How does embedding size affect the accuracy of question answering models?

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 ... 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. The first text embedding model with a 8192 context-length that outperforms OpenAI Ada-002 and text-embedding-3-small on both short and long context tasks. Jun 12, 2024 ... How models can be used to generate vector embeddings. How vector embeddings represent data. In machine learning, the “dimensions” of data do not ... May 4, 2019 ... The most common powers of 2 for word embeddings are 128 or 256, depending on which order of magnitude is preferred. Share. Share a link to this ... 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 ... 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 ... For other users embedding many documents, this batch size may be too small. # set the batch size to 42 embed_model = OpenAIEmbedding(embed_batch_size=42) ... Jul 14, 2018 ... I have a Word Template (DOTX), and a PowerPoint Template (POTX). I have embedded an OpenType font into each. In word, the file size shoots from 30kb to 4mb. Choose your embed sizing. Wistia embeds can remain at a fixed size or be fully responsive to any screen or browser. Sizing is a bit ...