Modern approaches to Named Entity Recognition (NER) use neural networks (NN) to automatically extract features from text and seamlessly integrate them with sequence taggers in an end-to-end fashion. Word embeddings, which are a side product of pre-trained neural language models (LMs), are key ingredients to boost the performance of NER systems. More recently, contextual word embeddings, which adapt according to the context where the word appears, have proved to be an invaluable resource to improve NER systems. In this work, we assess how different combinations of (shallow) word embeddings and contextual embeddings impact NER for the Portuguese Language. We show a comparative study of 16 different combinations of shallow and contextual embeddings and explore how textual diversity and the size of training corpora used in LMs impact our NER results. We evaluate NER performance using the HAREM corpus. Our best NER system outperforms the state-of-the-art in Portuguese NER by 5.99 in absolute percentage points.