llama2_13B_LORA_FOR_CLASSIFICATION

• Loss: 0.5708
• Balanced Accuracy: 0.7079
• Accuracy: 0.7530
• Micro F1: 0.7530
• Macro F1: 0.6771
• Weighted F1: 0.7669
• Classification Report: precision recall f1-score support

       0       0.89      0.79      0.83       857
       1       0.44      0.63      0.52       232

  accuracy 0.75 1089 macro avg 0.67 0.71 0.68 1089 weighted avg 0.79 0.75 0.77 1089

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

• learning_rate: 0.0001
• train_batch_size: 24
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 4

Training results

       0       0.90      0.81      0.85       857
       1       0.48      0.65      0.55       232

accuracy                           0.78      1089

       0       0.89      0.80      0.84       857
       1       0.47      0.63      0.54       232

accuracy                           0.77      1089

       0       0.89      0.79      0.83       857
       1       0.44      0.63      0.52       232

accuracy                           0.75      1089

Framework versions

• PEFT 0.11.1
• Transformers 4.41.2
• Pytorch 2.3.0+cu118
• Datasets 2.20.0
• Tokenizers 0.19.1