How To Calculate The Sample Size Given The Confidence Level & Margin of Error

Proposed a new deep learning model called BERT that achieved state-of-the-art performance on multiple NLP tasks

BERT uses bidirectional training of Transformers, allowing the model to learn contextual relations between words

The pretrained BERT model can be fine-tuned for downstream tasks using additional output layers

BERT large model achieves F1 score of 93.2 on SQuAD question answering dataset, outperforming human performance

BERT advances state-of-the-art on GLUE natural language understanding benchmark by a large margin

Proposes masked language modeling objective to enable bidirectional representation learning in BERT

BERT representations capture rich contextual information about word usage and meaning

BERT has wide applicability for transfer learning on many NLP tasks with minimal task-specific architecture modifications

BERT has become a foundational model for pretraining in NLP, enabling significant progress on benchmark tasks

Code and pretrained models released publicly, enabling wide adoption of BERT in industry and academia

BERT highlights the effectiveness of large-scale bidirectional pretraining for learning universal language representations

Authors foresee BERT driving progress in conversational AI and language understanding

BERT has motivated follow-up work investigating bidirectional pretraining techniques and model architectures

The pretrained BERT model is highly generalizable and applicable to many languages beyond English

BERT is considered a milestone in NLP, opening new research directions in pretrained language models