BERTUncased_Fake_news_classification
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0027
- Accuracy: 0.9997
- F1: 0.9998
- Percision: 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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 | Percision |
|---|---|---|---|---|---|---|
| 0.0081 | 1.0 | 3865 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0015 | 2.0 | 7730 | 0.0027 | 0.9997 | 0.9998 | 0.9995 |
Framework versions
- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2