---

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: "generate question: Beyonce 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: "generate question: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records." example_title: "Question Generation Example 2"
• text: "generate question: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records ." example_title: "Question Generation Example 3" model-index:
• name: lmqg/t5-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: 27.21
    • name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 54.13
    • name: METEOR (Question Generation) type: meteor_question_generation value: 27.7
    • name: BERTScore (Question Generation) type: bertscore_question_generation value: 91.0
    • name: MoverScore (Question Generation) type: moverscore_question_generation value: 65.29
    • 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: 95.57
    • 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: 95.51
    • 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: 95.62
    • 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: 71.1
    • 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: 70.8
    • 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: 71.41
    • name: QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer value: 92.97
    • name: QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_recall_bertscore_question_answer_generation_gold_answer value: 93.14
    • name: QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_precision_bertscore_question_answer_generation_gold_answer value: 92.83
    • name: QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer value: 64.72
    • name: QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_recall_moverscore_question_answer_generation_gold_answer value: 64.66
    • name: QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_precision_moverscore_question_answer_generation_gold_answer value: 64.87
  • 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: 0.06900290231938097
    • name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 0.2533914694448162
    • name: METEOR (Question Generation) type: meteor_question_generation value: 0.23008771718972076
    • name: BERTScore (Question Generation) type: bertscore_question_generation value: 0.911505327721968
    • name: MoverScore (Question Generation) type: moverscore_question_generation value: 0.6121573406359604
  • 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: 0.11180552552578073
    • name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 0.30058260713604856
    • name: METEOR (Question Generation) type: meteor_question_generation value: 0.2792115028015132
    • name: BERTScore (Question Generation) type: bertscore_question_generation value: 0.9316688723462665
    • name: MoverScore (Question Generation) type: moverscore_question_generation value: 0.6630609588403827
  • 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: 0.08047293820182351
    • name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 0.2518886524420378
    • name: METEOR (Question Generation) type: meteor_question_generation value: 0.2567360224537303
    • name: BERTScore (Question Generation) type: bertscore_question_generation value: 0.9241819763475975
    • name: MoverScore (Question Generation) type: moverscore_question_generation value: 0.6437327703980464
  • 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: 0.059479733408388684
    • name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 0.21988765767997162
    • name: METEOR (Question Generation) type: meteor_question_generation value: 0.21853957131436155
    • name: BERTScore (Question Generation) type: bertscore_question_generation value: 0.909493447578926
    • name: MoverScore (Question Generation) type: moverscore_question_generation value: 0.6064107011094938
  • 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: 8.038380813854933e-07
    • name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 0.09871887977864714
    • name: METEOR (Question Generation) type: meteor_question_generation value: 0.11967515095282454
    • name: BERTScore (Question Generation) type: bertscore_question_generation value: 0.879356137120911
    • name: MoverScore (Question Generation) type: moverscore_question_generation value: 0.5548471413251269
  • 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: 0.008434036066953862
    • name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 0.14134333081097744
    • name: METEOR (Question Generation) type: meteor_question_generation value: 0.1616192221446712
    • name: BERTScore (Question Generation) type: bertscore_question_generation value: 0.8786280911509731
    • name: MoverScore (Question Generation) type: moverscore_question_generation value: 0.560488065035827
  • 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: 0.007639835274564104
    • name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 0.105046370156132
    • name: METEOR (Question Generation) type: meteor_question_generation value: 0.1540402363682146
    • name: BERTScore (Question Generation) type: bertscore_question_generation value: 0.8749810194969178
    • name: MoverScore (Question Generation) type: moverscore_question_generation value: 0.56763136192963
  • 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: 1.149076256883913e-06
    • name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 0.12272623105315689
    • name: METEOR (Question Generation) type: meteor_question_generation value: 0.13027427314652157
    • name: BERTScore (Question Generation) type: bertscore_question_generation value: 0.8733754583767482
    • name: MoverScore (Question Generation) type: moverscore_question_generation value: 0.5536261740282519
  • 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: 1.8508536550762953e-10
    • name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 0.1192666899417942
    • name: METEOR (Question Generation) type: meteor_question_generation value: 0.12447769563902232
    • name: BERTScore (Question Generation) type: bertscore_question_generation value: 0.8825407926650608
    • name: MoverScore (Question Generation) type: moverscore_question_generation value: 0.5591163692270524
  • 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: 0.007817275411070228
    • name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 0.14594416096461188
    • name: METEOR (Question Generation) type: meteor_question_generation value: 0.16297700667338805
    • name: BERTScore (Question Generation) type: bertscore_question_generation value: 0.8928685000227912
    • name: MoverScore (Question Generation) type: moverscore_question_generation value: 0.5681021918513103

---

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

This model is fine-tuned version of t5-large for question generation task on the lmqg/qg_squad (dataset_name: default) via lmqg.

Overview

• Language model: t5-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/t5-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/t5-large-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

• Metric (Question Generation): raw metric file

• Metric (Question & Answer Generation, Reference Answer): Each question is generated from the gold answer. raw metric file

• Metric (Question & Answer Generation, Pipeline Approach): Each question is generated on the answer generated by lmqg/t5-large-squad-ae. raw metric file

• Metrics (Question Generation, Out-of-Domain)

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-large
• max_length: 512
• max_length_output: 32
• epoch: 6
• batch: 16
• 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",
}