distilbert-base-uncased-lora-text-classification

This model is a fine-tuned version of distilbert-base-uncased on three separate log datasets. It achieves the following results on the evaluation set:

• Loss: 0.0058
• Accuracy: {'accuracy': 0.9991534286141128}

Model description

Intended uses & limitations

Log Classification

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: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.0	1.0	5021	0.0058	{'accuracy': 0.9991534286141128}

Framework versions

• PEFT 0.8.2
• Transformers 4.38.1
• Pytorch 2.1.0+cu121
• Datasets 2.17.1
• Tokenizers 0.15.2