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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.
Large scale online learning of image similarity through ranking
Gal Chechik, Varun Sharma, Uri Shalit, and Samy Bengio · 2010
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Distilling the knowledge in a neural network, 2015
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Bag of tricks for efficient text classification
Armand Joulin, Édouard Grave, Piotr Bojanowski, and Tomáš Mikolov · 2017
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Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing
Taku Kudo and John Richardson · 2018
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Subword regularization: Improving neural network translation models with multiple subword candidates
Taku Kudo · 2018
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Representation learning with contrastive predictive coding
Aäron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych · 2019
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
Cited alongside, same era.
Zero: Memory optimizations toward training trillion parameter models
Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, and Yuxiong He · 2020
Cited alongside, same era.
Rocketqa: An optimized training approach to dense passage retrieval for open-domain question answering
Yingqi Qu, Yuchen Ding, Jing Liu, Kai Liu, Ruiyang Ren, Wayne Xin Zhao, Daxiang Dong, Hua Wu, and Haifeng Wang · 2021
Cited alongside, same era.
Beir: A heterogenous benchmark for zero-shot evaluation of information retrieval models, 2021
Nandan Thakur, Nils Reimers, Andreas Rücklé, Abhishek Srivastava, and Iryna Gurevych · 2021
Cited alongside, same era.
Sentence-t5: Scalable sentence encoders from pre-trained text-to-text models
Jianmo Ni, Gustavo Hernandez Abrego, Noah Constant, Ji Ma, Keith Hall, Daniel Cer, and Yinfei Yang · 2022
Cited alongside, same era.
Text embeddings by weakly-supervised contrastive pre-training
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei · 2022
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Text and code embeddings by contrastive pre-training
Arvind Neelakantan, Tao Xu, Raul Puri, Alec Radford, Jesse Michael Han, Jerry Tworek, Qiming Yuan, Nikolas Tezak, Jong Wook Kim, Chris Hallacy, Johannes Heidecke, Pranav Shyam, Boris Power, Tyna Eloundou Nekoul, Girish Sastry, Gretchen Krueger, David Schnurr, Felipe Petroski Such, Kenny Hsu, Madeleine Thompson, Tabarak Khan, Toki Sherbakov andcalculating Joanne Jang, Peter Welinder, and Lilian Weng · 2022
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Sgpt: Gpt sentence embeddings for semantic search, 2022
Niklas Muennighoff · 2022
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Promptagator: Few-shot dense retrieval from 8 examples
Zhuyun Dai, Vincent Y Zhao, Ji Ma, Yi Luan, Jianmo Ni, Jing Lu, Anton Bakalov, Kelvin Guu, Keith Hall, and Ming-Wei Chang · 2023
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Large dual encoders are generalizable retrievers
Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernandez Abrego, Ji Ma, Vincent Zhao, Yi Luan, Keith Hall, Ming-Wei Chang, et al · 2022
Cited alongside, same era.
Scaling laws and interpretability of learning from repeated data
Danny Hernandez, Tom Brown, Tom Conerly, Nova DasSarma, Dawn Drain, Sheer El-Showk, Nelson Elhage, Zac Hatfield-Dodds, Tom Henighan, Tristan Hume, et al · 2022
Cited alongside, same era.
Nils Reimers · 2023
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Mteb: Massive text embedding benchmark, 2023
Niklas Muennighoff, Nouamane Tazi, Loïc Magne, and Nils Reimers · 2023
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One embedder, any task: Instruction-finetuned text embeddings, 2023
Hongjin Su, Weijia Shi, Jungo Kasai, Yizhong Wang, Yushi Hu, Mari Ostendorf, Wen tau Yih, Noah A. Smith, Luke Zettlemoyer, and Tao Yu · 2023
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