Transaction Categoriser
Fine-tuned DistilBERT model that classifies raw bank transaction descriptions into 17 merchant spending categories with 99.88% test accuracy.
Try it live: 🤗 Spaces Demo
Model Details
| 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
{
0: "Education", 1: "Entertainment", 2: "Fees",
3: "Groceries", 4: "Healthcare", 5: "Income",
6: "Insurance", 7: "Mortgage", 8: "Personal Care",
9: "Rent", 10: "Restaurants", 11: "Shopping",
12: "Subscription", 13: "Transfer", 14: "Transportation",
15: "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
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
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
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
@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)