ModernBERT-Turkish-FakeNews-Classifier
This model is a fine-tuned version of artiwise-ai/modernbert-base-tr-uncased on a Fake News Detection dataset, which is constructed by combining various publicly available datasets, incorporating additional resources curated by me, and translating English-language data into Turkish using large language models. It achieves the following results on the evaluation set:
- Loss: 0.2734
- Accuracy: 0.9514
- Precision: 0.9180
- Recall: 0.9825
- F1: 0.9492
Training and evaluation data
The data cannot be shared.
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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.2843 | 1.0 | 124 | 0.2695 | 0.9150 | 0.8780 | 0.9474 | 0.9114 |
| 0.1242 | 2.0 | 248 | 0.2612 | 0.9514 | 0.9113 | 0.9912 | 0.9496 |
| 0.009 | 3.0 | 372 | 0.2455 | 0.9514 | 0.9180 | 0.9825 | 0.9492 |
| 0.0017 | 4.0 | 496 | 0.2675 | 0.9514 | 0.9180 | 0.9825 | 0.9492 |
| 0.0 | 5.0 | 620 | 0.2734 | 0.9514 | 0.9180 | 0.9825 | 0.9492 |
Framework versions
- Transformers 4.52.2
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1