Indo T5 base

Introduction

IndoT5-base is a variant of the Text-to-Text Transfer Transformer (T5) model, specifically pretrained on the Indonesian mC4 dataset with additional filtering. This model is designed for text-to-text generation tasks and requires fine-tuning for specific applications.

Architecture

The IndoT5-base follows the architecture of Google's T5 model, utilizing a transformer-based framework optimized for text-to-text tasks. This model is built using the PyTorch library and is part of the Transformers ecosystem.

Training

The model was pretrained for 1 million steps using the Indonesian portion of the mC4 dataset. The pretraining process was supported by Tensorflow Research Cloud with the use of TPU v3-8s. Although the model is pretrained, it requires further fine-tuning to achieve optimal performance on specific language tasks.

Guide: Running Locally

To run the IndoT5-base model locally, follow these steps:

1. Install Dependencies: Ensure you have Python and PyTorch installed. Use pip install transformers to get the Transformers library.
2. Download the Model: Use the Hugging Face transformers library to load the model with from transformers import T5Tokenizer, T5ForConditionalGeneration.
3. Fine-Tune: Prepare your dataset and fine-tune the model using Trainer or a custom training loop.
4. Inference: Use the model for text generation tasks by passing inputs through the tokenizer and generating outputs.

For optimal performance, it is recommended to use cloud GPUs such as those available on AWS, Google Cloud, or Azure, to handle the computational load.

License

The IndoT5-base model does not specify a particular license in the provided documentation. Users should check the Hugging Face model card or contact the maintainers for specific licensing information.