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license: apache-2.0
tags:
- text-classification
- emotion
- NLP
- DeBERTa
language: en
datasets:
- GoEmotions
metrics:
- Training Accuracy
- Validation Accuracy
- Testing Accuracy
- Precisionm
- Recall
- F-1(micro)
pipeline_tag: text-classification
---
# 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
```python
from transformers import pipeline
classifier = pipeline(
task="text-classification",
model="Sadman4701/Apricity-Final",
return_all_scores=True
)
text = "I feel scared but also strangely hopeful about the future."
outputs = classifier(text)
THRESHOLD = 0.5 #change it according to your preferences
predicted_emotions = [
o["label"] for o in outputs[0] if o["score"] >= THRESHOLD
]
print(predicted_emotions) |