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README.md

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+---
+license: apache-2.0
+tags:
+  - text-classification
+  - transformers
+  - onnx
+  - safetensors
+  - transformers.js
+  - distilbert
+  - finance
+  - transactions
+  - english
+language:
+  - en
+datasets:
+  - DoDataThings/us-bank-transaction-categories-v2
+pipeline_tag: text-classification
+---
+
+# DistilBERT US Bank Transaction Classifier v2
+
+**Successor to [v1](https://huggingface.co/DoDataThings/distilbert-us-transaction-classifier).** Sign-aware classification with expanded merchant coverage, PayPal wrapper handling, and a refined 16-category taxonomy.
+
+## What Changed from v1
+
+| | v1 | v2 |
+|---|---|---|
+| **Input format** | Description only | `[debit]` / `[credit]` prefix + full description |
+| **Categories** | 16 (includes Housing) | 16 (Housing split into Rent + Mortgage removed) |
+| **Training data** | 16,000 samples | 24,000 samples |
+| **Merchant coverage** | ~300 merchants | ~500+ merchants |
+| **PayPal awareness** | Limited | Full — PreApproved, Express Checkout, PP*, PAYPAL * |
+| **POS prefix awareness** | SQ* only | SQ*, TST* (Toast), CLV* (Clover) |
+| **Transfer patterns** | Basic | Brokerage sweeps, fintech platforms, wire, cashier's checks, ATM |
+
+### Why v2?
+
+v1 confused Income and Transfer because it only saw the description text. A "VENMO CASHOUT" deposit looks the same regardless of direction. v2 prepends `[credit]` or `[debit]` based on the transaction sign (after normalization to cardholder perspective), giving the model a strong directional signal.
+
+Mortgage was removed as a model category because mortgage account transactions are better classified by account type — every transaction on a mortgage account is a mortgage payment by definition.
+
+## What This Is (and Isn't)
+
+A fine-tuned DistilBERT model for classifying US bank transaction descriptions into 16 spending categories. Designed as a **fallback layer** in a multi-tier classification pipeline — not a standalone classifier.
+
+1. **User rules** — pattern matching catches known merchants (highest accuracy)
+2. **This model** — classifies everything else, with sign awareness
+3. **Bank-provided categories** — fallback when model confidence is low
+4. **User overrides** — manual corrections for edge cases
+
+## Training
+
+```
+Model:       DistilBERT-base-uncased + LoRA (r=32, alpha=64)
+Dataset:     24,000 synthetic samples, 1,500 per category
+Trainable:   1.8M / 68.7M parameters (2.6%)
+Training:    20 epochs, ~8 minutes on consumer GPU
+Best epoch:  17 (99.1% validation accuracy)
+```
+
+### Loss Curve
+
+| Epoch | Train Loss | Val Loss | Train Acc | Val Acc |
+|-------|-----------|----------|-----------|---------|
+| 1     | 2.590     | 1.936    | 20.8%     | 52.6%   |
+| 5     | 0.325     | 0.214    | 90.5%     | 93.9%   |
+| 10    | 0.078     | 0.055    | 97.8%     | 98.3%   |
+| 15    | 0.034     | 0.026    | 99.0%     | 99.2%   |
+| 17    | 0.029     | 0.030    | 99.1%     | 99.1%   |
+
+### Honest Assessment
+
+Validation accuracy (99.1%) is on synthetic data. Real-world performance on ~2,000 transactions:
+
+- **86% of model classifications at 0.90+ confidence**
+- **< 0.4% below 0.50 confidence** (9 of 2,038 transactions)
+- Income and Transfer both at 100% on synthetic validation
+- Shopping remains the weakest category (~93%) due to overlap with Subscription and Groceries
+
+The sign prefix resolved the Income/Transfer confusion from v1. The main remaining challenge is niche merchants the model hasn't seen — diminishing returns territory best handled by user rules.
+
+## Categories (16)
+
+| Category | What it covers |
+|----------|----------------|
+| Restaurants | Fast food, sit-down, coffee shops, food delivery, POS systems (TST*, SQ*, CLV*) |
+| Groceries | Supermarkets, warehouse clubs, farmers markets, convenience stores |
+| Shopping | Retail, online purchases, department stores, pet stores, liquor stores, e-commerce marketplaces |
+| Transportation | Gas, EV charging, rideshare, auto maintenance, parking, tolls, DMV |
+| Entertainment | Movies, events, gaming (Steam, PlayStation), gambling/sportsbooks |
+| Utilities | Electric, internet, phone, water, waste/trash, solar |
+| Subscription | Streaming, SaaS, AI tools, VPNs, social media premium, dating apps, news |
+| Healthcare | Pharmacy, doctor, dentist, telehealth, vision, hospital |
+| Insurance | Auto, home, health, life insurance |
+| Rent | Property management companies, lease payments |
+| Travel | Hotels, airlines, car rental, cruise lines, airport services |
+| Education | Online courses, tutoring, books, tuition, certification |
+| Personal Care | Salon, gym, beauty, spa, barber |
+| Transfer | CC autopay, Zelle/Venmo sends, bank transfers, brokerage sweeps, BNPL, wire transfers, ATM, cashier's checks |
+| Income | Payroll, direct deposit, interest, refunds, government benefits, gig economy payouts |
+| Fees | Bank fees, late fees, service charges, ATM fees |
+
+### Account-Type-Implied Categories (not model-classified)
+
+These categories are determined by the account type, not the model:
+
+| Account Type | Category |
+|---|---|
+| Mortgage | Mortgage |
+| Auto Loan | Transportation |
+| Student Loan | Education |
+| Personal Loan | Transfer |
+| HELOC | Transfer |
+| CD | Income |
+
+## Usage
+
+### Python
+
+```python
+from transformers import pipeline
+
+classifier = pipeline("text-classification", model="DoDataThings/distilbert-us-transaction-classifier-v2")
+
+# v2 requires sign prefix
+result = classifier("[debit] STARBUCKS #1234 SAN FRANCISCO CA")
+print(result)  # [{'label': 'Restaurants', 'score': 0.98}]
+
+# Sign matters for ambiguous transactions
+classifier("[credit] VENMO CASHOUT PPD ID: 12345678")
+# [{'label': 'Income', 'score': 0.95}]
+
+classifier("[debit] VENMO PAYMENT TO JOHN SMITH")
+# [{'label': 'Transfer', 'score': 0.97}]
+```
+
+### JavaScript (Transformers.js)
+
+```javascript
+const { pipeline } = require('@xenova/transformers');
+
+const classifier = await pipeline(
+  'text-classification',
+  'DoDataThings/distilbert-us-transaction-classifier-v2'
+);
+
+const result = await classifier('[debit] STARBUCKS #1234');
+// [{ label: 'Restaurants', score: 0.98 }]
+```
+
+An ONNX export is included in the `onnx/` subdirectory.
+
+### Sign Prefix Convention
+
+Prepend `[credit]` or `[debit]` based on the **normalized** transaction amount (cardholder perspective):
+- `[debit]` — money left the account (purchases, payments out, fees)
+- `[credit]` — money entered the account (income, refunds, payments received)
+
+If your data uses issuer perspective (e.g., Apple Card where purchases are positive), normalize the sign first, then apply the prefix.
+
+## Training Data
+
+The synthetic dataset is published at [`DoDataThings/us-bank-transaction-categories-v2`](https://huggingface.co/datasets/DoDataThings/us-bank-transaction-categories-v2). The generator script is open source — you can extend the merchant pools, add format templates, or increase sample counts.
+
+## Limitations
+
+- **US bank formats only** — Trained on Chase, Apple Card, PayPal, Capital One, and US Bank statement patterns
+- **Synthetic training data** — May miss patterns from banks not represented
+- **Shopping is the weakest category** (~93%) due to overlap with Subscription and Groceries
+- **Niche merchants** may classify with low confidence — use merchant rules for known edge cases
+- **Sign prefix required** — The model expects `[debit]` or `[credit]` prefix. Passing raw descriptions without the prefix will degrade accuracy.
+- **Not a standalone solution** — Best results come from combining with merchant rules and account-type-implied classifications
+
+## License
+
+Apache 2.0

