Modelpublic

HR26kk/modernbert-emotion-classifier

sourceHugging Faceapache-2.0updated 21d agoView on Hugging Face

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Model Card

ModernBERT Emotion Classifier

A fine-grained, 6-class emotion classification model built on top of `answerdotai/ModernBERT-base` (149M parameters) and trained on the `dair-ai/emotion` benchmark.

Model Details

—Architecture: ModernBERT Encoder with Sequence Classification Head
—Context Length: 8,192 tokens native support
—Embedding Dimension: 768
—Number of Labels: 6 (sadness, joy, love, anger, fear, surprise)
—Weights Format: SafeTensors

Quantitative Benchmark Results (Test Split)

Evaluation Metric	Score
Accuracy	92.25%
Macro F1	87.02%
Weighted F1	92.15%
Weighted Precision	92.16%
Weighted Recall	92.25%

Classification Breakdown

text

              precision    recall  f1-score   support
     sadness     0.9652    0.9535    0.9593       581
         joy     0.9371    0.9640    0.9504       695
        love     0.8699    0.7987    0.8328       159
       anger     0.9061    0.9127    0.9094       275
        fear     0.8596    0.9018    0.8802       224
    surprise     0.7736    0.6212    0.6891        66

Usage Example

python

from transformers import pipeline

classifier = pipeline("text-classification", model="HR26kk/modernbert-emotion-classifier")

result = classifier("I am genuinely proud of what we accomplished today.")
print(result)