cirimus/modernbert-large-bias-type-classifier

⚠️ This model has been superseded by cirimus/ultrabert-380M-bias-type-classifier.
💡 It is part of the collection Frontier BERT Workhorses.

Overview

This model was fine-tuned from ModernBERT-large on a synthetic dataset of biased statements and questions, generated by Mistral 7B as part of the GUS-Net paper. The model is designed to identify and classify text bias into multiple categories, including racial, religious, gender, age, and other biases, making it a valuable tool for bias detection and mitigation in natural language processing tasks.

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Model Details

• Base Model: ModernBERT-large
• Fine-Tuning Dataset: Synthetic biased corpus
• Number of Labels: 11
• Problem Type: Multi-label classification
• Language: English
• License: MIT
• Fine-Tuning Framework: Hugging Face Transformers

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Example Usage

Here’s how to use the model with Hugging Face Transformers:

from transformers import pipeline

# Load the model
classifier = pipeline(
    "text-classification",
    model="cirimus/modernbert-large-bias-type-classifier",
    top_k=None
)

text = "Tall people are so clumsy."
predictions = classifier(text)

# Print predictions
for pred in sorted(predictions[0], key=lambda x: x['score'], reverse=True)[:5]:
    print(f"{pred['label']}: {pred['score']:.3f}")

# Output:
# physical: 1.000
# socioeconomic: 0.002
# gender: 0.002
# racial: 0.001
# age: 0.001

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How the Model Was Created

The model was fine-tuned for bias detection using the following hyperparameters:

• Learning Rate: 3e-5
• Batch Size: 16
• Weight Decay: 0.01
• Warmup Steps: 500
• Optimizer: AdamW
• Evaluation Metrics: Precision, Recall, F1 Score (weighted), Accuracy

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Dataset

The synthetic dataset consists of biased statements and questions generated by Mistral 7B as part of the GUS-Net paper. It covers 11 bias categories:

1. Racial
2. Religious
3. Gender
4. Age
5. Nationality
6. Sexuality
7. Socioeconomic
8. Educational
9. Disability
10. Political
11. Physical

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Evaluation Results

The model was evaluated on the synthetic dataset’s test split. The overall metrics using a threshold of 0.5 are as follows:

Macro Averages:

Per-Label Results:

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Intended Use

The model is designed to detect and classify bias in text across 11 categories. It can be used in applications such as:

• Content moderation
• Bias analysis in research
• Ethical AI development

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Limitations and Biases

• Synthetic Nature: The dataset consists of synthetic text, which may not fully represent real-world biases.
• Category Overlap: Certain biases may overlap, leading to challenges in precise classification.
• Domain-Specific Generalization: The model may not generalize well to domains outside the synthetic dataset’s scope.

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Environmental Impact

• Hardware Used: NVIDIA RTX4090
• Training Time: ~2 hours
• Carbon Emissions: ~0.08 kg CO2 (calculated via ML CO2 Impact Calculator).

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Citation

If you use this model, please cite it as follows:

@misc{junque2025emotion,
  title        = {Emotion and Bias Classification with Modern Encoders},
  author       = {Junqu\'e de Fortuny, Enric},
  year         = {2025},
  howpublished = {\url{https://huggingface.co/spaces/cirimus/emotion-detection}}
}