Model Overview

This is a checkpoint for BioMegatron 345m uncased. BioMegatron is Megatron language model pretrained on uncased PubMed https://catalog.data.gov/dataset/pubmed, a biomedical domain dataset, which gives state-of-the-art results on a range of biomedical natural language downstream tasks. The model has around 345 million paramters.\nThe model is trained with https://github.com/NVIDIA/Megatron-LM.

Model Architecture

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-LM/BERT, 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.\nIt uses the original BERT uncased vocabulary learned from Wikipedia and Books corpus.\nFor more detail about the Megatron-LM/BERT architecture, please refer to the Megatron-LM or BERT papers.

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

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.\nLoss 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.

Performance

The model achieves state-of-the-art results on BioASQ-7b-factoid biomedical question answering:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Benchmark	SAcc	LAcc	MRR
BioASQ-7b-factoid	46.2	62.6	52.5

How to use this Model

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

Ulternatively, users can also choose to develop their own fine-tuning script from the Megatron-LM codebase, or use any other PyTorch-based framework such as Huggingface Transformers by installing the Megatron-LM package from PyPi.

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](https://arxiv.org/abs/2010.06060) is [Megatron](https://arxiv.org/abs/1909.08053) language model pretrained on uncased PubMed https://catalog.data.gov/dataset/pubmed, a biomedical domain dataset, which gives state-of-the-art results on a range of biomedical natural language downstream tasks. The model has around 345 million paramters.\nThe model is trained with https://github.com/NVIDIA/Megatron-LM.\n\n## Model Architecture\n\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-LM/BERT, 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. \nIt uses the original [BERT](https://arxiv.org/pdf/1810.04805.pdf) uncased vocabulary learned from Wikipedia and Books corpus.\nFor more detail about the Megatron-LM/BERT architecture, please refer to the [Megatron-LM](https://arxiv.org/abs/1909.08053) or [BERT](https://arxiv.org/pdf/1810.04805.pdf) papers.\n\nFor more information about BioBERT or Megatron visit https://ngc.nvidia.com/catalog/models/nvidia:biobertbasecasedfornemo or https://github.com/NVIDIA/Megatron-LM.\n\n## Training\n\nTraining BioMegatron was done using the [Megatron-LM codebase](https://github.com/NVIDIA/Megatron-LM) based on [PyTorch](https://pytorch.org/).\n\nThe entire pre-training takes about 400 hours on 8 DGX-2 machines with Tesla V100 GPUs.\nLoss 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/) abstract set and the 1.6 billion-word CC0-licensed Commercial Use Collection of the [PMC full-text corpus](www.ncbi.nlm.nih.gov/pmc).\n\n## Performance\n\nThe model achieves state-of-the-art results on BioASQ-7b-factoid biomedical question answering:\n\n| Benchmark | SAcc | LAcc | MRR |\n|------------------------|------|------|------|\n| BioASQ-7b-factoid | 46.2 | 62.6 | 52.5 |\n\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.\nTutorial notebooks on fine-tuning the model for [Named Entity Recognition](https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Token_Classification-BioMegatron.ipynb), [Relation Extraction](https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Relation_Extraction-BioMegatron.ipynb), and [Question Answering](https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Question_Answering_Squad.ipynb) can be found on the [tutorials page](https://github.com/NVIDIA/NeMo/tree/main/tutorials/nlp) of NeMo.\n\nUlternatively, users can also choose to develop their own fine-tuning script from the [Megatron-LM codebase](https://github.com/NVIDIA/Megatron-LM), or use any other PyTorch-based framework such as [Huggingface Transformers](https://github.com/huggingface/transformers) by installing the [Megatron-LM package](https://pypi.org/project/megatron-lm/) from PyPi.