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-large-squad
    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: 26.17
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 53.85
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 27.07
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 91
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 64.99
          - name: QAAlignedF1Score-BERTScore (Gold Answer)
            type: qa_aligned_f1_score_bertscore_gold_answer
            value: 95.54
          - name: QAAlignedRecall-BERTScore (Gold Answer)
            type: qa_aligned_recall_bertscore_gold_answer
            value: 95.49
          - name: QAAlignedPrecision-BERTScore (Gold Answer)
            type: qa_aligned_precision_bertscore_gold_answer
            value: 95.59
          - name: QAAlignedF1Score-MoverScore (Gold Answer)
            type: qa_aligned_f1_score_moverscore_gold_answer
            value: 70.82
          - name: QAAlignedRecall-MoverScore (Gold Answer)
            type: qa_aligned_recall_moverscore_gold_answer
            value: 70.54
          - name: QAAlignedPrecision-MoverScore (Gold Answer)
            type: qa_aligned_precision_moverscore_gold_answer
            value: 71.13
      - 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: 0.06530369842068952
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 0.25030985091008146
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 0.2229994442645732
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 0.9092814804525936
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 0.6086538514008419
      - 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: 0.11118273173452982
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 0.2967546690273089
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 0.27315087810722966
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 0.9322739617807421
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 0.6623000084761579
      - 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: 0.08117757543966063
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 0.25292097720734297
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 0.25254205113198686
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 0.9249009759439454
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 0.6406329128556304
      - 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: 0.059525104157825456
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 0.22365090580055863
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 0.21499800504546457
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 0.9095144685254328
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 0.6059332247878408
      - 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: 0.006278914808207679
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 0.12368226019088967
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 0.11576293675813865
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 0.8807110440044503
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 0.5555905941686486
      - 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: 0.00866799444965211
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 0.1601628874804186
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 0.15348605312210778
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 0.8783386920680519
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 0.5634845371093992
      - 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: 0.00528043272450429
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 0.12343711316491492
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 0.15133496445452477
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 0.8778951253890991
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 0.5701949938103265
      - 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: 0.0000010121579426501661
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 0.12508697028506718
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 0.11862284941640638
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 0.8748829724726739
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 0.5528899173535703
      - 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: 0.0000011301750984972448
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 0.13083168975354642
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 0.12419733006916912
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 0.8797711839570719
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 0.5542757411268555
      - 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: 8.380171318718442e-7
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 0.1402922852924756
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 0.1372146070365174
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 0.8891002409937424
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 0.5604572211470809

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

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

Language model: facebook/bart-large
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-large-squad")

# 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")
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	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",
}