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**[news]** A cross-lingual extension of SapBERT will appear in the main onference of **ACL 2021**! **[news]** SapBERT will appear in the conference proceedings of **NAACL 2021**! SapBERT (Liu et al. 2020) trained with UMLS 2020AB, using xlm-roberta-base as the base model. Please use [CLS] as the representation of the input. For more details about training and eval, see SapBERT github repo.

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> Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih. Dense Passage Retrieval for Open-Domain Question Answering. _Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)_, pages 6769-6781, 2020. Our own efforts are described in the paper entitled: > Pre-Processing Matters! Improved Wikipedia Corpora for Open-Domain Question Answering. This is the passage encoder portion of a 2nd iteration DPR model for the wiki-all-8-4 corpus variant trained on the amalgamation of the NQ, TriviaQA, WQ, and CuratedTREC datasets.

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> Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih. Dense Passage Retrieval for Open-Domain Question Answering. _Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)_, pages 6769-6781, 2020. Our own efforts are described in the paper entitled: > Pre-Processing Matters! Improved Wikipedia Corpora for Open-Domain Question Answering. This is the query encoder portion of a 2nd iteration DPR model for the wiki-all-8-4 corpus variant trained on the amalgamation of the NQ, TriviaQA, WQ, and CuratedTREC datasets.

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NeoBERT

chandar-lab

NeoBERT is a **next-generation encoder** model for English text representation, pre-trained from scratch on the RefinedWeb dataset. NeoBERT integrates state-of-the-art advancements in architecture, modern data, and optimized pre-training methodologies. It is designed for seamless adoption: it serves as a plug-and-play replacement for existing base models, relies on an **optimal depth-to-width ratio**, and leverages an extended context length of **4,096 tokens**. Despite its compact 250M parameter footprint, it is the most efficient model of its kind and achieves **state-of-the-art results** on the massive MTEB benchmark, outperforming BERT large, RoBERTa large, NomicBERT, and ModernBERT under identical fine-tuning conditions. - Paper: paper - Repository: github. If you would like to use sequence packing (un-padding), you will need to also install flash-attention:

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