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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",[237,238,239,240,241,242,243,244,245,246,247,248],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_205":283,"_250":284,"_251":285,"_253":254},{"_205":281,"_250":281,"_251":282,"_253":254},{"_205":279,"_250":279,"_251":280,"_253":254},{"_205":275,"_250":276,"_251":277,"_253":254},{"_205":273,"_250":273,"_251":274,"_253":254},{"_205":269,"_250":270,"_251":271,"_253":254},{"_205":267,"_250":267,"_251":268,"_253":254},{"_205":265,"_250":265,"_251":266,"_253":254},{"_205":261,"_250":262,"_251":263,"_253":254},{"_205":259,"_250":259,"_251":260,"_253":254},{"_205":257,"_250":257,"_251":258,"_253":254},{"_205":249,"_250":249,"_251":252,"_253":254},"PyTorch","value","filter",{"_255":256,"_250":249},"category","label","field","framework","NLP",{"_255":254,"_250":257},"Natural-Language-Understanding",{"_255":254,"_250":259},"Natural Language Processing","uscs_natural_language_processing",{"_255":264,"_250":262},"application","Megatron",{"_255":254,"_250":265},"Language-Modeling",{"_255":254,"_250":267},"Healthcare","indus_healthcare",{"_255":272,"_250":270},"industry","Deep-Learning",{"_255":254,"_250":273},"Conversational AI","soln_conversational_ai",{"_255":278,"_250":276},"technology","BioMegatron",{"_255":254,"_250":279},"BERT",{"_255":254,"_250":281},"AI","soln_ai",{"_255":278,"_250":284},{"_106":1269,"_108":1270},"ngcApps",[1222],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1203":1204,"_1205":1206,"_1207":1208,"_1209":1210,"_1211":297,"_137":325,"_1212":1213,"_1214":56,"_1215":85,"_1216":85,"_1217":1218,"_1219":85,"_1220":1221},"amplitude",{"_1200":1201,"_837":-7,"_1202":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1194":1195,"_1196":1197,"_1198":1199},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1785904886258,"1.11.0","defaultSearchSuggestions",{"_1077":167,"_1078":1079,"_1080":1081,"_1082":1083},"announcements",[],"consentState",{"_362":1067,"_1068":85,"_1069":31,"_1070":31,"_1071":1072},"consentConfig",{"_344":345,"_346":347,"_348":349,"_350":351,"_352":353,"_354":355,"_356":357,"_358":359,"_360":361,"_362":363},"showContactAdmin",1785904886259,[],[],[339,340,341,342,343],{"_45":59,"_47":69,"_49":101,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":68,"_49":101,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":60,"_49":101,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":101,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56},"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 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.","linkedin.com",[454],{"_77":455,"_409":452,"_410":31,"_166":456},"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. 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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.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.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_zitok","_twpid","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. 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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.",4,[594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618],[565,566,567,568,569],{"_403":478,"_205":478,"_166":167,"_376":589},{"_403":452,"_205":452,"_166":167,"_376":584},{"_403":579,"_205":579,"_166":167,"_376":580},{"_403":574,"_205":574,"_166":167,"_376":575},{"_403":570,"_205":570,"_166":167,"_376":571},"nvidia.partners",[572],{"_77":573,"_409":570,"_410":31,"_166":446},"edge","app.hushly.com",[576],{"_77":577,"_409":574,"_410":31,"_166":578},"*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",[581],{"_77":582,"_409":579,"_410":31,"_166":583},"_octo","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_country_code","developer.nvidia.com","This cookie is likely used to store the country code of the visitor to provide localized content and possibly for legal compliance with regional data privacy laws.","_gdxxxxxxxxxxxxx","nvidianews.nvidia.com","docs.api.nvidia.com","research.nvidia.com","readme_library","wcmmode","ai-podcast.nvidia.com","resources.nvidia.com","geo_locator","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.","crowdsource.nvidia.com","mbox","Cookie set by Adobe SiteCatalyst analytics software - used for measuring the performance of page content using A/B split testing","readme_language","vid","This cookie name 'vid' is used for tracking the search request made by the user on the 'Find a practitioner' page.","at_check","Used by Adobe Target to check if cookies are enabled/supported on the browser","blogs.nvidia.com","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.","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 performance.",[677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705],[657,658,659,660],{"_403":672,"_205":672,"_166":167,"_376":673},{"_403":667,"_205":667,"_166":167,"_376":668},{"_403":465,"_205":465,"_166":167,"_376":664},{"_403":579,"_205":579,"_166":167,"_376":661},[662],{"_77":663,"_409":579,"_410":85,"_166":583},"_gh_sess",[665],{"_77":666,"_409":465,"_410":85,"_166":472},"YSC","bizible.com",[669],{"_77":670,"_409":667,"_410":31,"_166":671},"_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",[674],{"_77":675,"_409":672,"_410":31,"_166":676},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_77":747,"_409":524,"_410":31,"_166":748},{"_77":732,"_409":746,"_410":85,"_166":733},{"_77":715,"_409":527,"_410":31,"_166":640},{"_77":715,"_409":628,"_410":31,"_166":640},{"_77":744,"_409":524,"_410":31,"_166":745},{"_77":732,"_409":530,"_410":85,"_166":733},{"_77":742,"_409":524,"_410":31,"_166":743},{"_77":732,"_409":527,"_410":85,"_166":733},{"_77":740,"_409":627,"_410":31,"_166":741},{"_77":732,"_409":649,"_410":85,"_166":733},{"_77":712,"_409":527,"_410":31,"_166":714},{"_77":738,"_409":524,"_410":85,"_166":739},{"_77":736,"_409":524,"_410":31,"_166":737},{"_77":734,"_409":524,"_410":85,"_166":735},{"_77":732,"_409":623,"_410":85,"_166":733},{"_77":730,"_409":524,"_410":31,"_166":731},{"_77":728,"_409":524,"_410":31,"_166":729},{"_77":726,"_409":524,"_410":85,"_166":727},{"_77":724,"_409":632,"_410":31,"_166":725},{"_77":712,"_409":623,"_410":31,"_166":714},{"_77":722,"_409":524,"_410":31,"_166":723},{"_77":720,"_409":524,"_410":85,"_166":721},{"_77":718,"_409":524,"_410":31,"_166":719},{"_77":716,"_409":524,"_410":31,"_166":717},{"_77":715,"_409":524,"_410":31,"_166":640},{"_77":712,"_409":713,"_410":31,"_166":714},{"_77":710,"_409":524,"_410":31,"_166":711},{"_77":708,"_409":524,"_410":85,"_166":709},{"_77":706,"_409":524,"_410":31,"_166":707},"_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. It contains the current session ID, whether the session is excluded due to sampling, and the expiration date of the session. The cookie is extended for an extra 15 minutes every time the user interacts with the website, up to the maximum user session duration (4 hours).","nvweb_A","_ga","This cookie name is associated with Google Universal Analytics - which is a significant update to Google's more commonly used analytics service. 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.","_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.","tp","This cookie is associated with Adobe Analytics site tracking. It registers if the website's scroll-depth detection is active. This function remembers how far the user has scrolled on the current session across the website's sub-pages.","_hly_vid","This cookie _hly_vid 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.","_session_id","This cookie creates an interim session ID used as an in-session user ID.\n","s_cc","This cookie is associated with the Adobe Site Catalyst. It determines whether cookies are enabled in the web browser.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","_hjSessionUser_xxxxxx","Hotjar cookie that is set when a user first lands on a page with the Hotjar script. 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