Property	Value
Base model	`distilbert-base-uncased`
Task	Multi-class text classification (17 classes)
Parameters	66M
Training platform	Kaggle (GPU T4 x2)
Framework	HuggingFace Transformers 4.44.2
Inference	CPU-compatible

Labels

python

{
"Education",       1: "Entertainment",   2: "Fees",
"Groceries",       4: "Healthcare",       5: "Income",
"Insurance",       7: "Mortgage",         8: "Personal Care",
"Rent",           10: "Restaurants",     11: "Shopping",
"Subscription",   13: "Transfer",        14: "Transportation",
"Travel",         16: "Utilities"
}

Performance

Evaluated on a held-out test set of 4,298 samples (10% of 42,975 total).

Metric	V1 (baseline)	V2 (this model)
Accuracy	99.70%	99.88%
F1 (weighted)	99.70%	99.88%
F1 (macro)	99.71%	99.89%

All experiments tracked on W&B.

Usage

python

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="fahadkamraan/transaction-categorizer",
    device=-1,   # CPU
    top_k=3,
)

results = classifier("starbucks coffee purchase")
# [{'label': 'Restaurants', 'score': 0.9999}]

With HuggingFace Hub

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("fahadkamraan/transaction-categorizer")
model = AutoModelForSequenceClassification.from_pretrained("fahadkamraan/transaction-categorizer")

inputs = tokenizer("netflix monthly subscription", return_tensors="pt", truncation=True, max_length=64)
with torch.no_grad():
    logits = model(**inputs).logits
predicted_class = logits.argmax(-1).item()
print(model.config.id2label[predicted_class])  # → Subscription

Docker

bash

docker run --rm \
  -e HF_TOKEN=<token> \
  -e INPUT_TEXT="delta airlines flight booking" \
  fahadkamraan/mlops-transaction-classifier:latest

Training Data

Dataset: DoDataThings/us-bank-transaction-categories-v2
Licence: MIT
Raw size: 68,000 rows · 17 categories
Used: 42,975 rows (up to 3,000 per class after deduplication)
Split: 80% train / 10% val / 10% test

Training Procedure

Two versions were trained and compared on Kaggle (GPU T4 x2):

Hyperparameter	V1	V2 (this model)
Learning rate	2e-5	5e-5
Epochs	3	5
Weight decay	0.0	0.01
Preprocessing	Light	Extra normalisation
Batch size	32	32

V2 preprocessing additionally strips [debit]/[credit] prefix tags and standardises store number suffixes, giving the model cleaner input.

Limitations

Supports only the 17 categories present in the training dataset. Transactions from categories like "Investments" or "Taxes" will be mapped to the nearest available label.
Trained on US-style bank transaction descriptions. May generalise poorly to non-English or very different regional bank formats.
Maximum input length: 64 tokens (sufficient for typical transaction strings).

MLOps Pipeline

This model is part of a complete MLOps assignment pipeline:

GitHub: S-FK/transaction_categorization
Docker: fahadkamraan/mlops-transaction-classifier
W&B: mlops-transaction-classifier
Spaces Demo: transaction-categorizer-demo

Citation

bibtex

@misc{kamraan2025transactioncategorizer,
  author    = {S Fahad Kamraan and Dhruvi Patel and Mahesh V and Himanshu Choubey},
  title     = {Bank Transaction Merchant Categoriser},
  year      = {2025},
  publisher = {HuggingFace Hub},
  url       = {https://huggingface.co/fahadkamraan/transaction-categorizer}
}

IIT Jodhpur — ML Ops Assignment (CSL 7040)