What are some techniques for learning embeddings that facilitate zero-shot transfer?

Optional, starting in the 2024-05-01-preview REST API, the dimensions of embeddings that you would like to generate, assuming the model supports a range of ... 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 ... 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. We propose CustomizE, which customizes embedding sizes in a data-driven way, assigning larger sizes for high-frequency entities and smaller sizes for low- ... Specify a dimensionality to control the embedding size. from nomic import embed output = embed.text( texts=['Nomic Embedding API', '#keepAIOpen'], model ... ... dimension to produce a single embedding vector. The batch size is always 1 for Vespa embeddings. See onnx export for how to export models to ONNX format ... Jul 8, 2023 ... The 'dimension' of an embedding refers to the length of the vector representing an object. So, an embedding with 1536 dimensions means that ... 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 ... Aug 26, 2021 ... For variables with many unique categories, it creates an unmanageable number of dimensions. Since each item is technically equidistant in vector ... 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.