---

language: en license: apache-2.0 tags:

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

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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

Label Schema

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

Usage

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