hf-tuner/bert-banking-intent

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bert-banking-intent

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
• —trainbatchsize: 16
• —evalbatchsize: 16
• —seed: 42
• —optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• —lrschedulertype: linear
• —num_epochs: 10

Training results

Framework versions

• —Transformers 4.57.1
• —Pytorch 2.8.0+cu126
• —Datasets 4.0.0
• —Tokenizers 0.22.1