This project presents a machine learning model trained on a dataset of over 25,000 Persian news articles. The model is designed to classify news articles into one of seven categories: Sport, Science, Culture, Politics, International, Economic, and Social.

The dataset used for this project consists of more than 25,000 Persian news articles. These articles are categorized into seven distinct categories, providing a diverse range of topics for the model to learn from. The categories are as follows:

The model has been trained on this extensive dataset, learning to identify and understand the nuances of each category. This allows it to accurately classify new, unseen Persian news articles into the appropriate category.

To use this model, simply input a Persian news article and the model will output the predicted category. This can be useful for a variety of applications, such as news aggregation services, content recommendation systems, and more.

We plan to continuously improve and update this model, incorporating more data and refining the model's architecture to increase its accuracy and efficiency.

Contributions to this project are welcome. If you have suggestions or improvements, feel free to open an issue or submit a pull request.