readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: tiny-bert-mnli-distilled
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: glue
        type: glue
        args: mnli
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.5818644931227712
  ---
  
  <!-- 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. -->
  
  # tiny-bert-mnli-distilled
  
  It achieves the following results on the evaluation set:
  - Loss: 1.5018
  - Accuracy: 0.5819
  - F1 score: 0.5782
  - Precision score: 0.6036
  - Metric recall: 0.5819
  
  ## 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: 0.0005
  - train_batch_size: 64
  - eval_batch_size: 32
  - seed: 33
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 4
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 score | Precision score | Metric recall |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------------:|:-------------:|
  | 1.4475        | 1.0   | 614  | 1.4296          | 0.4521   | 0.4070   | 0.5621          | 0.4521        |
  | 1.3354        | 2.0   | 1228 | 1.4320          | 0.4805   | 0.4579   | 0.5276          | 0.4805        |
  | 1.2244        | 3.0   | 1842 | 1.4786          | 0.5699   | 0.5602   | 0.5865          | 0.5699        |
  | 1.1416        | 4.0   | 2456 | 1.5018          | 0.5819   | 0.5782   | 0.6036          | 0.5819        |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.9.1
  - Datasets 2.1.0
  - Tokenizers 0.11.6
  