emo-Classification

Welcome to the emo-Classification model by AI4free! This model is designed for sentiment analysis, specifically to classify emotions based on textual input. By utilizing cutting-edge natural language processing techniques, this model helps in identifying the emotional tone of sentences, making it ideal for various applications in social media monitoring, customer feedback analysis, and more.

Model Description

The emo-Classification model uses advanced machine learning algorithms to analyze text and classify it into different emotional categories. Whether you're dealing with positive, negative, or neutral sentiments, this model provides accurate and insightful results.

Key Features

• Text Classification: Classifies emotions based on input sentences.
• High Accuracy: Trained on diverse datasets for reliable performance.
• Easy to Use: Simple API interface for seamless integration into your projects.

Usage

To use the emo-Classification model, you can utilize the Hugging Face Transformers library. Below is a simple example to get you started:

from transformers import pipeline

# Initialize the classifier
classifier = pipeline(task="text-classification", model="AI4free/emo-Classification", top_k=1)

# Define the sentence to analyze# Define the sentence to analyze
sentences = ["I am not having a great day"]

# Get the model output
model_outputs = classifier(sentences)

# Print the top emotion (first one in the list)
print(f"emotion: {model_outputs[0][0]['label']} (Score: {model_outputs[0][0]['score']:.4f})")

Example Input and Output

• Input: "I am not having a great day"
• Output: emotion: <emotion_label> (Score: <score>)

Installation

To install the required library, use pip:

pip install transformers

Contribution

Contributions are welcome! If you want to enhance this model or fix any issues, please feel free to open a pull request.

License

This model is licensed under MIT License.

Community

Join our discussions and share your thoughts in the community forum.

Acknowledgments

We would like to thank the Hugging Face community for their support and contributions to the development of this model.