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---
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 |