mt5 small

Introduction

mT5 is a multilingual version of the T5 model, designed by Google, that utilizes a text-to-text format to achieve superior results across multiple languages. It is pre-trained on a diverse dataset, mC4, which covers 101 languages, but requires fine-tuning for specific downstream tasks.

Architecture

mT5 uses the same architecture as the original T5 model, which is a transformer-based text-to-text framework. The model is designed to handle various natural language processing tasks by transforming all inputs into a text format.

Training

mT5 is pre-trained on the mC4 dataset, derived from the Common Crawl corpus and spanning 101 languages. Notably, mT5 is pre-trained without supervised learning, meaning it needs further fine-tuning for specific applications.

Guide: Running Locally

1. Install Dependencies: Ensure that you have Python and PyTorch or TensorFlow installed. Use the Hugging Face Transformers library for easy integration.
2. Clone Repository: Clone the mT5 repository from Hugging Face.
3. Load Model: Use the Transformers library to load and initialize the mT5 model.
4. Fine-tune Model: Prepare your dataset and fine-tune the model for your specific task.
5. Run Inference: Use the fine-tuned model for text-to-text generation tasks.

Cloud GPUs: For faster training and inference, consider using cloud GPUs from providers like AWS, Google Cloud, or Azure.

License

mT5 is released under the Apache 2.0 License, allowing for free use and modification with proper attribution.