In natural language processing (NLP) and computer vision (CV), one of the most critical aspects of deep learning is the use of neural networks. These neural networks are responsible for transforming raw data into meaningful representations. In NLP and CV, the data is in the form of text, images, or videos, and each of these data types requires a different representation.

In NLP, the most common representation of text is embedding. An embedding is a dense vector that represents the meaning of a word or phrase. The embedding is learned by the neural network during training, and it is used as input to the network for downstream tasks like sentiment analysis, text classification, or machine translation. However, the text is a sequential data type, and the order of the words in a sentence is crucial to its meaning. This is where position embedding comes in.

https://www.ai-contentlab.com/2023/03/position-embedding-detailed-explanation.html