---
license: mit
datasets:
- sweatSmile/FinanceQA
language:
- en
base_model:
- google-bert/bert-base-uncased
---
# 💳 Credit Card Statement QA Model

**Model Name:** `yakul259/credit-statement-scraper`  
**Base Model:** [`distilbert-base-uncased`](https://huggingface.co/distilbert-base-uncased)  
**Task:** Question Answering (Extractive QA)  
**Framework:** 🤗 Transformers  
**Language:** English  
**Author:** Yakul259  
**License:** MIT  

---

## 🧠 Model Overview

This model is a fine-tuned version of **DistilBERT** for **question answering** tasks, specifically designed to extract structured financial details from **credit card statements** in PDF or text format.

It was trained on a custom dataset of anonymized statements to recognize and answer questions like:
- “Which bank issued this statement?”
- “What is the billing cycle?”
- “What is the payment due date?”
- “What are the last 4 digits of the card?”
- “What is the total amount due?”

---

## 🏗️ Architecture

| Property | Value |
|-----------|--------|
| Model Type | DistilBERT |
| Architecture | DistilBertForQuestionAnswering |
| Hidden Size | 768 |
| Layers | 6 |
| Attention Heads | 12 |
| Max Sequence Length | 512 |
| Activation | GELU |
| Dropout | 0.1 |
| QA Dropout | 0.1 |
| Vocabulary Size | 30,522 |
| Transformers Version | 4.57.0 |

---

## 🧾 Example Usage

You can load this model directly using the `pipeline` API from 🤗 Transformers:

```python
from transformers import pipeline

qa_pipeline = pipeline(
    "question-answering",
    model="yakul259/credit-statement-scraper",
    tokenizer="yakul259/credit-statement-scraper"
)

context = """
Bank: XYZ Bank
Credit Card Number: **** **** **** 4321
Billing Period: 01/10/2025 - 31/10/2025
Payment Due Date: 15/11/2025
Total Amount Due: $1,254.67
"""

question = "What is the payment due date?"
result = qa_pipeline(question=question, context=context)
print(result)

## License
This model is released under the [MIT License](https://opensource.org/licenses/MIT).

### Attribution
This model was fine-tuned from [DistilBERT base uncased](https://huggingface.co/distilbert-base-uncased),
originally released by Hugging Face under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).

While the fine-tuned weights are distributed under the MIT License, users should note that the underlying
DistilBERT architecture and tokenizer originate from the Apache 2.0–licensed release.