results

• Loss: 0.4732
• Accuracy: 0.7753

• Fine Tuned DistilBert LLM with LoRa: Accuracy = 0.78, Precision = 0.77, Recall = 0.78, F1 Score = 0.78
• Non-Fine Tuned DistilBert LLM: Accuracy = 0.49, Precision = 0.45, Recall = 0.14, F1 Score = 0.22

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

• learning_rate: 5e-05
• train_batch_size: 32
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 8

Training results

Framework versions

• Transformers 4.39.3
• Pytorch 2.2.1+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2