---
language:
- en
tags:
- Text Classification
co2_eq_emissions: 0.319355 Kg
widget:
- text: "Nevertheless, Trump and other Republicans have tarred the protests as havens for terrorists intent on destroying property."
  example_title: "Biased example 1"
- text: "Christians should make clear that the perpetuation of objectionable vaccines and the lack of alternatives is a kind of coercion."
  example_title: "Biased example 2"
- text: "Strategic purchases of U.S. businesses and the placement of Chinese companies on American stock exchanges and indexes have also given the PRC enormous suasion over the avenues of American soft power."
  example_title: "Non-Biased example 1"
- text: "While emphasizing he’s not singling out either party, Cohen warned about the danger of normalizing white supremacist ideology."
  example_title: "Non-Biased example 2"
---

## About the Model
This model is trained to detect bias in a sentence. 

- Dataset : MBAD Data
- Carbon emission 0.319355 Kg

- Train accuracy : 0.7697
- Test accuracy : 0.62
- Train loss : 0.4506
- Test loss : 0.9644

## Author
This model is part of the Research topic "Bias and Fairness in AI" conducted by Shaina Raza, Deepak John Reji, Chen Ding. If you use this work (code, model or dataset), please cite as:
> Bias & Fairness in AI, (2020), GitHub repository, <https://github.com/dreji18/Fairness-in-AI/tree/dev>