ALBERT : Training the Model
Explore the ALBERT model's unique training approach, which replaces BERT's next sentence prediction with sentence order prediction. Understand how ALBERT uses this binary classification task alongside masked language modeling to train efficiently with fewer parameters. Learn how ALBERT outperforms BERT on language benchmarks and how it can be fine-tuned for downstream NLP tasks.
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Similar to BERT, the ALBERT model is pre-trained using the English Wikipedia and Toronto BookCorpus datasets. The BERT model is pre-trained using the masked language modeling (MLM) and next sentence prediction (NSP) tasks. Similarly, the ALBERT model is pre-trained using the MLM task, but instead of using the NSP task, ALBERT uses a new task called sentence order prediction (SOP). But why not use the NSP task?
Sentence order prediction (SOP)
The researchers of ALBERT pointed out that pre-training with the NSP task is not really useful and isn't a difficult task to perform compared to the MLM task. Also, the NSP task combines both topic prediction and coherence ...