readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: tiny-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. -->
  
  # tiny-vanilla-target-glue-mnli
  
  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 None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.8100
  - Accuracy: 0.6375
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 1.0866        | 0.04  | 500   | 1.0515          | 0.4557   |
  | 1.0101        | 0.08  | 1000  | 0.9526          | 0.5612   |
  | 0.9599        | 0.12  | 1500  | 0.9195          | 0.5802   |
  | 0.9378        | 0.16  | 2000  | 0.9018          | 0.5930   |
  | 0.9229        | 0.2   | 2500  | 0.8904          | 0.5954   |
  | 0.9182        | 0.24  | 3000  | 0.8802          | 0.6033   |
  | 0.9019        | 0.29  | 3500  | 0.8738          | 0.6070   |
  | 0.8971        | 0.33  | 4000  | 0.8613          | 0.6154   |
  | 0.8788        | 0.37  | 4500  | 0.8593          | 0.6172   |
  | 0.8856        | 0.41  | 5000  | 0.8508          | 0.6194   |
  | 0.8751        | 0.45  | 5500  | 0.8404          | 0.6256   |
  | 0.8718        | 0.49  | 6000  | 0.8445          | 0.6248   |
  | 0.8739        | 0.53  | 6500  | 0.8333          | 0.6306   |
  | 0.8653        | 0.57  | 7000  | 0.8363          | 0.6280   |
  | 0.8588        | 0.61  | 7500  | 0.8213          | 0.6376   |
  | 0.8587        | 0.65  | 8000  | 0.8215          | 0.6360   |
  | 0.8544        | 0.69  | 8500  | 0.8268          | 0.6292   |
  | 0.8556        | 0.73  | 9000  | 0.8045          | 0.6463   |
  | 0.8445        | 0.77  | 9500  | 0.8187          | 0.6328   |
  | 0.836         | 0.81  | 10000 | 0.8021          | 0.6446   |
  | 0.8399        | 0.86  | 10500 | 0.8100          | 0.6375   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.1.dev0
  - Tokenizers 0.13.2
  