wildmann_german_proposal_0

Emotion Classification Model

Model Description

This model is a fine-tuned version of xlm-roberta-large for multilingual emotion classification tasks. It is trained to classify text into 9 distinct emotion categories:

• Anger (0)
• Fear (1)
• Disgust (2)
• Sadness (3)
• Joy (4)
• Enthusiasm (5)
• Hope (6)
• Pride (7)
• No emotion (8)

The model is designed to analyze input text and predict the corresponding emotion, including the neutral "No emotion" category.

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Model Performance

The model was evaluated on a dataset of 12,022 examples (10% of all data). Below is a summary of the performance metrics across all categories:

Overall Metrics

• Accuracy: 64%
• Macro Average: Precision: 0.58, Recall: 0.39, F1-Score: 0.44
• Weighted Average: Precision: 0.63, Recall: 0.64, F1-Score: 0.61

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Usage

Input

The model expects a text input in UTF-8 format. The input can be a sentence, paragraph, or any textual data.

Output

The model outputs a predicted emotion label from the predefined categories, along with the associated confidence scores.

Example

from transformers import pipeline

classifier = pipeline("text-classification", model="uvegesistvan/wildmann_german_proposal_0")

text = "Ich bin so glücklich über die Fortschritte, die ich gemacht habe!"
prediction = classifier(text)

print(prediction)
# Output: [{'label': 'Joy', 'score': 0.85}]

Training Data

The model was trained on a dataset containing labeled examples for 9 emotions. All training data was on german. The "No emotion" category is the most represented in the dataset.

Limitations and Bias

• The model's performance may vary across languages or cultural contexts not well-represented in the training data.
• The "Fear" and "Enthusiasm" categories have lower recall and F1 scores, indicating potential underperformance in these classes.