readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - f1
  model-index:
  - name: UNEDMediaBiasTeam_at_SemEval23_Task3_Subtask3_PRE_BABE_dataset
    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. -->
  
  # UNEDMediaBiasTeam_at_SemEval23_Task3_Subtask3_PRE_BABE_dataset
  
  This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.2188
  - F1: 0.5660
  
  ## 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: 64
  - eval_batch_size: 64
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 0.5684        | 1.0   | 24   | 0.8998          | 0.4355 |
  | 0.3           | 2.0   | 48   | 0.9073          | 0.4625 |
  | 0.3634        | 3.0   | 72   | 0.8815          | 0.4868 |
  | 0.2344        | 4.0   | 96   | 0.9457          | 0.4848 |
  | 0.1712        | 5.0   | 120  | 0.9737          | 0.4945 |
  | 0.148         | 6.0   | 144  | 1.0416          | 0.4896 |
  | 0.0662        | 7.0   | 168  | 1.1345          | 0.4838 |
  | 0.046         | 8.0   | 192  | 1.0935          | 0.5353 |
  | 0.0398        | 9.0   | 216  | 1.1288          | 0.5376 |
  | 0.0563        | 10.0  | 240  | 1.2188          | 0.5660 |
  | 0.0449        | 11.0  | 264  | 1.2390          | 0.5160 |
  | 0.0472        | 12.0  | 288  | 1.3779          | 0.5069 |
  | 0.0122        | 13.0  | 312  | 1.4218          | 0.5442 |
  | 0.0037        | 14.0  | 336  | 1.4859          | 0.5432 |
  | 0.0557        | 15.0  | 360  | 1.5124          | 0.5510 |
  | 0.0038        | 16.0  | 384  | 1.5364          | 0.5542 |
  | 0.0043        | 17.0  | 408  | 1.5484          | 0.5589 |
  | 0.0022        | 18.0  | 432  | 1.6063          | 0.5554 |
  | 0.0044        | 19.0  | 456  | 1.6013          | 0.5268 |
  | 0.0023        | 20.0  | 480  | 1.6161          | 0.4802 |
  | 0.002         | 21.0  | 504  | 1.6622          | 0.4783 |
  | 0.0016        | 22.0  | 528  | 1.6737          | 0.4812 |
  | 0.002         | 23.0  | 552  | 1.6776          | 0.5250 |
  | 0.0019        | 24.0  | 576  | 1.7027          | 0.4800 |
  | 0.0015        | 25.0  | 600  | 1.6897          | 0.5211 |
  | 0.0018        | 26.0  | 624  | 1.6982          | 0.5211 |
  | 0.0015        | 27.0  | 648  | 1.7174          | 0.4781 |
  | 0.0019        | 28.0  | 672  | 1.7269          | 0.4781 |
  | 0.0016        | 29.0  | 696  | 1.7323          | 0.5133 |
  | 0.0304        | 30.0  | 720  | 1.7265          | 0.5172 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  