Zoopa/emotion-classification-model

Emotion Classification Model

Model Description

This model fine-tunes DistilBERT for multi-class emotion classification on the dair-ai/emotion dataset. The model is designed to classify text into one of six emotions: sadness, joy, love, anger, fear, or surprise. It can be used in applications requiring emotional analysis in English text.

Training and Evaluation

• Training Dataset: dair-ai/emotion (16,000 examples)
• Training Time: 8 minutes and 51 seconds
• Training Hyperparameters:
  • Learning Rate: 3e-5
  • Batch Size: 32
  • Epochs: 4
  • Weight Decay: 0.01

Training results

Training Loss	Epoch	Step	Validation Loss	Val. Accuracy
0.5164	1.0	500	0.1887	0.9275
0.1464	2.0	1000	0.1487	0.9345
0.0994	3.0	1500	0.1389	0.94
0.0701	4.0	2000	0.1479	0.94

• Overall Training Loss: 0.2081
• Test Accuracy: 100% accuracy on the 10 examples tested. Confidence scores ranged from 90% to 100%.

Usage

from transformers import pipeline
classifier = pipeline("text-classification", model="Zoopa/emotion-classification-model")

text = "I am so happy today!"
result = classifier(text)
print(result)

Limitations

• The model only supports English.
• The training dataset may contain biases, affecting model predictions on test data.
• Edge Cases like mixed emotions might reduce accuracy.