readme: 
  ---
  license: cc-by-4.0
  metrics:
  - bleu4
  - meteor
  - rouge-l
  - bertscore
  - moverscore
  language: en
  datasets:
  - lmqg/qag_tweetqa
  pipeline_tag: text2text-generation
  tags:
  - questions and answers generation
  widget:
  - text: "Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records."
    example_title: "Questions & Answers Generation Example 1" 
  model-index:
  - name: research-backup/t5-base-tweetqa-qag-np
    results:
    - task:
        name: Text2text Generation
        type: text2text-generation
      dataset:
        name: lmqg/qag_tweetqa
        type: default
        args: default
      metrics:
      - name: BLEU4 (Question & Answer Generation)
        type: bleu4_question_answer_generation
        value: 13.4
      - name: ROUGE-L (Question & Answer Generation)
        type: rouge_l_question_answer_generation
        value: 37.23
      - name: METEOR (Question & Answer Generation)
        type: meteor_question_answer_generation
        value: 31.14
      - name: BERTScore (Question & Answer Generation)
        type: bertscore_question_answer_generation
        value: 90.8
      - name: MoverScore (Question & Answer Generation)
        type: moverscore_question_answer_generation
        value: 62.26
      - name: QAAlignedF1Score-BERTScore (Question & Answer Generation)
        type: qa_aligned_f1_score_bertscore_question_answer_generation
        value: 92.4
      - name: QAAlignedRecall-BERTScore (Question & Answer Generation)
        type: qa_aligned_recall_bertscore_question_answer_generation
        value: 92.03
      - name: QAAlignedPrecision-BERTScore (Question & Answer Generation)
        type: qa_aligned_precision_bertscore_question_answer_generation
        value: 92.78
      - name: QAAlignedF1Score-MoverScore (Question & Answer Generation)
        type: qa_aligned_f1_score_moverscore_question_answer_generation
        value: 64.83
      - name: QAAlignedRecall-MoverScore (Question & Answer Generation)
        type: qa_aligned_recall_moverscore_question_answer_generation
        value: 64.07
      - name: QAAlignedPrecision-MoverScore (Question & Answer Generation)
        type: qa_aligned_precision_moverscore_question_answer_generation
        value: 65.68
  ---
  
  # Model Card of `research-backup/t5-base-tweetqa-qag-np`
  This model is fine-tuned version of [t5-base](https://huggingface.co/t5-base) for question & answer pair generation task on the [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
  This model is fine-tuned without a task prefix.
  
  ### Overview
  - **Language model:** [t5-base](https://huggingface.co/t5-base)   
  - **Language:** en  
  - **Training data:** [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) (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="en", model="research-backup/t5-base-tweetqa-qag-np")
  
  # model prediction
  question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes")
  
  ```
  
  - With `transformers`
  ```python
  from transformers import pipeline
  
  pipe = pipeline("text2text-generation", "research-backup/t5-base-tweetqa-qag-np")
  output = pipe("Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")
  
  ```
  
  ## Evaluation
  
  
  - ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/research-backup/t5-base-tweetqa-qag-np/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_tweetqa.default.json) 
  
  |                                 |   Score | Type    | Dataset                                                              |
  |:--------------------------------|--------:|:--------|:---------------------------------------------------------------------|
  | BERTScore                       |   90.8  | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) |
  | Bleu_1                          |   40.49 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) |
  | Bleu_2                          |   27.77 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) |
  | Bleu_3                          |   19.18 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) |
  | Bleu_4                          |   13.4  | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) |
  | METEOR                          |   31.14 | default | [lmqg/qag_tweetqa](https://huggingface.c