PaymentNonPayment-ModernBERT
This model is a fine-tuned version of answerdotai/ModernBERT-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0157
- Accuracy: 0.9985
- F1: 0.9985
- Precision: 0.9985
- Recall: 0.9985
- Roc Auc: 0.9984
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: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Use paged_adamw_32bit 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: 0.03
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc |
|---|---|---|---|---|---|---|---|---|
| 16.0312 | 0.1996 | 79 | 0.1616 | 0.9823 | 0.9823 | 0.9829 | 0.9823 | 0.9803 |
| 0.0001 | 0.3992 | 158 | 0.1080 | 0.9882 | 0.9882 | 0.9884 | 0.9882 | 0.9890 |
| 0.0001 | 0.5989 | 237 | 0.0169 | 0.9985 | 0.9985 | 0.9985 | 0.9985 | 0.9984 |
| 0.0003 | 0.7985 | 316 | 0.0138 | 0.9985 | 0.9985 | 0.9985 | 0.9985 | 0.9984 |
| 0.0001 | 0.9981 | 395 | 0.0157 | 0.9985 | 0.9985 | 0.9985 | 0.9985 | 0.9984 |
Framework versions
- Transformers 4.48.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.21.0