Sulaiman Shamasna’s Post

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Data Scientist - Generative AI

Text-to-Text Transfer Transformer, T5 for short, is a special variation of transformers developed by Google that treats NLP tasks as text-to-text problems. This enables a unified and highly adaptable approach to a diversity of NLP tasks. In this artical, I am diving deeply into this model, highlighting: ❇ T5 Architecture and applications ❇ T5 fine-tuning using PyTorch ❇ Setting up training environment including GPU ❇ Containerizing the training pipeline with Docker ❇ Saving and loading the finetuned model ❇ Performing inference and evaluation of the model ✴ Despite the T5 model being relatively older compared to the latest advancements in large language models, the principles and techniques demonstrated here remain highly relevant and applicable to various modern architectures. #T5 #FineTuning #GPU #NLP #DataScience #Docker

T5 Model: Fine-Tuning on a Single GPU in a Docker Container

T5 Model: Fine-Tuning on a Single GPU in a Docker Container

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