readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - super_glue
  metrics:
  - accuracy
  model-index:
  - name: yes_no_qna_deberta_model
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: super_glue
        type: super_glue
        config: boolq
        split: train
        args: boolq
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8507645259938837
  ---
  
  <!-- 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. -->
  
  # yes_no_qna_deberta_model
  
  This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the super_glue dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5570
  - Accuracy: 0.8508
  
  ## 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: 2e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.583         | 1.0   | 590  | 0.4086          | 0.8251   |
  | 0.348         | 2.0   | 1180 | 0.4170          | 0.8465   |
  | 0.2183        | 3.0   | 1770 | 0.5570          | 0.8508   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  