What are some challenges in evaluating the performance of embeddings on tasks involving figurative language?

Oct 21, 2023 ... We propose the Dynamic Embedding Size Search (DESS) method to minimize the embedding size selection regret on both user and item sides in a non-stationary ... Nov 9, 2019 ... I'm aware that the num_embeddings argument refers to how many elements we have in our vocabulary, and embedding_dim is simply referring to how ... Model Choices Voyage currently provides the following text embedding models: Model Context Length (tokens) Embedding Dimension Description voyage-3-large ... Dec 9, 2023 ... ... size or length. Common vector dimensionalities include 384 ... A single dimension in a vector embedding does not mean anything, as ... An embedding refers to the reduction of high-dimensional data, such as unstructured text, to a representation with fewer dimensions, such as a vector. 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 ... 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. Here are some test forms on fitting-net size tuning or embedding-net size tuning performed on several different systems. Available Models. Voyage recommends using the following text embedding models: Model, Context Length, Embedding Dimension, Description. voyage-3-large, 32,000 ... 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 ...