valurank / distilroberta-mbfc-bias

distilroberta-mbfc-bias

This model is a fine-tuned version of 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 . 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 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

Framework versions

• Transformers 4.11.2
• Pytorch 1.7.1
• Datasets 1.11.0
• Tokenizers 0.10.3