HR26kk/modernbert-emotion-classifier

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

              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

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)