I’m not sure if I understand your questions, but a sentence is a sequence of words. So using word embeddings, a sentence is a sequence of embeddings (i.e., vectors). Thus a sentence is represented as a 2-dim tensor of shape (num_words, embed_dim).
In practice, you also of the dimension for the batch to handle multiple sentences in parallel. So the final representation is then a 3-dim tensor if shape (batch_size, num_words, embed_dim).
Note, in many documentations, you will probably see seq_len instead of num_words since LSTM are not only used for sentences.