banking77-deBERTa-v3-base – AI Model by hiudev | AlphaNeural AI

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hiudev/
banking77-deBERTa-v3-base

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text-classification

sequence-classification

generated_from_trainer

microsoft/deberta-v3-base

autotrain_compatible

endpoints_compatible

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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