Source: Times Higher Education

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Spacy and transformers : Complete Repo

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How to effectively and efficiently build AI products using Large Language Models

These years, there has been widely speared applications employing Natural Language Processing (NLP) technologies, including question answering system, Machine reading comprehension, summarization, dialogue, autocompletion, machine translation, etc.From 2013 to 2014, the applications of various neural network models on NLP have gradually increased, among which the most widely used are: convolutional neural networks (CNN), recurrent neural networks (RNN), and structural recurrent neural networks (SRN). Since the text is ordered, the sequential structure of RNN (recurrent neural network) makes it the   […]

The continuous innovation around contextual understanding of sentences has expanded significant bounds in NLP. The general idea of Transformer architecture is based on self-attention proposed in Attention is All You Need paper 2017.Self-attention is learning to weigh the relationship between each item/word to    

Text summarization is a text generation task, which generates a concise and precise summary of input texts. There are two kinds of summarization tasks in Natural Language Processing, one is the extractive approach, which is to identify the most important sentences or phrases in the original text and combine them to make a summary. The more advanced approach is the abstractive approach, which generates new phrases and sentences to represent the information from the input text.