distill-bert-finetune

This model is a fine-tuned version of distilbert/distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.5165
• Accuracy: 0.884
• Auc: 0.888

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: 2e-05
• train_batch_size: 32
• eval_batch_size: 32
• 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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Auc
No log	3.5088	200	0.3942	0.853	0.908
No log	7.0175	400	0.4839	0.857	0.897
0.2711	10.5263	600	0.4707	0.857	0.903
0.2711	14.0351	800	0.5121	0.871	0.886
0.1079	17.5439	1000	0.5165	0.884	0.888

Framework versions

• Transformers 4.48.0
• Pytorch 2.4.1+cu121
• Datasets 3.2.0
• Tokenizers 0.21.0