hw2advanced

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.2408
• Precision: {'precision': 0.9067133278302073}
• Recall: {'recall': 0.903472079391197}
• F1: {'f1': 0.9050586148832505}
• Accuracy: {'accuracy': 0.9160687311178247}

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: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.4744	1.0	1324	0.3413	{'precision': 0.8554480519619763}	{'recall': 0.8395979020979021}	{'f1': 0.8466159268992823}	{'accuracy': 0.866786253776435}
0.3566	2.0	2648	0.2408	{'precision': 0.9067133278302073}	{'recall': 0.903472079391197}	{'f1': 0.9050586148832505}	{'accuracy': 0.9160687311178247}

Framework versions

• Transformers 4.38.1
• Pytorch 2.1.0+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2