JagCorrect

This project aims to correct common typing errors using a deep learning model.

Setup Guide for Non-Experienced Users

This guide will walk you through setting up the project environment and running the scripts. We will use conda for managing our project's dependencies, which helps avoid conflicts with other Python projects on your system.

Step 1: Install Miniconda or Anaconda

If you don't have conda installed, you'll need to install either Miniconda (recommended for a minimal installation) or Anaconda. Follow the instructions on their respective websites for your operating system.

Step 2: Create and Activate the Conda Environment

This project requires a specific Python version (3.8) and TensorFlow 2.10.0, which is compatible with CUDA 11.2 and cuDNN 8.1.1 for GPU acceleration.

1. Open your terminal or Anaconda Prompt.
2. Create the tf210_py38 conda environment:

   conda create -n tf210_py38 python=3.8

   When prompted to proceed, type y and press Enter.
3. Activate the newly created environment:

   conda activate tf210_py38

   You should see (tf210_py38) at the beginning of your terminal prompt, indicating that the environment is active.

Step 3: Install Project Dependencies

With your tf210_py38 environment activated, you can now install the necessary libraries for the project.

1. Navigate to the project directory (where this README.md file is located) in your terminal.
2. Install the dependencies using pip:

   pip install -r requirements.txt

   This will install all the required Python packages.

Step 4: Prepare the Dataset

This project uses the GitHub Typo Corpus v1.0.0 for training.

To prepare the data, run the following scripts in order within your activated tf210_py38 environment:

1. Extract data:

   python extract.py

2. Generate dataset:

   python generate_dataset.py

Step 5: Run a Script

To run any of the project's scripts, ensure your tf210_py38 Conda environment is active.

• To start the main AI training script:

  start.bat

  This script is configured to use the Python executable from your tf210_py38 environment.

• To run other Python scripts (e.g., tests):

  python test_ai.py

  Make sure you are in the project's root directory and the tf210_py38 environment is active.

GPU Setup (Optional, for optimal performance)

For optimal performance with TensorFlow, especially for training, ensure you have the following installed on your system:

• CUDA Toolkit: Version 11.2
• cuDNN: Version 8.1.1

TensorFlow 2.10.0 is compatible with these versions. If you have a compatible NVIDIA GPU, installing these will allow TensorFlow to utilize it for faster computations. Refer to the official TensorFlow documentation for detailed installation instructions for GPU support.

Verification (Recommended)

To ensure you are using the correct Python environment:

1. After activating your tf210_py38 environment, run:

   python --version

   It should output Python 3.8.x.
2. To see which Python executable is being used, run:

   where python

   (on Windows) or

   which python

   (on Linux/macOS). The output should point to the python.exe within your tf210_py38 conda environment (e.g., C:\Users\Admin\miniconda3\envs\tf210_py38\python.exe).