ALBERT Reviews

ALBERT Description

ALBERT is a Transformer model that can be self-supervised and was trained on large amounts of English data. It does not need manual labelling and instead uses an automated process that generates inputs and labels from the raw text. It is trained with two distinct goals in mind. Masked Language Modeling is the first. This randomly masks 15% words in an input sentence and requires that the model predict them. This technique is different from autoregressive models such as GPT and RNNs in that it allows the model learn bidirectional sentence representations. Sentence Ordering Prediction is the second objective. This involves predicting the order of two consecutive text segments during pretraining.