AYNLP (Advanced Yet Simple NLP) is a modular and lightweight Python toolkit designed to make
Natural Language Processing (NLP) accessible, fun, and powerful. With AYNLP, you can:
- Tokenize text and filter stopwords
- Perform stemming and lemmatization
- Tag parts-of-speech (POS) and extract named entities (NER)
- Analyze sentiment using multiple approaches
The library is **easy to use**, highly **extensible**, and ideal for educational, research, and open-source projects.
📌 **Why Contribute?**
- Help enhance NLP functionalities and add new features
- Improve existing modules like tokenization, POS tagging, or sentiment analysis
- Optimize code performance and expand compatibility with other NLP libraries
- Report issues, suggest improvements, and participate in shaping the roadmap of a growing open-source project
💡 **Get Started**
Visit the GitHub repository: [AYNLP](https://github.com/aijadugar/AYNLP) to fork the project, raise issues, or contribute code. Every contribution helps the community and strengthens the toolkit for everyone.
Whether you are a student, researcher, or developer, AYNLP offers a playground for learning and contributing to real-world NLP projects.
---
## âš¡ How to Use AYNLP
```python
from aynlp import AYNLP
# Initialize the pipeline
aynlp = AYNLP()
# Analyze text
result = aynlp.analyze("The yesterday's festival was awesome!")
# Print the results
print(result)
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"description": "\r\nAYNLP (Advanced Yet Simple NLP) is a modular and lightweight Python toolkit designed to make \r\nNatural Language Processing (NLP) accessible, fun, and powerful. With AYNLP, you can:\r\n\r\n- Tokenize text and filter stopwords\r\n- Perform stemming and lemmatization\r\n- Tag parts-of-speech (POS) and extract named entities (NER)\r\n- Analyze sentiment using multiple approaches\r\n\r\nThe library is **easy to use**, highly **extensible**, and ideal for educational, research, and open-source projects.\r\n\r\n\ud83d\udccc **Why Contribute?**\r\n- Help enhance NLP functionalities and add new features\r\n- Improve existing modules like tokenization, POS tagging, or sentiment analysis\r\n- Optimize code performance and expand compatibility with other NLP libraries\r\n- Report issues, suggest improvements, and participate in shaping the roadmap of a growing open-source project\r\n\r\n\ud83d\udca1 **Get Started**\r\nVisit the GitHub repository: [AYNLP](https://github.com/aijadugar/AYNLP) to fork the project, raise issues, or contribute code. Every contribution helps the community and strengthens the toolkit for everyone.\r\n\r\nWhether you are a student, researcher, or developer, AYNLP offers a playground for learning and contributing to real-world NLP projects.\r\n\r\n---\r\n\r\n## \u26a1 How to Use AYNLP\r\n\r\n```python\r\nfrom aynlp import AYNLP\r\n\r\n# Initialize the pipeline\r\naynlp = AYNLP()\r\n\r\n# Analyze text\r\nresult = aynlp.analyze(\"The yesterday's festival was awesome!\")\r\n\r\n# Print the results\r\nprint(result)\r\n\r\n",
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