Overview

This is a nemo file for BioMegatron 345m with biomedical domain vocabulary (30k size), uncased. BioMegatron is Megatron pretrained on PubMed, a biomedical domain dataset, which gives improved results on a range of biomedical downstream tasks. The model has around 345 million paramters.

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

Model Architecture

NeMo Megatron is a new capability in the NeMo framework that allows developers to effectively train and scale language models to billions of parameters. 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. BioMegatron 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.

This 345m papameter model has 24 layers (Transformer blocks), 1024 hidden-units, and 16 attention heads. It uses the biomedical domain vocabulary (30k size), uncased.

For more information about NeMo Megatron visit https://github.com/NVIDIA/NeMo

Training

Training BioMegatron was done using the Megatron-LM codebase based on PyTorch.

The entire pre-training takes about 400 hours on 8 DGX-2 machines with Tesla V100 GPUs. Loss function and hyper-parameter settings are the same as pre-training the BERT language models with the Megatron-LM codebase.

Dataset

Pre-training was done on 4.5 billion-word PubMed abstract set and the 1.6 billion-word CC0-licensed Commercial Use Collection of the PMC full-text corpus.

How to use this Model

NVIDIA NeMo can be used for easy fine-tuning to a number of different tasks. Tutorial notebooks on fine-tuning the model for Named Entity Recognition, Relation Extraction can be found on the tutorials page of NeMo.

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

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

Usage example 1: Finetune on RE dataset ChemProt\nhttps://github.com/NVIDIA/NeMo/blob/r1.7.2/tutorials/nlp/Relation_Extraction-BioMegatron.ipynb

Usage example 2: Finetune on NER dataset NBCI\nhttps://github.com/NVIDIA/NeMo/blob/r1.7.2/tutorials/nlp/Token_Classification-BioMegatron.ipynb

Limitations

No known limitations available at this time.

References

Shin, Hoo-Chang and Zhang, Yang and Bakhturina, Evelina and Puri, Raul and Patwary, Mostofa and Shoeybi, Mohammad and Mani, Raghav, 2020, November. BioMegatron: Larger Biomedical Domain Language Model. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 4700--4706).

Licence

License to use this model is covered by the NGC TERMS OF USE unless another License/Terms Of Use/EULA is clearly specified. By downloading the public and release version of the model, you accept the terms and conditions of the NGC TERMS OF USE.

