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--- |
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license: cc-by-4.0 |
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metrics: |
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- bleu4 |
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- meteor |
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- rouge-l |
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- bertscore |
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- moverscore |
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language: en |
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datasets: |
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- lmqg/qg_squad |
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pipeline_tag: text2text-generation |
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tags: |
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- question generation |
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widget: |
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- text: "<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records." |
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example_title: "Question Generation Example 1" |
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- text: "Beyonce further expanded her acting career, starring as blues singer <hl> Etta James <hl> in the 2008 musical biopic, Cadillac Records." |
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example_title: "Question Generation Example 2" |
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- text: "Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, <hl> Cadillac Records <hl> ." |
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example_title: "Question Generation Example 3" |
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model-index: |
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- name: lmqg/bart-large-squad-qg |
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results: |
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- task: |
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name: Text2text Generation |
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type: text2text-generation |
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dataset: |
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name: lmqg/qg_squad |
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type: default |
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args: default |
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metrics: |
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- name: BLEU4 (Question Generation) |
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type: bleu4_question_generation |
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value: 26.17 |
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- name: ROUGE-L (Question Generation) |
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type: rouge_l_question_generation |
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value: 53.85 |
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- name: METEOR (Question Generation) |
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type: meteor_question_generation |
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value: 27.07 |
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- name: BERTScore (Question Generation) |
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type: bertscore_question_generation |
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value: 91.0 |
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- name: MoverScore (Question Generation) |
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type: moverscore_question_generation |
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value: 64.99 |
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- name: QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] |
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type: qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer_gold_answer |
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value: 95.54 |
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- name: QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] |
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type: qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer_gold_answer |
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value: 95.49 |
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- name: QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] |
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type: qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer_gold_answer |
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value: 95.59 |
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- name: QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] |
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type: qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer_gold_answer |
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value: 70.82 |
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- name: QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] |
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type: qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer_gold_answer |
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value: 70.54 |
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- name: QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] |
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type: qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer_gold_answer |
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value: 71.13 |
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- name: QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold Answer] |
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type: qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer |
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value: 93.23 |
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- name: QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold Answer] |
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type: qa_aligned_recall_bertscore_question_answer_generation_gold_answer |
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value: 93.35 |
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- name: QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold Answer] |
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type: qa_aligned_precision_bertscore_question_answer_generation_gold_answer |
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value: 93.13 |
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- name: QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold Answer] |
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type: qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer |
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value: 64.76 |
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- name: QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold Answer] |
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type: qa_aligned_recall_moverscore_question_answer_generation_gold_answer |
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value: 64.63 |
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- name: QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold Answer] |
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type: qa_aligned_precision_moverscore_question_answer_generation_gold_answer |
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value: 64.98 |
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- task: |
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name: Text2text Generation |
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type: text2text-generation |
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dataset: |
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name: lmqg/qg_squadshifts |
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type: amazon |
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args: amazon |
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metrics: |
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- name: BLEU4 (Question Generation) |
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type: bleu4_question_generation |
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value: 0.06530369842068952 |
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- name: ROUGE-L (Question Generation) |
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type: rouge_l_question_generation |
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value: 0.25030985091008146 |
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- name: METEOR (Question Generation) |
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type: meteor_question_generation |
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value: 0.2229994442645732 |
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- name: BERTScore (Question Generation) |
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type: bertscore_question_generation |
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value: 0.9092814804525936 |
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- name: MoverScore (Question Generation) |
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type: moverscore_question_generation |
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value: 0.6086538514008419 |
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- task: |
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name: Text2text Generation |
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type: text2text-generation |
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dataset: |
