This model is a fine-tuned version of microsoft/deberta-v3-large for multilabel emotion classification.
It was trained on a custom dataset where each text sample may contain multiple emotions.

📌 Model Card

Model: FurqonAryadana/deberta-emotion-multilabel-0.5007
Base: DeBERTa-v3-Large
Task: Multilabel Emotion Classification
License: Apache 2.0
Language: English
Threshold Tuning: Applied per-label

📊 Evaluation (Validation Set)

Detailed Classification Report (Threshold Tuned):

Emotion	Precision	Recall	F1-score	Support
amusement	0.61	0.72	0.66	851
anger	0.43	0.58	0.49	999
annoyance	0.34	0.70	0.46	1609
caring	0.41	0.57	0.48	594
confusion	0.49	0.71	0.58	800
disappointment	0.30	0.47	0.37	990
disgust	0.27	0.49	0.35	584
embarrassment	0.33	0.32	0.33	308
excitement	0.45	0.47	0.46	632
fear	0.49	0.54	0.52	321
gratitude	0.83	0.73	0.78	955
joy	0.43	0.58	0.50	876
love	0.64	0.79	0.71	701
sadness	0.40	0.57	0.47	714

F1 Micro: 0.5101
F1 Macro: 0.5107

🧠 Emotions (Label Order)

python

[
  'amusement', 'anger', 'annoyance', 'caring', 'confusion', 'disappointment',
  'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'joy',
  'love', 'sadness'
]

FurqonAryadana/deberta-emotion-multilabel-0.5007

DeBERTa-v3-Large for Multilabel Emotion Classification

📌 Model Card

📊 Evaluation (Validation Set)

🧠 Emotions (Label Order)

FurqonAryadana/
deberta-emotion-multilabel-0.5007