Are certain embedding sizes better suited for representing abstract concepts?

titan-embed-text-v2:0 ) can intake up to 8,192 tokens or 50,000 characters and outputs a vector of 1,024 dimensions. The model is optimized for text retrieval ... Feb 5, 2025 ... 1. Input Context Length · Rotary Position Embeddings (RoPE). RoPE encodes positional information using a rotation matrix, and YaRN modifies how ... Oct 1, 2024 ... I have a database on a Qdrant with a vector of 256 dimensions. The embedding was done using the “text-embedding-3-large” model with the dimensions parameter ... In data science, the term dimension typically refers to a feature or attribute of the data. Higher-dimensional data in AI refers to datasets with many features ... Aug 3, 2023 ... pgvector adds a new datatype vector, which developers use to store embeddings in regular columns within their database tables. Jun 18, 2021 ... Here, we propose a principled method such that all structural information of a network is parsimoniously encoded. Feb 8, 2022 ... It can, generally speaking the larger the embedding size the more information you can encode into your embeddings. But it doesn't ... Feb 28, 2025 ... In general, the dimensionality of word embedding refers to the number of dimensions in which the vector representation of a word is defined. Apr 3, 2024 ... The reason the size increases is due to storing the extra passages as nested fields as well as all the actual embedding representations ... The mistral-embed model generates embedding vectors of dimension 1024 for each text string, regardless of the text length. It's worth noting that while higher ...