The Transformers’ Ability to Implement for Solving Intricacies of Language Processing

AI (Artificial Intelligence) tools have reached a surprising level of linguistic frequency. The best and biggest (little complex) of these are based on an architecture called the transformers. A transformer in AI is a tool that acts as a kind of training model to train the pre-trained models. Available pre-trained models can reduce computational cost, carbon foot print and saves time. A transformer model can be used for different modalities such as text, images, audio and even for multi-model including video classification and visual question answering. Latest version of transformers (Transformers 4.18.0) has been released by Pypi.org on 6
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Apr 2022 as part of the Hugging Face logo which claims SOTA (state-of-the-art), NLP (Natural Language Processing for Tensorflow 2.0 and PyTorch. This paper is one the first papers using this latest release of transformer tool for NLP (Natural Language Processing) in Python. Using latest Transformers this paper overviews, represents implements its powerful tools BERT (Bidirectional Encoder Representation from Transformers), GPT (Generative Pre-Trained), BART (Bidirectional Abstractive Representation from Transformers), & other utilities of highly efficient linear algebra libraries of Python for Natural Language Processing. So, this proposal implements Natural Language Processing introducing POS (Parts of Speech) Tags, Bigrams and implementing pipeline, summarizer, paraphrasing, sentiment-analysis using transformers. For sentiment Analysis multilingual (English & French) have been selected & matching sentiment results are 94% to 98 % for four test cases which are far better than conventional methods.