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