semvec/pypi-packages huggingface dataset:
DTIGE 0.1.0
Deceptive Text Identification and Generation Engine (DTIGE)
License: MIT
Requires Python: <3.13,>=3.9

# Deceptive Text Identification and Generation Engine (DTIGE)

[![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]

<!-- Links: -->
[codecov-image]: https://codecov.io/gh/entelecheia/DTIGE/branch/main/graph/badge.svg?token=NWrQzoyGku
[codecov-url]: https://codecov.io/gh/entelecheia/DTIGE
[pypi-image]: https://img.shields.io/pypi/v/DTIGE
[license-image]: https://img.shields.io/github/license/entelecheia/DTIGE
[license-url]: https://github.com/entelecheia/DTIGE/blob/main/LICENSE
[version-image]: https://img.shields.io/github/v/release/entelecheia/DTIGE?sort=semver
[release-date-image]: https://img.shields.io/github/release-date/entelecheia/DTIGE
[release-url]: https://github.com/entelecheia/DTIGE/releases
[jupyter-book-image]: https://jupyterbook.org/en/stable/_images/badge.svg

[repo-url]: https://github.com/entelecheia/DTIGE
[pypi-url]: https://pypi.org/project/DTIGE
[docs-url]: https://dtige.entelecheia.ai
[changelog]: https://github.com/entelecheia/DTIGE/blob/main/CHANGELOG.md
[contributing guidelines]: https://github.com/entelecheia/DTIGE/blob/main/CONTRIBUTING.md
<!-- Links: -->

Deceptive Text Identification and Generation Engine (DTIGE)

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

The Deceptive Text Identification and Generation Engine (DTIGE) is a novel language model framework that aims to mimic deceptive marketing materials through its generator component while its discriminator component assesses and improves its capability to recognize deceptive texts. The ultimate goal is to enhance both the detection of deceptive content and the understanding of deceptive practices in digital marketing, thus aiding in regulatory compliance and content moderation.

## 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].
hyfi-ros 0.1.0
A Python pakckage for building ROS-based robotics applications powered by LLMs.
License: MIT
Requires Python: <3.13,>=3.9

# HyFI-ROS

[![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]

<!-- Links: -->
[codecov-image]: https://codecov.io/gh/entelecheia/hyfi-ros/branch/main/graph/badge.svg?token=qjqf3r3zVp
[codecov-url]: https://codecov.io/gh/entelecheia/hyfi-ros
[pypi-image]: https://img.shields.io/pypi/v/hyfi-ros
[license-image]: https://img.shields.io/github/license/entelecheia/hyfi-ros
[license-url]: https://github.com/entelecheia/hyfi-ros/blob/main/LICENSE
[version-image]: https://img.shields.io/github/v/release/entelecheia/hyfi-ros?sort=semver
[release-date-image]: https://img.shields.io/github/release-date/entelecheia/hyfi-ros
[release-url]: https://github.com/entelecheia/hyfi-ros/releases
[jupyter-book-image]: https://jupyterbook.org/en/stable/_images/badge.svg

[repo-url]: https://github.com/entelecheia/hyfi-ros
[pypi-url]: https://pypi.org/project/hyfi-ros
[docs-url]: https://hyros.entelecheia.ai
[changelog]: https://github.com/entelecheia/hyfi-ros/blob/main/CHANGELOG.md
[contributing guidelines]: https://github.com/entelecheia/hyfi-ros/blob/main/CONTRIBUTING.md
<!-- Links: -->

A Python pakckage for building ROS-based robotics applications powered by LLMs.

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

HyFI-ROS is a Python package that provides a framework for building ROS-based robotics applications powered by large language models (LLMs). It includes tools and utilities for integrating LLMs into ROS-based systems, as well as pre-built components and templates for common robotics tasks.

## 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].
demarc 0.1.0
A Python package for Deceptive Marketing Classification.
License: MIT
Requires Python: <3.13,>=3.9

# Deceptive Marketing Classification

[![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]

<!-- Links: -->
[codecov-image]: https://codecov.io/gh/entelecheia/demarc/branch/main/graph/badge.svg?token=44aJmHU2rm
[codecov-url]: https://codecov.io/gh/entelecheia/demarc
[pypi-image]: https://img.shields.io/pypi/v/demarc
[license-image]: https://img.shields.io/github/license/entelecheia/demarc
[license-url]: https://github.com/entelecheia/demarc/blob/main/LICENSE
[version-image]: https://img.shields.io/github/v/release/entelecheia/demarc?sort=semver
[release-date-image]: https://img.shields.io/github/release-date/entelecheia/demarc
[release-url]: https://github.com/entelecheia/demarc/releases
[jupyter-book-image]: https://jupyterbook.org/en/stable/_images/badge.svg

[repo-url]: https://github.com/entelecheia/demarc
[pypi-url]: https://pypi.org/project/demarc
[docs-url]: https://demarc.entelecheia.ai
[changelog]: https://github.com/entelecheia/demarc/blob/main/CHANGELOG.md
[contributing guidelines]: https://github.com/entelecheia/demarc/blob/main/CONTRIBUTING.md
<!-- Links: -->

A Python package for Deceptive Marketing Classification.

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

This study aims to compare and analyze Naver Map reviews of restaurants that closed within a short period and those that achieved long-term success, in order to identify the characteristics of fake promotional reviews and develop a methodology for detecting them. We will apply Natural Language Processing (NLP) techniques using Large Language Models (LLMs) and representation vectors, and conduct an integrated analysis with rental car stay point data to investigate the relationship with actual business performance.

## 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].