distilbert-base-uncased_fakenews_identification
This model is a fine-tuned version of distilbert-base-uncased on the below dataset. https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset It achieves the following results on the evaluation set:
- Loss: 0.0059
- Accuracy: 0.999
- F1: 0.9990
Label Description
LABEL_0 - Fake News LABEL_1 - Real News
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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.0014 | 1.0 | 1000 | 0.0208 | 0.9965 | 0.9965 |
| 0.0006 | 2.0 | 2000 | 0.0041 | 0.9994 | 0.9994 |
| 0.0006 | 3.0 | 3000 | 0.0044 | 0.9992 | 0.9993 |
| 0.0 | 4.0 | 4000 | 0.0059 | 0.999 | 0.9990 |
Framework versions
- Transformers 4.16.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
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