bert-base-uncased and roberta-base are both fill-mask models. See each entry for specifics.
Google's original BERT base model in uncased form, pre-trained on BookCorpus and English Wikipedia via masked language modeling. Tokens are lowercased before processing, making it insensitive to capitalization. It remains a standard fine-tuning base for classification, NER, and extractive QA, though newer encoders outperform it on most benchmarks.
RoBERTa base from Facebook AI, trained with the same architecture as BERT base but significantly more data, longer training schedules, larger batch sizes, and dynamic masking. Pre-trained on BookCorpus, Wikipedia, CC-News, OpenWebText, and Stories — substantially more data than the original BERT. MIT licensed with multi-framework support.
Pick based on your compute budget and specific task requirements.