guardrail_model

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: 0.1895

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: 5e-05
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
0.6697	0.2439	10	0.5664
0.4939	0.4878	20	0.3467
0.4060	0.7317	30	0.2569
0.4070	0.9756	40	0.3106
0.2764	1.2195	50	0.1922
0.2656	1.4634	60	0.2205
0.1668	1.7073	70	0.2065
0.2996	1.9512	80	0.1840
0.1620	2.1951	90	0.2130
0.1983	2.4390	100	0.1714
0.1965	2.6829	110	0.1795
0.1159	2.9268	120	0.1895

Framework versions

• Transformers 5.0.0
• Pytorch 2.10.0+cpu
• Datasets 4.0.0
• Tokenizers 0.22.2