|
--- |
|
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 |
