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---
license: openrail
---
# Classifier-Bias-SG Model Card
## Model Details
Classifier-Bias-SG is a proof of concept model designed to classify texts based on their bias levels. The model categorizes texts into 2 classes: "Biased", and "Non-Biased".
## Model Architecture
The model is built upon the distilbert-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 BABE dataset containing news articles from various sources, annotated with one of the 2 bias levels. The dataset contains:
- **Biased**: 1810 articles
- **Non-Biased**: 1810 articles
## Training Procedure
The model was trained using the Adam optimizer for 15 epochs.
## Performance
On our validation set, the model achieved:
- **Accuracy**: 78%
- **F1 Score (Biased)**: 79%
- **F1 Score (Non-Biased)**: 77%
## 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("Social-Media-Fairness/Classifier-Bias-SG")
model = AutoModelForSequenceClassification.from_pretrained("Social-Media-Fairness/Classifier-Bias-SG")
classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
result = classifier("Women are bad driver.")
print(result)
```
Developed by Shardul Ghuge |