readme: 
  ---
  license: cc-by-4.0
  metrics:
  - bleu4
  - meteor
  - rouge-l
  - bertscore
  - moverscore
  language: en
  datasets:
  - lmqg/qg_squad
  pipeline_tag: text2text-generation
  tags:
  - question generation
  - answer extraction
  widget:
  - text: "generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records."
    example_title: "Question Generation Example 1" 
  - text: "generate question: Beyonce further expanded her acting career, starring as blues singer <hl> Etta James <hl> in the 2008 musical biopic, Cadillac Records."
    example_title: "Question Generation Example 2" 
  - text: "generate question: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic,  <hl> Cadillac Records <hl> ."
    example_title: "Question Generation Example 3" 
  - text: "extract answers: <hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress."
    example_title: "Answer Extraction Example 1" 
  - text: "extract answers: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress. <hl>"
    example_title: "Answer Extraction Example 2" 
  model-index:
  - name: lmqg/t5-small-squad-qg-ae
    results:
    - task:
        name: Text2text Generation
        type: text2text-generation
      dataset:
        name: lmqg/qg_squad
        type: default
        args: default
      metrics:
      - name: BLEU4 (Question Generation)
        type: bleu4_question_generation
        value: 24.18
      - name: ROUGE-L (Question Generation)
        type: rouge_l_question_generation
        value: 51.12
      - name: METEOR (Question Generation)
        type: meteor_question_generation
        value: 25.58
      - name: BERTScore (Question Generation)
        type: bertscore_question_generation
        value: 90.18
      - name: MoverScore (Question Generation)
        type: moverscore_question_generation
        value: 63.72
      - name: QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer))
        type: qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer
        value: 91.74
      - name: QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer))
        type: qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer
        value: 92.01
      - name: QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer))
        type: qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer
        value: 91.49
      - name: QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer))
        type: qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer
        value: 63.23
      - name: QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer))
        type: qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer
        value: 63.29
      - name: QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer))
        type: qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer
        value: 63.26
      - name: BLEU4 (Answer Extraction)
        type: bleu4_answer_extraction
        value: 27.06
      - name: ROUGE-L (Answer Extraction)
        type: rouge_l_answer_extraction
        value: 66.52
      - name: METEOR (Answer Extraction)
        type: meteor_answer_extraction
        value: 40.9
      - name: BERTScore (Answer Extraction)
        type: bertscore_answer_extraction
        value: 90.77
      - name: MoverScore (Answer Extraction)
        type: moverscore_answer_extraction
        value: 79.49
      - name: AnswerF1Score (Answer Extraction)
        type: answer_f1_score__answer_extraction
        value: 66.92
      - name: AnswerExactMatch (Answer Extraction)
        type: answer_exact_match_answer_extraction
        value: 54.17
  ---
  
  # Model Card of `lmqg/t5-small-squad-qg-ae`
  This model is fine-tuned version of [t5-small](https://huggingface.co/t5-small) for question generation and answer extraction jointly on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
  
  
  ### Overview
  - **Language model:** [t5-small](https://huggingface.co/t5-small)   
  - **Language:** en  
  - **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (default)
  - **Online Demo:** [https://