What are some techniques for reducing the dimensionality of embeddings after training?

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 ... output_dimensionality. Optional: int. Used to specify output embedding size. If set, output embeddings will be truncated to the size specified. Sep 6, 2023 ... The authors of word2vec are somewhat vague, recommending somewhere between 100 and 1000 dimensions. Other authors pick numbers like 100 or 300. Apr 8, 2023 ... The output dimension is mantained, since values coming from each attention head are concatenated and then projected with a matrix WO∈Rhdv×dmodel ... Jun 18, 2024 ... I'm working on a page where I'm embedding form.io form into a squarespace page. However, embedding the code changes the width of all of the sections by making ... ... Scale Semantic Embedding. PLoS Comput Biol 7(1): e1001047. doi:10.1371/journal.pcbi.1001047. Editor: Michael Levitt, Stanford University, United States of ... 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) ... Currently, many state-of-the-art models produce embeddings with 1024 dimensions, each of which is encoded in float32 , i.e., they require 4 bytes per dimension. 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 ... Jun 12, 2024 ... How models can be used to generate vector embeddings. How vector embeddings represent data. In machine learning, the “dimensions” of data do not ...