readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: bert-mini-sst2-distilled
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: glue
        type: glue
        args: sst2
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.856651376146789
  ---
  
  <!-- 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. -->
  
  # bert-mini-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 glue dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.1792
  - Accuracy: 0.8567
  
  ## 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: 0.00021185586235152412
  - train_batch_size: 1024
  - eval_batch_size: 1024
  - seed: 33
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 8
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 2.1552        | 1.0   | 66   | 1.4847          | 0.8349   |
  | 0.8451        | 2.0   | 132  | 1.3495          | 0.8624   |
  | 0.5864        | 3.0   | 198  | 1.2257          | 0.8532   |
  | 0.4553        | 4.0   | 264  | 1.2571          | 0.8544   |
  | 0.3708        | 5.0   | 330  | 1.2132          | 0.8658   |
  | 0.3086        | 6.0   | 396  | 1.2370          | 0.8589   |
  | 0.2701        | 7.0   | 462  | 1.1900          | 0.8635   |
  | 0.246         | 8.0   | 528  | 1.1792          | 0.8567   |
  
  
  ### Framework versions
  
  - Transformers 4.12.3
  - Pytorch 1.9.1
  - Datasets 1.15.1
  - Tokenizers 0.10.3
  