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:
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
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