finmigodeveloper
/

distilbert-transaction-classifier-lora

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 ---
+language: en
+tags:
+- transaction-categorization
+- distilbert
+- lora
+- peft
+- finance
+- text-classification
+datasets:
+- mitulshah/transaction-categorization
+license: apache-2.0
 ---
+# 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](https://huggingface.co/distilbert-base-uncased)
+- **Fine-tuned Model:** [finmigodeveloper/distilbert-transaction-classifier](https://huggingface.co/finmigodeveloper/distilbert-transaction-classifier)
+- **Adapter Size:** ~2.5 MB (98.7% smaller than full model)
+- **Categories:** 10 transaction types
+## Performance
+| Metric | Value |
+|--------|-------|
+| Accuracy | 98.53% |
+| Loss | 0.0221 |
+| Training Samples | 80,000 |
+| Validation Samples | 20,000 |
+## Categories
+- Charity & Donations
+- Entertainment & Recreation
+- Financial Services
+- Food & Dining
+- Government & Legal
+- Healthcare & Medical
+- Income
+- Shopping & Retail
+- Transportation
+- Utilities & Services
+## How to Use
+```python
+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