Metadata-Version: 2.1
Name: UnBIASing
Version: 1.0
Summary: A package to detect and debias text using pretrained models
Home-page: https://github.com/your_github_username/UnBIASing
Author: Shaina Raza
Author-email: shaina.raza@utoronto.ca
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
License-File: License.txt

# UnBIASing

UnBIASing is a Python package that classifies, detects, and debiases textual content to promote unbiased information. By leveraging advanced machine learning models, UnBIASing provides users with tools to analyze and correct biases in their texts.

## Features

- **Bias Classification**: Classifies textual content based on its bias using state-of-the-art models.
  
- **Named Entity Recognition (NER)**: Detects named entities within the text that might be indicative of bias.
  
- **Text Debiasing**: Provides unbiased or debiased versions of the input text using an ensemble of advanced models.

## Installation

```bash
pip install UnBIASing
```

## Usage

Here's a basic example of how to use the `BiasPipeline` from UnBIASing:

```python
from unbias import BiasPipeline

pipeline = BiasPipeline()
texts = ["Your sample text goes here."]
classification_results, ner_results, debiaser_results = pipeline.process(texts)

# If you wish to print the results
pipeline.pretty_print(texts, classification_results, ner_results, debiaser_results)

# Convert results to a Pandas DataFrame
df = results_to_dataframe(texts, classification_results, ner_results, debiaser_results)
print(df)
```

## Dependencies

- Transformers
- Torch
- Pandas
- SentencePiece

## License

[MIT](https://opensource.org/licenses/MIT)

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We hope `UnBIASing` proves useful in your journey to make the digital world a more inclusive and unbiased space. For any queries or feedback, feel free to Shaina Raza at shaina.raza@utoronto.ca

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