Sadman4701/Deberta-v3-base-for-apricity

Emotion DeBERTa – 5-Class Emotion Classifier

Model Description

Emotion DeBERTa – 5-Class Emotion Classifier

Model Description

This model is a fine-tuned version of DeBERTa-v3-base for emotion classification. It predicts one of five emotional states from input text:

• anger
• fear
• joy
• sadness
• surprise

The model was trained as part of a university capstone project focused on building an emotion-aware mental healthcare companion.

Base Model

• microsoft/deberta-v3-base

Training Details

• Task: Text-based emotion classification
• Architecture: DeBERTa encoder with a custom classification head
• Number of labels: 5
• Training method: Supervised fine-tuning
• Output: Single-label emotion prediction

The model was originally trained using a custom PyTorch class and later converted into Hugging Face format for deployment and reproducibility.

Intended Use

This model is designed for:

• Emotion-aware chat applications
• Mental health companion systems
• Sentiment and emotional analysis in academic projects
• Research and educational purposes

It is not intended for clinical diagnosis or professional mental health decisions.

Limitations

• Trained on a limited dataset
• May not generalize well to:
  • Slang-heavy text
  • Code-mixed or multilingual inputs
  • Highly sarcastic or ambiguous sentences
• Predictions should be treated as probabilistic, not factual

Example Usage

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="Sadman4701/Deberta-v3-base-for-apricity"
)

classifier("I feel empty and tired all the time.")