results

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.1811
• Accuracy: {'accuracy': 0.9558}

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: 16
• eval_batch_size: 16
• 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
0.19	1.0	625	0.1811	{'accuracy': 0.9558}
0.1973	2.0	1250	0.1809	{'accuracy': 0.9558}
0.1758	3.0	1875	0.1819	{'accuracy': 0.9558}
0.189	4.0	2500	0.1907	{'accuracy': 0.9558}
0.1864	5.0	3125	0.1886	{'accuracy': 0.9558}
0.19	6.0	3750	0.1833	{'accuracy': 0.9558}
0.18	7.0	4375	0.1844	{'accuracy': 0.9558}
0.1863	8.0	5000	0.1843	{'accuracy': 0.9558}
0.1806	9.0	5625	0.1806	{'accuracy': 0.9558}
0.1765	10.0	6250	0.1811	{'accuracy': 0.9558}

Framework versions

• PEFT 0.14.0
• Transformers 4.48.2
• Pytorch 2.5.1+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0