Natural Language Processing

Overview

This repository contains various projects, scripts, and resources related to Natural Language Processing (NLP). It serves as a central location for my work and studies in NLP, including data preprocessing, model training, evaluation, and deployment.

Table of Contents

1. Introduction
2. Projects
   • Sentiment Analysis
   • Text Classification
   • Named Entity Recognition
   • Machine Translation
3. Configuration and Installation
4. Usage
5. Data
6. Contributing
7. License
8. Contact

Introduction

Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and humans through natural language. This repository includes a variety of NLP projects that demonstrate different techniques and methodologies used in the field.

Projects

Sentiment Analysis

This project involves analyzing the sentiment of text data, typically classifying text as positive, negative, or neutral. It includes data preprocessing, feature extraction, and the implementation of machine learning models.

• Check out this repository for sentiment analysis to learn more

Text Classification

Text classification involves categorizing text into predefined categories. This project demonstrates various text classification techniques using different models and datasets.

• Run notebook on Google colab

Named Entity Recognition

Named Entity Recognition (NER) is the process of identifying and classifying entities (such as names, dates, and locations) in text. This project includes NER model training and evaluation.

Machine Translation

Machine Translation is the task of automatically translating text from one language to another. This project explores different translation models and techniques.

Configuration and Installation

To get started with this repository, follow these steps:

1. Clone the repository:

   git clone https://github.com/swalehmwadime/NLP-Beginners-guide.git
   cd nlp-repository

2. Create a virtual environment:

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

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

Each project in this repository is contained within its own directory. Navigate to the desired project directory and follow the instructions in the respective README file.

Example:

cd sentiment-analysis
python train.py

Data

The data used in each project is typically not included in the repository due to size constraints. Instructions for obtaining the data are provided in each project's README file. Ensure to place the data in the appropriate directories as specified.

Contributing

Contributions are welcome! If you have any improvements or new projects to add, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Commit your changes (git commit -am 'Add new feature').
4. Push to the branch (git push origin feature-branch).
5. Create a new Pull Request.

License

This repository is licensed under the MIT License. See the LICENSE file for more information.

Contact

If you have any questions or suggestions, feel free to make a pull request or submit an issue.

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