readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: bert_uncased_L-2_H-128_A-2-nan-labels-new-longer
    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_uncased_L-2_H-128_A-2-nan-labels-new-longer
  
  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: 8.4563
  
  ## 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: 5e-05
  - train_batch_size: 32
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 1000
  
  ### Training results
  
  | Training Loss | Epoch  | Step  | Validation Loss |
  |:-------------:|:------:|:-----:|:---------------:|
  | 6.445         | 1.0    | 14    | 6.4988          |
  | 5.9033        | 2.0    | 28    | 6.4333          |
  | 5.6732        | 3.0    | 42    | 6.4295          |
  | 5.6525        | 4.0    | 56    | 6.3895          |
  | 5.4781        | 5.0    | 70    | 6.3660          |
  | 5.31          | 6.0    | 84    | 6.4213          |
  | 5.1629        | 7.0    | 98    | 6.3842          |
  | 5.1541        | 8.0    | 112   | 6.4043          |
  | 5.0659        | 9.0    | 126   | 6.4686          |
  | 4.8597        | 10.0   | 140   | 6.4760          |
  | 4.983         | 11.0   | 154   | 6.4753          |
  | 4.9563        | 12.0   | 168   | 6.4612          |
  | 4.8113        | 13.0   | 182   | 6.5363          |
  | 4.86          | 14.0   | 196   | 6.5400          |
  | 4.8254        | 15.0   | 210   | 6.5595          |
  | 4.8032        | 16.0   | 224   | 6.5575          |
  | 4.7343        | 17.0   | 238   | 6.5832          |
  | 4.835         | 18.0   | 252   | 6.5729          |
  | 4.6986        | 19.0   | 266   | 6.5705          |
  | 4.6607        | 20.0   | 280   | 6.6127          |
  | 4.6594        | 21.0   | 294   | 6.6145          |
  | 4.6936        | 22.0   | 308   | 6.6280          |
  | 4.6328        | 23.0   | 322   | 6.6249          |
  | 4.6668        | 24.0   | 336   | 6.6779          |
  | 4.5523        | 25.0   | 350   | 6.6236          |
  | 4.3964        | 26.0   | 364   | 6.6895          |
  | 4.4736        | 27.0   | 378   | 6.6899          |
  | 4.5163        | 28.0   | 392   | 6.6832          |
  | 4.5136        | 29.0   | 406   | 6.7013          |
  | 4.542         | 30.0   | 420   | 6.6983          |
  | 4.4362        | 31.0   | 434   | 6.7516          |
  | 4.4466        | 32.0   | 448   | 6.7302          |
  | 4.4834        | 33.0   | 462   | 6.7404          |
  | 4.4824        | 34.0   | 476   | 6.7525          |
  | 4.4302        | 35.0   | 490   | 6.7623          |
  | 4.5098        | 36.0   | 504   | 6.7622          |
  | 4.396         | 37.0   | 518   | 6.8025          |
  | 4.3747        | 38.0   | 532   | 6.7847          |
  | 4.2892        | 39.0   | 546   | 6.8394          |
  | 4.3897        | 40.0   | 560   | 6.8372          |
  | 4.2554        | 41.0   | 574   | 6.7644          |
  | 4.3336        | 42.0   | 588   | 6.7843          |
  | 4.2943        | 43.0   | 602   | 6.7806          |
  | 4.3001        | 44.0   | 616   | 6.7864          |
  | 4.1161        | 45.0   | 630   | 6.8487          |
  | 4.1357        | 46.0   | 644   | 6.8556          |
  | 4.2713        | 47.0   | 658   | 6.7995          |
  | 4.2779        | 48.0   | 672   | 6.8441          |
  | 4.2564        | 49.0   | 686   | 6.8388          |
  | 4.192         | 50.0   | 700   | 6.7896          |
  | 4.1692        | 51.0   | 714   | 6.8261          |
  | 4.2694        | 52.0   | 728   | 6.8535          |
  | 4.2109        | 53.0   | 742   | 6.8533          |
  | 4.1504        | 54.0   | 756   | 6.8946          |
  | 4.1131        | 55.0   | 770   | 6.8696          |
  | 4.1799        | 56.0   | 784   | 6.8739          |
  | 4.1055        | 57.0   | 798   | 6.8651          |
  | 4.0118        | 58.0   | 812   | 6.8689          |
  | 4.176         | 59.0   | 826   | 6.8895          |
  | 4.1773        | 60.0   | 840   | 6.8385          |
  | 3.9761        | 61.0   | 854   | 6.8580          |
  | 4.1002        | 62.0   | 868   | 6.8556          |
  | 3.9164        | 63.0   | 882   | 6.9133          |
  | 4.0154        | 64.0   | 896   | 6.8968          |
  | 4.008         | 65.0   | 910   | 6.8973          |
  | 4.0876        | 66.0   | 924   | 6.8768          |
  | 3.9527        | 67.0   | 938   | 6.9413          |
  | 3.9351        | 68.0   | 952   | 6.9106          |
  | 4.0333        | 69.0   | 966   | 6.9012          |
  | 3.896         | 70.0   | 980   | 6.9127          |
  | 4.0042        | 71.0   | 994   | 6.9211          |
  | 3.9151        | 72.0   | 1008  | 6.9585 