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: 0.5043
• Accuracy: {'accuracy': 0.9479166666666666}

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: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	431	0.2331	{'accuracy': 0.90625}
0.4451	2.0	862	0.3140	{'accuracy': 0.90625}
0.2963	3.0	1293	0.3216	{'accuracy': 0.9322916666666666}
0.25	4.0	1724	0.2690	{'accuracy': 0.9270833333333334}
0.2261	5.0	2155	0.2707	{'accuracy': 0.9479166666666666}
0.1511	6.0	2586	0.2543	{'accuracy': 0.9427083333333334}
0.1401	7.0	3017	0.3120	{'accuracy': 0.9375}
0.1401	8.0	3448	0.2845	{'accuracy': 0.953125}
0.086	9.0	3879	0.4018	{'accuracy': 0.921875}
0.0583	10.0	4310	0.4593	{'accuracy': 0.9427083333333334}
0.0475	11.0	4741	0.4401	{'accuracy': 0.953125}
0.0515	12.0	5172	0.4631	{'accuracy': 0.9479166666666666}
0.0291	13.0	5603	0.4593	{'accuracy': 0.9479166666666666}
0.0319	14.0	6034	0.5292	{'accuracy': 0.9479166666666666}
0.0319	15.0	6465	0.5043	{'accuracy': 0.9479166666666666}

Framework versions

• PEFT 0.12.0
• Transformers 4.44.2
• Pytorch 2.4.1+cu124
• Datasets 3.0.0
• Tokenizers 0.19.1