readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - augmented_glue_sst2
  metrics:
  - accuracy
  model-index:
  - name: miny-bert-aug-sst2-distilled
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: augmented_glue_sst2
        type: augmented_glue_sst2
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9128440366972477
  ---
  
  <!-- 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. -->
  
  # miny-bert-aug-sst2-distilled
  
  This model is a fine-tuned version of [google/bert_uncased_L-4_H-256_A-4](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4) on the augmented_glue_sst2 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2643
  - Accuracy: 0.9128
  
  ## 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: 6e-05
  - train_batch_size: 128
  - eval_batch_size: 128
  - seed: 33
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 7
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.602         | 1.0   | 6227  | 0.3389          | 0.9186   |
  | 0.4195        | 2.0   | 12454 | 0.2989          | 0.9151   |
  | 0.3644        | 3.0   | 18681 | 0.2794          | 0.9117   |
  | 0.3304        | 4.0   | 24908 | 0.2793          | 0.9106   |
  | 0.3066        | 5.0   | 31135 | 0.2659          | 0.9186   |
  | 0.2881        | 6.0   | 37362 | 0.2668          | 0.9140   |
  | 0.2754        | 7.0   | 43589 | 0.2643          | 0.9128   |
  
  
  ### Framework versions
  
  - Transformers 4.16.2
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.3
  - Tokenizers 0.11.0
  