distilbert-base-uncased-lora-text-classification

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: 1.0205
• Accuracy: {'accuracy': 0.886}

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 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
No log	1.0	250	0.3867	{'accuracy': 0.874}
0.4338	2.0	500	0.3422	{'accuracy': 0.886}
0.4338	3.0	750	0.5465	{'accuracy': 0.876}
0.2056	4.0	1000	0.5987	{'accuracy': 0.887}
0.2056	5.0	1250	0.8393	{'accuracy': 0.888}
0.0888	6.0	1500	0.7664	{'accuracy': 0.888}
0.0888	7.0	1750	0.8778	{'accuracy': 0.881}
0.0233	8.0	2000	0.9618	{'accuracy': 0.889}
0.0233	9.0	2250	1.0294	{'accuracy': 0.888}
0.0114	10.0	2500	1.0205	{'accuracy': 0.886}

Framework versions

• PEFT 0.13.2
• Transformers 4.46.3
• Pytorch 2.5.1+cu121
• Datasets 3.1.0
• Tokenizers 0.20.3