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We provide preprocessed PubMed texts that were used to pre-train the BlueBERT models. The corpus contains ~4000M words extracted from the PubMed ASCII code version. This work was supported by the Intramural Research Programs of the National Institutes of Health, National Library of Medicine and Clinical Center. This work was supported by the National Library of Medicine of the National Institutes of Health under award number 4R00LM013001-01. This tool shows the results of research conducted in the Computational Biology Branch, NCBI. The information produced on this website is not intended for direct diagnostic use or medical decision-making without review and oversight by a clinical professional. Individuals should not change their health behavior solely on the basis of information produced on this website. NIH does not independently verify the validity or utility of the information produced by this tool. If you have questions about the information produced on this website, please see a health care professional. More information about NCBI's disclaimer policy is available.

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We provide preprocessed PubMed texts that were used to pre-train the BlueBERT models. The corpus contains ~4000M words extracted from the PubMed ASCII code version.

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H-optimus-0

bioptimus

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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H-optimus-1

bioptimus

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H0-mini

bioptimus

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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`vit_reg4_so150m_p14_ls_dino-v2-bio` is a Bio-DINO image encoder for natural photographs of living organisms. It uses a SoViT-150M/14 Vision Transformer with 4 register tokens and 133.6M backbone parameters, trained with a DINOv2-style self-supervised objective on approximately 31 million curated images spanning plants, fungi, insects, fish, corals, birds, mammals and other biodiversity subjects. The model is released as a single repository with three checkpoints: 224 x 224, 252 x 252 and 336 x 336. The 252px checkpoint is the recommended default, the 224px checkpoint is a faster and lower-cost variant, and the 336px checkpoint is a short high-resolution adaptation for users who want to trade latency for more input detail. All checkpoints are intended primarily as frozen image encoders for embeddings, retrieval, clustering, probing and transfer learning, rather than as ready-made species classifiers. This repository releases the Birder backbone checkpoints for direct use as image encoders. The full DINO training weights, including the DINO head and training state needed for continued self-supervised work, are released separately in the full DINO training weights companion repository.

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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bmd_watermark_n

BitcrushedHeart

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Object detection
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f-coref

biu-nlp

Please check the official repository for more details and updates.

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Please check the official repository for more details and updates.

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