Demo emotion detection
Retrieve news articles based on a query
Explore BERT model interactions
Deduplicate HuggingFace datasets in seconds
Generative Tasks Evaluation of Arabic LLMs
Upload a PDF or TXT, ask questions about it
"One-minute creation by AI Coding Autonomous Agent MOUSE"
Rerank documents based on a query
Load documents and answer questions from them
Embedding Leaderboard
Compare different tokenizers in char-level and byte-level.
Humanize AI-generated text to sound like it was written by a human
Extract relationships and entities from text
Modernbert Base Go Emotions is a text analysis tool designed to detect emotions within a given text. It leverages advanced AI models to provide insights into the emotional tone of written content, making it useful for understanding user sentiment, analyzing feedback, or categorizing text based on emotional context.
• Emotion Detection: Identifies emotions such as happiness, sadness, anger, fear, and more from text. • Multilingual Support: Works with multiple languages, enabling global sentiment analysis. • Ease of Use: Simple API integration for seamless integration into applications. • High Accuracy: Utilizes state-of-the-art AI models for precise emotion recognition. • Real-Time Analysis: Provides instant results for timely decision-making.
Example usage:
from modernbert_base_go_emotions import EmotionAnalyzer
analyzer = EmotionAnalyzer()
results = analyzer.analyze("I'm so excited about this!")
print(results) # Returns emotion predictions
What is Modernbert Base Go Emotions used for?
It is used to analyze text and detect the emotions expressed within it, helping to understand sentiment and emotional tone.
What languages does it support?
Modernbert Base Go Emotions supports multiple languages, making it versatile for global applications.
How accurate is the emotion detection?
Accuracy depends on the quality of the input text and the complexity of the emotions expressed. Modernbert Base Go Emotions is optimized for high accuracy using advanced AI.