Category: 01. Advantage

Neural networks are often trained in batches for efficiency. To create batches, inputs need to have the same dimensions. pad_sequences allows you to efficiently create batches from sequences of varying lengths.

In NLP, text data is inherently sequential, and maintaining the sequence structure is crucial for capturing contextual information. pad_sequences retains this sequence structure while ensuring uniformity in input dimensions.

pad_sequences is a built-in function in libraries like TensorFlow and Keras, making it easy to use and integrate into your data preprocessing pipeline.

Many neural network architectures, especially recurrent neural networks (RNNs) and transformers, require input sequences to have a fixed length. pad_sequences helps ensure that all input sequences are of the same length, making them compatible with such models.