readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: BERiT_2000_ls
    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_ls
  
  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: 6.9311
  
  ## 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: 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: 3
  - label_smoothing_factor: 0.2
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss |
  |:-------------:|:-----:|:----:|:---------------:|
  | 7.0431        | 0.19  | 500  | 6.9715          |
  | 6.9481        | 0.39  | 1000 | 6.9600          |
  | 6.9487        | 0.58  | 1500 | 6.9444          |
  | 6.9495        | 0.77  | 2000 | 6.9505          |
  | 6.9373        | 0.97  | 2500 | 6.9556          |
  | 6.9332        | 1.16  | 3000 | 6.9371          |
  | 6.9295        | 1.36  | 3500 | 6.9256          |
  | 6.9367        | 1.55  | 4000 | 6.9137          |
  | 6.9387        | 1.74  | 4500 | 6.9337          |
  | 6.9237        | 1.94  | 5000 | 6.9251          |
  | 6.9363        | 2.13  | 5500 | 6.9490          |
  | 6.9591        | 2.32  | 6000 | 6.9261          |
  | 6.9066        | 2.52  | 6500 | 6.9322          |
  | 6.9385        | 2.71  | 7000 | 6.9222          |
  | 6.9293        | 2.9   | 7500 | 6.9311          |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.2
  