About MAMMOTH

What are the key features and functionalities of the MAMMOTH?

MAMMOTH is a flexible for building modular sequence-to-sequence models. Its key features include modularity with different parameter sharing strategies and adapters, useful tools such as automatic configure generation, and hyper-parameter optimization in scalable multilingual training.

Are there any specific use cases or domains where MAMMOTH is particularly well-suited?

MAMMOTH can be use for training neural machine translation models from scratch using data in any domains. It is particularly well-suited for massively multilingual scenario.

What unique approaches or algorithms does MAMMOTH uses?

MAMMOTH uses the transformer-based sequence-to-sequence model. It adopts modular neural architectures with flexible sharing mechanisms.

What is the development status of MAMMOTH, and are there plans for future enhancements or features?

MAMMOTH is under active development now. We are testing MAMMOTH in massively multilingual setting, and plan to improve the modularity by adding support for different adapter networks, and consider sparse architectures with mixture-of-expert.

How can users contribute to the development of MAMMOTH or report issues with the repository and documentation?

As a open-source project, we love contributions from the community. Users can refer to CONTRIBUTING.md for details.

MAMMOTH and OpenNMT

What the relationship between MAMMOTH and the original OpenNMT project?

MAMMOTH build upon OpenNMT-py - the PyTorch version of OpenNMT project.

How does MAMMOTH leverage the capabilities of OpenNMT while introducing its own innovations?

MAMMOTH chooses PyTorch-based OpenNMT, which allows it to fully leverage the capabilities of OpenNMT and PyTorch ecosystem to enable user friendly development.

What are the modifications that MAMMOTH has made to OpenNMT-py?

MAMMOTH improves OpenNMT-py to enable more flexible modularity.