aesthetic_attribute_classifier

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

• Loss: 0.3976
• Precision: {'precision': 0.877129341279301}
• Recall: {'recall': 0.8751381215469614}
• F1: {'f1': 0.875529982855803}
• Accuracy: {'accuracy': 0.8751381215469614}

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: 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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.452	1.0	1528	0.4109	{'precision': 0.8632779077963935}	{'recall': 0.8615101289134438}	{'f1': 0.8618616182904953}	{'accuracy': 0.8615101289134438}
0.3099	2.0	3056	0.3976	{'precision': 0.877129341279301}	{'recall': 0.8751381215469614}	{'f1': 0.875529982855803}	{'accuracy': 0.8751381215469614}
0.227	3.0	4584	0.4320	{'precision': 0.876211408446225}	{'recall': 0.874401473296501}	{'f1': 0.8747427955387239}	{'accuracy': 0.874401473296501}
0.1645	4.0	6112	0.4840	{'precision': 0.8724641667216837}	{'recall': 0.8714548802946593}	{'f1': 0.8714577820909117}	{'accuracy': 0.8714548802946593}
0.1141	5.0	7640	0.5083	{'precision': 0.8755445355051571}	{'recall': 0.8747697974217311}	{'f1': 0.8748766125899489}	{'accuracy': 0.8747697974217311}

Framework versions

• Transformers 4.16.2
• Pytorch 1.10.2+cu113
• Datasets 1.18.3
• Tokenizers 0.11.0