metadata

language: en
tags:
  - emotion-classification
  - text-classification
  - distilbert
datasets:
  - dair-ai/emotion
metrics:
  - accuracy

Emotion Classification Model

Model Description

This model fine-tunes DistilBERT for a multi-class emotion classification task. The dataset that is used is dair-ai/emotion containing six emotion classes: sadness, joy, love, anger, fear and suprise

Training and Evaluation

Training Dataset: dair-ai/emotion (16,000 examples)
Validation Dataset: dair-ai/emotion (2,000 examples)
Test Dataset: dair-ai/emotion (2,000 examples)
Validation Accuracy: 96.6 %
Test Accuracy: 93.2 %
Training Time: 18,078.18s (5h 1m 18s)

Hyperparameters

Learning Rate: 5e-5
Batch Size: 16
Epochs: 4
Weight Decay: 0.01

Usage

from transformers import pipeline
classifier = pipeline("text-classification",
model="your-username/emotion-classification-model")
text = "I’m so happy today!"
result = classifier(text)
print(result)
# Output: [{'label': 'LABEL_1', 'score': 0.9996680021286011}] ; which corresponds to 'joy' with a 99.9% accuracy

Limitations

Performance on Non-English Texts: The model works best with English texts. It may not handle other languages or regional dialects.
Bias From Training Data: Reflects patterns from the training data, which may not cover all cases it could see. May lead to unfair predictns
Issues with Sarcasm and Complex Language: Model might misinterpret sarcasm or subtle language, ex, "Great product... not!"
No Confidence Scores: Model does not judge it's own output, so need some form of double check system, ie. yourself

License

The MIT License (MIT)