\n\n## Limitations\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).","BioMegatron345mUncased",["D",1680636830656],"/api/image/nvidia/-/models/biomegatron345muncased","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","bias","explainability","privacy","safetyAndSecurity",[],[266,267,268,269,270,271,272,273,274,275,276,277,278],[],[],[],{"_87":313,"_120":314,"_121":315,"_123":124},{"_87":311,"_120":311,"_121":312,"_123":124},{"_87":309,"_120":309,"_121":310,"_123":124},{"_87":306,"_120":307,"_121":308,"_123":124},{"_87":302,"_120":303,"_121":304,"_123":124},{"_87":298,"_120":299,"_121":300,"_123":124},{"_87":295,"_120":296,"_121":297,"_123":124},{"_87":293,"_120":293,"_121":294,"_123":124},{"_87":290,"_120":291,"_121":292,"_123":124},{"_87":286,"_120":287,"_121":288,"_123":124},{"_87":284,"_120":284,"_121":285,"_123":124},{"_87":282,"_120":282,"_121":283,"_123":124},{"_87":279,"_120":279,"_121":280,"_123":124},"PyTorch",{"_125":281,"_120":279},"framework","NLP",{"_125":124,"_120":282},"NeMo",{"_125":281,"_120":284},"Natural Language Understanding","uscs_natural_language_understanding",{"_125":289,"_120":287},"application","Natural Language Processing","uscs_natural_language_processing",{"_125":289,"_120":291},"Megatron",{"_125":124,"_120":293},"Language Modeling","uscs_language_modeling",{"_125":289,"_120":296},"Healthcare","indus_healthcare",{"_125":301,"_120":299},"industry","DL","soln_dl",{"_125":305,"_120":303},"technology","Conversational AI","soln_conversational_ai",{"_125":305,"_120":307},"BioMegatron",{"_125":124,"_120":309},"BERT",{"_125":124,"_120":311},"AI","soln_ai",{"_125":305,"_120":314},{"_159":1294,"_161":1295},"ngcApps",[1247],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1228":1229,"_1230":1231,"_1232":1233,"_1234":1235,"_1236":327,"_190":355,"_1237":1238,"_1239":50,"_1240":135,"_1241":135,"_1242":1243,"_1244":135,"_1245":1246},"amplitude",{"_1225":1226,"_867":-7,"_1227":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1219":1220,"_1221":1222,"_1223":1224},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1786051661412,"1.11.0","defaultSearchSuggestions",{"_1107":435,"_1108":1109,"_1110":1111,"_1112":1113},"announcements",[],"consentState",{"_392":1097,"_1098":135,"_1099":26,"_1100":26,"_1101":1102},"consentConfig",{"_374":375,"_376":377,"_378":379,"_380":381,"_382":383,"_384":385,"_386":387,"_388":389,"_390":391,"_392":393},"showContactAdmin",[],[],[368,369,370,371,372,373],{"_39":54,"_41":63,"_43":60,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":61,"_43":60,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":59,"_43":60,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":55,"_43":60,"_45":-5,"_47":-5,"_49":50},{"_39":51,"_41":52,"_43":60,"_45":-5,"_47":-5,"_49":50},{"_39":40,"_41":42,"_43":60,"_45":-5,"_47":-5,"_49":50},"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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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648],[595,596,597,598,599],{"_433":509,"_87":509,"_86":435,"_406":619},{"_433":483,"_87":483,"_86":435,"_406":614},{"_433":609,"_87":609,"_86":435,"_406":610},{"_433":604,"_87":604,"_86":435,"_406":605},{"_433":600,"_87":600,"_86":435,"_406":601},"nvidia.partners",[602],{"_245":603,"_440":600,"_441":26,"_86":477},"edge","app.hushly.com",[606],{"_245":607,"_440":604,"_441":26,"_86":608},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[611],{"_245":612,"_440":609,"_441":26,"_86":613},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735],[687,688,689,690],{"_433":702,"_87":702,"_86":435,"_406":703},{"_433":697,"_87":697,"_86":435,"_406":698},{"_433":496,"_87":496,"_86":435,"_406":694},{"_433":609,"_87":609,"_86":435,"_406":691},[692],{"_245":693,"_440":609,"_441":135,"_86":613},"_gh_sess",[695],{"_245":696,"_440":496,"_441":135,"_86":503},"YSC","bizible.com",[699],{"_245":700,"_440":697,"_441":26,"_86":701},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[704],{"_245":705,"_440":702,"_441":26,"_86":706},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_245":777,"_440":555,"_441":26,"_86":778},{"_245":762,"_440":776,"_441":135,"_86":763},{"_245":745,"_440":558,"_441":26,"_86":670},{"_245":745,"_440":658,"_441":26,"_86":670},{"_245":774,"_440":555,"_441":26,"_86":775},{"_245":762,"_440":561,"_441":135,"_86":763},{"_245":772,"_440":555,"_441":26,"_86":773},{"_245":762,"_440":558,"_441":135,"_86":763},{"_245":770,"_440":657,"_441":26,"_86":771},{"_245":762,"_440":679,"_441":135,"_86":763},{"_245":742,"_440":558,"_441":26,"_86":744},{"_245":768,"_440":555,"_441":135,"_86":769},{"_245":766,"_440":555,"_441":26,"_86":767},{"_245":764,"_440":555,"_441":135,"_86":765},{"_245":762,"_440":653,"_441":135,"_86":763},{"_245":760,"_440":555,"_441":26,"_86":761},{"_245":758,"_440":555,"_441":26,"_86":759},{"_245":756,"_440":555,"_441":135,"_86":757},{"_245":754,"_440":662,"_441":26,"_86":755},{"_245":742,"_440":653,"_441":26,"_86":744},{"_245":752,"_440":555,"_441":26,"_86":753},{"_245":750,"_440":555,"_441":135,"_86":751},{"_245":748,"_440":555,"_441":26,"_86":749},{"_245":746,"_440":555,"_441":26,"_86":747},{"_245":745,"_440":555,"_441":26,"_86":670},{"_245":742,"_440":743,"_441":26,"_86":744},{"_245":740,"_440":555,"_441":26,"_86":741},{"_245":738,"_440":555,"_441":135,"_86":739},{"_245":736,"_440":555,"_441":26,"_86":737},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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