metadata

license: cc-by-4.0
metrics:
  - bleu4
  - meteor
  - rouge-l
  - bertscore
  - moverscore
language: en
datasets:
  - lmqg/qg_squad
pipeline_tag: text2text-generation
tags:
  - question generation
widget:
  - text: >-
      <hl> Beyonce <hl> further expanded her acting career, starring as blues
      singer Etta James in the 2008 musical biopic, Cadillac Records.
    example_title: Question Generation Example 1
  - text: >-
      Beyonce further expanded her acting career, starring as blues singer <hl>
      Etta James <hl> in the 2008 musical biopic, Cadillac Records.
    example_title: Question Generation Example 2
  - text: >-
      Beyonce further expanded her acting career, starring as blues singer Etta
      James in the 2008 musical biopic,  <hl> Cadillac Records <hl> .
    example_title: Question Generation Example 3
model-index:
  - name: lmqg/bart-base-squad-qg
    results:
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_squad
          type: default
          args: default
        metrics:
          - name: BLEU4 (Question Generation)
            type: bleu4_question_generation
            value: 24.68
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 52.66
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 26.05
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 90.87
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 64.47
          - 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: 95.49
          - 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: 95.44
          - 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: 95.55
          - 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: 70.38
          - 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: 70.1
          - 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: 70.67
          - name: >-
              QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer
            value: 92.84
          - name: >-
              QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold
              Answer]
            type: qa_aligned_recall_bertscore_question_answer_generation_gold_answer
            value: 92.95
          - name: >-
              QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_precision_bertscore_question_answer_generation_gold_answer
            value: 92.75
          - name: >-
              QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer
            value: 64.24
          - name: >-
              QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_recall_moverscore_question_answer_generation_gold_answer
            value: 64.11
          - name: >-
              QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_precision_moverscore_question_answer_generation_gold_answer
            value: 64.46
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_squadshifts
          type: amazon
          args: amazon
        metrics:
          - name: BLEU4 (Question Generation)
            type: bleu4_question_generation
            value: 0.05824165264328302
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 0.23816054441894524
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 0.2126541577267873
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 0.9049284884636415
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 0.6026811246610306
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_squadshifts
          type: new_wiki
          args: new_wiki
        metrics:
          - name: BLEU4 (Question Generation)
            type: bleu4_question_generation
            value: 0.10732253983426589
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 0.2843539251435107
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 0.26233713078026283
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 0.9307303692241476
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 0.656720781293701
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_squadshifts
          type: nyt
          args: nyt
        metrics:
          - name: BLEU4 (Question Generation)
            type: bleu4_question_generation
            value: 0.07645313983751752
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 0.2390325229516282
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 0.244330483594333
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 0.9235989114144583
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 0.6368628469746445
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_squadshifts
          type: reddit
          args: reddit
        metrics:
          - name: BLEU4 (Question Generation)
            type: bleu4_question_generation
            value: 0.053789810023704955
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 0.2141155595451475
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 0.20395821936787215
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 0.905714302466044
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 0.6013927660089013
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_subjqa
          type: books
          args: books
        metrics:
          - name: BLEU4 (Question Generation)
            type: bleu4_question_generation
            value: 1.4952813458186383e-10
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 0.10769136267285535
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 0.11520101781020654
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 0.8774975922095214
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 0.5520873074919223
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_subjqa
          type: electronics
          args: electronics
        metrics:
          - name: BLEU4 (Question Generation)
            type: bleu4_question_generation
            value: 0.0000013766381900873328
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 0.14287460464803423
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 0.14866637711177003
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 0.8759880110997111
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 0.5607199201429516
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_subjqa
          type: grocery
          args: grocery
        metrics:
          - name: BLEU4 (Question Generation)
            type: bleu4_question_generation
            value: 0.006003840641121225
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 0.1248840598199836
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 0.1553374628831024
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 0.8737966828346252
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 0.5662545638649026
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_subjqa
          type: movies
          args: movies
        metrics:
          - name: BLEU4 (Question Generation)
            type: bleu4_question_generation
            value: 0.0108258720771249
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 0.1389815289507374
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 0.12855849168399078
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 0.8773110466344016
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 0.5555164603510797
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_subjqa
          type: restaurants
          args: restaurants
        metrics:
          - name: BLEU4 (Question Generation)
            type: bleu4_question_generation
            value: 1.7873892359263582e-10
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 0.12160976589996819
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 0.1146979295288459
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 0.8771339668070569
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 0.5490739019998478
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_subjqa
          type: tripadvisor
          args: tripadvisor
        metrics:
          - name: BLEU4 (Question Generation)
            type: bleu4_question_generation
            value: 0.010174680918435602
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 0.1341425139885307
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 0.1391725168440533
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 0.8877592491739579
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 0.5590591813016728

