This project demonstrates tokenization and stemming using NLTK's Porter and Lancaster stemmers, containerized with Docker.

Clone the repository:

git clone https://github.com/learningwithmainsh/Stemming.git
cd Stemming

.
├── Dockerfile
├── requirements.txt
├── stemmer.py
├── README.md

Ensure you have Docker installed. You can verify by running:

docker --version

docker build -t nlp-stemmer .

docker run --rm nlp-stemmer

If you want to check logs from a running container:

docker run -d --name nlp-stemmer nlp-stemmer

docker logs nlp-stemmer

• Dockerfile: Contains instructions to build the Docker image.
• requirements.txt: Lists required Python packages.
• stemmer.py: Python script for tokenization and stemming.
• README.md: This documentation.

The script processes the following sample text:

text = "Running ran easily quickly."

Tokenized Words: ['Running', 'ran', 'easily', 'quickly', '.']

Porter Stemmed Words: ['run', 'ran', 'easili', 'quickli', '.']

Lancaster Stemmed Words: ['run', 'ran', 'easy', 'quick', '.']

To remove all stopped containers and dangling images:

docker system prune -f

Feel free to fork this repo and open a pull request with any improvements!

Manish Pandey

Created by Manish Pandey. Feel free to reach out for any queries or collaborations!

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Happy Coding! 🚀