llm_firewall_distilbert-base-uncased

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llm_firewall_distilbert-base-uncased

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1218
• Accuracy: 0.9451

Latest finetune 5 Dec 2023

{'eval_loss': 0.12179878354072571, 'eval_accuracy': 0.9450980392156862, 'eval_runtime': 5.8053, 'eval_samples_per_second': 43.925, 'eval_steps_per_second': 2.756, 'epoch': 20.0}

Model description

Finetuned distilbert-uncased on prompts that are either malicious or benign.

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: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 20

Training results

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.1
• Datasets 2.15.0
• Tokenizers 0.15.0