Modernbert Base Emotions

🚀 ModernBERT-base for Emotion Classification

This model is fine - tuned from ModernBERT - base for multi - class emotion classification, predicting seven emotional states in English text.

🚀 Quick Start

This model was fine - tuned from [ModernBERT - base](https://huggingface.co/answerdotai/ModernBERT - base) on the [Super Emotion](https://huggingface.co/datasets/cirimus/super - emotion) dataset for multi - class emotion classification. It predicts emotional states in text across seven labels: joy, sadness, anger, fear, love, neutral, surprise.


✨ Features

• Fine - Tuned Model: Based on ModernBERT - base, fine - tuned for emotion classification.
• Multi - Class Classification: Predicts seven different emotional states in text.
• Fast and Accurate: Designed for quick and precise emotion detection.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic 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 - base - emotions",
    top_k=5
)

text = "I can't believe this just happened!"
predictions = classifier(text)

# Print top 3 detected emotions
sorted_preds = sorted(predictions[0], key=lambda x: x['score'], reverse=True)
top_3 = sorted_preds[:3]

print("\nTop 3 emotions detected:")
for pred in top_3:
    print(f"\t{pred['label']:10s} : {pred['score']:.3f}")

# Example output:
# Top 3 emotions detected:
#        SURPRISE   : 0.913
#        SADNESS    : 0.033
#        NEUTRAL    : 0.021

📚 Documentation

Model Details

How the Model Was Created

The model was fine - tuned for 2 epochs using the following hyperparameters:

• Learning Rate: 2e - 5
• Batch Size: 16
• Weight Decay: 0.01
• Warmup Steps: Cosine decay scheduling
• Optimizer: AdamW
• Evaluation Metrics: Precision, Recall, F1 Score (macro), Accuracy

Evaluation Results

As evaluated on the joint test - set:

Intended Use

The model is designed for emotion classification in English - language text, particularly useful for:

• Social media sentiment analysis
• Customer feedback evaluation
• Large scale behavioral or psychological research

The model is designed for fast and accurate emotion detection but struggles with subtle expressions or indirect references to emotions (e.g., "I find myself remembering the little things you say, long after you've said them.")

Limitations and Biases

• Data Bias: The dataset is aggregated from multiple sources and may contain biases in annotation and class distribution.
• Underrepresented Classes: Some emotions have fewer samples, affecting their classification performance.
• Context Dependence: The model classifies individual sentences and may not perform well on multi - sentence contexts.

Environmental Impact

• Hardware Used: NVIDIA RTX 4090
• Training Time: < 1 hour
• Carbon Emissions: ~0.04 kg CO2 (estimated via ML CO2 Impact Calculator)

📄 License

This model is licensed under CC - BY - 4.0.

🔧 Technical Details

The model was fine - tuned for 2 epochs using the following hyperparameters:

• Learning Rate: 2e - 5
• Batch Size: 16
• Weight Decay: 0.01
• Warmup Steps: Cosine decay scheduling
• Optimizer: AdamW
• Evaluation Metrics: Precision, Recall, F1 Score (macro), Accuracy

📖 Citation

If you use this model, please cite:

@inproceedings{JdFE2025b,
  title = {Emotion Classification with ModernBERT},
  author = {Enric Junqu\'e de Fortuny},
  year = {2025},
  howpublished = {\url{https://huggingface.co/your_model_name_here}},
}