bert-base-uncased and roberta-large 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 large, the 355M-parameter version of Facebook AI's strongly trained BERT variant, offering doubled hidden size and additional attention heads over RoBERTa base. It provides stronger NLU accuracy at roughly 4x the inference compute cost of the base variant. Used where task accuracy on complex English language understanding outweighs latency constraints.
Pick based on your compute budget and specific task requirements.