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: 0.0000
• Accuracy: {'accuracy': 1.0}

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.4805	1.0	500	0.2170	{'accuracy': 0.921}
0.3815	2.0	1000	0.1949	{'accuracy': 0.948}
0.2958	3.0	1500	0.1315	{'accuracy': 0.965}
0.2432	4.0	2000	0.0774	{'accuracy': 0.981}
0.1623	5.0	2500	0.0234	{'accuracy': 0.99}
0.1051	6.0	3000	0.0123	{'accuracy': 0.998}
0.0886	7.0	3500	0.0011	{'accuracy': 0.999}
0.043	8.0	4000	0.0002	{'accuracy': 1.0}
0.0285	9.0	4500	0.0000	{'accuracy': 1.0}
0.0076	10.0	5000	0.0000	{'accuracy': 1.0}

Framework versions

• PEFT 0.13.1
• Transformers 4.45.2
• Pytorch 2.4.1+cpu
• Datasets 3.0.1
• Tokenizers 0.20.1