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README.md

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+---
+license: mit
+tags:
+  - text-classification
+  - finance
+  - transactions
+  - english
+  - synthetic
+task_categories:
+  - text-classification
+language:
+  - en
+size_categories:
+  - 10K<n<100K
+---
+
+# US Bank Transaction Categories v2 — Synthetic Dataset
+
+**Successor to [v1](https://huggingface.co/datasets/DoDataThings/us-bank-transaction-categories).** 24,000 sign-prefixed transaction descriptions with expanded merchant coverage, PayPal wrapper patterns, and a refined 16-category taxonomy.
+
+## What Changed from v1
+
+| | v1 | v2 |
+|---|---|---|
+| **Format** | `description, category` | `[debit]/[credit] description, category` |
+| **Categories** | 16 (includes Housing) | 16 (Housing split into Rent + Mortgage removed) |
+| **Samples** | 16,000 (1,000/category) | 24,000 (1,500/category) |
+| **Merchants** | ~300 | ~500+ |
+| **PayPal wrappers** | Few categories | All spending categories (8% of samples) |
+| **POS prefixes** | SQ* | SQ*, TST* (Toast), CLV* (Clover) |
+| **Transfer patterns** | Basic P2P + CC payments | + brokerage sweeps, fintech, wire, cashier's checks, ATM, crypto, BNPL |
+
+## Why Sign Prefixes?
+
+v1 couldn't distinguish Income from Transfer because the description alone is ambiguous. "VENMO CASHOUT" as a credit (money arriving) is Income. As a debit (money leaving) it's Transfer. The `[debit]`/`[credit]` prefix encodes the transaction direction from the cardholder's perspective.
+
+Sign distributions per category reflect real-world patterns — spending categories are 95%+ debit, Income is 97% credit, Transfer is 50/50.
+
+## Format
+
+CSV with two columns:
+
+```
+description,category
+"[debit] AMAZON MKTPL*K8R2M5VN7","Shopping"
+"[credit] Zelle payment from JAMES GARCIA COFZ4RFI1WBQ","Income"
+"[debit] PAYPAL           INST XFER  NETFLIX         WEB ID: PAYPALSI42","Subscription"
+"[debit] SCHWAB MONEYLINK PPD ID: 207802622","Transfer"
+"[credit] AUTOMATIC PAYMENT - THANK","Transfer"
+"[debit] PreApproved Payment Bill User Payment: Google","Subscription"
+```
+
+## Categories (16)
+
+| Category | Count | Examples |
+|----------|-------|----------|
+| Restaurants | 1,500 | `[debit] MCDONALD'S F28471`, `[debit] TST*SAKURA SUSHI`, `[debit] CLV*BURGER JOINT` |
+| Groceries | 1,500 | `[debit] SAFEWAY #1197`, `[debit] WHOLEFDS FRE #10467`, `[debit] 365 MARKET` |
+| Shopping | 1,500 | `[debit] Amazon.com*K8R2M5VN7`, `[debit] TIKTOK SHOP`, `[debit] TOTAL WINE` |
+| Transportation | 1,500 | `[debit] CHEVRON 0385291`, `[debit] CHARGEPOINT *STATION 1234`, `[debit] CA DMV FEE` |
+| Entertainment | 1,500 | `[debit] VALVE STEAM PURCHASE`, `[debit] DRAFTKINGS SPORTSBOOK`, `[debit] AMC THEATRES` |
+| Utilities | 1,500 | `[debit] PG&E`, `[debit] REPUBLIC SERVICES TRASH`, `[debit] SUNRUN SOLAR` |
+| Subscription | 1,500 | `[debit] PreApproved Payment Bill User Payment: Microsoft`, `[debit] OBSIDIAN.MD`, `[debit] RAPIDAPI` |
+| Healthcare | 1,500 | `[debit] TELADOC TELEHEALTH`, `[debit] ASPEN DENTAL`, `[debit] LENSCRAFTERS` |
+| Insurance | 1,500 | `[debit] FARMERS INS BILLING`, `[debit] STATE FARM PREMIUM`, `[debit] GEICO` |
+| Rent | 1,500 | `[debit] EQUITY RESIDENTIAL RENT PAYMENT`, `[debit] GREYSTAR LEASE PAYMENT` |
+| Travel | 1,500 | `[debit] ROYAL CARIBBEAN`, `[debit] TSA PRECHECK`, `[debit] MARRIOTT` |
