semvec/pypi-packages huggingface dataset:
hyfi-absa 0.5.1
HyFI-ABSA is a Python package developed as a plugin for the Hydra Fast Interface (HyFI).
License: MIT
Requires Python: <3.13,>=3.9

# HyFI-ABSA

[![pypi-image]][pypi-url]
[![version-image]][release-url]
[![release-date-image]][release-url]
[![license-image]][license-url]
[![codecov][codecov-image]][codecov-url]
[![jupyter-book-image]][docs-url]

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[codecov-url]: https://codecov.io/gh/entelecheia/hyfi-absa
[pypi-image]: https://img.shields.io/pypi/v/hyfi-absa
[license-image]: https://img.shields.io/github/license/entelecheia/hyfi-absa
[license-url]: https://github.com/entelecheia/hyfi-absa/blob/main/LICENSE
[version-image]: https://img.shields.io/github/v/release/entelecheia/hyfi-absa?sort=semver
[release-date-image]: https://img.shields.io/github/release-date/entelecheia/hyfi-absa
[release-url]: https://github.com/entelecheia/hyfi-absa/releases
[jupyter-book-image]: https://jupyterbook.org/en/stable/_images/badge.svg

[repo-url]: https://github.com/entelecheia/hyfi-absa
[pypi-url]: https://pypi.org/project/hyfi-absa
[docs-url]: https://hyfi-absa.entelecheia.ai
[changelog]: https://github.com/entelecheia/hyfi-absa/blob/main/CHANGELOG.md
[contributing guidelines]: https://github.com/entelecheia/hyfi-absa/blob/main/CONTRIBUTING.md
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HyFI-ABSA is a Python package developed as a plugin for the Hydra Fast Interface (HyFI).

- Documentation: [https://hyfi-absa.entelecheia.ai][docs-url]
- GitHub: [https://github.com/entelecheia/hyfi-absa][repo-url]
- PyPI: [https://pypi.org/project/hyfi-absa][pypi-url]

The main function of HyFI-ABSA is to facilitate Aspect-Based Sentiment Analysis (ABSA), a specialized form of sentiment analysis that allows you to evaluate specific components or aspects of a given text. It also performs general sentiment analysis tasks. One feature of the HyFI-ABSA package is its capacity to employ generative language models for creating training datasets. This feature not only eliminates the extensive manual labor typically associated with data collection and preparation, but also ensures a more diverse and balanced dataset, which subsequently improves the overall performance and accuracy of sentiment analysis. In addition to these primary features, HyFI-ABSA boasts seamless integration with the existing HyFI architecture, thus allowing you to effortlessly combine it with other HyFI plugins and tools.

## Changelog

See the [CHANGELOG] for more information.

## Contributing

Contributions are welcome! Please see the [contributing guidelines] for more information.

## License

This project is released under the [MIT License][license-url].
ai-forecasting 0.0.10
An AI Forecasting package
License: MIT
Requires Python: >=3.9

AI Integration in White-Label Apps: 
======================================================

Overview
--------
This project aims to seamlessly integrate AI technology into Zeal's white-label applications, exemplified by our ongoing projects with Mohamed and Ramy. Our objective is to establish a scalable and replicable model, ensuring that this AI integration can be efficiently applied across all similar applications within our portfolio.

Key Goals
---------
1. **Scalability and Replicability:** Design a framework that can be easily adapted to various white-label apps, ensuring consistency and efficiency in AI integration.

2. **Standardization of Protocols:** Develop fundamental protocols to facilitate effective communication and collaboration with engineering and product teams across Zeal.

3. **Practical Deployment:** Focus on real-world application and deployment strategies, using this project as a hands-on opportunity to refine our deployment processes.

Contributing
------------
We welcome contributions and ideas from all team members. If you have suggestions or want to be a part of this initiative, please reach out to the project leads.

Stay Updated
------------
For ongoing updates and discussions related to this project, keep an eye on this repository and join our designated communication channels.

Let's make AI integration a seamless and standard part of our white-label app development at Zeal!