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BioMegatron is [Megatron](https://arxiv.org/abs/1909.08053) pretrained on [PubMed](https://pubmed.ncbi.nlm.nih.gov/download/), a biomedical domain dataset, which gives improved results on a range of biomedical downstream tasks. The model has around 345 million paramters. \n\nPlease be sure to download the latest version in order to ensure compatibility with the latest NeMo release.\n\n## Model Architecture\n\nNeMo Megatron is a new capability in the NeMo framework that allows developers to effectively train and scale language models to billions of parameters. 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. BioMegatron 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\nThis 345m papameter model has 24 layers (Transformer blocks), 1024 hidden-units, and 16 attention heads. It uses the biomedical domain vocabulary (30k size), uncased.\n\nFor more information about NeMo Megatron visit https://github.com/NVIDIA/NeMo\n\n## Training\n\nTraining BioMegatron was done using the [Megatron-LM](https://github.com/NVIDIA/Megatron-LM) codebase based on PyTorch.\n\nThe entire pre-training takes about 400 hours on 8 DGX-2 machines with Tesla V100 GPUs. Loss function and hyper-parameter settings are the same as pre-training the BERT language models with the Megatron-LM codebase.\n\n## Dataset\n\nPre-training was done on 4.5 billion-word [PubMed](https://pubmed.ncbi.nlm.nih.gov/download/) abstract set and the 1.6 billion-word CC0-licensed Commercial Use Collection of the PMC full-text corpus.\n\n## How to use this Model\n\nNVIDIA [NeMo](https://github.com/NVIDIA/NeMo) can be used for easy fine-tuning to a number of different tasks. Tutorial notebooks on fine-tuning the model for [Named Entity Recognition](https://github.com/NVIDIA/NeMo/blob/r1.7.2/tutorials/nlp/Token_Classification-BioMegatron.ipynb), [Relation Extraction](https://github.com/NVIDIA/NeMo/blob/r1.7.2/tutorials/nlp/Relation_Extraction-BioMegatron.ipynb) can be found on the [tutorials page](https://github.com/NVIDIA/NeMo/tree/r1.7.2/tutorials/nlp) of NeMo.\n\nSource code and developer guide is available at https://github.com/NVIDIA/NeMo Refer to documentation at https://docs.nvidia.com/deeplearning/nemo/neural-modules-release-notes/index.html\n\nIn the following we show examples for how to finetune BioMegatron on different downstream tasks.\n\nUsage example 1: Finetune on RE dataset ChemProt\nhttps://github.com/NVIDIA/NeMo/blob/r1.7.2/tutorials/nlp/Relation_Extraction-BioMegatron.ipynb\n\nUsage example 2: Finetune on NER dataset NBCI\nhttps://github.com/NVIDIA/NeMo/blob/r1.7.2/tutorials/nlp/Token_Classification-BioMegatron.ipynb\n\n## Limitations\n\nNo known limitations available at this time.\n\n## References\n\n1. Shin, Hoo-Chang and Zhang, Yang and Bakhturina, Evelina and Puri, Raul and Patwary, Mostofa and Shoeybi, Mohammad and Mani, Raghav, 2020, November. BioMegatron: Larger Biomedical Domain Language Model. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 4700--4706).\n\n## Licence\n\nLicense to use this model is covered by the NGC [TERMS OF USE](https://ngc.nvidia.com/legal/terms) unless another License/Terms Of Use/EULA is clearly specified. By downloading the public and release version of the model, you accept the terms and conditions of the NGC [TERMS OF USE](https://ngc.nvidia.com/legal/terms).","displayName","BioMegatron345m-biovocab-50k-uncased",["D",1680636368638],"publisher","NVIDIA NeMo","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/biomegatron345m_biovocab_50k_uncased","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[238,239,240,241,242,243,244,245,246,247,248,249],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_206":284,"_251":285,"_252":286,"_254":255},{"_206":282,"_251":282,"_252":283,"_254":255},{"_206":280,"_251":280,"_252":281,"_254":255},{"_206":276,"_251":277,"_252":278,"_254":255},{"_206":274,"_251":274,"_252":275,"_254":255},{"_206":270,"_251":271,"_252":272,"_254":255},{"_206":268,"_251":268,"_252":269,"_254":255},{"_206":266,"_251":266,"_252":267,"_254":255},{"_206":262,"_251":263,"_252":264,"_254":255},{"_206":260,"_251":260,"_252":261,"_254":255},{"_206":258,"_251":258,"_252":259,"_254":255},{"_206":250,"_251":250,"_252":253,"_254":255},"PyTorch","value","filter",{"_256":257,"_251":250},"category","label","field","framework","NLP",{"_256":255,"_251":258},"Natural-Language-Understanding",{"_256":255,"_251":260},"Natural Language Processing","uscs_natural_language_processing",{"_256":265,"_251":263},"application","Megatron",{"_256":255,"_251":266},"Language-Modeling",{"_256":255,"_251":268},"Healthcare","indus_healthcare",{"_256":273,"_251":271},"industry","Deep-Learning",{"_256":255,"_251":274},"Conversational AI","soln_conversational_ai",{"_256":279,"_251":277},"technology","BioMegatron",{"_256":255,"_251":280},"BERT",{"_256":255,"_251":282},"AI","soln_ai",{"_256":279,"_251":285},{"_106":1461,"_108":1462},"ngcApps",[1414],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1395":1396,"_1397":1398,"_1399":1400,"_1401":1402,"_1403":298,"_137":322,"_1404":1405,"_1406":55,"_1407":85,"_1408":85,"_1409":1410,"_1411":85,"_1412":1413},"amplitude",{"_1392":1393,"_994":-7,"_1394":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1386":1387,"_1388":1389,"_1390":1391},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1269":167,"_1270":1271,"_1272":1273,"_1274":1275},"announcements",[],"consentState",{"_360":1259,"_1260":85,"_1261":30,"_1262":30,"_1263":1264},"consentConfig",{"_342":343,"_344":345,"_346":347,"_348":349,"_350":351,"_352":353,"_354":355,"_356":357,"_358":359,"_360":361},"showContactAdmin",1783974824944,[],[],[336,337,338,339,340,341],{"_44":59,"_46":70,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":68,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":66,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":60,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":45,"_46":47,"_48":101,"_50":-5,"_52":-5,"_54":55},"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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This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","research.nvidia.com","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","s_pltp","This cookie is typically associated with Adobe Analytics (formerly SiteCatalyst). It is used to identify the previous link clicked within the site, which helps in analyzing site usage patterns.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","This cookie name is used as a long-lived unique identifier of the visitor on the website. 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