Generate a one-hot encoding for each word, resulting in a sparse matrix of size 100,000, and use these encodings as inputs to your model.

Utilize word embeddings from a pre-trained model such as Word2Vec or GloVe, which provide dense vector representations of words, capturing semantic relationships, and input these embeddings into your model.

Assign a unique numerical identifier to each word, ranging from 1 to 100,000, and use these identifiers directly as inputs to your model.

Order the words based on their frequency of occurrence in the dataset and use the rank order as the encoding for each word in your model.

Combine both one-hot encoding and word embeddings by first generating one-hot encodings and then applying a dimensionality reduction technique to obtain dense vectors.