README.md

---
title: Question Answering
emoji: ❓
colorFrom: green
colorTo: blue
sdk: gradio
sdk_version: 3.50.2
app_file: app.py
pinned: false
license: apache-2.0
---

## Introduction

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.

## Getting Started

### Prerequisites

Before you run the application, ensure that you have the following prerequisites installed:

- **Python 3.6+**: Make sure you have Python 3.6 or higher installed on your system.

- **Hugging Face Transformers**: Install the Hugging Face Transformers library, which is used for powerful natural language processing tasks.

    ```bash
    pip install transformers
    ```

- **Gradio**: Install Gradio, a user-friendly Python library for creating web-based UIs for machine learning models.

    ```bash
    pip install gradio
    ```

- **Datasets**: Depending on your specific dataset requirements, make sure to install any additional datasets you might need for training or evaluation.

    ```bash
    pip install datasets
    ```

### Configuration Reference

For detailed configuration options and fine-tuning, please refer to the [Hugging Face Spaces Config Reference](https://huggingface.co./docs/hub/spaces-config-reference).

## Usage

Follow these steps to get started with the Question Answering project:

1. Clone this repository to your local machine.

    ```bash
    # Make sure you have git-lfs installed (https://git-lfs.com)
    git lfs install
    git clone https://huggingface.co./spaces/xjlulu/question_answering
    cd question_answering

    # if you want to clone without large files – just their pointers
    # prepend your git clone with the following env var:
    GIT_LFS_SKIP_SMUDGE=1
    ```

2. Install the necessary dependencies as mentioned in the "Prerequisites" section.

3. Prepare your data if you're using a custom dataset. Ensure that your dataset is in the right format for your model.

4. Run the application:

    ```bash
    python app.py
    ```

    You can customize `app.py` to modify the appearance and behavior of the application as needed.

5. Open your web browser and navigate to [http://localhost:7860](http://localhost:7860) to access the Question Answering interface.

## Acknowledgments

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.

## License

This project is open-source and available under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).

## Contact

For any questions, feedback, or support, please feel free to reach out at [email protected].