emotion-classification-model

This model is a fine-tuned version of distilbert-base-uncased on the dair-ai/emotion dataset. It is designed to classify text into the following emotional categories: sadness (0), joy (1), love (2), anger (3), fear (4), surprise (5).

It achieves the following results:

• Validation Accuracy: 94.25%
• Test Accuracy: 93.2%

Model Description

This model uses the DistilBERT architecture, which is a lighter and faster variant of BERT. It has been fine-tuned specifically for emotion classification, making it suitable for tasks such as sentiment analysis, customer feedback analysis, and user emotion detection.

Key Features

• Efficient and lightweight for deployment.
• High accuracy for emotion detection tasks.
• Pretrained on a diverse dataset and fine-tuned for high specificity to emotions.

Intended Uses & Limitations

Intended Uses

• Emotion analysis in text data.
• Sentiment detection in customer reviews, tweets, or user feedback.
• Psychological or behavioral studies to analyze emotional tone in communications.

Limitations

• May not generalize well to datasets with highly domain-specific language.
• Might struggle with sarcasm, irony, or other nuanced forms of language.
• The model is English-specific and may not perform well on non-English text.

Training and Evaluation Data

Training Dataset

• Dataset: dair-ai/emotion
• Training Set Size: 16,000 examples
• Dataset Description: The dataset contains English sentences labeled with six emotional categories: sadness (0), joy (1), love (2), anger (3), fear (4), surprise (5)

Results

• Training Time: ~226 seconds
• Training Loss: 0.0520
• Validation Accuracy: 94.25%
• Test Accuracy: 93.2%

Training Procedure

Hyperparameters

• Learning Rate: 5e-05
• Batch Size: 16 (train and evaluation)
• Epochs: 3
• Seed: 42
• Optimizer: AdamW (betas=(0.9,0.999), epsilon=1e-08)
• Learning Rate Scheduler: Linear
• Mixed Precision Training: Native AMP

Training and Validation Results

Epoch	Training Loss	Validation Loss	Validation Accuracy
1	0.5383	0.1845	92.90%
2	0.2254	0.1589	93.55%
3	0.0520	0.1485	94.25%

Final Evaluation

• Validation Loss: 0.1485
• Validation Accuracy: 94.25%
• Test Loss: 0.1758
• Test Accuracy: 93.2%

Performance Metrics

• Training Speed: ~212 samples/second
• Evaluation Speed: ~1144 samples/second

Usage Example

from transformers import pipeline

# Load the fine-tuned model
classifier = pipeline("text-classification", model="Panda0116/emotion-classification-model")

# Example usage
text = "I am so happy to see you!"
emotion = classifier(text)
print(emotion)