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README.md

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# Suggestion-system
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This repository contains the suggestion system for Codex projects.
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<a id="readme-top"></a>
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[![Contributors][contributors-shield]][contributors-url]
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[![Forks][forks-shield]][forks-url]
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[![Stargazers][stars-shield]][stars-url]
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[![Issues][issues-shield]][issues-url]
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[![MIT License][license-shield]][license-url]
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[![LinkedIn][linkedin-shield]][linkedin-url]
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<!-- PROJECT LOGO -->
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<br />
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<div align="center">
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<a href="https://github.com/CodexEsto/textpipe">
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<img src="images/logo.png" alt="Logo" width="120" height="140">
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</a>
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<h3 align="center">textpipe</h3>
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<p align="center">
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Modern text processing pipeline for machine learning applications
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<br />
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<br />
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<a href="https://github.com/CodexEsto/textpipe/issues/new?labels=bug&template=bug-report---.md">Report Bug</a>
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&middot;
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<a href="https://github.com/CodexEsto/textpipe/issues/new?labels=enhancement&template=feature-request---.md">Request Feature</a>
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</p>
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</div>
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<!-- TABLE OF CONTENTS -->
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<details>
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<summary>Table of Contents</summary>
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<ol>
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<li>
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<a href="#about-the-project">About The Project</a>
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</li>
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<li>
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<a href="#getting-started">Getting Started</a>
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<ul>
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<li><a href="#installation">Installation</a></li>
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<li><a href="#usage">Usage</a></li>
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</ul>
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</li>
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<li><a href="#contributing">Contributing</a></li>
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<li><a href="#license">License</a></li>
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<li><a href="#contact">Contact</a></li>
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</ol>
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</details>
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<!-- ABOUT THE PROJECT -->
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## About The Project
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textpipe is an end-to-end text processing pipeline designed for modern NLP workflows. It provides:
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- **Configurable Processing**: YAML-based configuration for all processing steps
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- **Modular Architecture**: Clean separation of data loading, cleaning, vectorization, and modeling
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- **Production Ready**: Built-in logging, error handling, and type validation
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- **ML Integration**: Seamless integration with scikit-learn models
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- **Customizable Components**:
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- Multiple text cleaning strategies
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- Configurable tokenization (stemming, stopwords)
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- TF-IDF vectorization with automatic feature management
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- Extensible model architecture
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<p align="right">(<a href="#readme-top">back to top</a>)</p>
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<!-- GETTING STARTED -->
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## Getting Started
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### Installation
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Install the package with pip:
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```bash
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pip install textpipe
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```
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**Update existing installation:**
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```bash
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pip install textpipe --upgrade
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```
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### Usage
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Basic text processing pipeline example:
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```python
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from textpipe import Config, load_csv, SentimentPipeline
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# Initialize configuration
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config = Config.get()
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# Load training data
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texts, labels = load_csv("data/train.csv")
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# Initialize and train pipeline
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pipeline = SentimentPipeline(config)
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pipeline.train(texts, labels)
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# Make predictions
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new_texts = ["I love this product!", "Terrible service..."]
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predictions = pipeline.predict(new_texts)
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print(predictions)
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```
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Advanced configuration example (`config.yml`):
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```yaml
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processing:
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language: english
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remove_stopwords: true
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use_stemming: false
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max_features: 5000
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min_text_length: 3
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logging:
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level: INFO
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```
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<p align="right">(<a href="#readme-top">back to top</a>)</p>
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<!-- CONTRIBUTING -->
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## Contributing
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Contributions are what make the open source community an amazing place to learn, inspire, and create. Any contributions are **greatly appreciated**.
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1. Fork the Project
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2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
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3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
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4. Push to the Branch (`git push origin feature/AmazingFeature`)
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5. Open a Pull Request
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### Top Contributors:
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<a href="https://github.com/CodexEsto/textpipe/graphs/contributors">
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<img src="https://contrib.rocks/image?repo=CodexEsto/textpipe" alt="Project Contributors" />
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</a>
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<p align="right">(<a href="#readme-top">back to top</a>)</p>
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<!-- LICENSE -->
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## License
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Distributed under the MIT License. See `LICENSE` for more information.
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<p align="right">(<a href="#readme-top">back to top</a>)</p>
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<!-- CONTACT -->
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## Contact
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Textpipe Team - [email protected]
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Project Link: [https://github.com/CodexEsto/textpipe](https://github.com/CodexEsto/textpipe)
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<p align="right">(<a href="#readme-top">back to top</a>)</p>
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<!-- ACKNOWLEDGMENTS -->
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## Acknowledgments
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- Scikit-learn community for foundational ML components
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- NLTK team for language processing resources
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- Pandas for data handling capabilities
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- All contributors and open-source maintainers who inspired this work
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<p align="right">(<a href="#readme-top">back to top</a>)</p>
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<!-- MARKDOWN LINKS & IMAGES -->
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[contributors-shield]: https://img.shields.io/github/contributors/CodexEsto/textpipe.svg?style=for-the-badge
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[contributors-url]: https://github.com/CodexEsto/textpipe/graphs/contributors
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[forks-shield]: https://img.shields.io/github/forks/CodexEsto/textpipe.svg?style=for-the-badge
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[forks-url]: https://github.com/CodexEsto/textpipe/network/members
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[stars-shield]: https://img.shields.io/github/stars/CodexEsto/textpipe.svg?style=for-the-badge
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[stars-url]: https://github.com/CodexEsto/textpipe/stargazers
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[issues-shield]: https://img.shields.io/github/issues/CodexEsto/textpipe.svg?style=for-the-badge
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[issues-url]: https://github.com/CodexEsto/textpipe/issues
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[license-shield]: https://img.shields.io/github/license/CodexEsto/textpipe.svg?style=for-the-badge
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[license-url]: https://github.com/CodexEsto/textpipe/blob/master/LICENSE.txt
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[linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=for-the-badge&logo=linkedin&colorB=555
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[linkedin-url]: https://www.linkedin.com/in/your-profile/
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```

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