readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: mini-mlm-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. -->
  
  # mini-mlm-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: 2.7643
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|
  | 3.2543        | 0.16  | 500   | 3.0042          |
  | 3.1664        | 0.32  | 1000  | 2.9716          |
  | 3.1428        | 0.48  | 1500  | 2.9460          |
  | 3.1363        | 0.64  | 2000  | 2.9316          |
  | 3.1088        | 0.8   | 2500  | 2.9068          |
  | 3.0943        | 0.96  | 3000  | 2.9046          |
  | 3.0436        | 1.12  | 3500  | 2.8918          |
  | 3.0326        | 1.28  | 4000  | 2.8762          |
  | 3.0302        | 1.44  | 4500  | 2.8765          |
  | 3.0232        | 1.6   | 5000  | 2.8658          |
  | 3.0123        | 1.76  | 5500  | 2.8538          |
  | 3.0164        | 1.92  | 6000  | 2.8530          |
  | 2.992         | 2.08  | 6500  | 2.8487          |
  | 2.9922        | 2.24  | 7000  | 2.8440          |
  | 2.9862        | 2.4   | 7500  | 2.8348          |
  | 2.9621        | 2.56  | 8000  | 2.8324          |
  | 2.9926        | 2.72  | 8500  | 2.8235          |
  | 2.9871        | 2.88  | 9000  | 2.8223          |
  | 2.9593        | 3.04  | 9500  | 2.8131          |
  | 2.9404        | 3.2   | 10000 | 2.8119          |
  | 2.9278        | 3.36  | 10500 | 2.8076          |
  | 2.943         | 3.52  | 11000 | 2.8015          |
  | 2.9074        | 3.68  | 11500 | 2.8067          |
  | 2.9247        | 3.84  | 12000 | 2.8027          |
  | 2.9188        | 4.0   | 12500 | 2.7975          |
  | 2.9011        | 4.16  | 13000 | 2.7905          |
  | 2.8973        | 4.32  | 13500 | 2.7893          |
  | 2.8796        | 4.48  | 14000 | 2.7915          |
  | 2.9026        | 4.64  | 14500 | 2.7787          |
  | 2.9022        | 4.8   | 15000 | 2.7819          |
  | 2.8942        | 4.96  | 15500 | 2.7843          |
  | 2.8844        | 5.12  | 16000 | 2.7771          |
  | 2.8777        | 5.28  | 16500 | 2.7701          |
  | 2.8899        | 5.44  | 17000 | 2.7778          |
  | 2.8973        | 5.6   | 17500 | 2.7702          |
  | 2.877         | 5.76  | 18000 | 2.7592          |
  | 2.8704        | 5.92  | 18500 | 2.7711          |
  | 2.8649        | 6.08  | 19000 | 2.7610          |
  | 2.8619        | 6.24  | 19500 | 2.7643          |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.12.1
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  