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Jun 29, 2020 · What is a Transformer? The Transformer in NLP is a novel architecture that aims to solve sequence-to-sequence tasks while handling long-range dependencies with ease. It relies entirely on self-attention to compute representations of its input and output WITHOUT using sequence-aligned RNNs or convolution. 🤯.
Aug 30, 2024 · Transformers is used for NLP tasks like, machine translation, text summarization, name entity recognition and sentimental analysis. Another application is speech recognition system, where audio signals are processed to provide transcribed text.
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Learn how Transformers work as language models for various NLP tasks, from translation to summarization. Discover the different types of Transformers, their pretraining and fine-tuning processes, and their environmental impact.
Jun 7, 2024 · Learn how transformers in NLP work and how they handle long-range dependencies with self-attention and encoder-decoder stacks. Explore the state-of-the-art models based on transformers, such as BERT, GPT-2, and XLNet.
A transformer model is a type of deep learning model that was introduced in 2017. These models have quickly become fundamental in natural language processing (NLP), and have been applied to a wide range of tasks in machine learning and artificial intelligence.
Jan 9, 2024 · Learn how Transformers, the models that have revolutionized data handling through self-attention mechanisms, work in NLP. Explore the historical context, the encoder-decoder structure, and the attention mechanism of Transformers.
Nov 29, 2023 · A paper that explains basic concepts and key techniques of Transformers, a dominant model for natural language processing. It covers the standard Transformer architecture, model refinements, and common applications, with insights into the strengths and limitations of these models.
