linlin336/distilbert-finetuned-emotion

DistilBERT Fine-Tuned for Emotion Detection

Model Description

This model is a fine-tuned version of distilbert-base-uncased on the Emotion dataset for detecting emotions in English Twitter text.

The model classifies text into six basic emotions:

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

Intended Use

• Detecting emotional states in short English text (tweets, messages, comments)
• Social media monitoring and sentiment analysis
• Customer feedback emotion classification

Training Details

Parameter	Value
Base Model	distilbert-base-uncased
Dataset	emotion (16,000 train / 2,000 validation)
Epochs	1
Learning Rate	1e-3
Batch Size	8
Weight Decay	0.01

Evaluation Results

• Accuracy: ~92%
• F1-Score (weighted): ~92%

Usage

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="<your-username>/distilbert-finetuned-emotion"
)

result = classifier("I am feeling great today!")
print(result)
# [{'label': 'joy', 'score': 0.98}]

Limitations

• Trained on Twitter data — may not generalize well to formal text
• Class imbalance: joy and sadness are overrepresented
• surprise and love are the most challenging categories
• Only 1 epoch of fine-tuning — additional training may improve performance

License

Apache 2.0