readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: bert-tiny-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-tiny-mlm-finetuned-imdb
  
  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: 3.4487
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|
  | 4.1774        | 1.04  | 500   | 3.7705          |
  | 4.041         | 2.09  | 1000  | 3.7196          |
  | 3.9982        | 3.13  | 1500  | 3.6826          |
  | 3.9614        | 4.18  | 2000  | 3.6543          |
  | 3.9274        | 5.22  | 2500  | 3.6438          |
  | 3.9089        | 6.26  | 3000  | 3.6294          |
  | 3.8929        | 7.31  | 3500  | 3.6217          |
  | 3.873         | 8.35  | 4000  | 3.6083          |
  | 3.8659        | 9.39  | 4500  | 3.5900          |
  | 3.8484        | 10.44 | 5000  | 3.5791          |
  | 3.8261        | 11.48 | 5500  | 3.5731          |
  | 3.8228        | 12.53 | 6000  | 3.5579          |
  | 3.8098        | 13.57 | 6500  | 3.5576          |
  | 3.8028        | 14.61 | 7000  | 3.5532          |
  | 3.7881        | 15.66 | 7500  | 3.5440          |
  | 3.7829        | 16.7  | 8000  | 3.5440          |
  | 3.7727        | 17.75 | 8500  | 3.5372          |
  | 3.7648        | 18.79 | 9000  | 3.5248          |
  | 3.7504        | 19.83 | 9500  | 3.5223          |
  | 3.7487        | 20.88 | 10000 | 3.5212          |
  | 3.7497        | 21.92 | 10500 | 3.5166          |
  | 3.7344        | 22.96 | 11000 | 3.5103          |
  | 3.7339        | 24.01 | 11500 | 3.5052          |
  | 3.722         | 25.05 | 12000 | 3.5067          |
  | 3.7188        | 26.1  | 12500 | 3.4941          |
  | 3.7127        | 27.14 | 13000 | 3.4951          |
  | 3.7113        | 28.18 | 13500 | 3.4904          |
  | 3.7042        | 29.23 | 14000 | 3.4813          |
  | 3.7011        | 30.27 | 14500 | 3.4805          |
  | 3.6936        | 31.32 | 15000 | 3.4886          |
  | 3.6889        | 32.36 | 15500 | 3.4825          |
  | 3.6771        | 33.4  | 16000 | 3.4785          |
  | 3.6753        | 34.45 | 16500 | 3.4819          |
  | 3.6743        | 35.49 | 17000 | 3.4744          |
  | 3.6686        | 36.53 | 17500 | 3.4658          |
  | 3.669         | 37.58 | 18000 | 3.4607          |
  | 3.6623        | 38.62 | 18500 | 3.4688          |
  | 3.6648        | 39.67 | 19000 | 3.4676          |
  | 3.6574        | 40.71 | 19500 | 3.4581          |
  | 3.652         | 41.75 | 20000 | 3.4601          |
  | 3.6506        | 42.8  | 20500 | 3.4630          |
  | 3.6466        | 43.84 | 21000 | 3.4530          |
  | 3.637         | 44.89 | 21500 | 3.4507          |
  | 3.6428        | 45.93 | 22000 | 3.4557          |
  | 3.6408        | 46.97 | 22500 | 3.4483          |
  | 3.6368        | 48.02 | 23000 | 3.4505          |
  | 3.6322        | 49.06 | 23500 | 3.4494          |
  | 3.6256        | 50.1  | 24000 | 3.4487          |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  