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/mt5-small-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: 16.31
      - name: ROUGE-L (Question Generation)
        type: rouge_l_question_generation
        value: 31.39
      - name: METEOR (Question Generation)
        type: meteor_question_generation
        value: 26.39
      - name: BERTScore (Question Generation)
        type: bertscore_question_generation
        value: 84.27
      - name: MoverScore (Question Generation)
        type: moverscore_question_generation
        value: 62.49
      - 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: 90.17
      - 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: 90.16
      - 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: 90.17
      - 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: 68.22
      - 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: 68.21
      - 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: 68.23
      - name: QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold Answer]
        type: qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer
        value: 76.96
      - name: QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold Answer]
        type: qa_aligned_recall_bertscore_question_answer_generation_gold_answer
        value: 81.05
      - name: QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold Answer]
        type: qa_aligned_precision_bertscore_question_answer_generation_gold_answer
        value: 73.41
      - name: QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold Answer]
        type: qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer
        value: 55.53
      - name: QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold Answer]
        type: qa_aligned_recall_moverscore_question_answer_generation_gold_answer
        value: 58.25
      - name: QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold Answer]
        type: qa_aligned_precision_moverscore_question_answer_generation_gold_answer
        value: 53.24
  ---
  
  # Model Card of `lmqg/mt5-small-ruquad-qg`
  This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) 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:** [google/mt5-small](https://huggingface.co/google/mt5-small)   
  - **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-question-generation-)
  ```python
  from lmqg import Trans