Archives for bert - Page 5

05 Aug

What Is Google’s Recently Launched BigBird

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Recently, Google Research introduced a new sparse attention mechanism that improves performance on a multitude of tasks that require long contexts known as BigBird. The researchers took inspiration from the graph sparsification methods. They understood where the proof for the expressiveness of Transformers breaks down when full-attention is relaxed to form the proposed attention pattern.…

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05 Jun

How Syntactic Biases Help BERT To Achieve Better Language Understanding

Recently, researchers from DeepMind, UC Berkeley and the University of Oxford introduced a knowledge distillation strategy for injecting syntactic biases into BERT pre-training in order to benchmark natural language understanding. Bidirectional Encoder Representation from Transformers or BERT is one of the most popular neural network-based techniques for natural language processing (NLP) while pre-training.  At the…

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26 Feb

Does Neural Network Compression Impact Transfer Learning

The popularity of compression techniques grew with the increasing sizes of machine learning models, which grew in order to cater to the growing number of parameters (we are talking billions) among other factors. Compression comes in handy when a model has to be downloaded and run on a smartphone, which runs on low resources like…

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04 Dec

Transformers Simplified: A Hands-On Intro To Text Classification Using Simple Transformers 

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In the past few years, we have seen tremendous improvements in the ability of machines to deal with Natural Language. We saw algorithms breaking the state-of-the-art one after the other on a variety of language-specific tasks, all thanks to transformers. In this article, we will discuss and implement transformers in the simplest way possible using…

The post Transformers Simplified: A Hands-On Intro To Text Classification Using Simple Transformers  appeared first on Analytics India Magazine.

02 Dec

How To Build A BERT Classifier Model With TensorFlow 2.0

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BERT is one of the most popular algorithms in the NLP spectrum known for producing state-of-the-art results in a variety of language modeling tasks. Built on top of transformers and seq-to-sequence models, the Bidirectional Encoder Representations from Transformers is a very powerful NLP model that has outperformed many. What Is The Big Deal About BERT?…

The post How To Build A BERT Classifier Model With TensorFlow 2.0 appeared first on Analytics India Magazine.

31 Oct

Behind Google’s BERT Implementation In Search Queries

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On 25 October this year, Google announced changes in its search algorithm, a major step towards integrating natural language processing for optimising the search results. The tech giant boasts that with this tweak, they can deliver the most relevant results to queries. The modification in the algorithm is considered as one of the most significant…

The post Behind Google’s BERT Implementation In Search Queries appeared first on Analytics India Magazine.

14 Oct

Why Transformers Play A Crucial Role In NLP Development

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Recent advances in modern Natural Language Processing (NLP) research have been dominated by the combination of Transfer Learning methods with large-scale Transformer language models. Creating these general-purpose models remains an expensive and time-consuming process restricting the use of these methods to a small subset of the wider NLP community. With Transformers, came a paradigm shift…

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27 Sep

Google Turns 21! Here’s A Look At The Search Giant’s Top 21 Machine Learning Contributions 

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Google revolutionised the way the world uses the internet with its landmark PageRank algorithm. Today, after two decades, Google has grown into an AI powerhouse that generates state-of-the-art algorithms that touch almost every domain known to mankind.  As Google turns 21, we have compiled a list of 21 notable contributions from Google which has enriched…

The post Google Turns 21! Here’s A Look At The Search Giant’s Top 21 Machine Learning Contributions  appeared first on Analytics India Magazine.

12 Sep

How Language Models Can Be Used In Real-Time Use Cases

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Recent advancements in natural language processing (NLP) have touched many heights over the past few years. Pre-trained high-capacity language models such as ELMo and BERT have gained popularity in NLP.  Language modelling has been implementing in a number of applications such as machine translation, speech recognition, question answering and sentiment analysis, among others. Basically, the…

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