Model overview
This model, `squeezebert-uncased`, is a pretrained model for the English language using a masked language modeling (MLM) and Sentence Order Prediction (SOP) objective. SqueezeBERT was introduced in this paper. This model is case-insensitive. The model architecture is similar to BERT-base, but with the pointwise fully-connected layers replaced with grouped convolutions. The authors found that SqueezeBERT is 4.3x faster than `bert-base-uncased` on a Google Pixel 3 smartphone. The model is pretrained using the Masked Language Model (MLM) and Sentence Order Prediction (SOP) tasks. (Author's note: If you decide to pretrain your own model, and you prefer to train with MLM only, that should work too.) From the SqueezeBERT paper: > We pretrain SqueezeBERT from scratch (without distillation) using the LAMB optimizer, and we employ the hyperparameters recommended by the LAMB authors: a global batch size of 8192, a learning rate of 2.5e-3, and a warmup proportion of 0.28. Following the LAMB paper's recommendations, we pretrain for 56k steps with a maximum sequence length of 128 and then for 6k steps with a maximum sequence length of 512.
Key features
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Limitations & considerations
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Source prices & cost analysis
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Public benchmarks & run configuration
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Sources & traceability
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