With the growing number of electronic health record data, clinical NLP tasks have be-come increasingly relevant to unlock valu-able information from unstructured clinical text. Although the performance of down-stream NLP tasks, such as named-entity recog-nition (NER), in English corpus has recently improved by contextualised language models, less research is available for clinical texts in low resource languages. Our goal is to assess a deep contextual embedding model for Portuguese, so called BioBERTpt, to sup-port clinical and biomedical NER. We transfer learned information encoded in a multilingual-BERT model to a corpora of clinical narratives and biomedical-scientific papers in Brazilian Portuguese. To evaluate the performance of BioBERTpt, we ran NER experiments on two annotated corpora containing clinical narra-tives and compared the results with existing BERT models. Our in-domain model out-performed the baseline model in F1-score by 2.72%, achieving higher performance in 11 out of 13 assessed entities. We demonstrate that enriching contextual embedding models with domain literature can play an important role in improving performance for specific NLP tasks. The transfer learning process en-hanced the Portuguese biomedical NER model by reducing the necessity of labeled data and the demand for retraining a whole new model.