Publications — Sentence Transformers documentation (original) (raw)

If you find this repository helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:

@inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084", }

If you use one of the multilingual models, feel free to cite our publication Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation:

@inproceedings{reimers-2020-multilingual-sentence-bert, title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2020", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2004.09813", }

If you use the code for data augmentation, feel free to cite our publication Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks:

@inproceedings{thakur-2020-AugSBERT, title = "Augmented {SBERT}: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks", author = "Thakur, Nandan and Reimers, Nils and Daxenberger, Johannes and Gurevych, Iryna", booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = "6", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2010.08240", pages = "296--310", }

If you use the models for MS MARCO, feel free to cite the paper: The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes

@inproceedings{reimers-2020-Curse_Dense_Retrieval, title = "The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)", month = "8", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2012.14210", pages = "605--611", }

When you use the unsupervised learning example, please have a look at: TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning:

@inproceedings{wang-2021-TSDAE, title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning", author = "Wang, Kexin and Reimers, Nils and Gurevych, Iryna", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021", month = nov, year = "2021", address = "Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", pages = "671--688", url = "https://arxiv.org/abs/2104.06979", }

When you use the GenQ learning example, please have a look at: BEIR: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models:

@inproceedings{thakur-2021-BEIR, title = "BEIR: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models", author = {Thakur, Nandan and Reimers, Nils and R{"{u}}ckl{'{e}}, Andreas and Srivastava, Abhishek and Gurevych, Iryna}, booktitle={Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021) - Datasets and Benchmarks Track (Round 2)}, month = "4", year = "2021", url = "https://arxiv.org/abs/2104.08663", }

When you use GPL, please have a look at: GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval:

@inproceedings{wang-2021-GPL, title = "GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval", author = "Wang, Kexin and Thakur, Nandan and Reimers, Nils and Gurevych, Iryna", journal= "arXiv preprint arXiv:2112.07577", month = "12", year = "2021", url = "https://arxiv.org/abs/2112.07577", }

Repositories using SentenceTransformers

SentenceTransformers in Articles

In the following you find a (selective) list of articles / applications using SentenceTransformers to do amazing stuff. Feel free to contact me (info@nils-reimers.de) to add you application here.

SentenceTransformers used in Research

SentenceTransformers is used in hundreds of research projects. For a list of publications, see Google Scholar or Semantic Scholar.