readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: bert-mini-mlm-finetuned-imdb
    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. -->
  
  # bert-mini-mlm-finetuned-imdb
  
  This model is a fine-tuned version of [google/bert_uncased_L-4_H-256_A-4](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.6935
  
  ## 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: 128
  - eval_batch_size: 128
  - 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 |
  |:-------------:|:-----:|:-----:|:---------------:|
  | 3.2058        | 0.64  | 500   | 2.9411          |
  | 3.1048        | 1.28  | 1000  | 2.9042          |
  | 3.0631        | 1.92  | 1500  | 2.8780          |
  | 3.0197        | 2.56  | 2000  | 2.8667          |
  | 3.0071        | 3.2   | 2500  | 2.8503          |
  | 2.9886        | 3.84  | 3000  | 2.8319          |
  | 2.9577        | 4.48  | 3500  | 2.8127          |
  | 2.9498        | 5.12  | 4000  | 2.8080          |
  | 2.9301        | 5.75  | 4500  | 2.7894          |
  | 2.9229        | 6.39  | 5000  | 2.7912          |
  | 2.9027        | 7.03  | 5500  | 2.7874          |
  | 2.8961        | 7.67  | 6000  | 2.7785          |
  | 2.8869        | 8.31  | 6500  | 2.7619          |
  | 2.8793        | 8.95  | 7000  | 2.7607          |
  | 2.8729        | 9.59  | 7500  | 2.7581          |
  | 2.8523        | 10.23 | 8000  | 2.7593          |
  | 2.8525        | 10.87 | 8500  | 2.7433          |
  | 2.8403        | 11.51 | 9000  | 2.7505          |
  | 2.8318        | 12.15 | 9500  | 2.7444          |
  | 2.8314        | 12.79 | 10000 | 2.7352          |
  | 2.8136        | 13.43 | 10500 | 2.7334          |
  | 2.8161        | 14.07 | 11000 | 2.7280          |
  | 2.7955        | 14.71 | 11500 | 2.7342          |
  | 2.7951        | 15.35 | 12000 | 2.7237          |
  | 2.7878        | 15.98 | 12500 | 2.7171          |
  | 2.7816        | 16.62 | 13000 | 2.7160          |
  | 2.7805        | 17.26 | 13500 | 2.7120          |
  | 2.7776        | 17.9  | 14000 | 2.7078          |
  | 2.7661        | 18.54 | 14500 | 2.7086          |
  | 2.7678        | 19.18 | 15000 | 2.7017          |
  | 2.7613        | 19.82 | 15500 | 2.7015          |
  | 2.7516        | 20.46 | 16000 | 2.6958          |
  | 2.7529        | 21.1  | 16500 | 2.6909          |
  | 2.7422        | 21.74 | 17000 | 2.6966          |
  | 2.738         | 22.38 | 17500 | 2.7034          |
  | 2.7303        | 23.02 | 18000 | 2.6935          |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  