Overview

This is a checkpoint for BioMegatron 345m cased. BioMegatron is Megatron https://arxiv.org/abs/1909.08053 pretrained on cased PubMed https://catalog.data.gov/dataset/pubmed, a biomedical domain dataset, which gives improved results on a range of biomedical downstream tasks. The model has around 345 million paramters.\nThe model is trained with https://github.com/NVIDIA/Megatron-LM.

The model achieves weighted SAcc/MRR/LAcc of 45.12/61.17/51.4 on BioASQ-7b-factoid test set (after finetuned on SQuADv1.1 dataset), macro precision/recall/f1 of 82.46/78.79/80.52 on RE dataset ChemProt.

Please be sure to download the latest version in order to ensure compatibility with the latest NeMo release.

MegatronBERT.pt - pretrained Megatron model weights
config.json - the config file used to initialize model network architecture in NeMo
vocab.txt - vocabulary file used for train this checkpoint

More Details

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.

For more information about BioBERT or Megatron visit https://ngc.nvidia.com/catalog/models/nvidia:biobertbaseuncasedfornemo, https://ngc.nvidia.com/catalog/models/nvidia:biobertlargeuncasedfornemo or https://github.com/NVIDIA/Megatron-LM.

Documentation

Source code and developer guide is available at https://github.com/NVIDIA/NeMo\nRefer to documentation at https://docs.nvidia.com/deeplearning/nemo/neural-modules-release-notes/index.html

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 BioBERT on different downstream tasks.

Usage example 1: Finetune on BioASQ-factoid dataset

Visit https://github.com/NVIDIA/NeMo/blob/master/examples/nlp/biobert_notebooks/biobert_qa.ipynb

Usage example 2: Finetune on RE dataset ChemProt

Visit https://github.com/NVIDIA/NeMo/blob/master/examples/nlp/biobert_notebooks/biobert_re.ipynb

Usage example 2: Finetune on NER dataset NBCI

Visit https://github.com/NVIDIA/NeMo/blob/master/examples/nlp/biobert_notebooks/biobert_ner.ipynb

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BioMegatron is Megatron https://arxiv.org/abs/1909.08053 pretrained on cased PubMed https://catalog.data.gov/dataset/pubmed, a biomedical domain dataset, which gives improved results on a range of biomedical downstream tasks. The model has around 345 million paramters.\nThe model is trained with https://github.com/NVIDIA/Megatron-LM.\n\nThe model achieves weighted SAcc/MRR/LAcc of 45.12/61.17/51.4 on BioASQ-7b-factoid test set (after finetuned on SQuADv1.1 dataset), macro precision/recall/f1 of 82.46/78.79/80.52 on RE dataset ChemProt.\n\nPlease be sure to download the latest version in order to ensure compatibility with the latest NeMo release.\n\n* MegatronBERT.pt - pretrained Megatron model weights \n* config.json - the config file used to initialize model network architecture in NeMo\n* vocab.txt - vocabulary file used for train this checkpoint\n\n# More Details\nMegatron 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.\n\nFor more information about BioBERT or Megatron visit https://ngc.nvidia.com/catalog/models/nvidia:biobertbaseuncasedfornemo, https://ngc.nvidia.com/catalog/models/nvidia:biobertlargeuncasedfornemo or https://github.com/NVIDIA/Megatron-LM.\n\n# Documentation\nSource code and developer guide is available at https://github.com/NVIDIA/NeMo\nRefer to documentation at https://docs.nvidia.com/deeplearning/nemo/neural-modules-release-notes/index.html\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 BioBERT on different downstream tasks. \n\n## Usage example 1: Finetune on BioASQ-factoid dataset\nVisit https://github.com/NVIDIA/NeMo/blob/master/examples/nlp/biobert_notebooks/biobert_qa.ipynb\n\n## Usage example 2: Finetune on RE dataset ChemProt\nVisit https://github.com/NVIDIA/NeMo/blob/master/examples/nlp/biobert_notebooks/biobert_re.ipynb\n\n## Usage example 2: Finetune on NER dataset NBCI \nVisit https://github.com/NVIDIA/NeMo/blob/master/examples/nlp/biobert_notebooks/biobert_ner.ipynb","displayName","BioMegatron345mCased",["D",1680636364996],"publisher","","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/-/models/biomegatron345mcased","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[241,242,243,244,245,246,247,248],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_210":274,"_250":274,"_251":275,"_253":254},{"_210":270,"_250":271,"_251":272,"_253":254},{"_210":268,"_250":268,"_251":269,"_253":254},{"_210":265,"_250":266,"_251":267,"_253":254},{"_210":261,"_250":262,"_251":263,"_253":254},{"_210":258,"_250":258,"_251":259,"_253":254},{"_210":256,"_250":256,"_251":257,"_253":254},{"_210":249,"_250":249,"_251":252,"_253":254},"NLU","value","filter",{"_255":254,"_250":249},"category","label","field","NLP",{"_255":254,"_250":256},"NeMo",{"_255":260,"_250":258},"framework","Natural Language Understanding","uscs_natural_language_understanding",{"_255":264,"_250":262},"application","Natural Language Processing","uscs_natural_language_processing",{"_255":264,"_250":266},"Megatron",{"_255":254,"_250":268},"Conversational AI","soln_conversational_ai",{"_255":273,"_250":271},"technology","BERT",{"_255":254,"_250":274},{"_121":1255,"_123":1256},"ngcApps",[1208],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1189":1190,"_1191":1192,"_1193":1194,"_1195":1196,"_1197":287,"_152":311,"_1198":1199,"_1200":59,"_1201":100,"_1202":100,"_1203":1204,"_1205":100,"_1206":1207},"amplitude",{"_1186":1187,"_823":-7,"_1188":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMSUyMiUzQXRydWUlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1180":1181,"_1182":1183,"_1184":1185},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.10.0","defaultSearchSuggestions",{"_1063":214,"_1064":1065,"_1066":1067,"_1068":1069},"announcements",[],"consentState",{"_349":1053,"_1054":100,"_1055":34,"_1056":34,"_1057":1058},"consentConfig",{"_331":332,"_333":334,"_335":336,"_337":338,"_339":340,"_341":342,"_343":344,"_345":346,"_347":348,"_349":350},"showContactAdmin",1785066628041,[],[],[325,326,327,328,329,330],{"_48":62,"_50":72,"_52":116,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":69,"_52":116,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":67,"_52":116,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":63,"_52":116,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":116,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":116,"_54":-5,"_56":-5,"_58":59},"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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