readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: BERiT_2000_enriched_optimized
    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_enriched_optimized
  
  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: 6.5710
  
  ## 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: 6.732413659252984e-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: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss |
  |:-------------:|:-----:|:-----:|:---------------:|
  | 6.4676        | 0.19  | 500   | 6.1516          |
  | 6.0191        | 0.39  | 1000  | 5.8660          |
  | 5.9008        | 0.58  | 1500  | 5.9956          |
  | 5.7806        | 0.77  | 2000  | 5.7032          |
  | 5.6932        | 0.97  | 2500  | 5.6910          |
  | 6.4953        | 1.16  | 3000  | 6.6394          |
  | 6.6419        | 1.36  | 3500  | 6.6176          |
  | 6.6462        | 1.55  | 4000  | 6.5961          |
  | 6.6402        | 1.74  | 4500  | 6.6224          |
  | 6.6169        | 1.94  | 5000  | 6.6091          |
  | 6.6396        | 2.13  | 5500  | 6.6443          |
  | 6.6599        | 2.32  | 6000  | 6.6150          |
  | 6.5956        | 2.52  | 6500  | 6.6173          |
  | 6.6397        | 2.71  | 7000  | 6.6038          |
  | 6.6261        | 2.9   | 7500  | 6.6214          |
  | 6.6162        | 3.1   | 8000  | 6.6271          |
  | 6.6102        | 3.29  | 8500  | 6.5843          |
  | 6.6116        | 3.49  | 9000  | 6.6044          |
  | 6.6146        | 3.68  | 9500  | 6.6092          |
  | 6.5922        | 3.87  | 10000 | 6.6182          |
  | 6.6246        | 4.07  | 10500 | 6.5832          |
  | 6.6124        | 4.26  | 11000 | 6.6141          |
  | 6.6002        | 4.45  | 11500 | 6.6385          |
  | 6.6015        | 4.65  | 12000 | 6.5984          |
  | 6.6024        | 4.84  | 12500 | 6.6236          |
  | 6.6097        | 5.03  | 13000 | 6.6254          |
  | 6.5937        | 5.23  | 13500 | 6.6154          |
  | 6.5973        | 5.42  | 14000 | 6.5731          |
  | 6.6141        | 5.62  | 14500 | 6.6308          |
  | 6.5976        | 5.81  | 15000 | 6.5824          |
  | 6.5982        | 6.0   | 15500 | 6.6024          |
  | 6.6032        | 6.2   | 16000 | 6.5891          |
  | 6.603         | 6.39  | 16500 | 6.5926          |
  | 6.6089        | 6.58  | 17000 | 6.6090          |
  | 6.6067        | 6.78  | 17500 | 6.6137          |
  | 6.5718        | 6.97  | 18000 | 6.5817          |
  | 6.6036        | 7.16  | 18500 | 6.6008          |
  | 6.6001        | 7.36  | 19000 | 6.5571          |
  | 6.6203        | 7.55  | 19500 | 6.5778          |
  | 6.6055        | 7.75  | 20000 | 6.5805          |
  | 6.6168        | 7.94  | 20500 | 6.6099          |
  | 6.5874        | 8.13  | 21000 | 6.6125          |
  | 6.5932        | 8.33  | 21500 | 6.5701          |
  | 6.5984        | 8.52  | 22000 | 6.5719          |
  | 6.5753        | 8.71  | 22500 | 6.6199          |
  | 6.599         | 8.91  | 23000 | 6.5756          |
  | 6.579         | 9.1   | 23500 | 6.5926          |
  | 6.5805        | 9.3   | 24000 | 6.5623          |
  | 6.5753        | 9.49  | 24500 | 6.5818          |
  | 6.5645        | 9.68  | 25000 | 6.5726          |
  | 6.6094        | 9.88  | 25500 | 6.5710          |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.2
  