readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: BERiT_2000_custom_architecture_40_epochs_ls_.2
    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. -->
  
  # BERiT_2000_custom_architecture_40_epochs_ls_.2
  
  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 6.3120
  
  ## 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: 0.0005
  - 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: 40
  - label_smoothing_factor: 0.2
  
  ### Training results
  
  | Training Loss | Epoch | Step   | Validation Loss |
  |:-------------:|:-----:|:------:|:---------------:|
  | 15.998        | 0.19  | 500    | 8.5537          |
  | 7.8818        | 0.39  | 1000   | 7.3646          |
  | 7.2781        | 0.58  | 1500   | 7.1307          |
  | 7.1073        | 0.77  | 2000   | 7.0462          |
  | 7.0749        | 0.97  | 2500   | 7.0667          |
  | 7.0373        | 1.16  | 3000   | 6.9511          |
  | 6.9767        | 1.36  | 3500   | 6.8339          |
  | 6.9483        | 1.55  | 4000   | 6.7795          |
  | 6.9071        | 1.74  | 4500   | 6.7828          |
  | 6.8591        | 1.94  | 5000   | 6.7164          |
  | 6.8595        | 2.13  | 5500   | 6.7705          |
  | 6.8406        | 2.32  | 6000   | 6.6906          |
  | 6.7861        | 2.52  | 6500   | 6.6878          |
  | 6.8103        | 2.71  | 7000   | 6.6486          |
  | 6.7724        | 2.9   | 7500   | 6.6703          |
  | 6.7563        | 3.1   | 8000   | 6.6626          |
  | 6.7567        | 3.29  | 8500   | 6.6603          |
  | 6.7315        | 3.49  | 9000   | 6.6392          |
  | 6.7443        | 3.68  | 9500   | 6.6306          |
  | 6.7244        | 3.87  | 10000  | 6.6456          |
  | 6.7464        | 4.07  | 10500  | 6.6224          |
  | 6.7008        | 4.26  | 11000  | 6.6138          |
  | 6.7076        | 4.45  | 11500  | 6.6783          |
  | 6.6944        | 4.65  | 12000  | 6.6147          |
  | 6.6993        | 4.84  | 12500  | 6.6466          |
  | 6.6893        | 5.03  | 13000  | 6.6369          |
  | 6.6905        | 5.23  | 13500  | 6.6293          |
  | 6.6899        | 5.42  | 14000  | 6.6271          |
  | 6.6835        | 5.62  | 14500  | 6.6566          |
  | 6.6746        | 5.81  | 15000  | 6.6385          |
  | 6.68          | 6.0   | 15500  | 6.6309          |
  | 6.6776        | 6.2   | 16000  | 6.6069          |
  | 6.6714        | 6.39  | 16500  | 6.5991          |
  | 6.6766        | 6.58  | 17000  | 6.6180          |
  | 6.6591        | 6.78  | 17500  | 6.6212          |
  | 6.6396        | 6.97  | 18000  | 6.5804          |
  | 6.6575        | 7.16  | 18500  | 6.6096          |
  | 6.6506        | 7.36  | 19000  | 6.5579          |
  | 6.6618        | 7.55  | 19500  | 6.5911          |
  | 6.6581        | 7.75  | 20000  | 6.5870          |
  | 6.6703        | 7.94  | 20500  | 6.6062          |
  | 6.6392        | 8.13  | 21000  | 6.5962          |
  | 6.6343        | 8.33  | 21500  | 6.5903          |
  | 6.6426        | 8.52  | 22000  | 6.6010          |
  | 6.6227        | 8.71  | 22500  | 6.6060          |
  | 6.6392        | 8.91  | 23000  | 6.5935          |
  | 6.6198        | 9.1   | 23500  | 6.6293          |
  | 6.6372        | 9.3   | 24000  | 6.5594          |
  | 6.6146        | 9.49  | 24500  | 6.5917          |
  | 6.6119        | 9.68  | 25000  | 6.5694          |
  | 6.6292        | 9.88  | 25500  | 6.6230          |
  | 6.634         | 10.07 | 26000  | 6.5857          |
  | 6.5863        | 10.26 | 26500  | 6.5938          |
  | 6.5957        | 10.46 | 27000  | 6.6256          |
  | 6.5928        | 10.65 | 27500  | 6.6111          |
  | 6.5948        | 10.84 | 28000  | 6.6031          |
  | 6.6131        | 11.04 | 28500  | 6.5582          |
  | 6.5946        | 11.23 | 29000  | 6.6093          |
  | 6.6155        | 11.43 | 29500  | 6.5670          |
  | 6.6051        | 11.62 | 30000  | 6.6016          |
  | 6.5917        | 11.81 | 30500  | 6.6045          |
  | 6.5918        | 12.01 | 31000  | 6.5802          |
  | 6.558         | 12.2  | 31500  | 6.5195          |
  | 6.5896        | 12.39 | 32000  | 6.6315          |
  | 6.5662        | 12.59 | 32500  | 6.6112          |
  | 6.5702        | 12.78 | 33000  | 6.5779          |
  | 6.5798        | 12.97 | 33500  | 6.5662          |
  | 6.5963        | 13.17 | 34000  | 6.5776          |
  | 6.5733        | 13.36 | 34500  | 6.5870          |
  | 6.5499        | 13.56 | 35000  | 6.5850          |
  | 6.5492        | 13.75 | 35500  | 6.5957          |
  | 6.5466        | 13.94 | 36000  | 6.5812          |
  | 6.5741    