Position Embedding in NLP

Position embedding is a powerful concept in the world of NLP and deep learning, especially when dealing with sequences, such as sentences or time series data. Imagine reading a book; the order of the words matters, right? Similarly, when we feed sequences into neural networks, the position or order of data points can be critical. While word embedding gives a numerical representation to words, position embedding adds another layer by giving a numerical representation to their positions in a sequence. This is particularly useful in models such as Transformers and Large Language Models, which don't inherently understand the order of the data. By adding position embedding, we ensure that our model not only understands the meaning of each word, but also understands its context based on its position.

4 Ways to Learn