What are some techniques for learning compositional embeddings?

model = keras.Sequential() >>> model.add(keras.layers.Embedding(1000, 64)) >>> # The model will take as input an integer matrix of size (batch, > ... We present a complementary system, AdaEmbed, to reduce the size of embeddings needed for the same DLRM accuracy via in-training embedding pruning. Specify a dimensionality to control the embedding size. from nomic import embed output = embed.text( texts=['Nomic Embedding API', '#keepAIOpen'], model ... Model size: the size of a model indicates how easy it will be to run. All ... embeddings # openai embedding function def embed(docs: list[str]) -> list ... 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 ... Mar 19, 2023 ... I was wondering if ml-common has (or will have) options to reduce the default size using techniques such as PCA, t-SNE (t-SNE-Java), LDA, Autoencoders etc. The Granite Embedding lineup includes four different models of varying sizes: granite-embedding-30m-english: English only model that produces embedding vectors ... 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 ... Dec 9, 2023 ... ... size or length. Common vector dimensionalities include 384 ... A single dimension in a vector embedding does not mean anything, as ... Dec 19, 2024 ... It balances size and sophistication, outperforming smaller models like nomic-embed-text thanks to its higher dimensions. How fast do you need ...