asahi417's picture
model update
177afce
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-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: 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 (Question & Answer Generation (with
              Gold Answer)) [Gold Answer]
            type: >-
              qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer_gold_answer
            value: 95.54
          - 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: 95.49
          - 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: 95.59
          - 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: 70.82
          - 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: 70.54
          - 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: 71.13
          - name: >-
              QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer
            value: 93.23
          - name: >-
              QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold
              Answer]
            type: qa_aligned_recall_bertscore_question_answer_generation_gold_answer
            value: 93.35
          - name: >-
              QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_precision_bertscore_question_answer_generation_gold_answer
            value: 93.13
          - name: >-
              QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer
            value: 64.76
          - name: >-
              QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_recall_moverscore_question_answer_generation_gold_answer
            value: 64.63
          - name: >-
              QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_precision_moverscore_question_answer_generation_gold_answer
            value: 64.98
      - 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-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, Reference Answer): Each question is generated from the gold 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
Score Type Dataset
QAAlignedF1Score (BERTScore) 93.23 default lmqg/qg_squad
QAAlignedF1Score (MoverScore) 64.76 default lmqg/qg_squad
QAAlignedPrecision (BERTScore) 93.13 default lmqg/qg_squad
QAAlignedPrecision (MoverScore) 64.98 default lmqg/qg_squad
QAAlignedRecall (BERTScore) 93.35 default lmqg/qg_squad
QAAlignedRecall (MoverScore) 64.63 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",
}