Transformer

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Transformers are Neural Network architectures using self-attention to understand context and long-term dependencies in language, used in many Natural Language Processing (NLP) applications such as chatbots and Sentiment Analysis tools. Transformer Model uniquely have attention such that every output element is connected to every input element. The weightings between them are calculated dynamically, effectively. | Kyle Wiggers The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an Autoencoder (AE) / Encoder-Decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Attention Is All You Need | A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, and I. Polosukhin


The transformer model in artificial intelligence, specifically in the realm of natural language processing, can be likened to a complex system of interconnected mini neural networks. Each of these mini networks is assigned to a token, which represents a piece of information or a word in a sentence. In each mini neural network, there is a hierarchy of layers that perform computations. The main pathway, which can also be referred to as the 'residual stream' or 'main artery,' is where the information primarily flows. Occasionally, there are branches or 'offshoots' from this main pathway where additional computations are performed to enhance the understanding of the token. The results of these computations are then merged back into the main artery. The transformer model is hence a collection of these mini neural networks, each with a certain number of these branch-and-merge pathways. Smaller models may have around 12 to 24 of these, while larger models can have up to 100. Imagine a sentence, where each word or 'token' has its own dedicated mini transformer. These mini transformers continuously refine their understanding of their assigned tokens through iterative computations. What makes the transformer model unique and powerful is its ability to allow these mini transformers to exchange information with each other. Each mini transformer broadcasts information about what data they hold and what data they are looking for. Based on this, they fetch relevant information from other mini transformers and merge this new data into their own main artery. This process of information exchange and iterative computation is repeated cycle after cycle, refining the understanding of each token. After multiple cycles, the information reaches the bottom of the transformer stack. This information is then processed through a function called the Softmax, which provides the final output in the form of probability distribution. In summary, the transformer model is an iterative information processing system. It processes each token independently while also allowing for information exchange between different tokens. This makes it a powerful tool for understanding the context and nuances in language.


Competitive programming, conversational question answering, combinatorial optimization issues, and graph learning tasks all incorporate transformers as key components. Transformers models are used in competitive programming to produce solutions from textual descriptions. The well-known chatbot ChatGPT, which is a GPT-based model and a well-liked conversational question-answering model, is the best example of a transformer model. Transformers have also been used to resolve combinatorial optimization issues like the Travelling Salesman Problem, and they have been successful in graph learning tasks, especially when it comes to predicting the characteristics of molecules. Transformer models have shown great versatility in modalities, such as images, audio, video, and undirected Graphs. Click here for directed graphs.

The Transformer is a deep machine learning model introduced in 2017, used primarily in the field of natural language processing (NLP). Like Recurrent Neural Network (RNN), Transformers are designed to handle ordered sequences of data, such as natural language, for various tasks such as machine translation and text summarization. However, unlike RNNs, Transformers do not require that the sequence be processed in order. So, if the data in question is natural language, the Transformer does not need to process the beginning of a sentence before it processes the end. Due to this feature, the Transformer allows for much more parallelization than RNNs during training. Since their introduction, Transformers have become the basic building block of most state-of-the-art architectures in Natural Language Processing (NLP), replacing gated recurrent neural network models such as the Long Short-Term Memory (LSTM) in many cases. Since the Transformer architecture facilitates more parallelization during training computations, it has enabled training on much more data than was possible before it was introduced. This led to the development of pretrained systems such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer (GPT)-2, which have been trained with huge amounts of general language data prior to being released, and can then be fine-tune trained to specific language tasks.Wikipedia


Tensor2Tensor (T2T) | Google Brain

Tensor2Tensor, or T2T for short, is a library of deep learning models and datasets designed to make deep learning more accessible and [accelerate ML research](https://research.googleblog.com/2017/06/accelerating-deep-learning-research.html). T2T is actively used and maintained by researchers and engineers within the [Google Brain team](https://research.google.com/teams/brain/) and a community of users. This colab shows you some datasets we have in T2T, how to download and use them, some models we have, how to download pre-trained models and use them, and how to create and train your own models. | Jay Alammar]


Multi-head scaled dot-product attention mechanism. (Image source: Fig 2 in Vaswani, et al., 2017)

Transformers Course | HuggingFace

This course will teach you about natural language processing (NLP) using libraries from the Hugging Face ecosystem — 🤗 Transformers, 🤗 Datasets, 🤗 Tokenizers, and 🤗 Accelerate — as well as 🤗 the Hugging Face Hub.


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  • Chapters 1 to 4 provide an introduction to the main concepts of the 🤗 Transformers library. By the end of this part of the course, you will be familiar with how Transformer models work and will know how to use a model from the Hugging Face Hub, fine-tune it on a dataset, and share your results on the Hub!
  • Chapters 5 to 8 teach the basics of 🤗 Datasets and 🤗 Tokenizers before diving into classic NLP tasks. By the end of this part, you will be able to tackle the most common NLP problems by yourself.
  • Chapters 9 to 12 go beyond NLP, and explore how Transformer models can be used to tackle tasks in speech processing and computer vision. Along the way, you’ll learn how to build and share demos of your models, and optimize them for production environments. By the end of this part, you will be ready to apply 🤗 Transformers to (almost) any machine learning problem!

This course:

After you’ve completed this course, we recommend checking out DeepLearning.AI’s Natural Language Processing Specialization, which covers a wide range of traditional NLP models like naive Bayes and Long Short-Term Memory (LSTM)s that are well worth knowing about!

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