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
  widget:
  - text: "generate question: <hl>  Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl>"
    example_title: "Question Generation Example 1" 
  - text: "generate question: <hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl>"
    example_title: "Question Generation Example 2" 
  - text: "generate question: <hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records . <hl>"
    example_title: "Question Generation Example 3" 
  model-index:
  - name: research-backup/t5-small-squad-qg-no-answer
    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: 21.12
      - name: ROUGE-L (Question Generation)
        type: rouge_l_question_generation
        value: 47.47
      - name: METEOR (Question Generation)
        type: meteor_question_generation
        value: 23.38
      - name: BERTScore (Question Generation)
        type: bertscore_question_generation
        value: 89.64
      - name: MoverScore (Question Generation)
        type: moverscore_question_generation
        value: 62.07
  ---
  
  # Model Card of `research-backup/t5-small-squad-qg-no-answer`
  This model is fine-tuned version of [t5-small](https://huggingface.co/t5-small) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
  This model is fine-tuned without answer information, i.e. generate a question only given a paragraph (note that normal model is fine-tuned to generate a question given a pargraph and an associated answer in the paragraph).
  
  ### 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://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-small-squad-qg-no-answer")
  
  # model prediction
  questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner")
  
  ```
  
  - With `transformers`
  ```python
  from transformers import pipeline
  
  pipe = pipeline("text2text-generation", "research-backup/t5-small-squad-qg-no-answer")
  output = pipe("generate question: <hl>  Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl>")
  
  ```
  
  ## Evaluation
  
  
  - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/t5-small-squad-qg-no-answer/raw/main/eval/metric.first.sentence.paragraph_sentence.question.lmqg_qg_squad.default.json) 
  
  |            |   Score | Type    | Dataset                                                        |
  |:-----------|--------:|:--------|:---------------------------------------------------------------|
  | BERTScore  |   89.64 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
  | Bleu_1     |   53.37 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
  | Bleu_2     |   36.67 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
  | Bleu_3     |   27.4  | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
  | Bleu_4     |   21.12 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
  | METEOR     |   23.38 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
  | MoverScore |   62.07 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
  | ROUGE_L    |   47.47 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
  
  
  
  ## Training hyperparameters
  
  The following hyperparameters were used during fine-tuning:
   - dataset_path: lmqg/qg_squad
   - dataset_name: default
   - input_types: ['paragraph_sentence']
   - output_types: ['question']
   - prefix_types: ['qg']
   - model: t5-small
   - max_length: 512
   - max_length_output: 32
   - epoch: 7
   - batch: 64
   - lr: 0.0001
   - fp16: False