Jina Embeddings: A Novel Set of High-Performance Sentence Embedding Models
This work provides improved sentence embeddings for NLP applications, though it appears incremental as it builds on existing embedding techniques with enhanced data and training.
The paper introduces Jina Embeddings, a set of sentence embedding models that achieve high performance in tasks like dense retrieval and semantic textual similarity, as evaluated on the Massive Text Embedding Benchmark (MTEB). It also creates a novel dataset for improving grammatical negation awareness, which is publicly released.
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.