This repository serves as a central location for my work and studies in NLP.
Natural Language Processing is a field of artificial intelligence that focuses on the interaction between computers and humans through natural language. You will get a variety of projects that demonstrate different techniques and methodologies used in the field.
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 sentiment analysis to learn more
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 (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 is the task of automatically translating text from one language to another. This project explores different translation models and techniques.
To get started with this repository, follow these steps:
-
Clone the repository:
git clone https://github.com/swalehmwadime/NLP-Beginners-guide.git cd nlp-repository
-
Create a virtual environment:
python -m venv venv source venv/bin/activate # On Windows, use `venv\Scripts\activate`
-
Install the required dependencies:
pip install -r requirements.txt
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
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.
Contributions are welcome! If you have any improvements or new projects to add, please follow these steps:
- Fork the repository.
- Create a new branch (
git checkout -b feature-branch
). - Commit your changes (
git commit -am 'Add new feature'
). - Push to the branch (
git push origin feature-branch
). - Create a new Pull Request.
This repository is licensed under the MIT License. See the LICENSE file for more information.
If you have any questions or suggestions, feel free to make a pull request or submit an issue.