finmigodeveloper/distilbert-transaction-classifier-lora

Transaction Category Classifier - LoRA Version

This is a LoRA adapter for DistilBERT that classifies bank transactions into 10 categories with 98.53% accuracy.

Model Details

• —Base Model: distilbert-base-uncased
• —Fine-tuned Model: finmigodeveloper/distilbert-transaction-classifier
• —Adapter Size: ~2.5 MB (98.7% smaller than full model)
• —Categories: 10 transaction types

Performance

Categories

• —Charity & Donations
• —Entertainment & Recreation
• —Financial Services
• —Food & Dining
• —Government & Legal
• —Healthcare & Medical
• —Income
• —Shopping & Retail
• —Transportation
• —Utilities & Services

How to Use

from transformers import pipeline

# Load directly
classifier = pipeline("text-classification", 
                     model="finmigodeveloper/distilbert-transaction-classifier-lora")

# Test it
transactions = [
    "Starbucks coffee",
    "Monthly salary deposit", 
    "Uber ride to airport"
]

for text in transactions:
    result = classifier(text)[0]
    print(f"{text}: {result['label']} ({result['score']:.2%})")

Training Details

• —LoRA Rank (r): 8
• —LoRA Alpha: 16
• —Target Modules: qlin, klin, vlin, outlin
• —Dropout: 0.1
• —Epochs: 3
• —Batch Size: 64
• —Learning Rate: 2e-5

Why LoRA?

• —98.7% smaller than the full model
• —Faster loading (~0.3 seconds vs 2-3 seconds)
• —Same accuracy as the full model
• —Perfect for mobile apps and edge deployment

Files in this repository

• —adapter_model.safetensors: The LoRA adapter weights (2.5 MB)
• —adapter_config.json: LoRA configuration
• —training_stats.json: Detailed training statistics
• —tokenizer.json & tokenizer_config.json: Tokenizer files