readme: ---
  license: other
  tags:
  - generated_from_trainer
  model-index:
  - name: distilroberta-mbfc-bias
    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. -->
  
  # distilroberta-mbfc-bias
  
  This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the Proppy dataset, using political bias from mediabiasfactcheck.com as labels.
  
  It achieves the following results on the evaluation set:
  - Loss: 1.4130
  - Acc: 0.6348
  
  ## Training and evaluation data
  
  The training data used is the [proppy corpus](https://zenodo.org/record/3271522). Articles are labeled for political bias using the political bias of the source publication, as scored by mediabiasfactcheck.com. See [Proppy: Organizing the News Based on Their Propagandistic Content](https://propaganda.qcri.org/papers/elsarticle-template.pdf) for details.
  
  To create a more balanced training set, common labels are downsampled to have a maximum of 2000 articles. The resulting label distribution in the training data is as follows:
  
  ```
  extremeright     689
  leastbiased     2000
  left             783
  leftcenter      2000
  right           1260
  rightcenter     1418
  unknown         2000
  ```
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 3e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 12345
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 16
  - num_epochs: 20
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Acc    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 0.9493        | 1.0   | 514  | 1.2765          | 0.4730 |
  | 0.7376        | 2.0   | 1028 | 1.0003          | 0.5812 |
  | 0.6702        | 3.0   | 1542 | 1.1294          | 0.5631 |
  | 0.6161        | 4.0   | 2056 | 1.0439          | 0.6058 |
  | 0.4934        | 5.0   | 2570 | 1.1196          | 0.6028 |
  | 0.4558        | 6.0   | 3084 | 1.0993          | 0.5977 |
  | 0.4717        | 7.0   | 3598 | 1.0308          | 0.6373 |
  | 0.3961        | 8.0   | 4112 | 1.1291          | 0.6234 |
  | 0.3829        | 9.0   | 4626 | 1.1554          | 0.6316 |
  | 0.3442        | 10.0  | 5140 | 1.1548          | 0.6465 |
  | 0.2505        | 11.0  | 5654 | 1.3605          | 0.6169 |
  | 0.2105        | 12.0  | 6168 | 1.3310          | 0.6297 |
  | 0.262         | 13.0  | 6682 | 1.2706          | 0.6383 |
  | 0.2031        | 14.0  | 7196 | 1.3658          | 0.6378 |
  | 0.2021        | 15.0  | 7710 | 1.4130          | 0.6348 |
  
  
  ### Framework versions
  
  - Transformers 4.11.2
  - Pytorch 1.7.1
  - Datasets 1.11.0
  - Tokenizers 0.10.3
  