readme: 
  ---
  license: cc-by-4.0
  metrics:
  - bleu4
  - meteor
  - rouge-l
  - bertscore
  - moverscore
  language: it
  datasets:
  - lmqg/qag_itquad
  pipeline_tag: text2text-generation
  tags:
  - questions and answers generation
  widget:
  - text: "Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento."
    example_title: "Questions & Answers Generation Example 1" 
  model-index:
  - name: lmqg/mt5-base-itquad-qag
    results:
    - task:
        name: Text2text Generation
        type: text2text-generation
      dataset:
        name: lmqg/qag_itquad
        type: default
        args: default
      metrics:
      - name: QAAlignedF1Score-BERTScore (Question & Answer Generation)
        type: qa_aligned_f1_score_bertscore_question_answer_generation
        value: 79.93
      - name: QAAlignedRecall-BERTScore (Question & Answer Generation)
        type: qa_aligned_recall_bertscore_question_answer_generation
        value: 78.87
      - name: QAAlignedPrecision-BERTScore (Question & Answer Generation)
        type: qa_aligned_precision_bertscore_question_answer_generation
        value: 81.06
      - name: QAAlignedF1Score-MoverScore (Question & Answer Generation)
        type: qa_aligned_f1_score_moverscore_question_answer_generation
        value: 53.8
      - name: QAAlignedRecall-MoverScore (Question & Answer Generation)
        type: qa_aligned_recall_moverscore_question_answer_generation
        value: 53.02
      - name: QAAlignedPrecision-MoverScore (Question & Answer Generation)
        type: qa_aligned_precision_moverscore_question_answer_generation
        value: 54.64
  ---
  
  # Model Card of `lmqg/mt5-base-itquad-qag`
  This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for question & answer pair generation task on the [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) (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:** it  
  - **Training data:** [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) (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="it", model="lmqg/mt5-base-itquad-qag")
  
  # model prediction
  question_answer_pairs = model.generate_qa("Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.")
  
  ```
  
  - With `transformers`
  ```python
  from transformers import pipeline
  
  pipe = pipeline("text2text-generation", "lmqg/mt5-base-itquad-qag")
  output = pipe("Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.")
  
  ```
  
  ## Evaluation
  
  
  - ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-itquad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_itquad.default.json) 
  
  |                                 |   Score | Type    | Dataset                                                            |
  |:--------------------------------|--------:|:--------|:-------------------------------------------------------------------|
  | QAAlignedF1Score (BERTScore)    |   79.93 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
  | QAAlignedF1Score (MoverScore)   |   53.8  | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
  | QAAlignedPrecision (BERTScore)  |   81.06 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
  | QAAlignedPrecision (MoverScore) |   54.64 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
  | QAAlignedRecall (BERTScore)     |   78.87 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
  | QAAlignedRecall (MoverScore)    |   53.02 | default | [lmqg/qag_itquad](https://huggingface.co/datasets/lmqg/qag_itquad) |
  
  
  
  ## Training hyperparameters
  
  The following hyperparameters were used during fine-tuning:
   - dataset_path: lmqg/qag_itquad
   - dataset_name: default
   - input_types: ['paragraph']
   - output_types: ['questions_answers']
   - prefix_types: None
   - model: google/mt5-base
   - max_length: 512
   - max_length_output: 256
   - epoch: 4
   - batch: 2
   - lr: 0.0005
   - fp16: False
   - random_seed: 1
   - gradient_accumulation_steps: 32
   - label_smoothing: 0.15
  
  The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-base-itquad-qag/raw/main/trainer_config.json).
  
  ## Citation
  ```
  @inproceedings{ushio-etal-2022-generative,
      title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uesti