Metadata-Version: 2.1
Name: Text_Labling_EN
Version: 0.0.7
Summary: Auto labeling English text package
Author-email: Hisham Altayieb <20hishamibrahim@gmail.com>
License: MIT License
        
        Copyright (c) [2022] [AR_NLP_TopicModling]
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
License-File: LICENSE
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.7
Requires-Dist: matplotlib
Requires-Dist: nltk
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: transformers
Requires-Dist: wordcloud
Description-Content-Type: text/markdown

# Text_Labling_EN

Text_Labling_EN is a Python package for Auto labeling English text using the Bart model and then exporting visuals to a pptx file

## Installation
```bash

pip install Text_Labling_EN
```

## Usage

```python
from Text_Labling_EN import *


Text_Labling=Text_Labling(r'path','col_name','NewCol','Lem' or 'stem' or 'None')
Text_Labling.cleanData()

Text_Labling.DoLabels([labels])

Text_Labling.doVis(number of top N want to visualize)

```

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

## License
[MIT](https://choosealicense.com/licenses/mit/)


