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Natural Language Processing (NLP) encompasses computer science, human language, and artificial intelligence in an attempt to close the gap between human communication and computer understanding.The purpose of NLP is to train machines to understand...
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Transformer-based language models have revolutionized the NLP space since the introduction of the Transformer, a novel neural network architecture, in 2017. Today, the most advanced language models heavily rely on transformers and are now consider...
All transformative technologies come with a problem—they are really difficult to explain. Try explaining to a communications mogul from the past century that people in 2021 will not only communicate through their tiny slabs of glass they can carry...
Today, we are in an age of business dubbed the “Age of the Customer”. Businesses today are more focused on customer experience than ever before. Customers interact with your business through your customer support team. Your agents are how your cus...
Today, it is easier than ever for customers to communicate with businesses. In fact, a study conducted by Statista in 2020, revealed that 58 percent of Americans surveyed had contacted some form of customer service over the prior one month period....
If you’ve ever trained an ML model from the scikit-learn python library, you’ve probably noticed that each model has certain options that can be specified prior to training. For example, the Decision Tree classifier lets you specify how many featu...
For the past two years, the “transformer” architecture has achieved state-of-the-art performance on most benchmark NLP tasks. A transformer model considers the entire input sequence of text at once - rather than one word or character at a time - a...
When machine learning models are trained to perform a particular task, we usually collect a set of samples, the “test set”, that is representative of the task at hand in order to measure the model’s performance. If there are any limitations on th...
