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BERT & It’s Variants in Artificial Intelligence

  • July 12, 2023
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Meet the Author : Mr. Bharani Kumar

Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 17 years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.

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BERT (Bidirectional Encoder Representations from Transformers) is a popular natural language processing (NLP) model developed by Google researchers in 2018. It revolutionized the field of NLP by introducing a new approach to pretraining language models using unsupervised learning.

BERT belongs to the Transformer architecture family, which is based on the attention mechanism and has been highly successful in various NLP tasks. BERT specifically focuses on the task of "masked language modeling" (MLM) and "next sentence prediction" (NSP).

In MLM, BERT is pretrained on a large corpus of text where a certain percentage of words are randomly masked, and the model is trained to predict the original words based on the context. This allows BERT to learn contextualized word representations, capturing the meaning of words based on their surrounding context.

In NSP, BERT is trained to predict whether two sentences appear consecutively in a given text. This helps BERT learn the relationship between sentences and enables it to understand the context and meaning of a sentence in relation to the previous sentence.

BERT has several variants that have been developed to improve upon its architecture and performance. Some notable variants include:


RoBERTa (Robustly Optimized BERT Approach) is a variant of BERT introduced by Facebook AI in 2019. It addresses some of the limitations of BERT by using a larger training corpus, longer training duration, and removing the NSP task. RoBERTa achieves state-of-the-art results on various NLP benchmarks.


ALBERT (A Lite BERT) is a more efficient variant of BERT proposed by Google researchers in 2019. ALBERT reduces the number of parameters in BERT by factorizing the embedding matrices, using shared layers, and applying parameter sharing across layers. This results in a more parameter-efficient model without sacrificing performance.


ELECTRA (Efficiently Learning an Encoder that Classifies Token Replacements Accurately) is a variant of BERT introduced by Google researchers in 2020. It introduces a new pretraining task called "discriminative masked language modeling" (DMLM). ELECTRA replaces some tokens in the input with plausible alternatives and trains a generator and discriminator to distinguish between the original and replaced tokens. This approach improves the training efficiency of BERT.


DistilBERT is a distilled version of BERT introduced by Hugging Face in 2019. It aims to reduce the size and computational requirements of BERT while maintaining a similar level of performance. DistilBERT achieves this by using a smaller architecture, removing certain components, and applying knowledge distillation from the larger BERT model.


MobileBERT is designed to be more lightweight and efficient for mobile and edge devices. It was introduced by Google researchers in 2020 and achieves a smaller model size and faster inference speed compared to BERT. MobileBERT achieves this by applying various architectural modifications and optimization techniques.


TinyBERT is another approach to compressing BERT models, introduced by researchers from the University of Waterloo in 2020. It aims to reduce the model size while preserving performance by leveraging a teacher-student framework. TinyBERT distills knowledge from a larger BERT model to a smaller student model, resulting in a more compact representation.


SpanBERT, introduced by researchers at the University of Washington in 2019, extends BERT to handle span-level tasks, where the model predicts relations between pairs of spans in a sentence. By considering the context and relationships between spans, SpanBERT improves performance on tasks such as question answering and coreference resolution.


CamemBERT is a variant of BERT specifically designed for the French language. It was developed by researchers at INRIA and Ecole Normale Supérieure in 2019. CamemBERT is pretrained on a large French corpus and achieves state-of-the-art performance on various French NLP benchmarks.


SciBERT is a BERT variant specifically designed for scientific text and domain-specific language. It was developed by researchers at the Allen Institute for Artificial Intelligence in 2019. SciBERT is pretrained on a large corpus of scientific papers, allowing it to capture the specific terminology and context used in scientific literature. It has been shown to improve performance on various scientific text analysis tasks.


BioBERT is an adaptation of BERT tailored for biomedical text and the life sciences domain. It was developed by researchers at the Korea University in 2019. BioBERT is pretrained on a large biomedical corpus, including biomedica…


MathBERT is a BERT variant developed specifically for mathematical language processing. It was introduced by researchers at the University of Illinois at Urbana-Champaign in 2020. MathBERT is pretrained on a large corpus of mathematical documents and captures the unique syntax and semantics of mathematical expressions. It has been utilized for tasks such as mathematical question answering, equation parsing, and math word problem solving.


ClinicalBERT is a BERT variant designed for clinical text analysis and healthcare applications. It was developed by researchers at the University of California, San Diego in 2019. ClinicalBERT is pretrained on a large corpus of electronic health records (EHRs), clinical notes, and medical literature, enabling it to understand medical terminology, clinical context, and domain-specific information. It has been employed for tasks like clinical named entity recognition, medical code prediction, and clinical text classification.


FinanceBERT is a BERT variant tailored for financial text analysis and applications in the finance domain. It was introduced by researchers at Alliance Manchester Business School in 2020. FinanceBERT is pretrained on a large corpus of financial news articles, reports, and other financial documents. It captures financial jargon, market sentiment, and contextual information relevant to the finance domain. FinanceBERT has been utilized for tasks such as sentiment analysis, financial news classification, and stock price prediction.


VideoBERT-HL (VideoBERT with Hierarchical Learning) is an extension of VideoBERT that incorporates hierarchical learning for video understanding. It was proposed by researchers at Google Research in 2021. VideoBERT-HL leverages the hierarchical structure of videos, allowing it to capture both low-level visual features and high-level temporal semantics. It has been applied to tasks like video action recognition, video summarization, and video retrieval.

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