transformer model

A transformer model is a type of deep learning architecture that has become the foundation for many state-of-the-art natural language processing (NLP) systems. Introduced in the paper “Attention Is All You Need” by Vaswani et al. in 2017, transformers are designed to handle sequential data, like text, in a way that allows for parallel processing and effectively captures the context and relationships between elements in the sequence[1][2][6].

Transformers are characterized by their use of self-attention mechanisms, which allow the model to weigh the importance of different parts of the input data. This self-attention enables the model to focus on different parts of the input sequence when predicting each part of the output sequence, making it particularly adept at understanding the context and dependencies between words or tokens that are far apart in the sequence[1][2][4].

Unlike previous sequence modeling architectures such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks, transformers do not process data in a sequential manner, which allows them to be more parallelizable and thus significantly faster to train on modern hardware like GPUs[1][2][4]. This parallelization capability is one of the reasons why transformers have largely replaced RNNs and LSTMs in many NLP tasks.

The transformer architecture typically consists of an encoder and a decoder, each made up of multiple layers that include self-attention and feedforward neural network components. The encoder processes the input sequence, and the decoder generates the output sequence, with each layer in both the encoder and decoder contributing to the model’s ability to capture complex data relationships[1][4].

Transformers have been the backbone of many recent advancements in AI, including models like BERT, GPT (Generative Pretrained Transformer), and T5 (Text-to-Text Transfer Transformer), which have set new benchmarks in a variety of NLP tasks[1][4][6]. They are also being explored for applications beyond NLP, such as computer vision and multi-modality tasks[3].

Citations:

[1] https://blogs.nvidia.com/blog/what-is-a-transformer-model/

[2] https://datagen.tech/guides/computer-vision/transformer-architecture/

[3] https://arxiv.org/abs/2306.07303

[4] https://blog.knoldus.com/what-are-transformers-in-nlp-and-its-advantages/

[5] https://openreview.net/pdf?id=OCm0rwa1lx1

[6] https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)

[7] https://www.datacamp.com/tutorial/how-transformers-work

[8] https://www.marktechpost.com/2023/01/24/what-are-transformers-concept-and-applications-explained/

[9] https://blog.pangeanic.com/what-are-transformers-in-nlp

[10] https://aiml.com/what-are-the-drawbacks-of-transformer-models/

[11] https://www.techtarget.com/searchenterpriseai/definition/transformer-model

[12] https://huggingface.co/learn/nlp-course/en/chapter1/4

[13] https://towardsdatascience.com/what-are-transformers-and-how-can-you-use-them-f7ccd546071a

[14] https://www.capitalone.com/tech/machine-learning/transformer-nlp/

[15] https://aws.amazon.com/what-is/transformers-in-artificial-intelligence/

[16] https://www.sciencedirect.com/science/article/abs/pii/S0957417423031688

[17] https://aiml.com/what-are-the-main-advantages-of-the-transformer-models/

[18] https://towardsdatascience.com/transformers-141e32e69591

[19] https://towardsai.net/p/l/a-journey-into-the-fabulous-applications-of-transformers%E2%80%8A-%E2%80%8Apart-1

[20] https://whites.agency/blog/what-is-the-benefit-of-using-transformer-in-nlp/

[21] https://txt.cohere.com/what-are-transformer-models/

[22] https://www.techtarget.com/searchenterpriseai/feature/Transformer-neural-networks-are-shaking-up-AI

[23] https://serokell.io/blog/transformers-in-ml

[24] https://originality.ai/blog/what-are-transformer-models

[25] https://machinelearningmastery.com/the-transformer-model/