readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: small-vanilla-target-glue-mnli
    results: []
  ---
  
  <!-- 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. -->
  
  # small-vanilla-target-glue-mnli
  
  This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6020
  - Accuracy: 0.7618
  
  ## 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: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: constant
  - num_epochs: 200
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.9249        | 0.04  | 500   | 0.8197          | 0.6419   |
  | 0.8154        | 0.08  | 1000  | 0.7776          | 0.6651   |
  | 0.7747        | 0.12  | 1500  | 0.7455          | 0.6749   |
  | 0.7523        | 0.16  | 2000  | 0.7315          | 0.6853   |
  | 0.7445        | 0.2   | 2500  | 0.7097          | 0.6977   |
  | 0.7337        | 0.24  | 3000  | 0.6981          | 0.7026   |
  | 0.7165        | 0.29  | 3500  | 0.6876          | 0.7117   |
  | 0.704         | 0.33  | 4000  | 0.6669          | 0.7192   |
  | 0.68          | 0.37  | 4500  | 0.6773          | 0.7249   |
  | 0.6834        | 0.41  | 5000  | 0.6513          | 0.7228   |
  | 0.6808        | 0.45  | 5500  | 0.6467          | 0.7260   |
  | 0.671         | 0.49  | 6000  | 0.6416          | 0.7275   |
  | 0.6733        | 0.53  | 6500  | 0.6434          | 0.7372   |
  | 0.6714        | 0.57  | 7000  | 0.6358          | 0.7394   |
  | 0.6552        | 0.61  | 7500  | 0.6243          | 0.7423   |
  | 0.6519        | 0.65  | 8000  | 0.6272          | 0.7380   |
  | 0.6546        | 0.69  | 8500  | 0.6207          | 0.7440   |
  | 0.6457        | 0.73  | 9000  | 0.6293          | 0.7424   |
  | 0.6458        | 0.77  | 9500  | 0.6084          | 0.7492   |
  | 0.6349        | 0.81  | 10000 | 0.6248          | 0.7431   |
  | 0.6376        | 0.86  | 10500 | 0.6035          | 0.7531   |
  | 0.6294        | 0.9   | 11000 | 0.5969          | 0.7561   |
  | 0.6264        | 0.94  | 11500 | 0.5981          | 0.7518   |
  | 0.6297        | 0.98  | 12000 | 0.5953          | 0.7539   |
  | 0.5935        | 1.02  | 12500 | 0.6093          | 0.7602   |
  | 0.5726        | 1.06  | 13000 | 0.5991          | 0.7628   |
  | 0.5571        | 1.1   | 13500 | 0.5921          | 0.7606   |
  | 0.5535        | 1.14  | 14000 | 0.6011          | 0.7559   |
  | 0.5427        | 1.18  | 14500 | 0.6020          | 0.7618   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.1.dev0
  - Tokenizers 0.13.2
  