Bias Detection Model

🚀 Bias Detection Model

An English sequence classification model for detecting bias and fairness in sentences.

🚀 Quick Start

This English sequence classification model is trained on the MBAD Dataset to detect bias and fairness in sentences (news articles). It's built on the distilbert - base - uncased model and trained for 30 epochs with a batch size of 16, a learning rate of 5e - 5, and a maximum sequence length of 512.

✨ Features

• Bias Detection: Effectively detects bias and fairness in English sentences, especially in news articles.
• Trained on MBAD Dataset: Utilizes the MBAD Data for training to ensure high - quality performance.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

The easiest way is to load the inference api from huggingface and the second method is through the pipeline object offered by the transformers library.

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("d4data/bias-detection-model")
model = TFAutoModelForSequenceClassification.from_pretrained("d4data/bias-detection-model")

classifier = pipeline('text-classification', model=model, tokenizer=tokenizer) # cuda = 0,1 based on gpu availability
classifier("The irony, of course, is that the exhibit that invites people to throw trash at vacuuming Ivanka Trump lookalike reflects every stereotype feminists claim to stand against, oversexualizing Ivanka’s body and ignoring her hard work.")

📚 Documentation

Model Information

Performance Metrics

🔧 Technical Details

This model was built on top of the distilbert - base - uncased model. It was trained for 30 epochs with a batch size of 16, a learning rate of 5e - 5, and a maximum sequence length of 512.

📄 License

No license information is provided in the original document.

👨‍💻 Author

This model is part of the Research topic "Bias and Fairness in AI" conducted by Deepak John Reji, Shaina Raza. If you use this work (code, model or dataset), please star at:

Bias & Fairness in AI, (2022), GitHub repository, https://github.com/dreji18/Fairness-in-AI

Example Widgets

• Biased example 1: "Nevertheless, Trump and other Republicans have tarred the protests as havens for terrorists intent on destroying property."
• Biased example 2: "Billie Eilish issues apology for mouthing an anti - Asian derogatory term in a resurfaced video."
• Biased example 3: "Christians should make clear that the perpetuation of objectionable vaccines and the lack of alternatives is a kind of coercion."
• Non - Biased example 1: "There have been a protest by a group of people"
• Non - Biased example 2: "While emphasizing he’s not singling out either party, Cohen warned about the danger of normalizing white supremacist ideology."