😄 Emotion Classifier — Fine-tuned DistilBERT

This is a DistilBERT-based model fine-tuned for emotion classification on the dair-ai/emotion dataset.

It predicts one of 6 emotion labels for a given text:

sadness
joy
love
anger
fear
surprise

🧠 Model Details

Base model: distilbert-base-uncased
Fine-tuned on: dair-ai/emotion
Task: Multi-class single-label text classification
Framework: 🤗 Transformers

📊 Dataset

Dataset: dair-ai/emotion

Split	Samples
Train	16,000
Validation	2,000
Test	2,000
Total	20,000

Classes (labels):

0 → sadness
1 → joy
2 → love
3 → anger
4 → fear
5 → surprise

🚀 Usage

You can use the model directly with the 🤗 Transformers pipeline:

from transformers import pipeline

classifier = pipeline("text-classification", model="shivvamm/emotion-distilbert-finetuned", top_k=None)

text = "I feel hopeful and excited about the future."
results = classifier(text)
print(results)

Example output:

[{'label': 'joy', 'score': 0.9876}]

✅ Intended Use

Social media and product review emotion analysis
Sentiment & psychological tone detection
Personal journaling or well-being apps
Academic research in NLP and affective computing

⚠️ Limitations

Handles English language only
Classifies only one dominant emotion per text
May misclassify mixed emotions, sarcasm, or idioms

📄 Citation

@misc{shivvamm2025emotion,
  title={Emotion Classification using DistilBERT},
  author={Shivvamm},
  year={2025},
  howpublished={\url{https://huggingface.co/shivvamm/emotion-distilbert-finetuned}},
  note={Fine-tuned on the dair-ai/emotion dataset}
}

👤 Author

Shivvamm
Model: shivvamm/emotion-distilbert-finetuned
License: MIT

💡 Fine-tuned with 🤗 Hugging Face Transformers and Accelerate

shivvamm
/

emotion-distilbert-finetuned