Are larger embeddings always better for capturing subtle semantic distinctions?

Jan 25, 2024 ... ... embedding model and creates embeddings with up to 3072 dimensions. ... embedding-ada-002 embedding with a size of 1536. ada v2, text ... 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. Oct 17, 2023 ... We've uncovered the fascinating world of distributed representations, high-dimensional spaces, and emergent properties in this exploration of word embeddings. The Granite Embedding lineup includes four different models of varying sizes: granite-embedding-30m-english: English only model that produces embedding vectors ... Specify a dimensionality to control the embedding size. from nomic import embed output = embed.text( texts=['Nomic Embedding API', '#keepAIOpen'], model ... 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 ... 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 ... Aug 13, 2024 ... Bi-Encoders produce a vector representation for a given sentence or document chunk; which is usually a single vector of a fixed dimension. Note ... For other users embedding many documents, this batch size may be too small. # set the batch size to 42 embed_model = OpenAIEmbedding(embed_batch_size=42) ... Aug 17, 2023 ... Word2Vec can be used to create dense word embeddings that are vectors that have a fixed size, regardless of the size of the vocabulary. This ...