+| Education | 1,500 | `[debit] WYZANT TUTORING`, `[debit] COURSERA`, `[debit] UNIVERSITY OF TUITION` |
+| Personal Care | 1,500 | `[debit] SPORT CLIPS`, `[debit] SEPHORA`, `[debit] MASSAGE ENVY` |
+| Transfer | 1,500 | `[debit] COINBASE ACH TRANSFER`, `[debit] WIRE TRANSFER TO NAME`, `[credit] ATM DEPOSIT` |
+| Income | 1,500 | `[credit] EMPLOYER PAYROLL PPD ID:`, `[credit] SSA TREAS 310 FED SAL`, `[credit] DOORDASH DASHERPAY` |
+| Fees | 1,500 | `[debit] OVERDRAFT FEE`, `[debit] ATM SURCHARGE`, `[debit] PAPER STATEMENT FEE` |
+
+## Eight Format Templates
+
+1. **ACH (Chase checking)** — `INSTITUTION PURPOSE DETAILS WEB/PPD ID: CODE`
+2. **Merchant + ID (Chase credit)** — `MERCHANT #1234` or `MERCHANT*ORDERID`
+3. **Full address (Apple Card)** — `MERCHANT ADDRESS CITY ZIP STATE COUNTRY`
+4. **PayPal wrappers** — `PreApproved Payment Bill User Payment: MERCHANT`, `Express Checkout Payment: MERCHANT`, `PP*MERCHANT`, `PAYPAL *MERCHANT`, `PAYPAL INST XFER MERCHANT WEB ID:`
+5. **Action prefix (Capital One)** — `Withdrawal from DESCRIPTION` / `Deposit from DESCRIPTION`
+6. **POS prefixes** — `SQ *MERCHANT`, `TST*MERCHANT`, `CLV*MERCHANT`
+7. **Compressed names** — `DAVESHOTCHICKEN`, `CHICKFILA`, `WHOLEFDS FRE`
+8. **Simple** — `MERCHANT` or `MERCHANT.COM`
+
+## Variation Dimensions
+
+- **Capitalization:** ALL CAPS, Title Case, lowercase, mixed
+- **Spacing:** normal, extra-padded (ACH style), compressed
+- **Store numbers:** `#1234`, `1234`, `#01234`, absent
+- **Order IDs:** `*ORDERID` (Amazon style)
+- **POS prefixes:** `SQ *` (Square), `TST*` (Toast), `CLV*` (Clover)
+- **Addresses:** full, partial, zip-smashed-into-city (Apple Card quirk)
+- **Cities:** 36 US cities across multiple states
+- **Sign prefixes:** `[debit]` / `[credit]` with category-appropriate distributions
+- **Abbreviated merchant names:** real bank statement abbreviations
+
+## Design Decisions
+
+- **No "Mortgage" category.** Mortgage transactions are classified by account type, not description. Every transaction on a mortgage account is a mortgage payment by definition.
+- **No "Business" category.** Whether a transaction is a business expense depends on the _account_, not the description.
+- **Transfer vs Income.** Sign prefix is the primary signal. `[credit] VENMO CASHOUT` = Income. `[debit] VENMO PAYMENT TO` = Transfer.
+- **Subscription vs Shopping.** `Amazon.com*ORDERID` = Shopping. `AMAZON WEB SERVICES` = Subscription. `X CORP. PAID FEATURES` = Subscription. `TIKTOK SHOP` = Shopping.
+- **PayPal wrappers across all categories.** The model learns to look past `PreApproved Payment Bill User Payment:` and classify based on the merchant inside.
+- **Balanced classes.** 1,500 samples per category prevents the classifier from defaulting to the most common class.
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("DoDataThings/us-bank-transaction-categories-v2")
+print(ds["train"][0])
+# {'description': '[debit] AMAZON MKTPL*K8R2M5VN7', 'category': 'Shopping'}
+```
+
+## Trained Model
+
+A DistilBERT model fine-tuned on this dataset is available at [DoDataThings/distilbert-us-transaction-classifier-v2](https://huggingface.co/DoDataThings/distilbert-us-transaction-classifier-v2).
+
+## Generator
+
+The synthetic data generator script is open source:
+
+```bash
+node scripts/generate-training-data.js --count 1500
+```
+
+Available at [github.com/wnstnb/foliome](https://github.com/wnstnb/foliome).
+
+## License
+
+MIT