NLP in the age of monolithic large language models is approaching its limits in terms of size and information that can be handled. The trend goes to modularization, a necessary step into the direction of designing smaller sub-networks and components with specialized functionality. In this paper, we present the MAMMOTH toolkit: a framework designed for training massively multilingual modular machine translation systems at scale, initially derived from OpenNMT-py and then adapted to ensure efficient training across computation clusters. We showcase its efficiency across clusters of A100 and V100 NVIDIA GPUs, and discuss our design philosophy and plans for future information. The toolkit is publicly available online.

We have developed two types of embeddings using We evaluated the Biomedical Word Embeddings two different corpora: the Spanish Biomedical Corpora obtaining better results than previous versions showing and the Spanish Clinical Corpora. Since the the implication that with more data, we obtain Spanish Biomedical Corpora is of a much larger magnitude better representations.