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distilbert-base-uncased-finetuned-fake-news

This model is a fine-tuned version of distilbert-base-uncased on a fake news dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0403
  • Accuracy: 0.9892
  • F1: 0.9892

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: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 2

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
0.03 1.0 762 0.0364 0.9880 0.9881
0.0121 2.0 1524 0.0403 0.9892 0.9892

Framework versions

  • Transformers 4.41.0
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1
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Dataset used to train jaranohaal/distilbert-base-uncased-finetuned-fake-news