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Last updated : Oct 26, 2022
This blip is not on the current edition of the Radar. If it was on one of the last few editions, it is likely that it is still relevant. If the blip is older, it might no longer be relevant and our assessment might be different today. Unfortunately, we simply don't have the bandwidth to continuously review blips from previous editions of the Radar. Understand more
Oct 2022
Trial ?

Since we last talked about BERT (Bidirectional Encoder Representations from Transformers) in the Radar, our teams have successfully used it in a few natural language processing (NLP) projects. In one of our engagements, we observed significant improvements when we switched from the default BERT tokenizer to a domain-trained word-piece tokenizer for queries that contain nouns like brand names or dimensions. Although NLP has several new transformer models, BERT is well understood with good documentation and a vibrant community, and we continue to find it effective in an enterprise NLP context.

Nov 2019
Assess ?

BERT stands for Bidirectional Encoder Representations from Transformers; it's a new method of pretraining language representations which was published by researchers at Google in October 2018. BERT has significantly altered the natural language processing (NLP) landscape by obtaining state-of-the-art results on a wide array of NLP tasks. Based on Transformer architecture, it learns from both the left and right side of a token's context during training. Google has also released pretrained general-purpose BERT models that have been trained on a large corpus of unlabelled text including Wikipedia. Developers can use and fine-tune these pre-trained models on their task-specific data and achieve great results. We talked about transfer learning for NLP in our April 2019 edition of the Radar; BERT and its successors continue to make transfer learning for NLP a very exciting field with significant reduction in effort for users dealing with text classification.

Published : Nov 20, 2019

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