Update README.md

README.md

@@ -13,8 +13,40 @@ datasets:
 # SpendingCategoryBERT
 
 Fine-tuned BERT for classifying raw bank transaction strings into 8 spending categories.
-Built as part of Foresight.
+Built as part of [Foresight](https://github.com/DikshithPulakanti/foresight) — a proactive AI financial OS with 12 LangGraph agents and 6 custom MCP servers.
+
+## Categories
+
+| Label | Category | Example |
+|-------|----------|---------|
+| 0 | grocery | WHOLEFDS MKT #1523 |
+| 1 | restaurant | STARBUCKS #12345 |
+| 2 | transport | UBER *TRIP |
+| 3 | shopping | AMZN MKTP US*2K4J9 |
+| 4 | entertainment | NETFLIX.COM |
+| 5 | utilities | ATT*BILL PAYMENT |
+| 6 | healthcare | CVS PHARMACY #1234 |
+| 7 | general | VENMO PAYMENT |
 
 ## Performance
-- Accuracy: 100%
-- F1: 1.0
+
+| Metric | Score |
+|--------|-------|
+| Accuracy | 100% |
+| F1 (weighted) | 1.0 |
+| Precision | 1.0 |
+| Recall | 1.0 |
+
+## Usage
+
+from transformers import pipeline
+classifier = pipeline("text-classification", model="Dikshith4500/SpendingCategoryBERT")
+classifier("WHOLEFDS MKT #1523")
+
+## Training
+- Base model: bert-base-uncased
+- Dataset: Dikshith4500/bank-transaction-categories-50k
+- Examples: 6,973 labeled bank transaction strings
+- Epochs: 3
+- Learning rate: 2e-5
+- Hardware: T4 GPU