This model is a fine-tuned version of google-bert/bert-base-uncased on hf-tuner/banking-intent dataset. It achieves the following results on the evaluation set:

Loss: 0.0079
Accuracy: 0.9993

How to Get Started with the Model

from transformers import pipeline

classifier = pipeline("text-classification", model = "hf-tuner/bert-banking-intent")
classifier("Please help me get a new card, I reside in the United States.")
## [{'label': 'country_support', 'score': 0.997}]

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_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.9901	1.0	626	1.5437	0.8104
0.8228	2.0	1252	0.5328	0.9335
0.3901	3.0	1878	0.2214	0.9678
0.1889	4.0	2504	0.1041	0.9830
0.0973	5.0	3130	0.0518	0.9920
0.0733	6.0	3756	0.0322	0.9944
0.0405	7.0	4382	0.0167	0.9976
0.0214	8.0	5008	0.0114	0.9988
0.0175	9.0	5634	0.0091	0.9993
0.0138	10.0	6260	0.0079	0.9993

Framework versions

Transformers 4.57.1
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.22.1