Positional Encoding

Positional Encoding

Since the transformer architecture does not have any inherent notion of the order of the input data, positional encodings are added to the input embeddings to provide information about the position of each element in the sequence.

It involves adding a vector to each input token's embedding that represents its position in the sequence. This ensures that the transformer can account for the order of the tokens and recognize patterns based on their relative positions.

These encodings are usually sinusoidal functions of different frequencies, which allows the model to learn and represent positional relationships in the input data.

Positional Encoding is all about helping the model figure out where words are in a sequence. It doesn't deal with the meaning of words or how they relate to each other, like how "cat" and "dog" are pretty similar. Instead, positional encoding is all about keeping track of word order. For example, when translating a sentence like "The cat is on the mat" to another language, it's crucial to know that "cat" comes before "mat." Word order is super important for tasks like translation, summarizing stuff, and answering questions.

During the training phase, the neural network is presented with a vast corpus of text data and is trained to make predictions based on that data. The weights of the neurons in the network are adjusted iteratively using a backpropagation algorithm in order to minimize the difference between the predicted output and the actual output.

Positional encoding: Since transformers do not maintain an internal state, they need a way to incorporate the order of the input tokens. Positional encoding is a method used to inject positional information into the input embeddings. It involves adding a vector to each input token's embedding that represents its position in the sequence. This ensures that the transformer can account for the order of the tokens and recognize patterns based on their relative positions.