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Masked language modeling (MLM) trains a bidirectional encoder to predict selected input tokens from surrounding context.
Teerera kugwaro iriAI-yakarondedzerwa · ~1 min
BERT uses a token-corruption scheme; later methods alter the masking or prediction objective.
BERT’s pretraining combines masked language modeling with a sentence-pair objective in its original formulation. For MLM, selected tokens are hidden or altered and the model predicts the original tokens using both left and right context. RoBERTa removes the next-sentence prediction objective in its experiments and uses dynamic masking. ELECTRA instead trains a discriminator to classify original versus generator-replaced tokens. These approaches differ in objective, data handling and model design.
BERT masks selected token positions and computes prediction loss for those positions.
RoBERTa uses dynamic masking so selected positions can vary across passes.
ELECTRA trains a discriminator to detect tokens replaced by a generator.
Compare MLM with autoregressive next-token training, which uses a causal context.
Choose a pretraining objective based on the model’s intended use and available compute/data. Report tokenizer, masking policy and evaluation tasks. MLM’s bidirectional context supports representation learning, while autoregressive training naturally matches left-to-right generation; neither objective is universally best.
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Masked language modeling (MLM) trains a bidirectional encoder to predict selected input tokens from surrounding context. BERT uses a token-corruption scheme; later methods alter the masking or prediction objective.
MLM inovanza chikamu chema tokens uye inodzidzisa modhi kuti ivadzore vachishandisa zvese zviri zviviri kuruboshwe uye kurudyi mamiriro.
BERT masks ingangoita gumi neshanu muzana yematokeni ekupinza, chiyero pakati pekupa chiratidzo chakakwana uye kusiya mamiriro akakwana.
Iyo Transformer encoder inoshandisa isiri-causal yekuzvitarisa, saka chiratidzo chega chega chinogona kuona mamwe ese pamativi ese.
Kuti uvandudze kusimba, 10% yezvinzvimbo zvakafukidzwa zvinochinjirwa chiratidzo chisina kujairika uye 10% inosiiwa isina kuchinjwa.
Kurasikirwa kwemuchinjika-entropy kunoshandiswa chete kune akafukidzwa zvinzvimbo, kuenzanisa kufanotaura kune yekutanga tokeni ID.
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InoteveraGaidhi rinotevera
Mutauro Modelling
Mutauro AI