📊 Datasets

This project uses multiple datasets to train, evaluate, and analyze the model. Below is a detailed description of each dataset and its purpose within the project.

1. Old English Corpora

• Name: Old English Corpora (Kielipankki / ORACC 2017-09)
• Source: [Link to the website]
• Description:
  Contains:
  • Corpus of Ancient Mesopotamian Scholarship (CAMS)
  • Digital Corpus of Cuneiform Lexical Texts (DCCLT)
  • Royal Inscriptions of Babylonia Online (RIBO)
  • Royal Inscriptions of the Neo-Assyrian Period (RINAP)
  • State Archives of Assyria Online (SAAO)
• Format: VRT
• Size: 23 MB

2. Modern English Corpus

• Name: BBC News Dataset (Kaggle)
• Source: [Link to the website]
• Description:
  Self updating dataset. It collects RSS Feeds from BBC News using a Kernel: https://www.kaggle.com/gpreda/bbc-news-rss-feeds. The Kernel is run with a fixed frequency and the dataset is updated using the output of the Notebook. BBC News RSS Feeds. The data contains the following columns:
  • title
  • pubDate
  • guid
  • link
  • description
• Format: CSV
• Size: 14 MB

💻 Installation and Setup

Follow these steps in your terminal or command prompt:

1. Clone the Repository Get a local copy of the project files using Git.

   git clone https://github.com/Sahe00/NLPTM_finalproject.git
   cd NLPTM_finalproject

2. Create and Activate a Virtual Environment (Recommended) Use a virtual environment to manage dependencies and avoid conflicts.

   python -m venv venv
   # On Windows (Command Prompt):
   .\venv\Scripts\activate
   # On macOS/Linux:
   source venv/bin/activate

3. Install Dependencies Install all required libraries listed in requirements.txt:

   pip install -r requirements.txt

🏃 Running the Project

The core analysis is performed within the primary Jupyter Notebook.

Option A: Running via Terminal/Browser (Standard)

1. Launch Jupyter Lab/Notebook Start the local server from the project root directory:

   jupyter lab
   # OR
   jupyter notebook

2. Execute the Code A web browser will open. Navigate to and click on project.ipynb. Run the cells sequentially to reproduce the results.

Option B: Running via Visual Studio Code (IDE)

1. Open the Project in VS Code Open the NLPTM_finalproject folder in VS Code.

2. Select the Python Kernel

   • Open project.ipynb.
   • In the top right corner of the notebook interface, select the Python kernel associated with your newly created venv environment.
   • Note: Ensure you have the Python and Jupyter extensions installed in VS Code.

3. Execute the Code Run the cells one by one or select "Run All" within the notebook interface to perform the analysis.

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