Modelpublic

criticalEdgeK/distilbert-emotion-goemotions-8class

sourceHugging Faceapache-2.0updated 4mo agoView on Hugging Face

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Model Card

language: en license: apache-2.0 tags:

—emotion-detection
—text-classification
—distilbert
—goemotions
—multi-label-classification datasets:
—go_emotions metrics:
—f1 ---

Emotion Classifier — DistilBERT fine-tuned on GoEmotions (8 classes)

Fine-tuned for the Privacy-Preserving Emotional AI Agent project (B.Tech Software Engineering, Delhi Technological University).

Model Details

—Base model: distilbert-base-uncased
—Task: Multi-label emotion classification
—Dataset: GoEmotions (43,410 training samples after preprocessing)
—Classes: joy, sadness, anger, fear, surprise, trust, disgust, neutral

Performance

Metric	Score
Accuracy	87.2%
Precision	86.1%
Recall	84.9%
Macro F1	85.5%
Latency CPU	~148ms

Label Schema

27 GoEmotions fine-grained labels consolidated into 8 coarse categories based on Plutchik's wheel of emotions.

Usage

python

from src.emotion.detector import EmotionDetector
detector = EmotionDetector()
result = detector.detect("I feel really happy today!")
print(result.primary_emotion)  # "joy"