Jina Embeddings: A Novel Set of High-Performance Sentence Embedding Models

• URL: http://arxiv.org/abs/2307.11224v3
• Date: Fri, 20 Oct 2023 14:09:36 GMT
• Title: Jina Embeddings: A Novel Set of High-Performance Sentence Embedding Models
• Authors: Michael G\"unther, Louis Milliken, Jonathan Geuter, Georgios Mastrapas, Bo Wang, Han Xiao
• Abstract summary: Jina Embeddings constitutes a set of high-performance sentence embedding models adept at translating textual inputs into numerical representations. This paper details the development of Jina Embeddings, starting with the creation of high-quality pairwise and triplet datasets. It concludes with a comprehensive performance evaluation using the Massive Text Embedding Benchmark (MTEB)
• Score: 4.451741472324815
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Jina Embeddings constitutes a set of high-performance sentence embedding models adept at translating textual inputs into numerical representations, capturing the semantics of the text. These models excel in applications like dense retrieval and semantic textual similarity. This paper details the development of Jina Embeddings, starting with the creation of high-quality pairwise and triplet datasets. It underlines the crucial role of data cleaning in dataset preparation, offers in-depth insights into the model training process, and concludes with a comprehensive performance evaluation using the Massive Text Embedding Benchmark (MTEB). Furthermore, to increase the model's awareness of grammatical negation, we construct a novel training and evaluation dataset of negated and non-negated statements, which we make publicly available to the community.

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