Classifier-Bias-MR Model Card
1. Introduction
This model is designed to classify text data into 'biased' and 'non-biased' categories, utilizing the power of the BERT architecture.
2. Model Details
Architecture: BERT (base, uncased)
Fine-tuning Objective: Binary classification (Bias / Non-Bias)
3. Dataset Details
3.1 Source
The model is trained on the BABE dataset, which contains news articles from various sources.
3.2 Categories
Biased: 1863 articles
Non-Biased: 1810 articles
4. Training Procedure
Optimizer: AdamW
Fine tuned: Optuna
5. Model Performance
On a random split validation dataset:
Accuracy: 81%
F1 Score (Biased): 81%
F1 Score (Non-Biased): 81%
6. Usage
#Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Social-Media-Fairness/Classifier_bias_mizanur_base_model")
#Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Social-Media-Fairness/Classifier_bias_mizanur_base_model")
model = AutoModelForSequenceClassification.from_pretrained("Social-Media-Fairness/Classifier_bias_mizanur_base_model")
7. Potential Applications
Content Moderation: To identify and filter out biased content from platforms.
Research: Assisting in bias-related studies and media analysis.
Education: For teaching and understanding media bias.
8. Caveats and Limitations
The model's training data originates from a specific dataset (BABE) which might not represent all kinds of biases or content.
The performance metrics are based on a random validation split, so the model's performance might vary in real-world applications.
9. Maintenance and Updates
For updates on model performance, improvements, or changes, please refer to the repository Social-Media-Fairness/Classifier_bias_mizanur_base_model.
10. Licensing
This model is licensed under OpenRail.
