Payment-NonPayment-distilbert-base-uncased_70_10_10
This model is a fine-tuned version of distilbert/distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0003
- Accuracy: 0.9999
- F1: 0.9999
- Precision: 0.9999
- Recall: 0.9999
- Roc Auc: 0.9999
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: 0.0002
- train_batch_size: 64
- eval_batch_size: 128
- seed: 42
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc |
|---|---|---|---|---|---|---|---|---|
| 0.0018 | 1.0 | 693 | 0.0645 | 0.9918 | 0.9918 | 0.9919 | 0.9918 | 0.9923 |
| 0.0016 | 2.0 | 1386 | 0.0020 | 0.9999 | 0.9999 | 0.9999 | 0.9999 | 0.9999 |
| 0.0022 | 3.0 | 2079 | 0.0009 | 0.9999 | 0.9999 | 0.9999 | 0.9999 | 0.9999 |
| 0.0003 | 4.0 | 2772 | 0.0003 | 0.9999 | 0.9999 | 0.9999 | 0.9999 | 0.9999 |
| 0.0031 | 5.0 | 3465 | 0.0003 | 0.9999 | 0.9999 | 0.9999 | 0.9999 | 0.9999 |
Framework versions
- Transformers 4.48.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.21.0