roberta-large and xlm-roberta-base are both fill-mask models. See each entry for specifics.
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.
XLM-RoBERTa base from Facebook AI, pre-trained on 2.5TB of filtered CommonCrawl text across 100 languages using the RoBERTa training procedure. Enables cross-lingual transfer — models fine-tuned on labeled English data can infer on other languages without parallel annotations. The standard starting point for multilingual classification and token-level tasks.
Pick based on your compute budget and specific task requirements.