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Introduction

Zabanshenas is a language detection model based on the RoBERTa architecture. It is designed to identify the most likely language of a given text. The model is capable of recognizing 231 languages and is developed using both PyTorch and TensorFlow frameworks. Zabanshenas is particularly useful for text classification tasks.

Architecture

Zabanshenas employs the RoBERTa architecture, a variant of the BERT model optimized for language understanding. This architecture is particularly effective in capturing the nuances of different languages, enabling the model to perform accurately across a broad spectrum of linguistic inputs.

Training

The model is trained on the wili_2018 dataset, which includes a diverse range of languages. Evaluation metrics such as precision, recall, and F1-score are calculated for each language, demonstrating high performance across various text lengths including paragraphs, sentences, and tokens.

Guide: Running Locally

To run the Zabanshenas model locally, follow these steps:

1. Clone the Repository: Clone the Zabanshenas repository from GitHub.
2. Install Dependencies: Ensure you have Python and the necessary packages installed. Use pip install -r requirements.txt to install dependencies.
3. Download the Model: Load the model using the transformers library from Hugging Face.
4. Run Inference: Use the provided scripts to input text and receive language predictions.

For optimal performance, especially with large datasets, consider using cloud-based GPUs such as AWS EC2 with GPU instances or Google Cloud's AI Platform.

License

The Zabanshenas model is released under the Apache 2.0 License. This license permits use, distribution, and modification of the software, ensuring both open access and broad usability.