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)
Validation Accuracy: [Your Results]
Test Accuracy: [Your Results]
Training Time: [Your Time]

Hyperparameters

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

Usage

‘‘‘python 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) ‘‘‘

Limitations

[Discuss any limitations you observed...]