Are there any techniques for improving the embeddings of rare entities?

An embedding refers to the reduction of high-dimensional data, such as unstructured text, to a representation with fewer dimensions, such as a vector. Apr 11, 2023 ... My main concern is determining the optimal or maximum sentence size for encoding each embedding. I understand that sentence length plays a crucial role. Feb 13, 2023 ... The size of the embedding matrix (WE) is (50000 * 768) for small models, where there are 50000 tokens and 768 dimensions. The unembedding ... Model Choices Voyage currently provides the following text embedding models: Model Context Length (tokens) Embedding Dimension Description voyage-3-large ... Jul 29, 2017 ... Answer. A Word Embedding is just a mapping from words to vectors. Dimensionality in word embeddings refers to the length of these vectors. Nov 9, 2019 ... I'm aware that the num_embeddings argument refers to how many elements we have in our vocabulary, and embedding_dim is simply referring to how ... Aug 26, 2021 ... For variables with many unique categories, it creates an unmanageable number of dimensions. Since each item is technically equidistant in vector ... Jan 28, 2020 ... Let's talk about Spatial Embedding. Consider a Basket Ball: and be yourself my friendly Amoeba Scientist Amoeba Joe: You live your full life ... Apr 16, 2024 ... Explore Snowflake Arctic's new open-source embedding models, delivering top retrieval performance with smaller sizes for faster deployment ... The embedding dimension is the length of the produced vectors. A greater dimension offers a greater precision, but is more costly to operate over. The optimal ...