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

arXiv: https://arxiv.org/abs/2307.11224 · HTML: https://mlsanity.com/paper/2307.11224/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 24 references that are in the map.
- First posted on arXiv 20 Jul 2023.

## Builds on (most cited first, 18 of 18)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://mlsanity.com/paper/1910.02054/) (2019)
- [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://mlsanity.com/paper/2212.03533/) (2022)
- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://mlsanity.com/paper/2104.08663/) (2021)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates](https://mlsanity.com/paper/1804.10959/) (2018)
- [Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models](https://mlsanity.com/paper/2108.08877/) (2021)
- [Large Dual Encoders Are Generalizable Retrievers](https://mlsanity.com/paper/2112.07899/) (2021)
- [RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2010.08191/) (2020)
- [Text and Code Embeddings by Contrastive Pre-Training](https://mlsanity.com/paper/2201.10005/) (2022)
- [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://mlsanity.com/paper/2212.09741/) (2022)
- [Promptagator: Few-shot Dense Retrieval From 8 Examples](https://mlsanity.com/paper/2209.11755/) (2022)
- [SGPT: GPT Sentence Embeddings for Semantic Search](https://mlsanity.com/paper/2202.08904/) (2022)
- [Scaling Laws and Interpretability of Learning from Repeated Data](https://mlsanity.com/paper/2205.10487/) (2022)

## Built on by (most cited first, 30 of 35)

- [Generative Representational Instruction Tuning](https://mlsanity.com/paper/2402.09906/) (2024)
- [Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents](https://mlsanity.com/paper/2310.19923/) (2023)
- [LocAgent: Graph-Guided LLM Agents for Code Localization](https://mlsanity.com/paper/2503.09089/) (2025)
- [Arctic-Embed 2.0: Multilingual Retrieval Without Compromise](https://mlsanity.com/paper/2412.04506/) (2024)
- [Jina CLIP: Your CLIP Model Is Also Your Text Retriever](https://mlsanity.com/paper/2405.20204/) (2024)
- [CoRNStack: High-Quality Contrastive Data for Better Code Retrieval and Reranking](https://mlsanity.com/paper/2412.01007/) (2024)
- [Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings](https://mlsanity.com/paper/2402.17016/) (2024)
- [SweRank: Software Issue Localization with Code Ranking](https://mlsanity.com/paper/2505.07849/) (2025)
- [CLERC: A Dataset for Legal Case Retrieval and Retrieval-Augmented Analysis Generation](https://mlsanity.com/paper/2406.17186/) (2024)
- [Improving General Text Embedding Model: Tackling Task Conflict and Data Imbalance through Model Merging](https://mlsanity.com/paper/2410.15035/) (2024)
- [Interpretable Text Embeddings and Text Similarity Explanation: A Survey](https://mlsanity.com/paper/2502.14862/) (2025)
- [CoFE-RAG: A Comprehensive Full-chain Evaluation Framework for Retrieval-Augmented Generation with Enhanced Data Diversity](https://mlsanity.com/paper/2410.12248/) (2024)
- [Position: Text Embeddings Should Capture Implicit Semantics, Not Just Surface Meaning](https://mlsanity.com/paper/2506.08354/) (2025)
- [SAIL-Embedding Technical Report: Omni-modal Embedding Foundation Model](https://mlsanity.com/paper/2510.12709/) (2025)
- [Effective and Efficient Conversation Retrieval for Dialogue State Tracking with Implicit Text Summaries](https://mlsanity.com/paper/2402.13043/) (2024)
- [Graph-Based Captioning: Enhancing Visual Descriptions by Interconnecting Region Captions](https://mlsanity.com/paper/2407.06723/) (2024)
- [CUPCase: Clinically Uncommon Patient Cases and Diagnoses Dataset](https://mlsanity.com/paper/2503.06204/) (2025)
- [Training LLMs to be Better Text Embedders through Bidirectional Reconstruction](https://mlsanity.com/paper/2509.03020/) (2025)
- [Repurposing Language Models into Embedding Models: Finding the Compute-Optimal Recipe](https://mlsanity.com/paper/2406.04165/) (2024)
- [Sentence Smith: Controllable Edits for Evaluating Text Embeddings](https://mlsanity.com/paper/2502.14734/) (2025)
- [Balancing Exploration and Exploitation in LLM using Soft RLLF for Enhanced Negation Understanding](https://mlsanity.com/paper/2403.01185/) (2024)
- [Agentic RAG with Knowledge Graphs for Complex Multi-Hop Reasoning in Real-World Applications](https://mlsanity.com/paper/2507.16507/) (2025)
- [On The Role of Pretrained Language Models in General-Purpose Text Embeddings: A Survey](https://mlsanity.com/paper/2507.20783/) (2025)
- [Do You Know What You Are Talking About? Characterizing Query-Knowledge Relevance For Reliable Retrieval Augmented Generation](https://mlsanity.com/paper/2410.08320/) (2024)
- [T"ubingen-CL at SemEval-2024 Task 1:Ensemble Learning for Semantic Relatedness Estimation](https://mlsanity.com/paper/2410.10585/) (2024)
- [ALEE: Any-Language Evaluation of Embeddings via English-Centric Minimal Pairs](https://mlsanity.com/paper/2607.00171/) (2026)
- [Data-CUBE: Data Curriculum for Instruction-based Sentence Representation Learning](https://mlsanity.com/paper/2401.03563/) (2024)
- [How Similar Are Elected Politicians and Their Constituents? Quantitative Evidence From Online Social Networks](https://mlsanity.com/paper/2407.03255/) (2024)
- [GEAR: A Simple GENERATE, EMBED, AVERAGE AND RANK Approach for Unsupervised Reverse Dictionary](https://mlsanity.com/paper/2412.06654/) (2024)
- [Multi-Task Pre-Finetuning of Lightweight Transformer Encoders for Text Classification and NER](https://mlsanity.com/paper/2510.07566/) (2025)

## Abstract (arXiv)

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.