readme: 
  ---
  license: cc-by-4.0
  metrics:
  - bleu4
  - meteor
  - rouge-l
  - bertscore
  - moverscore
  language: de
  datasets:
  - lmqg/qg_dequad
  pipeline_tag: text2text-generation
  tags:
  - question generation
  widget:
  - text: "Empfangs- und Sendeantenne sollen in ihrer Polarisation übereinstimmen, andernfalls <hl> wird die Signalübertragung stark gedämpft. <hl>"
    example_title: "Question Generation Example 1" 
  - text: "das erste weltweit errichtete Hermann Brehmer <hl> 1855 <hl> im niederschlesischen ''Görbersdorf'' (heute Sokołowsko, Polen)."
    example_title: "Question Generation Example 2" 
  - text: "Er muss Zyperngrieche sein und wird direkt für <hl> fünf Jahre <hl> gewählt (Art. 43 Abs. 1 der Verfassung) und verfügt über weitreichende Exekutivkompetenzen."
    example_title: "Question Generation Example 3" 
  model-index:
  - name: lmqg/mbart-large-cc25-dequad-qg
    results:
    - task:
        name: Text2text Generation
        type: text2text-generation
      dataset:
        name: lmqg/qg_dequad
        type: default
        args: default
      metrics:
      - name: BLEU4 (Question Generation)
        type: bleu4_question_generation
        value: 0.75
      - name: ROUGE-L (Question Generation)
        type: rouge_l_question_generation
        value: 11.19
      - name: METEOR (Question Generation)
        type: meteor_question_generation
        value: 13.71
      - name: BERTScore (Question Generation)
        type: bertscore_question_generation
        value: 80.77
      - name: MoverScore (Question Generation)
        type: moverscore_question_generation
        value: 55.88
      - 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: 90.66
      - 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: 90.69
      - 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: 90.64
      - 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: 65.36
      - 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: 65.36
      - 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: 65.37
      - name: QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold Answer]
        type: qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer
        value: 0.0
      - name: QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold Answer]
        type: qa_aligned_recall_bertscore_question_answer_generation_gold_answer
        value: 0.0
      - name: QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold Answer]
        type: qa_aligned_precision_bertscore_question_answer_generation_gold_answer
        value: 0.0
      - name: QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold Answer]
        type: qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer
        value: 0.0
      - name: QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold Answer]
        type: qa_aligned_recall_moverscore_question_answer_generation_gold_answer
        value: 0.0
      - name: QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold Answer]
        type: qa_aligned_precision_moverscore_question_answer_generation_gold_answer
        value: 0.0
  ---
  
  # Model Card of `lmqg/mbart-large-cc25-dequad-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_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) (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:** de  
  - **Training data:** [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) (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-question-generation-)
  ```python
  from lmqg import TransformersQG
  
  # initi