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Overview

This is a nemo file for BioMegatron 345m uncased. BioMegatron is Megatron pretrained on uncased 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 original BERT uncased vocabulary learned from Wikipedia and Books corpus.

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.

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. 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 uncased [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 original BERT uncased vocabulary learned from Wikipedia and Books corpus. \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## Performance\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. 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","BioMegatron345mUncased",["D",1680635911500],"publisher","NVIDIA NeMo","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/biomegatron345muncased","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[239,240,241,242,243,244,245,246,247,248,249,250],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_207":285,"_252":286,"_253":287,"_255":256},{"_207":283,"_252":283,"_253":284,"_255":256},{"_207":281,"_252":281,"_253":282,"_255":256},{"_207":277,"_252":278,"_253":279,"_255":256},{"_207":275,"_252":275,"_253":276,"_255":256},{"_207":271,"_252":272,"_253":273,"_255":256},{"_207":269,"_252":269,"_253":270,"_255":256},{"_207":267,"_252":267,"_253":268,"_255":256},{"_207":263,"_252":264,"_253":265,"_255":256},{"_207":261,"_252":261,"_253":262,"_255":256},{"_207":259,"_252":259,"_253":260,"_255":256},{"_207":251,"_252":251,"_253":254,"_255":256},"PyTorch","value","filter",{"_257":258,"_252":251},"category","label","field","framework","NLP",{"_257":256,"_252":259},"Natural-Language-Understanding",{"_257":256,"_252":261},"Natural Language Processing","uscs_natural_language_processing",{"_257":266,"_252":264},"application","Megatron",{"_257":256,"_252":267},"Language-Modeling",{"_257":256,"_252":269},"Healthcare","indus_healthcare",{"_257":274,"_252":272},"industry","Deep-Learning",{"_257":256,"_252":275},"Conversational AI","soln_conversational_ai",{"_257":280,"_252":278},"technology","BioMegatron",{"_257":256,"_252":281},"BERT",{"_257":256,"_252":283},"AI","soln_ai",{"_257":280,"_252":286},{"_108":1462,"_110":1463},"ngcApps",[1415],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1396":1397,"_1398":1399,"_1400":1401,"_1402":1403,"_1404":299,"_139":323,"_1405":1406,"_1407":56,"_1408":87,"_1409":87,"_1410":1411,"_1412":87,"_1413":1414},"amplitude",{"_1393":1394,"_995":-7,"_1395":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1387":1388,"_1389":1390,"_1391":1392},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1270":169,"_1271":1272,"_1273":1274,"_1275":1276},"announcements",[],"consentState",{"_361":1260,"_1261":31,"_1262":31,"_1263":87,"_1264":1265},"consentConfig",{"_343":344,"_345":346,"_347":348,"_349":350,"_351":352,"_353":354,"_355":356,"_357":358,"_359":360,"_361":362},"showContactAdmin",1784098973717,[],[],[337,338,339,340,341],{"_45":60,"_47":71,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":50,"_51":52,"_53":342,"_55":56},{"_45":46,"_47":48,"_49":103,"_51":-5,"_53":-5,"_55":56},4,"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 is used as a URL shortening service, which allows Twitter to collect data on how links are shared.","snowplow.com",[441],{"_79":442,"_408":439,"_409":31,"_168":443},"sp","Testing","google.com",[446],{"_79":447,"_408":444,"_409":31,"_168":448},"NID","This domain is owned by Google Inc. Although Google is primarily known as a search engine, the company provides a diverse range of products and services. Its main source of revenue however is advertising. Google tracks users extensively both through its own products and sites, and the numerous technologies embedded into many millions of websites around the world. It uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","tags.srv.stackadapt.com",[451,452,453,454],{"_79":459,"_408":449,"_409":31,"_168":456},{"_79":458,"_408":449,"_409":31,"_168":456},{"_79":457,"_408":449,"_409":31,"_168":456},{"_79":455,"_408":449,"_409":31,"_168":456},"sa-user-id-v3","This domain is associated with StackAdapt, a platform for programmatic advertising, which suggests that it is used for targeting and advertising purposes.","sa-user-id","sa-user-id-v2","sa-user-id-v4","linkedin.com",[462],{"_79":463,"_408":460,"_409":31,"_168":464},"bcookie","This domain is owned by LinkedIn, the business networking platform. It typically acts as a third party host where website owners have placed one of its content sharing buttons in their pages, although its content and services can be embedded in other ways. Although such buttons add functionality to the website they are on, cookies are set regardless of whether or not the visitor has an active Linkedin profile, or agreed to their terms and conditions. For this reason it is classified as a primarily tracking/targeting domain.","twitter.com",[467,468,469,470],{"_79":476,"_408":465,"_409":31,"_168":472},{"_79":474,"_408":465,"_409":31,"_168":475},{"_79":473,"_408":465,"_409":31,"_168":472},{"_79":471,"_408":465,"_409":31,"_168":472},"personalization_id","This domain is owned by Twitter. The main business activity is: Social Networking Services. Where twitter acts as a third party host, it collects data through a range of plug-ins and integrations, that is primarily used for tracking and targeting.","guest_id_marketing","guest_id","Twitter does not currently provide information on the use of specific cookies.","guest_id_ads","youtube.com",[479,480,481,482],{"_79":488,"_408":477,"_409":31,"_168":486},{"_79":487,"_408":477,"_409":31,"_168":486},{"_79":485,"_408":477,"_409":31,"_168":486},{"_79":483,"_408":477,"_409":31,"_168":484},"VISITOR_INFO1_LIVE","This cookie