hjLabs AI Playground
Starting the on-device runtime…
Fill-mask is the pre-training objective behind BERT and the clearest window into what a language model has actually learned: hide a word, and the model ranks candidates with probabilities. It is genuinely useful for probing bias, testing tokenisation, checking whether a domain term is in-vocabulary, and teaching how transformers work. Type a sentence with [MASK] in it and the top predictions appear with their scores, computed on your GPU.
fill mask demobert masked language modelpredict missing word aimlm playgroundbert bias probingtransformer demo online
BERT-family models use [MASK]; RoBERTa-family models use <mask>. The page inserts the correct token for the model you have selected, so you can click rather than remember.
It is a fast way to audit a model before you fine-tune it — whether your jargon survives tokenisation, and what stereotypes the pre-training data baked in.