Open Sourcing BERT

Open Sourcing BERT is a revolutionary development in the field of Natural Language Processing (NLP). One of the major challenges in NLP is the scarcity of training data. However, modern deep learning-based NLP models require large amounts of data for optimal performance. To address this, researchers have turned to pre-training techniques using unannotated text from the web. BERT, in particular, is a novel pre-training method that stands out. Unlike previous models, BERT is the first deeply bidirectional, unsupervised language representation. It is trained using only a plain text corpus, in this case, Wikipedia. Contextual models are crucial in NLP, as they generate word representations based on the context of the sentence. BERT takes this a step further by representing each word using both its previous and next context, making it deeply bidirectional. This bidirectionality is achieved by masking out some words in the input and conditioning each word to predict the masked words. Additionally, BERT learns to model relationships between sentences by pre-training on a simple task. The success of BERT is also attributed to the use of Cloud TPUs, which allowed for rapid experimentation and model tweaking. The Transformer model architecture, developed by Google, provided the foundation for BERT's success. BERT has achieved state-of-the-art results on 11 NLP tasks, including the competitive Stanford Question Answering Dataset (SQuAD v1.1). It has surpassed previous benchmarks and even human-level scores in some cases. The released models can be fine-tuned on various NLP tasks in a few hours. Currently, the models are English-only, but the hope is to release multilingual versions in the future. Overall, Open Sourcing BERT has the potential to transform the field of NLP and enable researchers and developers to build more advanced NLP systems.