readme: 
  ---
  license: cc-by-4.0
  metrics:
  - bleu4
  - meteor
  - rouge-l
  - bertscore
  - moverscore
  language: ru
  datasets:
  - lmqg/qg_ruquad
  pipeline_tag: text2text-generation
  tags:
  - question generation
  widget:
  - text: "Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, <hl> в мае 1860 года <hl> провёл серию опытов."
    example_title: "Question Generation Example 1" 
  - text: "Однако, франкоязычный <hl> Квебек <hl> практически никогда не включается в состав Латинской Америки."
    example_title: "Question Generation Example 2" 
  - text: "Классическим примером международного синдиката XX века была группа компаний <hl> Де Бирс <hl> , которая в 1980-е годы контролировала до 90 % мировой торговли алмазами."
    example_title: "Question Generation Example 3" 
  model-index:
  - name: lmqg/mbart-large-cc25-ruquad-qg
    results:
    - task:
        name: Text2text Generation
        type: text2text-generation
      dataset:
        name: lmqg/qg_ruquad
        type: default
        args: default
      metrics:
      - name: BLEU4 (Question Generation)
        type: bleu4_question_generation
        value: 18.8
      - name: ROUGE-L (Question Generation)
        type: rouge_l_question_generation
        value: 34.18
      - name: METEOR (Question Generation)
        type: meteor_question_generation
        value: 29.3
      - name: BERTScore (Question Generation)
        type: bertscore_question_generation
        value: 87.18
      - name: MoverScore (Question Generation)
        type: moverscore_question_generation
        value: 65.88
      - 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: 92.08
      - 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: 92.08
      - 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: 92.09
      - 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: 71.45
      - 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: 71.45
      - 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: 71.46
      - name: QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold Answer]
        type: qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer
        value: 79.14
      - name: QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold Answer]
        type: qa_aligned_recall_bertscore_question_answer_generation_gold_answer
        value: 82.85
      - name: QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold Answer]
        type: qa_aligned_precision_bertscore_question_answer_generation_gold_answer
        value: 75.88
      - name: QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold Answer]
        type: qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer
        value: 56.25
      - name: QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold Answer]
        type: qa_aligned_recall_moverscore_question_answer_generation_gold_answer
        value: 58.93
      - name: QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold Answer]
        type: qa_aligned_precision_moverscore_question_answer_generation_gold_answer
        value: 54.01
  ---
  
  # Model Card of `lmqg/mbart-large-cc25-ruquad-qg`
  This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question generation task on the [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
  
  
  ### Overview
  - **Language model:** [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25)   
  - **Language:** ru  
  - **Training data:** [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (default)
  - **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
  - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
  - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
  
  ### Usage
  - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-questi