readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: bert_uncased_L-2_H-128_A-2-finetuned-parsed
    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-finetuned-parsed
  
  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 an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 4.2883
  
  ## 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: 2e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 200
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss |
  |:-------------:|:-----:|:-----:|:---------------:|
  | No log        | 1.0   | 59    | 4.6900          |
  | No log        | 2.0   | 118   | 4.6347          |
  | No log        | 3.0   | 177   | 4.6578          |
  | No log        | 4.0   | 236   | 4.5731          |
  | No log        | 5.0   | 295   | 4.6258          |
  | No log        | 6.0   | 354   | 4.6365          |
  | No log        | 7.0   | 413   | 4.7292          |
  | No log        | 8.0   | 472   | 4.4789          |
  | 4.5634        | 9.0   | 531   | 4.3161          |
  | 4.5634        | 10.0  | 590   | 4.6929          |
  | 4.5634        | 11.0  | 649   | 4.5543          |
  | 4.5634        | 12.0  | 708   | 4.3739          |
  | 4.5634        | 13.0  | 767   | 4.6118          |
  | 4.5634        | 14.0  | 826   | 4.4036          |
  | 4.5634        | 15.0  | 885   | 4.3940          |
  | 4.5634        | 16.0  | 944   | 4.5944          |
  | 4.0896        | 17.0  | 1003  | 4.3630          |
  | 4.0896        | 18.0  | 1062  | 4.0447          |
  | 4.0896        | 19.0  | 1121  | 4.3832          |
  | 4.0896        | 20.0  | 1180  | 4.0535          |
  | 4.0896        | 21.0  | 1239  | 4.5213          |
  | 4.0896        | 22.0  | 1298  | 4.5887          |
  | 4.0896        | 23.0  | 1357  | 4.5211          |
  | 4.0896        | 24.0  | 1416  | 4.1876          |
  | 4.0896        | 25.0  | 1475  | 4.5861          |
  | 3.9145        | 26.0  | 1534  | 4.3581          |
  | 3.9145        | 27.0  | 1593  | 4.6545          |
  | 3.9145        | 28.0  | 1652  | 4.4919          |
  | 3.9145        | 29.0  | 1711  | 4.1109          |
  | 3.9145        | 30.0  | 1770  | 4.2736          |
  | 3.9145        | 31.0  | 1829  | 4.6461          |
  | 3.9145        | 32.0  | 1888  | 4.3111          |
  | 3.9145        | 33.0  | 1947  | 4.2909          |
  | 3.8088        | 34.0  | 2006  | 4.1168          |
  | 3.8088        | 35.0  | 2065  | 4.2329          |
  | 3.8088        | 36.0  | 2124  | 4.5285          |
  | 3.8088        | 37.0  | 2183  | 4.4841          |
  | 3.8088        | 38.0  | 2242  | 4.2489          |
  | 3.8088        | 39.0  | 2301  | 4.2384          |
  | 3.8088        | 40.0  | 2360  | 4.3610          |
  | 3.8088        | 41.0  | 2419  | 4.2758          |
  | 3.8088        | 42.0  | 2478  | 4.2895          |
  | 3.7034        | 43.0  | 2537  | 4.2824          |
  | 3.7034        | 44.0  | 2596  | 4.4997          |
  | 3.7034        | 45.0  | 2655  | 4.5091          |
  | 3.7034        | 46.0  | 2714  | 4.0883          |
  | 3.7034        | 47.0  | 2773  | 4.2018          |
  | 3.7034        | 48.0  | 2832  | 4.3701          |
  | 3.7034        | 49.0  | 2891  | 4.0764          |
  | 3.7034        | 50.0  | 2950  | 4.6149          |
  | 3.6455        | 51.0  | 3009  | 4.3629          |
  | 3.6455        | 52.0  | 3068  | 4.2199          |
  | 3.6455        | 53.0  | 3127  | 4.3543          |
  | 3.6455        | 54.0  | 3186  | 4.7006          |
  | 3.6455        | 55.0  | 3245  | 4.1633          |
  | 3.6455        | 56.0  | 3304  | 4.5183          |
  | 3.6455        | 57.0  | 3363  | 4.1918          |
  | 3.6455        | 58.0  | 3422  | 4.4810          |
  | 3.6455        | 59.0  | 3481  | 4.1398          |
  | 3.5468        | 60.0  | 3540  | 3.9632          |
  | 3.5468        | 61.0  | 3599  | 4.4640          |
  | 3.5468        | 62.0  | 3658  | 4.0500          |
  | 3.5468        | 63.0  | 3717  | 4.3956          |
  | 3.5468        | 64.0  | 3776  | 4.3922          |
  | 3.5468        | 65.0  | 3835  | 4.2513          |
  | 3.5468        | 66.0  | 3894  | 4.4475          |
  | 3.5468        | 67.0  | 3953  | 4.3037          |
  | 3.4975        | 68.0  | 4012  | 4.1568          |
  | 3.4975        | 69.0  | 4071  | 4.2253          |
  | 3.4975        | 70.0  | 4130  | 4.1202          |
  | 3.4975        | 71.0  | 4189  | 4.4421          |
  | 3.4975        | 72.0  | 4248  | 4.3548          |
  | 3.4975        | 73.0  | 4307  | 4.1671          |
  | 3.4975        | 74.