This repository contains a model trained on the "Human Emotions Dataset (HES)" sourced from Kaggle. The model has achieved an accuracy of 78% in detecting human emotions. Model Description The model is designed to classify human emotions based on the input data provided. It utilizes machine learning techniques to predict emotions from the dataset with a 78% accuracy rate.
The model was trained on the Human Emotions Dataset (HES) available on Kaggle. This dataset contains labeled examples of human emotions, enabling the model to learn and make predictions based on emotional cues in the input data.
Researchers, developers, and enthusiasts interested in emotion detection, sentiment analysis, and machine learning can leverage this model for various applications related to understanding human emotions in text or other forms of data.
The model has demonstrated a 78% accuracy rate in detecting human emotions. While not perfect, it provides a solid foundation for further improvements and applications in emotion recognition tasks. Repository Structure
- model.py: Contains the code for training and evaluating the emotion detection model.
- requirements.txt: Lists the dependencies required to run the model.
- README.md: Provides an overview of the repository and instructions for usage.
We acknowledge the creators of the "Human Emotions Dataset (HES)" on Kaggle for providing the data that enabled the training of this emotion detection model. Feel free to explore, use, and contribute to this repository to enhance emotion detection capabilities. Happy coding! 😊🧡