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name: lmqg/qg_squadshifts |
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type: new_wiki |
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args: new_wiki |
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metrics: |
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- name: BLEU4 (Question Generation) |
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type: bleu4_question_generation |
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value: 0.11118273173452982 |
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- name: ROUGE-L (Question Generation) |
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type: rouge_l_question_generation |
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value: 0.2967546690273089 |
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- name: METEOR (Question Generation) |
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type: meteor_question_generation |
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value: 0.27315087810722966 |
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- name: BERTScore (Question Generation) |
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type: bertscore_question_generation |
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value: 0.9322739617807421 |
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- name: MoverScore (Question Generation) |
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type: moverscore_question_generation |
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value: 0.6623000084761579 |
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- task: |
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name: Text2text Generation |
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type: text2text-generation |
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dataset: |
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name: lmqg/qg_squadshifts |
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type: nyt |
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args: nyt |
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metrics: |
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- name: BLEU4 (Question Generation) |
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type: bleu4_question_generation |
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value: 0.08117757543966063 |
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- name: ROUGE-L (Question Generation) |
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type: rouge_l_question_generation |
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value: 0.25292097720734297 |
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- name: METEOR (Question Generation) |
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type: meteor_question_generation |
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value: 0.25254205113198686 |
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- name: BERTScore (Question Generation) |
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type: bertscore_question_generation |
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value: 0.9249009759439454 |
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- name: MoverScore (Question Generation) |
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type: moverscore_question_generation |
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value: 0.6406329128556304 |
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- task: |
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name: Text2text Generation |
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type: text2text-generation |
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dataset: |
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name: lmqg/qg_squadshifts |
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type: reddit |
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args: reddit |
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metrics: |
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- name: BLEU4 (Question Generation) |
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type: bleu4_question_generation |
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value: 0.059525104157825456 |
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- name: ROUGE-L (Question Generation) |
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type: rouge_l_question_generation |
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value: 0.22365090580055863 |
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- name: METEOR (Question Generation) |
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type: meteor_question_generation |
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value: 0.21499800504546457 |
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- name: BERTScore (Question Generation) |
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type: bertscore_question_generation |
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value: 0.9095144685254328 |
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- name: MoverScore (Question Generation) |
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type: moverscore_question_generation |
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value: 0.6059332247878408 |
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- task: |
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name: Text2text Generation |
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type: text2text-generation |
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dataset: |
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name: lmqg/qg_subjqa |
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type: books |
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args: books |
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metrics: |
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- name: BLEU4 (Question Generation) |
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type: bleu4_question_generation |
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value: 0.006278914808207679 |
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- name: ROUGE-L (Question Generation) |
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type: rouge_l_question_generation |
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value: 0.12368226019088967 |
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- name: METEOR (Question Generation) |
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type: meteor_question_generation |
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value: 0.11576293675813865 |
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- name: BERTScore (Question Generation) |
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type: bertscore_question_generation |
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value: 0.8807110440044503 |
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- name: MoverScore (Question Generation) |
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type: moverscore_question_generation |
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value: 0.5555905941686486 |
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- task: |
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name: Text2text Generation |
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type: text2text-generation |
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dataset: |
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name: lmqg/qg_subjqa |
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type: electronics |
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args: electronics |
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metrics: |
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- name: BLEU4 (Question Generation) |
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type: bleu4_question_generation |
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value: 0.00866799444965211 |
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- name: ROUGE-L (Question Generation) |
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type: rouge_l_question_generation |
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value: 0.1601628874804186 |
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- name: METEOR (Question Generation) |
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type: meteor_question_generation |
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value: 0.15348605312210778 |
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- name: BERTScore (Question Generation) |
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type: bertscore_question_generation |
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value: 0.8783386920680519 |
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- name: MoverScore (Question Generation) |
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type: moverscore_question_generation |
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value: 0.5634845371093992 |
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- task: |
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name: Text2text Generation |
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type: text2text-generation |
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dataset: |
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name: lmqg/qg_subjqa |
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type: grocery |
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args: grocery |
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metrics: |
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- name: BLEU4 (Question Generation) |
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type: bleu4_question_generation |
