What is BERT?
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.
Traditional models vs. BERT
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.
Usage of BERT
There are multiple usages for Bert like:
- Hate speech analysis
- Text classification (Sentiment Analysis)
- Sentence Prediction
- Model training etc.
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).
Architecture
There are currently two variants of BERT that are built on top of a transformer:
- 12 layers - BERT base
- 24 layers - BERT large
Sentiment analysis using BERT
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.
Sentence prediction with Bert
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
For detailed understanding
You can learn more about BERT and its implementation from the official Google blog.
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