distilbert-base-uncased-lora-text-classification
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: 0.0287
- Accuracy: {'accuracy': 0.992}
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: 0.001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.3979 | 1.0 | 1250 | 0.2949 | {'accuracy': 0.903} |
| 0.4384 | 2.0 | 2500 | 0.2595 | {'accuracy': 0.9152} |
| 0.3827 | 3.0 | 3750 | 0.2294 | {'accuracy': 0.9316} |
| 0.3539 | 4.0 | 5000 | 0.2089 | {'accuracy': 0.9394} |
| 0.331 | 5.0 | 6250 | 0.1202 | {'accuracy': 0.9594} |
| 0.2554 | 6.0 | 7500 | 0.1205 | {'accuracy': 0.9678} |
| 0.2312 | 7.0 | 8750 | 0.0965 | {'accuracy': 0.9778} |
| 0.1933 | 8.0 | 10000 | 0.0551 | {'accuracy': 0.9864} |
| 0.1389 | 9.0 | 11250 | 0.0404 | {'accuracy': 0.989} |
| 0.0822 | 10.0 | 12500 | 0.0287 | {'accuracy': 0.992} |
Framework versions
- PEFT 0.14.0
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1