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value: 0.00528043272450429 |
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- name: ROUGE-L (Question Generation) |
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type: rouge_l_question_generation |
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value: 0.12343711316491492 |
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- name: METEOR (Question Generation) |
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type: meteor_question_generation |
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value: 0.15133496445452477 |
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- name: BERTScore (Question Generation) |
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type: bertscore_question_generation |
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value: 0.8778951253890991 |
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- name: MoverScore (Question Generation) |
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type: moverscore_question_generation |
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value: 0.5701949938103265 |
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- task: |
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name: Text2text Generation |
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type: text2text-generation |
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dataset: |
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name: lmqg/qg_subjqa |
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type: movies |
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args: movies |
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metrics: |
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- name: BLEU4 (Question Generation) |
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type: bleu4_question_generation |
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value: 1.0121579426501661e-06 |
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- name: ROUGE-L (Question Generation) |
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type: rouge_l_question_generation |
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value: 0.12508697028506718 |
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- name: METEOR (Question Generation) |
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type: meteor_question_generation |
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value: 0.11862284941640638 |
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- name: BERTScore (Question Generation) |
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type: bertscore_question_generation |
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value: 0.8748829724726739 |
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- name: MoverScore (Question Generation) |
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type: moverscore_question_generation |
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value: 0.5528899173535703 |
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- task: |
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name: Text2text Generation |
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type: text2text-generation |
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dataset: |
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name: lmqg/qg_subjqa |
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type: restaurants |
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args: restaurants |
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metrics: |
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- name: BLEU4 (Question Generation) |
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type: bleu4_question_generation |
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value: 1.1301750984972448e-06 |
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- name: ROUGE-L (Question Generation) |
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type: rouge_l_question_generation |
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value: 0.13083168975354642 |
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- name: METEOR (Question Generation) |
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type: meteor_question_generation |
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value: 0.12419733006916912 |
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- name: BERTScore (Question Generation) |
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type: bertscore_question_generation |
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value: 0.8797711839570719 |
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- name: MoverScore (Question Generation) |
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type: moverscore_question_generation |
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value: 0.5542757411268555 |
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- task: |
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name: Text2text Generation |
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type: text2text-generation |
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dataset: |
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name: lmqg/qg_subjqa |
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type: tripadvisor |
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args: tripadvisor |
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metrics: |
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- name: BLEU4 (Question Generation) |
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type: bleu4_question_generation |
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value: 8.380171318718442e-07 |
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- name: ROUGE-L (Question Generation) |
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type: rouge_l_question_generation |
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value: 0.1402922852924756 |
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- name: METEOR (Question Generation) |
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type: meteor_question_generation |
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value: 0.1372146070365174 |
|
- name: BERTScore (Question Generation) |
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type: bertscore_question_generation |
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value: 0.8891002409937424 |
|
- name: MoverScore (Question Generation) |
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type: moverscore_question_generation |
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value: 0.5604572211470809 |
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--- |
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|
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# Model Card of `lmqg/bart-large-squad-qg` |
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This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). |
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### Overview |
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- **Language model:** [facebook/bart-large](https://huggingface.co/facebook/bart-large) |
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- **Language:** en |
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- **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (default) |
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- **Online Demo:** [https://autoqg.net/](https://autoqg.net/) |
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- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) |
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- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) |
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### Usage |
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- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) |
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```python |
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from lmqg import TransformersQG |
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# initialize model |
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model = TransformersQG(language="en", model="lmqg/bart-large-squad-qg") |
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# model prediction |
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questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") |
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``` |
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- With `transformers` |
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```python |
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from transformers import pipeline |
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pipe = pipeline("text2text-generation", "lmqg/bart-large-squad-qg") |
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output = pipe("<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") |
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``` |
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## Evaluation |
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- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/bart-large-squad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json) |
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|
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| | Score | Type | Dataset | |
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|:-----------|--------:|:--------|:---------------------------------------------------------------| |
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| BERTScore | 91 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | |
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| Bleu_1 | 58.79 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | |
