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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/mt5-small-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: 21.65 |
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- name: ROUGE-L (Question Generation) |
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type: rouge_l_question_generation |
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value: 48.95 |
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- name: METEOR (Question Generation) |
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type: meteor_question_generation |
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value: 23.83 |
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- name: BERTScore (Question Generation) |
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type: bertscore_question_generation |
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value: 90.01 |
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- name: MoverScore (Question Generation) |
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type: moverscore_question_generation |
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value: 62.75 |
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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_dequad |
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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: 9.242783121165897e-12 |
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- name: ROUGE-L (Question Generation) |
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type: rouge_l_question_generation |
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value: 0.01556150764938016 |
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- name: METEOR (Question Generation) |
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type: meteor_question_generation |
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value: 0.04809700451843158 |
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- name: BERTScore (Question Generation) |
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type: bertscore_question_generation |
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value: 0.7353078946893743 |
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- name: MoverScore (Question Generation) |
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type: moverscore_question_generation |
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value: 0.5036973829954939 |
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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_esquad |
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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: 0.0059191752064594125 |
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- name: ROUGE-L (Question Generation) |
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type: rouge_l_question_generation |
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value: 0.05208940592236566 |
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- name: METEOR (Question Generation) |
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type: meteor_question_generation |
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value: 0.06021086135293597 |
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- name: BERTScore (Question Generation) |
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type: bertscore_question_generation |
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value: 0.7494422899749911 |
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- name: MoverScore (Question Generation) |
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type: moverscore_question_generation |
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value: 0.5062373132800192 |
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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_frquad |
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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: 0.0171464639522496 |
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- name: ROUGE-L (Question Generation) |
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type: rouge_l_question_generation |
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value: 0.1583673053928925 |
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- name: METEOR (Question Generation) |
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type: meteor_question_generation |
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value: 0.08244973027319356 |
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- name: BERTScore (Question Generation) |
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type: bertscore_question_generation |
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value: 0.7291012183458674 |
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- name: MoverScore (Question Generation) |
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type: moverscore_question_generation |
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value: 0.509610854598101 |
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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_itquad |
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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: 0.005438910607183992 |
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- name: ROUGE-L (Question Generation) |
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type: rouge_l_question_generation |
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value: 0.05010570221421983 |
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- name: METEOR (Question Generation) |
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type: meteor_question_generation |
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value: 0.05890828426558759 |
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- name: BERTScore (Question Generation) |
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type: bertscore_question_generation |
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value: 0.7260160158030385 |
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- name: MoverScore (Question Generation) |
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type: moverscore_question_generation |
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value: 0.5023119088393686 |
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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_jaquad |
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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: 4.4114578660129224e-08 |
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- name: ROUGE-L (Question Generation) |
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type: rouge_l_question_generation |
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value: 0.06084267343290677 |
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- name: METEOR (Question Generation) |
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type: meteor_question_generation |
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value: 0.005149267426183168 |
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- name: BERTScore (Question Generation) |
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type: bertscore_question_generation |
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value: 0.6608093198082075 |
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- name: MoverScore (Question Generation) |
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type: moverscore_question_generation |
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value: 0.46526108687696893 |
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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_koquad |
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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: 1.4750917137316939e-12 |
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- name: ROUGE-L (Question Generation) |
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type: rouge_l_question_generation |
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value: 0.0006466767450454226 |
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- name: METEOR (Question Generation) |
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type: meteor_question_generation |
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value: 0.007310046912436679 |
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- name: BERTScore (Question Generation) |
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type: bertscore_question_generation |
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value: 0.6634288882769679 |
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- name: MoverScore (Question Generation) |
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type: moverscore_question_generation |
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value: 0.4586124640357038 |
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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_ruquad |
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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: 4.229109829516021e-12 |
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- name: ROUGE-L (Question Generation) |
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type: rouge_l_question_generation |
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value: 0.009881091250723615 |
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- name: METEOR (Question Generation) |
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type: meteor_question_generation |
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value: 0.017796529053904556 |
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- name: BERTScore (Question Generation) |
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type: bertscore_question_generation |
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value: 0.7089446693028568 |
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- name: MoverScore (Question Generation) |
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type: moverscore_question_generation |
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value: 0.49098728551715626 |
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--- |
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# Model Card of `lmqg/mt5-small-squad-qg` |
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This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) 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:** [google/mt5-small](https://huggingface.co/google/mt5-small) |
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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/mt5-small-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/mt5-small-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/mt5-small-squad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json) |
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| | Score | Type | Dataset | |
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|:-----------|--------:|:--------|:---------------------------------------------------------------| |
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| BERTScore | 90.01 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | |
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| Bleu_1 | 54.07 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | |
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| Bleu_2 | 37.62 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | |
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| Bleu_3 | 28.18 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | |
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| Bleu_4 | 21.65 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | |
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| METEOR | 23.83 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | |
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| MoverScore | 62.75 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | |
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| ROUGE_L | 48.95 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | |
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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_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) | default | 73.53 | 0.0 | 4.81 | 50.37 | 1.56 | [link](https://huggingface.co/lmqg/mt5-small-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_dequad.default.json) | |
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| [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | default | 74.94 | 0.59 | 6.02 | 50.62 | 5.21 | [link](https://huggingface.co/lmqg/mt5-small-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_esquad.default.json) | |
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| [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | default | 72.91 | 1.71 | 8.24 | 50.96 | 15.84 | [link](https://huggingface.co/lmqg/mt5-small-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_frquad.default.json) | |
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| [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | default | 72.6 | 0.54 | 5.89 | 50.23 | 5.01 | [link](https://huggingface.co/lmqg/mt5-small-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_itquad.default.json) | |
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| [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | default | 66.08 | 0.0 | 0.51 | 46.53 | 6.08 | [link](https://huggingface.co/lmqg/mt5-small-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_jaquad.default.json) | |
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| [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | default | 66.34 | 0.0 | 0.73 | 45.86 | 0.06 | [link](https://huggingface.co/lmqg/mt5-small-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_koquad.default.json) | |
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| [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | default | 70.89 | 0.0 | 1.78 | 49.1 | 0.99 | [link](https://huggingface.co/lmqg/mt5-small-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_ruquad.default.json) | |
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## Training hyperparameters |
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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: google/mt5-small |
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- max_length: 512 |
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- max_length_output: 32 |
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- epoch: 15 |
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- batch: 64 |
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- lr: 0.0005 |
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- fp16: False |
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- random_seed: 1 |
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- gradient_accumulation_steps: 1 |
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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/mt5-small-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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