What are some architectures that use embeddings effectively for question answering?

Aug 3, 2023 ... pgvector adds a new datatype vector, which developers use to store embeddings in regular columns within their database tables. In this paper, we suggest a simple method for estimating τ ∗ and τ w based on symbolic analysis and symbolic entropy. 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 ... ... dimension" to latitude and longitude: population size. ... A programmer may feed the neural network examples of how to create an embedding, which dimensions to ... The length of the embedding array is called the vector's dimensionality. A passage of text might be represented by a vector containing hundreds of dimensions. Specify a dimensionality to control the embedding size. from nomic import embed output = embed.text( texts=['Nomic Embedding API', '#keepAIOpen'], model ... The embedding dimension is the length of the produced vectors. A greater dimension offers a greater precision, but is more costly to operate over. The optimal ... The Granite Embedding lineup includes four different models of varying sizes: granite-embedding-30m-english: English only model that produces embedding vectors ... Jul 22, 2024 ... This paper proposes to learn Scalable Lightweight Embeddings for streaming recommendation, called SCALL, which can adaptively adjust the embedding sizes of ... We present a complementary system, AdaEmbed, to reduce the size of embeddings needed for the same DLRM accuracy via in-training embedding pruning.