Model Card of `lmqg/bart-base-squad-qg`

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

Overview

Language model: facebook/bart-base
Language: en
Training data: lmqg/qg_squad (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="en", model="lmqg/bart-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/bart-base-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

Metric (Question Generation): raw metric file

	Score	Type	Dataset
BERTScore	90.87	default	lmqg/qg_squad
Bleu_1	56.92	default	lmqg/qg_squad
Bleu_2	40.98	default	lmqg/qg_squad
Bleu_3	31.44	default	lmqg/qg_squad
Bleu_4	24.68	default	lmqg/qg_squad
METEOR	26.05	default	lmqg/qg_squad
MoverScore	64.47	default	lmqg/qg_squad
ROUGE_L	52.66	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)	95.49	default	lmqg/qg_squad
QAAlignedF1Score (MoverScore)	70.38	default	lmqg/qg_squad
QAAlignedPrecision (BERTScore)	95.55	default	lmqg/qg_squad
QAAlignedPrecision (MoverScore)	70.67	default	lmqg/qg_squad
QAAlignedRecall (BERTScore)	95.44	default	lmqg/qg_squad
QAAlignedRecall (MoverScore)	70.1	default	lmqg/qg_squad

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

	Score	Type	Dataset
QAAlignedF1Score (BERTScore)	92.84	default	lmqg/qg_squad
QAAlignedF1Score (MoverScore)	64.24	default	lmqg/qg_squad
QAAlignedPrecision (BERTScore)	92.75	default	lmqg/qg_squad
QAAlignedPrecision (MoverScore)	64.46	default	lmqg/qg_squad
QAAlignedRecall (BERTScore)	92.95	default	lmqg/qg_squad
QAAlignedRecall (MoverScore)	64.11	default	lmqg/qg_squad

Metrics (Question Generation, Out-of-Domain)

Dataset	Type	BERTScore	Bleu_4	METEOR	MoverScore	ROUGE_L	Link
lmqg/qg_squadshifts	amazon	90.49	5.82	21.27	60.27	23.82	link
lmqg/qg_squadshifts	new_wiki	93.07	10.73	26.23	65.67	28.44	link
lmqg/qg_squadshifts	nyt	92.36	7.65	24.43	63.69	23.9	link
lmqg/qg_squadshifts	reddit	90.57	5.38	20.4	60.14	21.41	link
lmqg/qg_subjqa	books	87.75	0.0	11.52	55.21	10.77	link
lmqg/qg_subjqa	electronics	87.6	0.0	14.87	56.07	14.29	link
lmqg/qg_subjqa	grocery	87.38	0.6	15.53	56.63	12.49	link
lmqg/qg_subjqa	movies	87.73	1.08	12.86	55.55	13.9	link
lmqg/qg_subjqa	restaurants	87.71	0.0	11.47	54.91	12.16	link
lmqg/qg_subjqa	tripadvisor	88.78	1.02	13.92	55.91	13.41	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-base
max_length: 512
max_length_output: 32
epoch: 7
batch: 32
lr: 0.0001
fp16: False
random_seed: 1
gradient_accumulation_steps: 8
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",
}