🧠 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.
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