lucky377/emotion_classifier

🎭 Fine-Tuned DistilBERT Emotion Classifier

This repository hosts a fine-tuned DistilBERT model for text emotion classification, trained on the dair-ai/emotion dataset.

It categorizes English text into 6 distinct emotional classes:

• sadness (0)
• joy (1)
• love (2)
• anger (3)
• fear (4)
• surprise (5)

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📌 Model Summary

• Model Architecture: DistilBERT (distilbert-base-uncased)
• Task: Text Sequence Classification (Emotion & Sentiment Analysis)
• Dataset: dair-ai/emotion (20,000 annotated text snippets)
• Language: English (en)
• License: Apache 2.0

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🏷️ Emotion Class Mapping

Label ID	Emotion Tag	Description & Typical Mood Indicators
0	sadness	Feelings of sorrow, heartbreak, grief, disappointment, or loneliness
1	joy	Happiness, excitement, achievement, satisfaction, and delight
2	love	Warmth, affection, appreciation, gratitude, and romantic connection
3	anger	Frustration, annoyance, rage, irritation, and hostility
4	fear	Anxiety, apprehension, nervousness, panic, and terror
5	surprise	Astonishment, awe, unexpected news, and shock

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🚀 Quickstart Usage

1. Using Hugging Face transformers Pipeline

from transformers import pipeline

# Initialize the text classification pipeline
classifier = pipeline(
    "text-classification",
    model="lucky377/emotion_classifier",
    return_all_scores=True
)

text = "I am so happy and excited about this new project!"
predictions = classifier(text)

print(predictions)