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: "<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: "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: "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" 
  model-index:
  - name: research-backup/bart-large-squad-qg-no-paragraph
    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: 23.86
      - name: ROUGE-L (Question Generation)
        type: rouge_l_question_generation
        value: 51.43
      - name: METEOR (Question Generation)
        type: meteor_question_generation
        value: 25.18
      - name: BERTScore (Question Generation)
        type: bertscore_question_generation
        value: 90.7
      - name: MoverScore (Question Generation)
        type: moverscore_question_generation
        value: 63.85
  ---
  
  # Model Card of `research-backup/bart-large-squad-qg-no-paragraph`
  This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) 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 pargraph information but only the sentence that contains the answer.
  
  ### Overview
  - **Language model:** [facebook/bart-large](https://huggingface.co/facebook/bart-large)   
  - **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/bart-large-squad-qg-no-paragraph")
  
  # 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/bart-large-squad-qg-no-paragraph")
  output = pipe("<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")
  
  ```
  
  ## Evaluation
  
  
  - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/bart-large-squad-qg-no-paragraph/raw/main/eval/metric.first.sentence.sentence_answer.question.lmqg_qg_squad.default.json) 
  
  |            |   Score | Type    | Dataset                                                        |
  |:-----------|--------:|:--------|:---------------------------------------------------------------|
  | BERTScore  |   90.7  | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
  | Bleu_1     |   55.85 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
  | Bleu_2     |   39.85 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
  | Bleu_3     |   30.44 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
  | Bleu_4     |   23.86 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
  | METEOR     |   25.18 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
  | MoverScore |   63.85 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
  | ROUGE_L    |   51.43 | 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: ['sentence_answer']
   - output_types: ['question']
   - prefix_types: None
   - model: facebook/bart-large
   - max_length: 128
   - max_length_output: 32
   - epoch: 8
   - batch: 32
   - lr: 5e-05
   - fp16: False
   - random_seed: 1
   - gradient_accumulation_steps: 16
   - label_smoothing: 0.15
  
  The full configuration can be found at [fine-tun