readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: tiny-bert-sst2-1_mobilebert_2_bert-only-distillation
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: glue
        type: glue
        config: sst2
        split: train
        args: sst2
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8291284403669725
  ---
  
  <!-- 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-sst2-1_mobilebert_2_bert-only-distillation
  
  This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the glue dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.5399
  - Accuracy: 0.8291
  
  ## 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: 5e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 33
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.2068        | 1.0   | 4210  | 1.5399          | 0.8291   |
  | 0.22          | 2.0   | 8420  | 1.5395          | 0.8234   |
  | 0.2171        | 3.0   | 12630 | 1.6631          | 0.8200   |
  | 0.2434        | 4.0   | 16840 | 1.6152          | 0.8234   |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  