DarioFrK/sift-banking77-modernbert

Sift — banking77 intent classifier (modernbert)

Fine-tuned `answerdotai/ModernBERT-base` on `PolyAI/banking77` (77 fine-grained banking intents). Part of Sift, a query-triage project showing a small fine-tuned model beats a zero-shot open 7B instruct LLM (Mistral-7B-Instruct, run locally and 4-bit quantised) on cost, latency, and privacy for narrow, high-volume classification.

Metrics — full test split (3076 examples)

Intent -> team routing

The 77 intents are mapped to six routing teams: Cards, Payments, Accounts, Fraud/Security, Onboarding, General.

Usage

from transformers import pipeline
clf = pipeline("text-classification", model="DarioFrK/sift-banking77-modernbert", top_k=5)
clf("My card was swallowed by the ATM")

• —Base model: answerdotai/ModernBERT-base
• —Dataset: PolyAI/banking77
• —Training: 3 epochs, lr=5e-05, batch=16, max_len=64