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: 2.0657
• Accuracy: {'accuracy': 0.7330827067669173}

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
0.9636	1.0	538	0.8582	{'accuracy': 0.6992481203007519}
0.7447	2.0	1076	1.0010	{'accuracy': 0.7030075187969925}
0.5876	3.0	1614	0.9129	{'accuracy': 0.7142857142857143}
0.4728	4.0	2152	1.1641	{'accuracy': 0.7255639097744361}
0.4145	5.0	2690	1.3646	{'accuracy': 0.7330827067669173}
0.2917	6.0	3228	1.4447	{'accuracy': 0.7556390977443609}
0.2485	7.0	3766	1.7574	{'accuracy': 0.7330827067669173}
0.1596	8.0	4304	1.9367	{'accuracy': 0.7330827067669173}
0.1468	9.0	4842	2.0091	{'accuracy': 0.7368421052631579}
0.1128	10.0	5380	2.0657	{'accuracy': 0.7330827067669173}

Framework versions

• PEFT 0.11.1
• Transformers 4.41.0
• Pytorch 2.3.0
• Datasets 2.19.1
• Tokenizers 0.19.1