Class	Precision	Recall	F1-score	Support
0 (sadness)	0.99	0.96	0.98	24,121
1 (joy)	0.93	0.99	0.96	28,220
2 (love)	1.00	0.71	0.83	6,824
3 (anger)	0.95	0.94	0.95	11,448
4 (fear)	0.90	0.91	0.91	9,574
5 (surprise)	0.74	0.99	0.85	3,038

Overall Performance:

Accuracy: 94%
Macro F1: 0.91
Weighted F1: 0.94

🧑‍💻 Usage

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

model_name = "YamenRM/distilbert-emotion-classifier"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

nlp = pipeline("text-classification", model=model, tokenizer=tokenizer)

print(nlp("I feel so happy and excited today!"))
# [{'label': 'joy', 'score': 0.98}]

YamenRM/Emotion_model

DistilBERT Emotion Classifier 🎭

📊 Evaluation

🧑‍💻 Usage

YamenRM/
Emotion_model