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The Text-to-Text Transfer Transformer (T5) is a cutting-edge text-to-text transformer model developed by Google Research and published in a research paper in October 2019. T5 is based on the transformer architecture, which Vaswani et al. introduced in their seminal paper "Attention Is All You Need" in 2017. Because of its effectiveness and scalability, the transformer architecture has since become the standard for many NLP tasks.T5 employs a unified framework that can convert any text-based language problem to a text-to-text format. This allows the model to be trained on various NLP tasks, including machine translation, document summarization, question answering, and classification tasks, using the same model, loss function, and hyperparameters.

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An Overview of T5

T5 is a cutting-edge natural language processing model designed as a text-to-text framework for various NLP tasks such as machine translation, summarization, question answering, and classification. Here are some of the model's key features.

Largest T5 variant has 11 billion parameters

11B parameters

The largest T5 model variant, T5-11B, has 11 billion parameters. The larger models are more powerful but also require more computational resources to train and use.

T5 model was trained on a massive dataset

750GB Data

750GB massive dataset used to train the T5 model is known as the "Colossal Clean Crawled Corpus" (C4) and is one of the largest publicly available text corpora.

General language learning abilities


The model has shown good performance on a diverse set of benchmarks, including machine translation, question answering, abstractive summarization, and text classification

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