---
language: en
tags:
- text-classification
- pytorch
- ModernBERT
- bias
- multi-class-classification
- multi-label-classification
datasets:
- synthetic-biased-corpus
license: mit
metrics:
- accuracy
- f1
- precision
- recall
- matthews_correlation
base_model:
- answerdotai/ModernBERT-large
widget:
- text: Women are bad at math.
library_name: transformers
---

![banner](https://huggingface.co/cirimus/modernbert-large-bias-type-classifier/resolve/main/banner.png)

### Overview

This model was fine-tuned from [ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) on a synthetic dataset of biased statements and questions, generated by Mistal 7B as part of the [GUS-Net paper](https://huggingface.co/papers/2410.08388). 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.

---

### Model Details

- **Base Model**: [ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large)
- **Fine-Tuning Dataset**: Synthetic biased corpus
- **Number of Labels**: 11
- **Problem Type**: Multi-label classification
- **Language**: English
- **License**: [MIT](https://opensource.org/licenses/MIT)
- **Fine-Tuning Framework**: Hugging Face Transformers

---

### Example Usage

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

```python
from transformers import pipeline

# Load the model
classifier = pipeline(
    "text-classification",
    model="cirimus/modernbert-large-bias-type-classifier",
    return_all_scores=True
)

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
```

---

### 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

---

### Dataset

The synthetic dataset consists of biased statements and questions generated by Mistal 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

---

### 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:

| Metric       | Value  |
|--------------|--------|
| Accuracy     | 0.983  |
| Precision    | 0.930  |
| Recall       | 0.914  |
| F1           | 0.921  |
| MCC          | 0.912  |

#### Per-Label Results:

| Label          | Accuracy | Precision | Recall | F1    | MCC   | Support | Threshold |
|----------------|----------|-----------|--------|-------|-------|---------|-----------|
| Racial         | 0.975    | 0.871     | 0.889  | 0.880 | 0.866 | 388     | 0.5       |
| Religious      | 0.994    | 0.962     | 0.970  | 0.966 | 0.962 | 335     | 0.5       |
| Gender         | 0.976    | 0.930     | 0.925  | 0.927 | 0.913 | 615     | 0.5       |
| Age            | 0.990    | 0.964     | 0.931  | 0.947 | 0.941 | 375     | 0.5       |
| Nationality    | 0.972    | 0.924     | 0.881  | 0.902 | 0.886 | 554     | 0.5       |
| Sexuality      | 0.993    | 0.960     | 0.957  | 0.958 | 0.955 | 301     | 0.5       |
| Socioeconomic  | 0.964    | 0.909     | 0.818  | 0.861 | 0.842 | 516     | 0.5       |
| Educational    | 0.982    | 0.873     | 0.933  | 0.902 | 0.893 | 330     | 0.5       |
| Disability     | 0.986    | 0.923     | 0.887  | 0.905 | 0.897 | 283     | 0.5       |
| Political      | 0.988    | 0.958     | 0.938  | 0.948 | 0.941 | 438     | 0.5       |
| Physical       | 0.993    | 0.961     | 0.920  | 0.940 | 0.936 | 238     | 0.5       |

---

### 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

---

### 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.

---

### Environmental Impact

- **Hardware Used**: NVIDIA RTX4090
- **Training Time**: ~2 hours
- **Carbon Emissions**: ~0.08 kg CO2 (calculated via [ML CO2 Impact Calculator](https://mlco2.github.io/impact)).

---

### Citation

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

```bibtex
@inproceedings{JunquedeFortuny2025c,
  title = {Bias Detection with ModernBERT-Large},
  author = {Enric Junqué de Fortuny},
  year = {2025},
  howpublished = {\url{https://huggingface.co/cirimus/modernbert-large-bias-type-classifier}},
}
```