metadata
language: en
license: apache-2.0
library_name: setfit
tags:
- text-classification
- transaction-classification
- banking
- finance
- setfit
- sentence-transformers
- few-shot-learning
- contrastive-learning
datasets:
- mitulshah/transaction-categorization
base_model: sentence-transformers/all-MiniLM-L6-v2
pipeline_tag: text-classification
model-index:
- name: transaction-classifier-setfit
results:
- task:
type: text-classification
name: Transaction Classification
metrics:
- name: Real-World Accuracy (Weighted)
type: accuracy
value: 0.805
- name: SetFit-Only Accuracy
type: accuracy
value: 0.667
- name: Validation Accuracy
type: accuracy
value: 0.98
Transaction Classifier — SetFit (v3)
A SetFit model built on sentence-transformers/all-MiniLM-L6-v2 that classifies bank transaction strings into 10 budget categories using contrastive few-shot learning.
This is version 3 in a progressive model development series. It demonstrated that pre-trained semantic embeddings dramatically outperform traditional NLP approaches for transaction classification, jumping from 55.7% to 80.5% real-world accuracy.
Model Details
| Property | Value |
|---|---|
| Base model | sentence-transformers/all-MiniLM-L6-v2 (22M params) |
| Framework | SetFit (contrastive learning + logistic head) |
| Task | Multi-class text classification (10 categories) |
| Training samples | 8,000 |
| Contrastive iterations | 20 |
| Epochs | 1 |
| Batch size | 32 |
| Format | SafeTensors + model_head.pkl |
| Trained | 2026-03-28 |
Categories
| ID | Category |
|---|---|
| 0 | Food & Dining |
| 1 | Transportation |
| 2 | Shopping & Retail |
| 3 | Entertainment & Recreation |
| 4 | Healthcare & Medical |
| 5 | Utilities & Services |
| 6 | Financial Services |
| 7 | Income |
| 8 | Government & Legal |
| 9 | Charity & Donations |
Performance
Evaluated on 505 unique real-world RBC transactions (3,113 weighted, 2019-2026).
Overall
| Metric | Score |
|---|---|
| Real-world accuracy (weighted) | 80.5% |
| SetFit-only accuracy | 66.7% |
| Validation accuracy | 98.0% |
Per-Category Accuracy
| Category | Accuracy |
|---|---|
| Income | 97.8% |
| Healthcare & Medical | 100.0% |
| Financial Services | 91.2% |
| Entertainment & Recreation | 88.6% |
| Food & Dining | 83.9% |
| Transportation | 83.3% |
| Shopping & Retail | 74.6% |
| Government & Legal | 54.5% |
| Utilities & Services | 34.2% |
| Charity & Donations | 0.0% |
Usage
from setfit import SetFitModel
model = SetFitModel.from_pretrained("maaz-zaidi/transaction-classifier-setfit")
predictions = model.predict([
"STARBUCKS STORE 12345",
"SHELL GAS STATION",
"NETFLIX.COM"
])
print(predictions)
Training Data
- Primary: mitulshah/transaction-categorization - 8K samples from 3.6M records (gated dataset)
- Evaluation: 505 real-world RBC bank transactions (2019-2026)
Key Breakthrough
SetFit's contrastive learning approach was the breakthrough moment in this project:
- v2 (FastText) -> v3 (SetFit): 55.7% -> 80.5% overall accuracy
- FastText's ML-only accuracy was 14.8% (severe Income category bias). SetFit's ML-only accuracy was 66.7%.
- Pre-trained sentence embeddings understand real-world merchant concepts that character n-grams cannot capture.
Part of a Series
See the Transaction Classifier collection for all 7 model versions.
Limitations
- Contrastive learning with a logistic regression head is outperformed by standard cross-entropy fine-tuning at this data scale (see v4)
- Utilities & Services at only 34.2% accuracy
- Domain-specific to Canadian banking transaction formats
Citation
@misc{zaidi2026txnclassifier,
title={Transaction Classifier: Multi-Stage Bank Transaction Categorization},
author={Maaz Zaidi},
year={2026},
url={https://huggingface.co/maaz-zaidi/transaction-classifier-setfit}
}