---
license: openrail
---
# 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:

```python
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