• Aruna Kumari Bellamkonda
• DAnveshini
• Thota Sravya Vardhani
• Perni Mythri
• Lankapalli Bharghavi, Dondapati Rachana

In the recent past, with the appearances of the transformer models, the sphere of NLP has certain advantages. Transformers have emerged as the new milestones in NLP tasks as they have shown the state-of-the-art performance in many tasks involving text classification, machine translation, for sentiment analysis and question answering. To be precise, main models like BERT (Bidirectional Encoder Representations from Transformers), GPT (Generative Pre-trained Transformer), and few more have shown a lot of results. In this study a comprehensive discussion is made on transformers and how they can be applied in natural language processing. Therefore, let’s begin with the most simplified idea of the nature of the NLP transformers. Letting these types of models to read and generate text very good by considering the importance aspect such as spatial encoding and self-attention mechanisms. In the next tutorial, we will be going to talk about the new transformer models and the internal architecture and training method of the current models like BERT and GPT-3 along with the traditional Transformer model itself. This has 88% of aaccuracy in researching related data from my research tool.

Natural Language Processing(NLP), Transformers, Tokenization, Text Classification, Web Scraping(WS

"RESEARCH TOOL USING TRANSFORMERS IN NATURAL LANGUAGE PROCESSING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.97-108, November-2024, Available :http://www.jetir.org/papers/JETIRGO06010.pdf

"RESEARCH TOOL USING TRANSFORMERS IN NATURAL LANGUAGE PROCESSING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. pp97-108, November-2024, Available at : http://www.jetir.org/papers/JETIRGO06010.pdf