Instructions to use mkianih/ai-banking-intent-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mkianih/ai-banking-intent-classifier with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
metadata
license: mit
base_model: roberta-base
tags:
- text-classification
- lora
- peft
- banking77
Banking Intent Classifier (RoBERTa + LoRA)
roberta-base fine-tuned with LoRA on Banking77
(77 customer-support intents). LoRA adapters have been merged into the base weights, so
this loads like any standard AutoModelForSequenceClassification checkpoint.
- Accuracy: 92.05% | Macro F1: 92.05% | Weighted F1: 92.05%
Code, training notebook, and the PII-redaction layer used in front of this model: https://github.com/mkianih/ai-banking-intent-classifier
label_encoder.joblib (scikit-learn LabelEncoder) is included alongside the model
weights to map class indices back to intent names.