readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: small-mlm-glue-qnli-custom-tokenizer
    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-mlm-glue-qnli-custom-tokenizer
  
  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: 5.5974
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|
  | 7.2117        | 0.4   | 500   | 6.7997          |
  | 6.5734        | 0.8   | 1000  | 6.6026          |
  | 6.4063        | 1.2   | 1500  | 6.5514          |
  | 6.2622        | 1.6   | 2000  | 6.4092          |
  | 6.2126        | 2.0   | 2500  | 6.3653          |
  | 6.1191        | 2.4   | 3000  | 6.3054          |
  | 6.0669        | 2.8   | 3500  | 6.2685          |
  | 5.9877        | 3.2   | 4000  | 6.2077          |
  | 5.8901        | 3.6   | 4500  | 6.1328          |
  | 5.8306        | 4.0   | 5000  | 6.1574          |
  | 5.8053        | 4.4   | 5500  | 6.0958          |
  | 5.7117        | 4.8   | 6000  | 6.0377          |
  | 5.7372        | 5.2   | 6500  | 6.0045          |
  | 5.6595        | 5.6   | 7000  | 5.9655          |
  | 5.6579        | 6.0   | 7500  | 5.9410          |
  | 5.6323        | 6.4   | 8000  | 5.9121          |
  | 5.5978        | 6.8   | 8500  | 5.8435          |
  | 5.5634        | 7.2   | 9000  | 5.9205          |
  | 5.4642        | 7.6   | 9500  | 5.8433          |
  | 5.4851        | 8.0   | 10000 | 5.8122          |
  | 5.4272        | 8.4   | 10500 | 5.8350          |
  | 5.357         | 8.8   | 11000 | 5.7860          |
  | 5.3638        | 9.2   | 11500 | 5.7262          |
  | 5.3088        | 9.6   | 12000 | 5.7529          |
  | 5.3052        | 10.0  | 12500 | 5.7783          |
  | 5.2628        | 10.4  | 13000 | 5.7124          |
  | 5.2923        | 10.8  | 13500 | 5.7053          |
  | 5.1727        | 11.2  | 14000 | 5.7031          |
  | 5.1474        | 11.6  | 14500 | 5.6445          |
  | 5.145         | 12.0  | 15000 | 5.6299          |
  | 5.102         | 12.4  | 15500 | 5.6858          |
  | 5.0612        | 12.8  | 16000 | 5.6089          |
  | 5.0928        | 13.2  | 16500 | 5.6404          |
  | 4.9953        | 13.6  | 17000 | 5.5769          |
  | 5.0163        | 14.0  | 17500 | 5.5935          |
  | 4.9591        | 14.4  | 18000 | 5.5862          |
  | 5.0046        | 14.8  | 18500 | 5.5974          |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.1.dev0
  - Tokenizers 0.13.2
  