What are some best practices for documenting and sharing information about the embedding sizes used in machine learning models?

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 ... Apr 16, 2024 ... Explore Snowflake Arctic's new open-source embedding models, delivering top retrieval performance with smaller sizes for faster deployment ... They feature lower costs, higher multilingual performance, and new parameters to control the overall size. What are embeddings? OpenAI's text embeddings measure ... Dec 9, 2023 ... ... size or length. Common vector dimensionalities include 384 ... A single dimension in a vector embedding does not mean anything, as ... model = keras.Sequential() >>> model.add(keras.layers.Embedding(1000, 64)) >>> # The model will take as input an integer matrix of size (batch, > ... The mistral-embed model generates embedding vectors of dimension 1024 for each text string, regardless of the text length. It's worth noting that while higher ... Selecting Embed only the characters used in the presentation reduces the file size but limits editing of the file using the same font. This checkbox is common ... Sep 24, 2021 ... As for the format, in the format section of each page you have a setting called "Page Size" where you can set a specific page size type, 16:9, 4 ... Feb 8, 2022 ... It can, generally speaking the larger the embedding size the more information you can encode into your embeddings. But it doesn't ... Higher dimensions allow more nuanced representations but require more computational resources. For instance, word embeddings like Word2Vec often use 300 ...