distilbert-finetuned-fakenews
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0049
- Accuracy: 0.9995
- F1: 0.9995
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.0392 | 1.0 | 500 | 0.0059 | 0.999 | 0.999 |
| 0.002 | 2.0 | 1000 | 0.0047 | 0.9995 | 0.9995 |
| 0.0001 | 3.0 | 1500 | 0.0047 | 0.9995 | 0.9995 |
| 0.0001 | 4.0 | 2000 | 0.0049 | 0.9995 | 0.9995 |
| 0.0 | 5.0 | 2500 | 0.0049 | 0.9995 | 0.9995 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.12.0