config.json

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+{
+  "_name_or_path": "data/models/foliome-classifier-v2\\",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
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+  "dim": 768,
+  "dropout": 0.1,
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+  "id2label": {
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+    "4": "Healthcare",
+    "5": "Income",
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+    "7": "Personal Care",
+    "8": "Rent",
+    "9": "Restaurants",
+    "10": "Shopping",
+    "11": "Subscription",
+    "12": "Transfer",
+    "13": "Transportation",
+    "14": "Travel",
+    "15": "Utilities"
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+    "Personal Care": 7,
+    "Rent": 8,
+    "Restaurants": 9,
+    "Shopping": 10,
+    "Subscription": 11,
+    "Transfer": 12,
+    "Transportation": 13,
+    "Travel": 14,
+    "Utilities": 15
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "problem_type": "single_label_classification",
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.49.0",
+  "vocab_size": 30522
+}

label_mapping.json

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+{
+  "id2label": {
+    "0": "Education",
+    "1": "Entertainment",
+    "2": "Fees",
+    "3": "Groceries",
+    "4": "Healthcare",
+    "5": "Income",
+    "6": "Insurance",
+    "7": "Personal Care",
+    "8": "Rent",
+    "9": "Restaurants",
+    "10": "Shopping",
+    "11": "Subscription",
+    "12": "Transfer",
+    "13": "Transportation",
+    "14": "Travel",
+    "15": "Utilities"
+  },
+  "label2id": {
+    "Education": 0,
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+    "Transfer": 12,
+    "Transportation": 13,
+    "Travel": 14,
+    "Utilities": 15
+  }
+}

onnx/model_quantized.onnx

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special_tokens_map.json

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tokenizer_config.json

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+}

training-report.json

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+{
+  "model": "distilbert-base-uncased + LoRA (r=32, alpha=64)",
+  "dataset": "24000 synthetic transactions, 16 categories, 1500/category",
+  "split": "20400 train / 3600 val (85/15)",
+  "epochs": 20,
+  "best_epoch": 18,
+  "best_val_accuracy": 0.9908,
+  "total_training_time_s": 458.7,
+  "device": "cuda",
+  "trainable_params": 1782544,
+  "total_params": 68748320,
+  "categories": [
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+    "Entertainment",
+    "Fees",
+    "Groceries",
+    "Healthcare",
+    "Income",
+    "Insurance",
+    "Personal Care",
+    "Rent",
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+    "Shopping",
+    "Subscription",
+    "Transfer",
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+        "Restaurants": 3,
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+    }
+  }
+}