Modernbert Large Go Emotions

🚀 ModernBERT-large for GoEmotions Multi-label Classification

This model fine-tunes ModernBERT-large on the GoEmotions dataset for multi-label emotion classification in English text, offering a practical solution for sentiment analysis and related research.

🚀 Quick Start

This model was fine-tuned from ModernBERT-large on the GoEmotions dataset for multi-label classification. It predicts emotional states in text, with a total of 28 possible labels. Each input text can have one or more associated labels, reflecting the multi-label nature of the task.

Try it out here.

✨ Features

• Multi-label Classification: Capable of predicting multiple emotional labels for a single text input.
• English Language Support: Specifically designed for English text emotion classification.
• Fine-tuned on GoEmotions: Utilizes the GoEmotions dataset for fine-tuning, enhancing its performance in emotion prediction.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import pipeline
import torch

# Load the model
classifier = pipeline(
    "text-classification", 
    model="cirimus/modernbert-large-go-emotions",
    top_k=5
)

text = "I am so happy and excited about this opportunity!"
predictions = classifier(text)

# Print top 5 detected emotions
sorted_preds = sorted(predictions[0], key=lambda x: x['score'], reverse=True)
top_5 = sorted_preds[:5]

print("\nTop 5 emotions detected:")
for pred in top_5:
    print(f"\t{pred['label']:10s} : {pred['score']:.3f}")

# Example output:
# Top 5 emotions detected:
#        joy        : 0.784
#        excitement : 0.735
#        admiration : 0.013
#        gratitude  : 0.003
#        amusement  : 0.003

📚 Documentation

Model Details

How the Model Was Created

The model was fine-tuned for 3 epochs using the following hyperparameters:

• Learning Rate: 2e-5
• Batch Size: 16
• Weight Decay: 0.01
• Optimizer: AdamW
• Evaluation Metrics: Precision, Recall, F1 Score (weighted), Accuracy

Dataset

The GoEmotions dataset is a multi-label emotion classification dataset derived from Reddit comments. It contains 58,000 examples with 28 emotion labels (e.g., admiration, amusement, anger, etc.), and it is annotated for multi-label classification.

Evaluation Results

Standard Results

Using the default threshold of 0.5.

Optimal Results

Using the best threshold for each label based on the training set (tuned on F1), tested on the test set:

Intended Use

The model is designed for emotion classification in English-language text, particularly in domains such as:

• Social media sentiment analysis
• Customer feedback evaluation
• Behavioral or psychological research

Limitations and Biases

⚠️ Important Note

• Data Bias: The dataset is based on Reddit comments, which may not generalize well to other domains or cultural contexts.
• Underrepresented Classes: Certain labels like "grief" and "relief" have very few examples, leading to lower performance for those classes.
• Ambiguity: Some training data contain annotation inconsistencies or ambiguities that may impact predictions.

Environmental Impact

📄 License

This model is released under the MIT license.

🔧 Technical Details

The model was fine-tuned for 3 epochs using a learning rate of 2e-5, a batch size of 16, a weight decay of 0.01, and the AdamW optimizer. Evaluation metrics included precision, recall, F1 score (weighted), and accuracy.

📄 Citation

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

@inproceedings{JdFE2025c,
  title = {Emotion Classification with ModernBERT},
  author = {Enric Junqu\'e de Fortuny},
  year = {2025},
  howpublished = {\url{https://huggingface.co/cirimus/modernbert-large-go-emotions}},
}