readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: BERiT_2000_custom_architecture_relu_40_epochs
    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_relu_40_epochs
  
  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: 6.3968
  
  ## 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 |
  |:-------------:|:-----:|:------:|:---------------:|
  | 14.8756       | 0.19  | 500    | 8.3404          |
  | 7.8098        | 0.39  | 1000   | 7.3110          |
  | 7.2696        | 0.58  | 1500   | 7.1646          |
  | 7.1277        | 0.77  | 2000   | 7.0953          |
  | 7.0939        | 0.97  | 2500   | 7.0701          |
  | 7.0621        | 1.16  | 3000   | 7.0003          |
  | 7.0236        | 1.36  | 3500   | 6.9189          |
  | 6.9898        | 1.55  | 4000   | 6.8536          |
  | 6.9625        | 1.74  | 4500   | 6.8450          |
  | 6.9125        | 1.94  | 5000   | 6.7799          |
  | 6.9115        | 2.13  | 5500   | 6.8028          |
  | 6.8954        | 2.32  | 6000   | 6.7288          |
  | 6.8289        | 2.52  | 6500   | 6.7664          |
  | 6.855         | 2.71  | 7000   | 6.7064          |
  | 6.8134        | 2.9   | 7500   | 6.7155          |
  | 6.7907        | 3.1   | 8000   | 6.7050          |
  | 6.791         | 3.29  | 8500   | 6.6695          |
  | 6.7659        | 3.49  | 9000   | 6.6815          |
  | 6.775         | 3.68  | 9500   | 6.6449          |
  | 6.7508        | 3.87  | 10000  | 6.6684          |
  | 6.7627        | 4.07  | 10500  | 6.6397          |
  | 6.7229        | 4.26  | 11000  | 6.6417          |
  | 6.7336        | 4.45  | 11500  | 6.6824          |
  | 6.7138        | 4.65  | 12000  | 6.6252          |
  | 6.7123        | 4.84  | 12500  | 6.6374          |
  | 6.703         | 5.03  | 13000  | 6.6400          |
  | 6.7054        | 5.23  | 13500  | 6.6264          |
  | 6.6978        | 5.42  | 14000  | 6.6094          |
  | 6.6944        | 5.62  | 14500  | 6.6627          |
  | 6.6857        | 5.81  | 15000  | 6.6363          |
  | 6.694         | 6.0   | 15500  | 6.6026          |
  | 6.6882        | 6.2   | 16000  | 6.6123          |
  | 6.6694        | 6.39  | 16500  | 6.5918          |
  | 6.6781        | 6.58  | 17000  | 6.6201          |
  | 6.6605        | 6.78  | 17500  | 6.6316          |
  | 6.6435        | 6.97  | 18000  | 6.5789          |
  | 6.6658        | 7.16  | 18500  | 6.5999          |
  | 6.6551        | 7.36  | 19000  | 6.5425          |
  | 6.6603        | 7.55  | 19500  | 6.5790          |
  | 6.6589        | 7.75  | 20000  | 6.5767          |
  | 6.675         | 7.94  | 20500  | 6.6005          |
  | 6.6362        | 8.13  | 21000  | 6.5962          |
  | 6.6391        | 8.33  | 21500  | 6.5716          |
  | 6.6379        | 8.52  | 22000  | 6.5830          |
  | 6.6164        | 8.71  | 22500  | 6.6137          |
  | 6.638         | 8.91  | 23000  | 6.5877          |
  | 6.6255        | 9.1   | 23500  | 6.6197          |
  | 6.6284        | 9.3   | 24000  | 6.5573          |
  | 6.6198        | 9.49  | 24500  | 6.5717          |
  | 6.6025        | 9.68  | 25000  | 6.5627          |
  | 6.6334        | 9.88  | 25500  | 6.5902          |
  | 6.6305        | 10.07 | 26000  | 6.5628          |
  | 6.5797        | 10.26 | 26500  | 6.5625          |
  | 6.5906        | 10.46 | 27000  | 6.5808          |
  | 6.5904        | 10.65 | 27500  | 6.5690          |
  | 6.5935        | 10.84 | 28000  | 6.5845          |
  | 6.6231        | 11.04 | 28500  | 6.5282          |
  | 6.5923        | 11.23 | 29000  | 6.6107          |
  | 6.6136        | 11.43 | 29500  | 6.5475          |
  | 6.5954        | 11.62 | 30000  | 6.5823          |
  | 6.5821        | 11.81 | 30500  | 6.5721          |
  | 6.5993        | 12.01 | 31000  | 6.5492          |
  | 6.5584        | 12.2  | 31500  | 6.4938          |
  | 6.5886        | 12.39 | 32000  | 6.6026          |
  | 6.5625        | 12.59 | 32500  | 6.5902          |
  | 6.572         | 12.78 | 33000  | 6.5436          |
  | 6.5807        | 12.97 | 33500  | 6.5588          |
  | 6.5853        | 13.17 | 34000  | 6.5555          |
  | 6.5727        | 13.36 | 34500  | 6.5606          |
  | 6.5456        | 13.56 | 35000  | 6.5386          |
  | 6.5538        | 13.75 | 35500  | 6.5712          |
  | 6.5456        | 13.94 | 36000  | 6.5582          |
  | 6.5734      