EPD_model

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.2236
• F1: {'f1': 0.9345610104602673}
• Recall: {'recall': 0.9336773252632005}
• Precision: {'precision': 0.9355536215201713}
• Accuracy: {'accuracy': 0.9358918447448757}

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: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 5
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Recall	Precision	Accuracy
No log	1.0	128	0.2695	{'f1': 0.8913898471010318}	{'recall': 0.8931338001905943}	{'precision': 0.8901158856369481}	{'accuracy': 0.8929350196249455}
No log	2.0	256	0.2055	{'f1': 0.9198169564205299}	{'recall': 0.9158104161980756}	{'precision': 0.9263523714174473}	{'accuracy': 0.9221543829044919}
No log	3.0	384	0.2831	{'f1': 0.8998826704095172}	{'recall': 0.891783385825231}	{'precision': 0.9209965165513254}	{'accuracy': 0.9044919319668556}
0.2073	4.0	512	0.2114	{'f1': 0.9308545622407456}	{'recall': 0.9279733477935482}	{'precision': 0.9349529033731581}	{'accuracy': 0.9326210204971653}
0.2073	5.0	640	0.2236	{'f1': 0.9345610104602673}	{'recall': 0.9336773252632005}	{'precision': 0.9355536215201713}	{'accuracy': 0.9358918447448757}

Framework versions

• Transformers 4.47.0
• Pytorch 2.5.1+cu121
• Datasets 3.2.0
• Tokenizers 0.21.0