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: q_lin, k_lin, v_lin, out_lin
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

finmigodeveloper/distilbert-transaction-classifier-lora

Transaction Category Classifier - LoRA Version

Model Details

Performance

Categories

How to Use

Training Details

Why LoRA?

Files in this repository

finmigodeveloper/
distilbert-transaction-classifier-lora