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| Bleu_2 | 42.79 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | |
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| Bleu_3 | 33.11 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | |
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| Bleu_4 | 26.17 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | |
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| METEOR | 27.07 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | |
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| MoverScore | 64.99 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | |
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| ROUGE_L | 53.85 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | |
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- ***Metric (Question & Answer Generation, Reference Answer)***: Each question is generated from *the gold answer*. [raw metric file](https://huggingface.co/lmqg/bart-large-squad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_squad.default.json) |
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|
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| | Score | Type | Dataset | |
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|:--------------------------------|--------:|:--------|:---------------------------------------------------------------| |
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| QAAlignedF1Score (BERTScore) | 95.54 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | |
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| QAAlignedF1Score (MoverScore) | 70.82 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | |
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| QAAlignedPrecision (BERTScore) | 95.59 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | |
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| QAAlignedPrecision (MoverScore) | 71.13 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | |
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| QAAlignedRecall (BERTScore) | 95.49 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | |
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| QAAlignedRecall (MoverScore) | 70.54 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | |
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|
|
|
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- ***Metric (Question & Answer Generation, Pipeline Approach)***: Each question is generated on the answer generated by [`lmqg/bart-large-squad-ae`](https://huggingface.co/lmqg/bart-large-squad-ae). [raw metric file](https://huggingface.co/lmqg/bart-large-squad-qg/raw/main/eval_pipeline/metric.first.answer.paragraph.questions_answers.lmqg_qg_squad.default.lmqg_bart-large-squad-ae.json) |
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|
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| | Score | Type | Dataset | |
|
|:--------------------------------|--------:|:--------|:---------------------------------------------------------------| |
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| QAAlignedF1Score (BERTScore) | 93.23 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | |
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| QAAlignedF1Score (MoverScore) | 64.76 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | |
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| QAAlignedPrecision (BERTScore) | 93.13 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | |
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| QAAlignedPrecision (MoverScore) | 64.98 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | |
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| QAAlignedRecall (BERTScore) | 93.35 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | |
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| QAAlignedRecall (MoverScore) | 64.63 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | |
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|
|
|
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- ***Metrics (Question Generation, Out-of-Domain)*** |
|
|
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| Dataset | Type | BERTScore| Bleu_4 | METEOR | MoverScore | ROUGE_L | Link | |
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|:--------|:-----|---------:|-------:|-------:|-----------:|--------:|-----:| |
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| [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | amazon | 90.93 | 6.53 | 22.3 | 60.87 | 25.03 | [link](https://huggingface.co/lmqg/bart-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.amazon.json) | |
|
| [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | new_wiki | 93.23 | 11.12 | 27.32 | 66.23 | 29.68 | [link](https://huggingface.co/lmqg/bart-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.new_wiki.json) | |
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| [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | nyt | 92.49 | 8.12 | 25.25 | 64.06 | 25.29 | [link](https://huggingface.co/lmqg/bart-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.nyt.json) | |
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| [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | reddit | 90.95 | 5.95 | 21.5 | 60.59 | 22.37 | [link](https://huggingface.co/lmqg/bart-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.reddit.json) | |
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| [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | books | 88.07 | 0.63 | 11.58 | 55.56 | 12.37 | [link](https://huggingface.co/lmqg/bart-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.books.json) | |
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| [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | electronics | 87.83 | 0.87 | 15.35 | 56.35 | 16.02 | [link](https://huggingface.co/lmqg/bart-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.electronics.json) | |
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| [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | grocery | 87.79 | 0.53 | 15.13 | 57.02 | 12.34 | [link](https://huggingface.co/lmqg/bart-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.grocery.json) | |
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| [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | movies | 87.49 | 0.0 | 11.86 | 55.29 | 12.51 | [link](https://huggingface.co/lmqg/bart-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.movies.json) | |
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| [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | restaurants | 87.98 | 0.0 | 12.42 | 55.43 | 13.08 | [link](https://huggingface.co/lmqg/bart-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.restaurants.json) | |
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| [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | tripadvisor | 88.91 | 0.0 | 13.72 | 56.05 | 14.03 | [link](https://huggingface.co/lmqg/bart-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.tripadvisor.json) | |
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## Training hyperparameters |
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|
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The following hyperparameters were used during fine-tuning: |
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- dataset_path: lmqg/qg_squad |
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- dataset_name: default |
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- input_types: ['paragraph_answer'] |
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- output_types: ['question'] |
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- prefix_types: None |
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- model: facebook/bart-large |
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- max_length: 512 |
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- max_length_output: 32 |
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- epoch: 4 |
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- batch: 32 |
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- lr: 5e-05 |
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- fp16: False |
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- random_seed: 1 |
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- gradient_accumulation_steps: 4 |
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- label_smoothing: 0.15 |
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The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/bart-large-squad-qg/raw/main/trainer_config.json). |
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## Citation |
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``` |
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@inproceedings{ushio-etal-2022-generative, |
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title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", |
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author = "Ushio, Asahi and |
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Alva-Manchego, Fernando and |
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Camacho-Collados, Jose", |
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booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", |
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month = dec, |
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year = "2022", |
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address = "Abu Dhabi, U.A.E.", |
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publisher = "Association for Computational Linguistics", |
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} |
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``` |
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|
