660
  • BLEU4 (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.078
  • ROUGE-L (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.246
  • METEOR (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.252
  • BERTScore (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.922
  • MoverScore (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.638
  • BLEU4 (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.057
  • ROUGE-L (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.220
  • METEOR (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.213
  • BERTScore (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.906
  • MoverScore (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.602
  • BLEU4 (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.005
  • ROUGE-L (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.094
  • METEOR (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.135
  • 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
    0.000
  • ROUGE-L (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.135
  • METEOR (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.165
  • BERTScore (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.877
  • MoverScore (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.558
  • BLEU4 (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.000
  • ROUGE-L (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.103
  • METEOR (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.162
  • BERTScore (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.875
  • MoverScore (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.566
  • BLEU4 (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.007
  • ROUGE-L (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.119
  • METEOR (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.131
  • BERTScore (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.877
  • MoverScore (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.555
  • BLEU4 (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.000
  • ROUGE-L (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.107
  • METEOR (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.133
  • BERTScore (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.878
  • MoverScore (Question Generation) on lmqg/qg_subjqa
    self-reported
    0.554