cirimus

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modernbert-large-go-emotions

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Overview

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.

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Model Details

• Base Model: ModernBERT-large
• Fine-Tuning Dataset: GoEmotions
• Number of Labels: 28
• 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
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:
#        excitement : 0.915
#        joy        : 0.771
#        admiration : 0.021
#        gratitude  : 0.015
#        surprise   : 0.006

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

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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.

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Evaluation Results

The model was evaluated on the test split of the GoEmotions dataset, using a threshold of 0.5 for binarizing predictions. The overall metrics were:

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:

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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

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Limitations and Biases

• 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.

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Environmental Impact

• Hardware Used: NVIDIA RTX4090
• Training Time: <1 hour
• Carbon Emissions: ~0.06 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}}
}