  Transformer-based language models have been changing the modern Natural
Language Processing (NLP) landscape for high-resource languages such as
English, Chinese, Russian, etc. However, this technology does not yet exist for
any Ghanaian language. In this paper, we introduce the first of such models for
Twi or Akan, the most widely spoken Ghanaian language. The specific
contribution of this research work is the development of several pretrained
transformer language models for the Akuapem and Asante dialects of Twi, paving
the way for advances in application areas such as Named Entity Recognition
(NER), Neural Machine Translation (NMT), Sentiment Analysis (SA) and
Part-of-Speech (POS) tagging. Specifically, we introduce four different
flavours of ABENA -- A BERT model Now in Akan that is fine-tuned on a set of
Akan corpora, and BAKO - BERT with Akan Knowledge only, which is trained from
scratch. We open-source the model through the Hugging Face model hub and
demonstrate its use via a simple sentiment classification example.
