Title: Neural Embeddings for Text. Abstract: We propose a new kind of embedding for natural language text that deeply represents semantic meaning. Standard text embeddings use the vector output of a pretrained language model. In our method, we let a language model learn from the text and then literally pick its brain, taking the actual weights of the model's neurons to generate a vector. We call this representation of the text a neural embedding. With analysis of its behavior on several datasets, we confirm the ability of this representation to reflect semantics of the text. We also compare neural embeddings with GPT sentence (SGPT) embeddings. We observe that neural embeddings achieve comparable performance with a far smaller model, and that the embeddings respond to semantics differently.