What are some linguistic properties that might be better captured by embeddings of certain dimensionalities?

Jul 8, 2023 ... The 'dimension' of an embedding refers to the length of the vector representing an object. So, an embedding with 1536 dimensions means that ... Mar 5, 2024 ... Storage cost scales linearly with dimension, and the choice of embeddings, such as OpenAI's in 1526 dimensions, impacts the overall cost. 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 ... The Granite Embedding lineup includes four different models of varying sizes: granite-embedding-30m-english: English only model that produces embedding vectors ... Jan 12, 2021 ... In this paper, we proposed a novel approach, named PEP (short for Plug-in Embedding Pruning), to reduce the size of the embedding table while avoiding the drop ... A simple lookup table that stores embeddings of a fixed dictionary and size. This module is often used to store word embeddings and retrieve them using indices. Higher dimensions allow more nuanced representations but require more computational resources. For instance, word embeddings like Word2Vec often use 300 ... Aug 30, 2024 ... So even embedding models with max tokens of 512 should be more than enough. Embedding Dimensions: Length of the embedding vector. Smaller ... Mar 7, 2025 ... We've improved our context length from previous models allowing you to embed large chunks of text, code, or other data. Output dimensions of 3K ... They feature lower costs, higher multilingual performance, and new parameters to control the overall size. What are embeddings? OpenAI's text embeddings measure ...