A fine-tuned multi-label emotion classification model based on microsoft/deberta-v3-small and trained on the GoEmotions dataset. This model is designed to power emotional tagging for personal journaling and mental wellness applications like MindMap.

🚀 Model Details

Base Model: DeBERTa v3 small
Task: Multi-label emotion classification
Dataset: GoEmotions (27 emotions + neutral)
Activation: Sigmoid
Loss: BCEWithLogitsLoss
Output: Probability scores for each of the 28 emotion labels

🏷️ Supported Emotions (28 classes):

admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire, disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief, joy, love, nervousness, optimism, pride, realization, relief, remorse, sadness, surprise

📊 Evaluation

This model was evaluated using:

Metrics: F1-score (micro/macro), Precision, Recall

Validation split: 90/10 on the simplified GoEmotions dataset

Threshold: 0.05 for emotion label activation

🧠 Use Case

Originally used for MindMap, a digital journaling app that helps users track and reflect on their emotional well-being. The model enables emotion-aware feedback and visualizations, offering therapeutic insight to users based on their writing.

📦 Model Files

model.safetensors: Model weights

config.json: Model configuration

tokenizer.json, tokenizer_config.json: Tokenizer details

special_tokens_map.json, vocab.json: Tokenizer vocabulary

📚 Citation / Credit

Base model: Microsoft DeBERTa v3-small

Dataset: GoEmotions by Google Research

🛠 Maintained by @wncelrcn

wncelrcn/mindmap-deBERTa-small-goemotions-v2

🧠 MindMap Emotion Classifier v2 - Small Version (deBERTa-v3-small + GoEmotions)

Note: This model is part of the experiment to find the best-performing emotion classification model for our digital journaling web application called MindMap.