NER using RoBERTa Model

This project focuses on implementing Named Entity Recognition (NER) utilizing the RoBERTa model. NER is a crucial task in Natural Language Processing (NLP) that involves identifying and classifying entities such as names of people, organizations, locations, and more within text.

Table of Contents

• Installation
• Usage
• Project Structure
• Contributing
• License

Installation

To set up the project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/SagarMaddela/NER-using-Roberta-Model.git
   cd NER-using-Roberta-Model

2. Create a virtual environment:

   python3 -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

To train and evaluate the NER model:

1. Prepare your dataset:

   • Place your training and evaluation data in the data/ directory. Ensure the data is in the correct format expected by the scripts.

2. Configure the model parameters:

   • Adjust the configuration settings in the config/ directory as needed.

3. Run the training script:

   python main.py

   This script will train the RoBERTa model on your dataset and evaluate its performance.

Project Structure

• config/: Contains configuration files for model parameters and training settings.
• data/: Directory to store training and evaluation datasets.
• scripts/: Includes utility scripts for data preprocessing and other tasks.
• main.py: The main script to train and evaluate the NER model.
• requirements.txt: Lists the Python dependencies required for the project.

Contributing

Contributions are welcome! If you'd like to contribute, please fork the repository and submit a pull request. For major changes, it's advisable to open an issue first to discuss the proposed modifications.

License

This project is licensed under the MIT License.