prompt-classification-bert
This model is a fine-tuned version of answerdotai/ModernBERT-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0666
- F1: 0.9905
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 4
- 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
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.1883 | 1.0 | 118 | 0.1625 | 0.9622 |
| 0.0718 | 2.0 | 236 | 0.0819 | 0.9716 |
| 0.0159 | 3.0 | 354 | 0.0610 | 0.9905 |
| 0.0 | 4.0 | 472 | 0.0656 | 0.9905 |
| 0.0 | 5.0 | 590 | 0.0666 | 0.9905 |
Framework versions
- Transformers 4.48.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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