readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: tiny-mlm-glue-qnli-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. -->
  
  # tiny-mlm-glue-qnli-target-glue-mnli
  
  This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-qnli](https://huggingface.co/muhtasham/tiny-mlm-glue-qnli) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.7907
  - Accuracy: 0.6507
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 1.0753        | 0.04  | 500   | 1.0327          | 0.4677   |
  | 1.0084        | 0.08  | 1000  | 0.9655          | 0.5434   |
  | 0.962         | 0.12  | 1500  | 0.9232          | 0.5779   |
  | 0.9358        | 0.16  | 2000  | 0.9087          | 0.5874   |
  | 0.9241        | 0.2   | 2500  | 0.8928          | 0.5963   |
  | 0.9157        | 0.24  | 3000  | 0.8772          | 0.5988   |
  | 0.8992        | 0.29  | 3500  | 0.8687          | 0.6088   |
  | 0.8928        | 0.33  | 4000  | 0.8571          | 0.6173   |
  | 0.8757        | 0.37  | 4500  | 0.8529          | 0.6164   |
  | 0.8774        | 0.41  | 5000  | 0.8438          | 0.6232   |
  | 0.8694        | 0.45  | 5500  | 0.8372          | 0.6246   |
  | 0.8653        | 0.49  | 6000  | 0.8350          | 0.6265   |
  | 0.8677        | 0.53  | 6500  | 0.8268          | 0.6292   |
  | 0.8584        | 0.57  | 7000  | 0.8270          | 0.6326   |
  | 0.8508        | 0.61  | 7500  | 0.8134          | 0.6391   |
  | 0.8521        | 0.65  | 8000  | 0.8110          | 0.6416   |
  | 0.8447        | 0.69  | 8500  | 0.8264          | 0.6323   |
  | 0.8466        | 0.73  | 9000  | 0.7951          | 0.6468   |
  | 0.8379        | 0.77  | 9500  | 0.8089          | 0.6401   |
  | 0.8277        | 0.81  | 10000 | 0.7941          | 0.6477   |
  | 0.8307        | 0.86  | 10500 | 0.7999          | 0.6437   |
  | 0.8289        | 0.9   | 11000 | 0.7874          | 0.6530   |
  | 0.8228        | 0.94  | 11500 | 0.7835          | 0.6524   |
  | 0.8228        | 0.98  | 12000 | 0.7851          | 0.6511   |
  | 0.8078        | 1.02  | 12500 | 0.7907          | 0.6507   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.1.dev0
  - Tokenizers 0.13.2
  