Banking intent router built with RoBERTa, LoRA, and privacy controls

A banking intent router was developed using the BANKING77 dataset, incorporating RoBERTa, LoRA, and calibration techniques. The project focused on privacy controls and uncertainty testing, ultimately finding that a model other than a Transformer architecture achieved the highest accuracy. AI

IMPACT Demonstrates alternative model architectures can outperform Transformers on specific tasks, highlighting the importance of tailored solutions.

RANK_REASON The item describes a specific model development and evaluation process, including dataset usage and architectural choices, which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Medium — MLOps tag →

• BANKING77
• Bert
• gated recurrent unit
• GPT-3
• long short-term memory
• LoRA
• OpenAI
• PyTorch
• RoBERTa
• Tensorflow
• Transformer++

• model release
• product

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