banking77-deBERTa-v3-base

This model is a fine-tuned version of microsoft/deberta-v3-base on the banking77 dataset. It achieves the following results on the evaluation set:

• Loss: 0.3281
• Accuracy: 0.9195
• F1 Macro: 0.9170
• Precision Macro: 0.9222
• Recall Macro: 0.9159
• F1 Weighted: 0.9194
• Precision Weighted: 0.9229
• Recall Weighted: 0.9195

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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	Precision Macro	Recall Macro	F1 Weighted	Precision Weighted	Recall Weighted
3.4666	1.0	501	3.1762	0.3548	0.2479	0.3016	0.3195	0.2774	0.3421	0.3548
1.2538	2.0	1002	1.0122	0.8141	0.7625	0.8091	0.7795	0.7946	0.8291	0.8141
0.5576	3.0	1503	0.4823	0.8941	0.8797	0.9012	0.8786	0.8915	0.9021	0.8941
0.3544	4.0	2004	0.3625	0.9110	0.9090	0.9170	0.9084	0.9108	0.9172	0.9110
0.2603	5.0	2505	0.3281	0.9195	0.9170	0.9222	0.9159	0.9194	0.9229	0.9195

Framework versions

• Transformers 4.51.3
• Pytorch 2.6.0+cu124
• Datasets 3.6.0
• Tokenizers 0.21.1