As we all know, technological advancements and new trends are introduced daily. In 2018, Google published a paper on
Every word in BERT has its own importance and plays a vital role in Natural Language Processing.
Unlike other models that have previously worked
Take a look at the example above, previously, trained models couldn’t actually figure out the main context of the sentence. So, a model would have struggled with the word bank since it has
BERT, on the other hand, checks both sides of the highlighted word and then generates its results accordingly. This is where the concept of transformers plays a major role.
There are multiple usages for Bert like:
Most developers use BERT as it is pretrained on a significantly large corpus of unlabelled text including the entire Wikipedia - which alone includes 2,500 million words, and Book Corpus of 800 million words).
There are currently two variants of BERT that are built on top of a transformer:
To classify any statement, whether politically or in positive or negative remarks, every model has to understand the nature of the words spoken in it.
The BERT framework uses pre-training and fine-tuning to create tasks that include question answering systems, sentiment analysis, and language inference.
The primary objective of any NLP technique is to study human language and learn how it is spoken.
The simplest example for actual understanding of BERT can be
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