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yakul259/credit-statement-scraper
credit-statement-scraper is a machine learning model from yakul259. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
Model Name: yakul259/credit-statement-scraper Base Model: distilbert-base-uncased Task: Question Answering (Extractive QA) Framework: 🤗 Transformers Language: English Author: Yakul259 License: MIT
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From the Hugging Face model README
Model Name: yakul259/credit-statement-scraper
Base Model: distilbert-base-uncased
Task: Question Answering (Extractive QA)
Framework: 🤗 Transformers
Language: English
Author: Yakul259
License: MIT
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:
| 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 |
You can load this model directly using the pipeline API from 🤗 Transformers:
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.