To utilize pretrained embeddings like GloVe or fastText, they must be parsed and loaded into memory using a dedicated data structure, such as a token embedding class. This structure processes the precomputed text files to create a vocabulary dictionary mapping each token to a unique integer index, alongside a tensor mapping these indices to their corresponding continuous vector representations. A special unknown token is consistently assigned to index 0 to handle out-of-vocabulary words encountered during subsequent processing.