distilbert-base-uncased-finetuned-fashion

This model is a fine-tuned version of distilbert-base-uncased on a munally created dataset in order to detect fashion (label_0) from non-fashion (label_1) items. It achieves the following results on the evaluation set:

• Loss: 0.0809
• Accuracy: 0.98
• F1: 0.9801

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 64
• eval_batch_size: 64
• 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	Accuracy	F1
0.4017	1.0	47	0.1220	0.966	0.9662
0.115	2.0	94	0.0809	0.98	0.9801

Framework versions

• Transformers 4.18.0
• Pytorch 1.11.0+cu113
• Datasets 2.1.0
• Tokenizers 0.12.1