readme: 
  ---
  license: cc-by-4.0
  metrics:
  - bleu4
  - meteor
  - rouge-l
  - bertscore
  - moverscore
  language: es
  datasets:
  - lmqg/qg_esquad
  pipeline_tag: text2text-generation
  tags:
  - question generation
  widget:
  - text: "del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India."
    example_title: "Question Generation Example 1" 
  - text: "a <hl> noviembre <hl> , que es también la estación lluviosa."
    example_title: "Question Generation Example 2" 
  - text: "como <hl> el gobierno de Abbott <hl> que asumió el cargo el 18 de septiembre de 2013."
    example_title: "Question Generation Example 3" 
  model-index:
  - name: lmqg/mt5-base-esquad-qg
    results:
    - task:
        name: Text2text Generation
        type: text2text-generation
      dataset:
        name: lmqg/qg_esquad
        type: default
        args: default
      metrics:
      - name: BLEU4 (Question Generation)
        type: bleu4_question_generation
        value: 10.15
      - name: ROUGE-L (Question Generation)
        type: rouge_l_question_generation
        value: 25.45
      - name: METEOR (Question Generation)
        type: meteor_question_generation
        value: 23.43
      - name: BERTScore (Question Generation)
        type: bertscore_question_generation
        value: 84.47
      - name: MoverScore (Question Generation)
        type: moverscore_question_generation
        value: 59.62
      - 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: 89.68
      - 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: 89.66
      - 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: 89.7
      - 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: 64.22
      - 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: 64.21
      - 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: 64.24
      - name: QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold Answer]
        type: qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer
        value: 80.79
      - name: QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold Answer]
        type: qa_aligned_recall_bertscore_question_answer_generation_gold_answer
        value: 83.34
      - name: QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold Answer]
        type: qa_aligned_precision_bertscore_question_answer_generation_gold_answer
        value: 78.45
      - name: QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold Answer]
        type: qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer
        value: 55.25
      - name: QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold Answer]
        type: qa_aligned_recall_moverscore_question_answer_generation_gold_answer
        value: 56.99
      - name: QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold Answer]
        type: qa_aligned_precision_moverscore_question_answer_generation_gold_answer
        value: 53.7
  ---
  
  # Model Card of `lmqg/mt5-base-esquad-qg`
  This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for question generation task on the [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
  
  
  ### Overview
  - **Language model:** [google/mt5-base](https://huggingface.co/google/mt5-base)   
  - **Language:** es  
  - **Training data:** [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (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
  
  # initialize model
  model = TransformersQG(language="es", model="lmqg/mt5-base-esquad-qg")
  
  # model prediction
  questions = model.generate_q(list_context="a noviembre , que es también la estación lluviosa.", list_answer="noviembre")
  
  ```
  
  - With `transformers`