readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: tiny-mlm-glue-sst2-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-sst2-target-glue-mnli
  
  This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-sst2](https://huggingface.co/muhtasham/tiny-mlm-glue-sst2) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.7870
  - Accuracy: 0.6519
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 1.076         | 0.04  | 500   | 1.0342          | 0.4657   |
  | 1.0114        | 0.08  | 1000  | 0.9714          | 0.5393   |
  | 0.9654        | 0.12  | 1500  | 0.9268          | 0.5736   |
  | 0.9381        | 0.16  | 2000  | 0.9120          | 0.5849   |
  | 0.9266        | 0.2   | 2500  | 0.8942          | 0.5953   |
  | 0.9171        | 0.24  | 3000  | 0.8783          | 0.6014   |
  | 0.9009        | 0.29  | 3500  | 0.8687          | 0.6085   |
  | 0.8932        | 0.33  | 4000  | 0.8567          | 0.6191   |
  | 0.8767        | 0.37  | 4500  | 0.8524          | 0.6171   |
  | 0.8768        | 0.41  | 5000  | 0.8436          | 0.6231   |
  | 0.8702        | 0.45  | 5500  | 0.8374          | 0.6220   |
  | 0.8673        | 0.49  | 6000  | 0.8345          | 0.6271   |
  | 0.8684        | 0.53  | 6500  | 0.8274          | 0.6274   |
  | 0.8606        | 0.57  | 7000  | 0.8282          | 0.6298   |
  | 0.8528        | 0.61  | 7500  | 0.8146          | 0.6363   |
  | 0.8529        | 0.65  | 8000  | 0.8103          | 0.6406   |
  | 0.8467        | 0.69  | 8500  | 0.8237          | 0.6320   |
  | 0.8478        | 0.73  | 9000  | 0.7964          | 0.6473   |
  | 0.8399        | 0.77  | 9500  | 0.8081          | 0.6391   |
  | 0.8295        | 0.81  | 10000 | 0.7954          | 0.6475   |
  | 0.833         | 0.86  | 10500 | 0.7994          | 0.6439   |
  | 0.8316        | 0.9   | 11000 | 0.7886          | 0.6513   |
  | 0.8239        | 0.94  | 11500 | 0.7847          | 0.6544   |
  | 0.8247        | 0.98  | 12000 | 0.7848          | 0.6512   |
  | 0.81          | 1.02  | 12500 | 0.7915          | 0.6507   |
  | 0.8059        | 1.06  | 13000 | 0.7870          | 0.6519   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.1.dev0
  - Tokenizers 0.13.2
  