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distilbert-base-uncased-fake-news-detection
tyaroshko/distilbert-base-uncased-fake-news-detection
distilbert-base-uncased-fake-news-detection is a text classification model from tyaroshko. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- distilbert
- Access
- Public
- Created
- Dec 18, 2025
- Updated
- Dec 18, 2025
- SHA
- c13be8be
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Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
fake_news_model
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: 0.0248
- Accuracy: 0.9924
- Precision: 0.9924
- Recall: 0.9924
- F1: 0.9924
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
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.6262 | 0.0718 | 100 | 0.5156 | 0.8468 | 0.8471 | 0.8468 | 0.8464 |
| 0.2347 | 0.1436 | 200 | 0.1725 | 0.9446 | 0.9451 | 0.9446 | 0.9445 |
| 0.1062 | 0.2154 | 300 | 0.0719 | 0.9752 | 0.9755 | 0.9752 | 0.9752 |
| 0.0731 | 0.2873 | 400 | 0.0485 | 0.9839 | 0.9839 | 0.9839 | 0.9839 |
| 0.049 | 0.3591 | 500 | 0.0479 | 0.9850 | 0.9852 | 0.9850 | 0.9850 |
| 0.0561 | 0.4309 | 600 | 0.0423 | 0.9847 | 0.9851 | 0.9847 | 0.9847 |
| 0.0276 | 0.5027 | 700 | 0.0324 | 0.9903 | 0.9903 | 0.9903 | 0.9903 |
| 0.0593 | 0.5745 | 800 | 0.0329 | 0.9888 | 0.9889 | 0.9888 | 0.9888 |
| 0.0302 | 0.6463 | 900 | 0.0343 | 0.9888 | 0.9889 | 0.9888 | 0.9888 |
| 0.035 | 0.7181 | 1000 | 0.0309 | 0.9905 | 0.9906 | 0.9905 | 0.9905 |
| 0.0326 | 0.7899 | 1100 | 0.0266 | 0.9924 | 0.9924 | 0.9924 | 0.9924 |
| 0.028 | 0.8618 | 1200 | 0.0236 | 0.9922 | 0.9922 | 0.9922 | 0.9922 |
| 0.0209 | 0.9336 | 1300 | 0.0285 | 0.9915 | 0.9915 | 0.9915 | 0.9915 |
| 0.0166 | 1.0050 | 1400 | 0.0248 | 0.9924 | 0.9924 | 0.9924 | 0.9924 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.1+cu128
- Datasets 4.4.1
- Tokenizers 0.22.1
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Dec 18, 2025
- Updated
- Dec 18, 2025