Emotion RoBERTa Pooled V4

🚀 Multilingual Fine-tuned RoBERTa Model for Emotion Classification

This multilingual fine-tuned RoBERTa model is designed for emotion classification in Hungarian, Slovak, Polish, and Czech. It can classify text into six emotional categories, offering valuable insights for various text - related applications.

🚀 Quick Start

You can use this model directly with the transformers library from Hugging Face. Below is an example of how to load and use the model:

from transformers import pipeline

# Load the fine-tuned model
classifier = pipeline("text-classification", model="visegradmedia-emotion/Emotion_RoBERTa_pooled_V4")

# Example usage
result = classifier("Nagyon örömtelinek érzem magam ma!")
print(result)

✨ Features

• Multilingual Support: Tailored for emotion classification in Hungarian, Slovak, Polish, and Czech languages.
• Six - Category Classification: Capable of classifying textual data into six emotional categories: anger, fear, disgust, sadness, joy, and none of them.
• Wide Application: Can be used in sentiment analysis, social media monitoring, customer feedback analysis, etc.

📚 Documentation

Model Description

This model is a multilingual fine-tuned version of the RoBERTa model, specifically tailored for emotion classification tasks in Hungarian, Slovak, Polish, and Czech languages. The model was trained to classify textual data into six emotional categories (anger, fear, disgust, sadness, joy, and none of them).

Intended Use

This model is intended for classifying textual data into emotional categories across multiple languages, including Hungarian, Slovak, Polish, and Czech. It can be used in applications such as sentiment analysis, social media monitoring, customer feedback analysis, and similar tasks. The model predicts the dominant emotion in a given text among the six predefined categories.

Metrics

Overall Performance

• Accuracy: 0.84
• Macro Average Precision: 0.86
• Macro Average Recall: 0.86
• Macro Average F1 - Score: 0.86

Class - wise Performance

The model demonstrates strong performance across different emotional categories, with particularly high precision, recall, and F1 scores in the fear, disgust, and joy categories. The model performs moderately well in detecting anger and none of them categories, but still achieves adequate accuracy in these cases.

Limitations

• Context Sensitivity: The model may struggle with recognizing emotions that require deeper contextual understanding.
• Class Imbalance: The model's performance on the "none of them" category suggests that further training with more balanced datasets could improve accuracy.
• Generalization: The model's performance may vary depending on the text's domain, language style, and length, especially across different languages.

📄 License

This project is licensed under the MIT license.