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--- |
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license: mit |
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datasets: |
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- sweatSmile/FinanceQA |
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language: |
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- en |
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base_model: |
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- google-bert/bert-base-uncased |
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--- |
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# 💳 Credit Card Statement QA Model |
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**Model Name:** `yakul259/credit-statement-scraper` |
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**Base Model:** [`distilbert-base-uncased`](https://huggingface.co/distilbert-base-uncased) |
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**Task:** Question Answering (Extractive QA) |
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**Framework:** 🤗 Transformers |
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**Language:** English |
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**Author:** Yakul259 |
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**License:** MIT |
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--- |
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## 🧠 Model Overview |
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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. |
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It was trained on a custom dataset of anonymized statements to recognize and answer questions like: |
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- “Which bank issued this statement?” |
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- “What is the billing cycle?” |
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- “What is the payment due date?” |
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- “What are the last 4 digits of the card?” |
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- “What is the total amount due?” |
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--- |
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## 🏗️ Architecture |
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| Property | Value | |
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|-----------|--------| |
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| Model Type | DistilBERT | |
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| Architecture | DistilBertForQuestionAnswering | |
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| Hidden Size | 768 | |
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| Layers | 6 | |
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| Attention Heads | 12 | |
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| Max Sequence Length | 512 | |
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| Activation | GELU | |
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| Dropout | 0.1 | |
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| QA Dropout | 0.1 | |
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| Vocabulary Size | 30,522 | |
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| Transformers Version | 4.57.0 | |
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--- |
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## 🧾 Example Usage |
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You can load this model directly using the `pipeline` API from 🤗 Transformers: |
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```python |
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from transformers import pipeline |
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qa_pipeline = pipeline( |
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"question-answering", |
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model="yakul259/credit-statement-scraper", |
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tokenizer="yakul259/credit-statement-scraper" |
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) |
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context = """ |
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Bank: XYZ Bank |
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Credit Card Number: **** **** **** 4321 |
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Billing Period: 01/10/2025 - 31/10/2025 |
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Payment Due Date: 15/11/2025 |
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Total Amount Due: $1,254.67 |
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""" |
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question = "What is the payment due date?" |
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result = qa_pipeline(question=question, context=context) |
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print(result) |
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## License |
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This model is released under the [MIT License](https://opensource.org/licenses/MIT). |
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### Attribution |
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This model was fine-tuned from [DistilBERT base uncased](https://huggingface.co/distilbert-base-uncased), |
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originally released by Hugging Face under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). |
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While the fine-tuned weights are distributed under the MIT License, users should note that the underlying |
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DistilBERT architecture and tokenizer originate from the Apache 2.0–licensed release. |
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