indobart v2

Introduction
IndoBART-v2 is a state-of-the-art language model for Indonesian, derived from the BART model. It is designed for text-to-text generation and has been trained using the BART training objective.

Architecture
IndoBART-v2 utilizes the architecture of the original BART model, adapted for the Indonesian language. It has been trained with 132 million parameters and utilizes the Indo4B-Plus dataset, comprising 26 GB of text data.

Training
The model was developed by a team of researchers, including Samuel Cahyawijaya, Genta Indra Winata, Bryan Wilie, and others. It was trained on the Indo4B-Plus dataset, which provides a rich corpus of Indonesian text, allowing the model to achieve state-of-the-art performance in natural language generation tasks.

Guide: Running Locally
To run IndoBART-v2 locally, follow these steps:

1. Clone the Repository: Obtain the model files from the Hugging Face repository.
2. Setup Environment: Install necessary dependencies, such as PyTorch and the Transformers library.
3. Load the Model: Use the Transformers library to load IndoBART-v2.
4. Inference: Run text-to-text generation tasks using the model.

For more efficient processing, especially for larger datasets or batch processing, consider using cloud GPUs from providers like AWS, Google Cloud, or Azure.

License
IndoBART-v2 is licensed under the MIT License, allowing for broad usage, modification, and distribution.