An embedding in machine learning (and thus in PyTorch) is a multi-dimensional vector representing a certain token, in this case a word. As a neural network is trained, the initially random values of the embeddings for each word shift to become more useful and encode semantic features, e.g. synonyms come closer together.
spam fails_task lang_mismatch pii not_appropriate hate_speech sexual_content quality toxicity humor helpfulness creativity violence