finipher/finipher-transaction-classifier

Finipher transaction classifier v1.0.0

Sorts one bank or card transaction line into one of 17 spending categories. It is a fine-tuned all-MiniLM-L6-v2 (BertForSequenceClassification), so it loads with the standard Transformers classes and needs no custom code.

Internal checkpoint name: finance-multitask-minilm-two-head-v2. Finipher's manifest version: minilm-two-head-20261027-v2.

Labels

Education, Entertainment, Fees, Groceries, Healthcare, Income, Insurance, Mortgage, Personal Care, Rent, Restaurants, Shopping, Subscription, Transfer, Transportation, Travel, Utilities.

Input

• One line per transaction, prefixed with its direction: [debit] STARBUCKS #1234 SEATTLE WA or [credit] ACME CORP PAYROLL.
• Maximum length 64 tokens.
• Below 0.50 confidence, don't trust the label.

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

repo = "finipher/finipher-transaction-classifier"
tok = AutoTokenizer.from_pretrained(repo, revision="v1.0.0")
model = AutoModelForSequenceClassification.from_pretrained(repo, revision="v1.0.0").eval()

x = tok(["[debit] NETFLIX.COM 866-579-7172 CA"], truncation=True, max_length=64, return_tensors="pt")
with torch.inference_mode():
    p = torch.softmax(model(**x).logits, dim=-1)[0]
i = int(p.argmax())
print(model.config.id2label[i], float(p[i]))

Results

Measured offline through Finipher's production code:

• 1,290 hand-written everyday vendor lines (Plaid names, card swipes, POS, ACH): 90.7% correct.
• DoDataThings 68k rows, final Finipher category: 98.7%.

Known mistakes

• Walmart paychecks ([credit] WALMART DIR DEP PPD) read as Shopping.
• FasTrak and Circle K card lines read as Groceries.
• Mortgage PRINCIPAL PMT reads as Utilities.

Training data

To be confirmed. This repo stays private until the training data and the rights to it are documented.

Use

The output is a category label, not tax or financial advice.

Files and SHA-256

File	SHA-256
config.json	6b8c456706017e7cbddd8a4826a88746294100336be5aa9e40f7730890f1b72b
model.safetensors	96d95c2520f9b8cee0a3e85e1440f541420c4c043e18a0c4579943e08e91a5a3
tokenizer.json	41b9d12a4c559e4098f30173bc0f76d092c43d7b1f873e7027da45a256f30f87
tokenizer_config.json	1128dd50ff3f913671a9405619c1bd8f9e9ddc28f62257cb94ed70683590b715