UnBIAS Classification Model Card
Model Description
UnBIAS is a state-of-the-art model designed to classify texts based on their bias levels. The model categorizes texts into three classes: "Highly Biased", "Slightly Biased", and "Neutral".
Model Architecture
The model is built upon the bert-base-uncased architecture and has been fine-tuned on a custom dataset for the specific task of bias detection.
Dataset
The model was trained on a dataset containing news articles from various sources, annotated with one of the three bias levels. The dataset contains:
- Highly Biased: 4000 articles
- Slightly Biased: 4000 articles
- Neutral: 4000 articles
Training Procedure
The model was trained using the Adam optimizer for 10 epochs.
Performance
On our validation set, the model achieved:
- Accuracy: 95%
- F1 Score (Highly Biased): 89%
- F1 Score (Slightly Biased): 85%
- F1 Score (Neutral): 82%
(Replace placeholders with actual performance metrics.)
How to Use
To use this model for text classification, use the following code:
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("newsmediabias/UnBIAS-classifier")
model = AutoModelForSequenceClassification.from_pretrained("newsmediabias/UnBIAS-classifier")
classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
result = classifier("Women are bad driver.")
print(result)
Developed by Shaina Raza