  Transformer language models have become fundamental components of natural
language processing based pipelines. Although several Transformer models have
been introduced to serve many languages, there is a shortage of models
pre-trained for low-resource and Indigenous languages. In this work, we
introduce IndT5, the first Transformer language model for Indigenous languages.
To train IndT5, we build IndCorpus--a new dataset for ten Indigenous languages
and Spanish. We also present the application of IndT5 to machine translation by
investigating different approaches to translate between Spanish and the
Indigenous languages as part of our contribution to the AmericasNLP 2021 Shared
Task on Open Machine Translation. IndT5 and IndCorpus are publicly available
for research
