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Fake_News_BERT_Classifier
This model is a fine-tuned version of distilbert-base-uncased trained on a Fake News Dataset It achieves the following results on the evaluation set:
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Loss: 0.2542
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Accuracy: 0.9688
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LABEL_0 = Fake news
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LABEL_1 = Real News
Model description
More information needed
Intended uses & limitations
This model was created for the purposes of UW IMT 575 project.
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.1284 | 1.0 | 4490 | 0.1488 | 0.9582 |
| 0.12 | 2.0 | 8980 | 0.1704 | 0.9627 |
| 0.0741 | 3.0 | 13470 | 0.1971 | 0.9674 |
| 0.0202 | 4.0 | 17960 | 0.2265 | 0.9677 |
| 0.0465 | 5.0 | 22450 | 0.2542 | 0.9688 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
