distilbert-base-uncased fine-tuned for banking customer-intent classification over the 77 intents of the BANKING77 dataset (card issues, transfers, top-ups, fees, verification, …).

Training

Method: full fine-tuning (all 66M parameters)
Objective: cross-entropy, HF Trainer
Data: mteb/banking77 — 10,003 train / 3,080 test, 77 classes
Hyperparameters: 5 epochs, batch 32, lr 5e-5, max_len 64
Hardware: CPU only (AMD Ryzen 9 7950X)

Results (test set)

Epoch	Accuracy	Macro-F1
1	0.8072	0.7850
2	0.9060	0.9061
3	0.9226	0.9225
5	0.9278	0.9278

Macro-F1 tracks accuracy closely, indicating balanced performance across all 77 intents rather than only the frequent ones.

Usage

python

from transformers import pipeline
clf = pipeline("text-classification", model="vivekkopthsd/banking-intent-distilbert")
clf("My card payment was declined at the store")
# [{'label': 'declined_card_payment', 'score': 0.98}]

Predictions return readable intent names (e.g. declined_card_payment, card_arrival, exchange_rate) via the model's id2label mapping.

Limitations

Trained on BANKING77, which is English-only, single-turn, and relatively clean. Real support traffic contains typos, code-switching, and multi-intent messages; expect degradation and re-tune on in-domain data before production use. Labels are BANKING77 class indices.