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Data Entry Bank

An AI model fine-tuned on DistilBERT to automatically extract key fields from bank and warehouse documents. Built for B2B licensing to banks and warehousing companies.

KindOtherVersionRevision d12d4f8Publisher@ArticWasHereCgrade

Model source

Kind
Other
File size
253 MB
Version
vRevision d12d4f8
Updated
Oct 21, 2026
Publisher
@ArticWasHere
Downloads
7
Source
Hugging Face

Source description

DataEntry AI — Bank & Warehouse Document Extraction

An AI model fine-tuned on DistilBERT to automatically extract key fields from bank and warehouse documents. Built for B2B licensing to banks and warehousing companies.

What it does

Paste any bank or warehouse document and it instantly extracts structured fields — no manual data entry needed.

Bank Documents

Extracts: ACCOUNT_NUMBER · ACCOUNT_HOLDER · DATE · TRANSACTION_TYPE · AMOUNT

Warehouse / Invoice Documents

Extracts: INVOICE_NUMBER · VENDOR · ITEM_NAME · QUANTITY · UNIT_PRICE · TOTAL_AMOUNT


Quick Start

from transformers import pipeline

# Bank documents
bank_model = pipeline("token-classification", model="ArticWasHere/data-entry-bank")
result = bank_model("Account Number: 4821039476 Amount: $4,250.00 Date: 03/15/2024")
print(result)

# Warehouse documents
warehouse_model = pipeline("token-classification", model="ArticWasHere/data-entry-warehouse")
result = warehouse_model("Invoice INV-20481 Vendor: Apex Supply Co Quantity: 12 units")
print(result)

Example Input & Output

Bank Document Input

Account Number: 4821039476
Account Holder: Sarah Johnson
Date: 03/15/2024
Transaction Type: deposit
Amount: $4,250.00

Extracted Output

{
  "ACCOUNT_NUMBER": "4821039476",
  "ACCOUNT_HOLDER": "Sarah Johnson",
  "DATE": "03/15/2024",
  "TRANSACTION_TYPE": "deposit",
  "AMOUNT": "$4,250.00"
}

Model Details

PropertyValue
Base Modeldistilbert-base-uncased
TaskToken Classification (NER)
Training Samples10 labeled documents
Epochs5
LanguagesEnglish

Intended Use

  • Automating data entry for banks and financial institutions
  • Processing warehouse invoices and purchase orders
  • Reducing manual data entry errors in enterprise workflows

Roadmap

  • Add more training data (100+ samples)
  • Support for more document types (KYC, loan applications)
  • Multi-language support
  • REST API endpoint

Built by ArticWasHere · GitHub · Contact for licensing inquiries

Sources

1 source
Hugging FacePrimaryVersion d12d4f818458553be58b8ed61d8517ea221ede12
huggingface.co/ArticWasHere/data-entry-bank/tree/d12d4f818458553be58b8ed61d8517ea221ede12

Verified Oct 21

Model artifacts

6 artifacts

checkpoint-1/model.safetensors

safetensors · 253 MB · SHA-256 57e559723d75…ee69 · Hugging Face

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checkpoint-2/model.safetensors

safetensors · 253 MB · SHA-256 012c0d9c3ebc…8dbe · Hugging Face

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checkpoint-3/model.safetensors

safetensors · 253 MB · SHA-256 d168e3c70352…6cc4 · Hugging Face

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checkpoint-4/model.safetensors

safetensors · 253 MB · SHA-256 0757143f6c27…5fd7 · Hugging Face

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checkpoint-5/model.safetensors

safetensors · 253 MB · SHA-256 738585db3f2c…7c57 · Hugging Face

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model.safetensors

safetensors · 253 MB · SHA-256 738585db3f2c…7c57 · Hugging Face

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Source excerpts

3 excerpts

--- language: en tags: - token-classification - ner - data-entry - banking - warehousing - document-extraction license: mit --- # DataEntry AI — Bank & Warehouse Document Extraction An AI model fine-tuned on **DistilBERT** to automatically extract key fields from bank and warehouse documents. Built for B2B licensing to banks and warehousing companies. ## What it does Paste any bank or warehouse document and it instantly extracts structured fields — no manual data entry needed. ### Bank Documents Extracts: `ACCOUNT_NUMBER` · `ACCOUNT_HOLDER` · `DATE` · `TRANSACTION_TYPE` · `AMOUNT` ### Warehouse / Invoice Documents Extracts: `INVOICE_NUMBER` · `VENDOR` · `ITEM_NAME` · `QUANTITY` · `UNIT_PRICE` · `TOTAL_AMOUNT` --- ## Quick Start ```python from transformers import pipeline # Bank documents bank_model = pipeline("token-classification", model="ArticWasHere/data-entry-bank") result = bank_model("Account Number: 4821039476 Amount: $4,250.00 Date: 03/15/2024") print(result) # Warehouse documents warehouse_model = pipeline("token-classification", model="ArticWasHere/data-entry-warehouse") result = warehouse_model("Invoice INV-20481 Vendor: Apex Supply Co Quantity: 12 units") print(result) ``` --- ## Example Input & Output ### Bank Document Input ``` Account Number: 4821039476 Account Holder: Sarah Johnson Date: 03/15/2024 Transaction Type: deposit Amount: $4,250.00 ``` ### Extracted Output ```json { "ACCOUNT_NUMBER": "4821039476", "ACCOUNT_HOLDER": "Sarah Johnson", "DATE": "03/15/2024", "TRANSACTION_TYPE": "deposit", "AMOUNT": "$4,250.00" } ``` --- ## Model Details | Property | Value | |---|---| | Base Model | distilbert-base-uncased | | Task | Token Classification (NER) | | Training Samples | 10 labeled documents | | Epochs | 5 | | Languages | English | --- ## Intended Use - Automating data entry for banks and financial institutions - Processing warehouse invoices and purchase orders - Reducing manual data entry errors in enterprise workflows --- ## Roadmap - [ ] Add more training data (100+ samples) - [ ] Support for more document types (KYC, loan applications) - [ ] Multi-language support - [ ] REST API endpoint --- Built by **ArticWasHere** · [GitHub](https://github.com) · Contact for licensing inquiries

Source context: 7 downloads · 0 likes · Pipeline token-classification · Repo ArticWasHere/data-entry-bank

Source context: 6 downloads · 0 likes · Pipeline token-classification · Repo ArticWasHere/data-entry-bank