readme: ---
  language:
  - en
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: pixel-base-finetuned-qnli
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE QNLI
        type: glue
        args: qnli
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8859600951857953
  ---
  
  <!-- 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. -->
  
  # pixel-base-finetuned-qnli
  
  This model is a fine-tuned version of [Team-PIXEL/pixel-base](https://huggingface.co/Team-PIXEL/pixel-base) on the GLUE QNLI dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.9503
  - Accuracy: 0.8860
  
  ## 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: 3e-05
  - train_batch_size: 64
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 100
  - training_steps: 15000
  - mixed_precision_training: Apex, opt level O1
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.5451        | 0.31  | 500   | 0.5379          | 0.7282   |
  | 0.4451        | 0.61  | 1000  | 0.3846          | 0.8318   |
  | 0.4567        | 0.92  | 1500  | 0.3543          | 0.8525   |
  | 0.3558        | 1.22  | 2000  | 0.3294          | 0.8638   |
  | 0.3324        | 1.53  | 2500  | 0.3221          | 0.8666   |
  | 0.3434        | 1.83  | 3000  | 0.2976          | 0.8774   |
  | 0.2573        | 2.14  | 3500  | 0.3193          | 0.8750   |
  | 0.2411        | 2.44  | 4000  | 0.3044          | 0.8794   |
  | 0.253         | 2.75  | 4500  | 0.2932          | 0.8834   |
  | 0.1653        | 3.05  | 5000  | 0.3364          | 0.8841   |
  | 0.1662        | 3.36  | 5500  | 0.3348          | 0.8797   |
  | 0.1816        | 3.67  | 6000  | 0.3440          | 0.8869   |
  | 0.1699        | 3.97  | 6500  | 0.3453          | 0.8845   |
  | 0.1027        | 4.28  | 7000  | 0.4277          | 0.8810   |
  | 0.0987        | 4.58  | 7500  | 0.4590          | 0.8832   |
  | 0.0974        | 4.89  | 8000  | 0.4311          | 0.8783   |
  | 0.0669        | 5.19  | 8500  | 0.5214          | 0.8819   |
  | 0.0583        | 5.5   | 9000  | 0.5776          | 0.8850   |
  | 0.065         | 5.8   | 9500  | 0.5646          | 0.8821   |
  | 0.0381        | 6.11  | 10000 | 0.6252          | 0.8796   |
  | 0.0314        | 6.41  | 10500 | 0.7222          | 0.8801   |
  | 0.0453        | 6.72  | 11000 | 0.6951          | 0.8823   |
  | 0.0264        | 7.03  | 11500 | 0.7620          | 0.8828   |
  | 0.0215        | 7.33  | 12000 | 0.8160          | 0.8834   |
  | 0.0176        | 7.64  | 12500 | 0.8583          | 0.8828   |
  | 0.0245        | 7.94  | 13000 | 0.8484          | 0.8867   |
  | 0.0124        | 8.25  | 13500 | 0.8927          | 0.8836   |
  | 0.0112        | 8.55  | 14000 | 0.9368          | 0.8827   |
  | 0.0154        | 8.86  | 14500 | 0.9405          | 0.8860   |
  | 0.0046        | 9.16  | 15000 | 0.9503          | 0.8860   |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.11.0+cu113
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  