cirimus / modernbert-base-go-emotions

Model Card for YourModelName

Overview

This model was fine-tuned from ModernBERT-base 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 .

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Model Details

• Base Model : ModernBERT-base
• 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-base-go-emotions",
    return_all_scores=True
)

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"{pred['label']}: {pred['score']:.3f}")

# Example output:
# Top 5 emotions detected:
# excitement: 0.937
# joy: 0.915
# desire: 0.022
# love: 0.020
# admiration: 0.017

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
• Warmup Steps : 500
• 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.

Macro Averages (test)

• Accuracy: 0.970
• Precision: 0.665
• Recall: 0.389
• F1: 0.465
• MCC: 0.477

Per-Label Results (test)

Optimal Results :

Using the best threshold for each label based on the training set (tuned on F1).

Macro Averages (test)

• Accuracy: 0.967
• Precision: 0.568
• Recall: 0.531
• F1: 0.541
• MCC: 0.526

Per-Label Results (test)

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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 : ~0.04 kg CO2 (calculated via ML CO2 Impact Calculator ).

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Citation

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

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