readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: rob-base-superqa
    results:
    - task:
        type: question-answering
        name: Question Answering
      dataset:
        name: adversarial_qa
        type: adversarial_qa
        config: adversarialQA
        split: validation
      metrics:
      - name: Exact Match
        type: exact_match
        value: 43.8667
        verified: true
      - name: F1
        type: f1
        value: 55.135
        verified: true
    - task:
        type: question-answering
        name: Question Answering
      dataset:
        name: squad_v2
        type: squad_v2
        config: squad_v2
        split: validation
      metrics:
      - name: Exact Match
        type: exact_match
        value: 79.2432
        verified: true
      - name: F1
        type: f1
        value: 82.336
        verified: true
    - task:
        type: question-answering
        name: Question Answering
      dataset:
        name: quoref
        type: quoref
        config: default
        split: validation
      metrics:
      - name: Exact Match
        type: exact_match
        value: 78.8581
        verified: true
      - name: F1
        type: f1
        value: 82.8261
        verified: true
  task:
  - question-answering
  datasets:
  - squad_v2
  - quoref
  - adversarial_qa
  - duorc
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # rob-base-superqa
  
  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 7e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 8
  - total_train_batch_size: 256
  - total_eval_batch_size: 256
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
  - lr_scheduler_type: linear
  - num_epochs: 3.0
  
  ### Training results
  
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.11.0a0+gita4c10ee
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  