Embeddings

Embeddings are dense vectors that represent tokens in a continuous space, capturing semantic and syntactic properties. They are often initialized randomly or with pretrained models like GloVe, then fine-tuned during training. For example, the embedding for “dog” might evolve to reflect its context in pet-related tasks, enhancing model accuracy.