Emotion RoBERTa Czech6

🚀 Fine-tuned RoBERTa Model for Emotion Classification in Czech

A fine-tuned RoBERTa model specifically designed for emotion classification in Czech, capable of categorizing text into six emotional states.

🚀 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_czech6")

# Example usage
result = classifier("Dnes se cítím velmi šťastný!")
print(result)

✨ Features

• This model is a fine-tuned version of the RoBERTa model, specifically tailored for emotion classification tasks in Czech.
• It can classify textual data into six emotional categories: anger, fear, disgust, sadness, joy, and none of them.
• It can be used in applications such as sentiment analysis, social media monitoring, customer feedback analysis, and similar tasks.

📚 Documentation

Model Description

This model is a fine-tuned version of the RoBERTa model, specifically tailored for emotion classification tasks in Czech. 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 in the Czech language. 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: 0.81
• Macro Average Precision: 0.84
• Macro Average Recall: 0.84
• Macro Average F1-Score: 0.84

Class-wise Performance

The model demonstrates strong performance in the fear, disgust, and joy categories, with particularly high precision, recall, and F1 scores. The model performs moderately well in detecting anger and none of them categories, indicating potential areas for improvement.

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.

Training Data

The model was fine-tuned on a custom Czech dataset containing textual samples labeled across six emotional categories. The dataset's distribution was considered during training to ensure balanced performance across classes.

📄 License

This model is released under the MIT license.