is used as a unique identifier to track viewing of videos","__Secure-YNID","YouTube is a Google owned platform for hosting and sharing videos. YouTube collects user data through videos embedded in websites, which is aggregated with profile data from other Google services in order to display targeted advertising to web visitors across a broad range of their own and other websites.","__Secure-YEC","__Secure-ROLLOUT_TOKEN","demdex.net",[491],{"_79":492,"_408":489,"_409":31,"_168":493},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[496],{"_79":497,"_408":494,"_409":31,"_168":498},"receive-cookie-deprecation","This domain is owned by im-apps.net which provides information and resources about various instant messaging (IM) applications to help users make informed decisions based on their communication needs.","influ2.com",[501],{"_79":502,"_408":499,"_409":31,"_168":443},"R","6sc.co",[505],{"_79":506,"_408":503,"_409":31,"_168":507},"6suuid","This domain is associated with the company 6Sense Insights, Inc., which provides predictive analytics and account engagement services. 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It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier.","kndctr_F207D74D549850760A4C98C6_AdobeOrg_identity","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","ai-podcast.nvidia.com","This cookie is used to identify the user's session on the website and is often associated with analytics or personalization.","_gcl_au","Used by Google AdSense for experimenting with advertisement efficiency across websites using their services","_mkto_trk","This cookie is associated with an email marketing service provided by Marketo. This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","_gat_gtag_UA_140825383_2","Google Analytics","_zitok","This cookie name is associated with ZoomInfo which is used to maintain a unique visitor identifier and store Personally Identifiable Information.","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","AMP_91bf70adfe","Supports Amplitude product analytics.","This cookie name is associated with StackAdapt Inc. which is used to support the effective management and optimization of digital advertising campaigns by handling information such as visitor actions on websites, visited pages, time spent, page load speed, and more.","build.nvidia.com","kndctr_F207D74D549850760A4C98C6_AdobeOrg_cluster","_cs_nnnnnnnnnnnnn","This is a session cookie. 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It is commonly used for analytics and personalization of content.","marketplace.nvidia.com","AMP_MKTG_92dea1e784","This cookie is typically associated with SnackTools, which is a suite of web applications for creating and sharing multimedia content. It is used to identify a user across sessions and track user activities.","forums.developer.nvidia.com","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","AMP_MKTG_91bf70adfe","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.",[634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654],[599,600,601,602,603],{"_402":494,"_207":494,"_168":169,"_375":625},{"_402":460,"_207":460,"_168":169,"_375":620},{"_402":613,"_207":613,"_168":169,"_375":614},{"_402":609,"_207":609,"_168":169,"_375":610},{"_402":604,"_207":604,"_168":169,"_375":605},"nvcrm.my.site.com",[606],{"_79":607,"_408":604,"_409":31,"_168":608},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[611],{"_79":612,"_408":609,"_409":31,"_168":443},"edge","github.com",[615,616],{"_79":619,"_408":613,"_409":31,"_168":618},{"_79":617,"_408":613,"_409":31,"_168":618},"logged_in","This domain is owned by Github. 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We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","nv.modal.cuda","This cookie name suggests it is related to a modal popup functionality on NVIDIA's website, possibly to remember user preferences or interactions with the modal.","readme_library","docs.api.nvidia.com","_im_ses.1006927","geo_locator","nv.modal.deepstream","openedx-language-preference","learn.nvidia.com","doxygen_width","raytracing-docs.nvidia.com","This cookie is likely used to remember the width preferences of a sidebar or interface element within a documentation platform like Doxygen.","_sfid_eb78","This cookie is categorized as Functionality Cookie.","_im_id.1006927","_hjUserAttributesHash","docs.nvidia.com","This cookie name is associated with Hotjar which is used to know when any user attribute has changed and needs to be updated.","mbox","Cookie set by Adobe SiteCatalyst analytics software - used for measuring the performance of page content using A/B split testing","nv.modal.cudnn","This cookie name suggests it is related to modal popup handling or user interface preferences on NVIDIA's website, specifically for the cuDNN library section.","readme_language","nv.modal.hpc","This cookie name suggests it is related to a modal pop-up on a website, possibly to control the display of a high-priority cookie notice or other important information to the user.","at_check","Used by Adobe Target to check if cookies are enabled/supported on the browser","_biz_uid","This cookie is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.\n","nvHeadless","This cookie is likely used to track if a user is using the site in a 'headless' non-GUI mode, which could be for accessibility reasons or for automated processes.","_biz_nA","C0002","Performance Cookies","These cookies are used to provide quantitative measures of our website visitors, such as the number of times you visit, time on page, your mouse movements, scrolling, clicks and keystroke activity on the websites; other browsing, search, or product research behavior; and what brought you to our site. 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 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