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license: apache-2.0
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This project provides a user-friendly interface for performing question-answering tasks using Hugging Face Transformers and Gradio. Users can input a question and a context paragraph, and the model will generate an answer.
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## Getting Started
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### Prerequisites
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Before
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- Hugging Face Transformers
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- Gradio
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- Datasets
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```shell
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pip install transformers gradio datasets
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```
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license: apache-2.0
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---
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## Introduction
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Welcome to the Question Answering project powered by Hugging Face Transformers and Gradio. This project provides a user-friendly interface for performing question-answering tasks, allowing users to input a question and a context paragraph, and the model will generate an answer.
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## Getting Started
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### Prerequisites
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Before you run the application, ensure that you have the following prerequisites installed:
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- **Python 3.6+**: Make sure you have Python 3.6 or higher installed on your system.
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- **Hugging Face Transformers**: Install the Hugging Face Transformers library, which is used for powerful natural language processing tasks.
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```bash
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pip install transformers
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```
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- **Gradio**: Install Gradio, a user-friendly Python library for creating web-based UIs for machine learning models.
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```bash
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pip install gradio
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```
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- **Datasets**: Depending on your specific dataset requirements, make sure to install any additional datasets you might need for training or evaluation.
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```bash
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pip install datasets
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```
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### Configuration Reference
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For detailed configuration options and fine-tuning, please refer to the [Hugging Face Spaces Config Reference](https://huggingface.co/docs/hub/spaces-config-reference).
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## Usage
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Follow these steps to get started with the Question Answering project:
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1. Clone this repository to your local machine.
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```bash
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# Make sure you have git-lfs installed (https://git-lfs.com)
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git lfs install
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git clone https://huggingface.co/spaces/xjlulu/question_answering
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cd question_answering
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# if you want to clone without large files – just their pointers
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# prepend your git clone with the following env var:
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GIT_LFS_SKIP_SMUDGE=1
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```
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2. Install the necessary dependencies as mentioned in the "Prerequisites" section.
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3. Prepare your data if you're using a custom dataset. Ensure that your dataset is in the right format for your model.
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4. Run the application:
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```bash
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python app.py
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```
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You can customize `app.py` to modify the appearance and behavior of the application as needed.
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5. Open your web browser and navigate to [http://localhost:7860](http://localhost:7860) to access the Question Answering interface.
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## Acknowledgments
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This project leverages the power of Hugging Face Transformers for state-of-the-art natural language understanding and Gradio for building an intuitive user interface.
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## License
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This project is open-source and available under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).
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## Contact
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For any questions, feedback, or support, please feel free to reach out at [email protected].
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