Overview

This is a checkpoint for BioMegatron 345m with biomedical domain vocabulary (30k size), uncased.

Megatron is a large, powerful transformer developed by the Applied Deep Learning Research team at NVIDIA, which was trained with multinode and using mixed precision. Unlike BERT, the position of the layer normalization and the residual connection in the model architecture (similar to GPT-2 architucture) are swapped, which allowed the models to continue to improve as they were scaled up. This model reaches higher scores compared to BERT on a range of Natural Language Processing (NLP) tasks.\nBioMegatron has the same network architecture as the Megatron, but is pretrained on a different dataset - PubMed, a large biomedical text corpus, which achieves better performance in biomedical downstream tasks than the original Megatron.

It contains

model_optim_rng.pt - pre-trained Megatron model weights
pubmed_merged-all-uncased.vocab.txt - biomedical vocabulary file (30k size, uncased) used for train this checkpoint

More details about the model can be found in the BioMegatron paper: https://arxiv.org/abs/2010.06060

Documentation

Source code and developer guide is available at https://github.com/NVIDIA/NeMo and https://github.com/NVIDIA/Megatron-LM

This model checkpoint can be used for finetuning on biomedical question answering datasets, such as named entity recognition(NER), question answering (QA), or relationship extraction (RE).

In the following we show examples for how to finetune BioMegatron on different downstream tasks.

Usage example 1: Finetune on Named Entity Recognition

https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Token_Classification-BioMegatron.ipynb

Usage example 2: Finetune on Relation Extraction

https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Relation_Extraction-BioMegatron.ipynb

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Unlike BERT, the position of the layer normalization and the residual connection in the model architecture (similar to GPT-2 architucture) are swapped, which allowed the models to continue to improve as they were scaled up. This model reaches higher scores compared to BERT on a range of Natural Language Processing (NLP) tasks. \nBioMegatron has the same network architecture as the Megatron, but is pretrained on a different dataset - PubMed, a large biomedical text corpus, which achieves better performance in biomedical downstream tasks than the original Megatron.\n\nIt contains\n* model_optim_rng.pt - pre-trained Megatron model weights\n* pubmed_merged-all-uncased.vocab.txt - biomedical vocabulary file (30k size, uncased) used for train this checkpoint\n\nMore details about the model can be found in the BioMegatron paper: https://arxiv.org/abs/2010.06060\n\n# Documentation\nSource code and developer guide is available at https://github.com/NVIDIA/NeMo and https://github.com/NVIDIA/Megatron-LM\n\nThis model checkpoint can be used for finetuning on biomedical question answering datasets, such as named entity recognition(NER), question answering (QA), or relationship extraction (RE). \n\nIn the following we show examples for how to finetune BioMegatron on different downstream tasks. \n\n## Usage example 1: Finetune on Named Entity Recognition\nhttps://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Token_Classification-BioMegatron.ipynb\n\n## Usage example 2: Finetune on Relation Extraction\nhttps://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Relation_Extraction-BioMegatron.ipynb","BioMegatron345m-biovocab-30k-uncased",["D",1603855049430],"","/api/image/nvidia/-/models/biomegatron345m_biovocab_30k_uncased","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","bias","explainability","privacy","safetyAndSecurity",[],[],[],[],[],{"_158":1241,"_160":1242},"ngcApps",[1194],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1175":1176,"_1177":1178,"_1179":1180,"_1181":1182,"_1183":278,"_189":304,"_1184":1185,"_1186":51,"_1187":136,"_1188":136,"_1189":1190,"_1191":136,"_1192":1193},"amplitude",{"_1172":1173,"_817":-7,"_1174":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1166":1167,"_1168":1169,"_1170":1171},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787602120125,"1.12.0","defaultSearchSuggestions",{"_1057":255,"_1058":1059,"_1060":1061,"_1062":1063},"announcements",[],"consentState",{"_343":1047,"_1048":136,"_1049":26,"_1050":26,"_1051":1052},"consentConfig",{"_325":326,"_327":328,"_329":330,"_331":332,"_333":334,"_335":336,"_337":338,"_339":340,"_341":342,"_343":344},"showContactAdmin",1787602120129,[],[],[318,319,320,321,322,323,324],{"_40":55,"_42":64,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":63,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":62,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":56,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":61,"_46":-5,"_48":-5,"_50":51},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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