mrm8488/squeezebert-finetuned-squadv1

SqueezeBERT is a pretrained model for the English language using a masked language modeling (MLM) and Sentence Order Prediction (SOP) objective . The model architecture is similar to BERT-base, but with the pointwise fully-connected layers replaced with grouped convolutions . The authors found that SqueezBERT was 4.3x faster than bert-base-uncased on a Google Pixel 3 smartphone . The question-answering dataset is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or,