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Model Overview

Description:

ESM-2nv is a protein language model that provides numerical embeddings for each amino acid in a protein sequence. It is developed using the BioNeMo Framework. The embeddings from its encoder can be used as features for predictive models. The 650M model has 33 layers, 20 attention heads, a hidden space dimension of 1280, and contains 650M parameters. This model is ready for commercial use.

Third-Party Community Consideration

This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case [1]; see link to Hugging Face Model Card for ESM-2 650M model.

References:

[1] Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y. and dos Santos Costa, A., 2023. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, 379(6637), pp.1123-1130.\n[2] \"UniProt: the universal protein knowledgebase in 2021.\" Nucleic acids research 49, no. D1 (2021): D480-D489.\n[3] Devlin, J., Chang, M.W., Lee, K. and Toutanova, K., 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.\n

Model Architecture:

Architecture Type: BERT
\nNetwork Architecture: ESM-2

Input:

Input Type(s): Text (Protein Sequences)
\nInput Parameters: 1D
\nOther Properties Related to Input: Protein sequence represented as a string of canonical amino acids, of maximum length 1022. Longer sequences are automatically truncated to this length.

Output:

Output Type(s): Text (Protein Sequences)
\nOutput Parameters: 1D
\nOther Properties Related to Output: Numeric vector with one float-point value corresponding to an embedding for each amino acid in the input protein sequence. Maximum output length is 1022 embeddings - one embedding vector per amino acid.

Software Integration:

Runtime Engine(s):

BioNeMo, NeMo 1.2

Supported Hardware Microarchitecture Compatibility:

Ampere
Hopper
Volta

Preferred/Supported Operating System(s):

Linux

Model Version(s):

esm2nv_650M_converted.nemo, version 1.0

Training & Evaluation:

Training Dataset:

Links:

UniRef50
UniRef90

Data Collection Method by dataset

Human

Properties: UniRef50 (release 04/2021) was used for training [2]. The representative sequence for each UniRef50 cluster was selected, resulting in 49,874,565 protein sequences. The sequences were randomly split with 249,372 sequences in validation and 49,625,193 in training. All training sequences that matched a validation sequence with 50% sequence identity were removed from the train set, resulting in 49,425,807 train sequences. A sampling dataset of UniRef90 sequences was created based on any UniRef90 representatives and cluster members that had complete sequences available from UniRef90 or UniRef100, and filtered to UniRef90 sequences for clusters that corresponded to the UniRef50 train set. The UniRef90 dataset was combined with the filtered UniRef50 training dataset to create the sampling fasta file. A mapping file was created to enable rapid replacement of UniRef50 sequences with a sequence sampled uniformly from the corresponding records in the sampling fasta file during each training update. The UniRef50 training fasta was sorted in the order of occurrence of records in column 1 of the mapping file. The UniRef90+UniRef50 sampling fasta file was sorted in the order of occurrence of records in column 2 of the mapping file. Protein sequences longer than 1024 amino acids were cropped to 1022 from sequence start [3].

Unlike ESM-2 pre-training data, the curated pre-training dataset provided with ESM-2nv release contains hits for de novo proteins, since sequences in UniRef100, UniRef90, and UniRef50 with high sequence similarity to a non-public 81 de novo proteins [1] are not filtered.

Evaluation Dataset:

Link: FLIP – secondary structure, conservation,subcellular localization, meltome, GB1 activity
\nData Collection Method by dataset

Hybrid: Human & Automated

Labeling Method by dataset

Hybrid: Human & Automated

Properties:\nThe FLIP datasets evaluate the performance of the model on five specific downstream tasks for proteins. It provides pre-defined splits for fine-tuning a pretrained model using task-specific train and validation examples, and subsequently evaluating it on a task-specific test split.

The secondary structure FLIP dataset contains experimental secondary structures, with 9712 proteins for model finetuning, 1080 proteins for validation, and 648 proteins for testing.

Conservation dataset contains conservation scores of the residues of protein sequences with 9392 proteins for training, 555 proteins for validation, and 519 proteins for testing.

Subcellular localization dataset contains protein subcellular locations with 9503 proteins for training, 1678 proteins for validation, and 2768 proteins for testing.

Meltome dataset contains experimental melting temperatures for proteins, with 22335 proteins for training, 2482 proteins for validation, and 3134 proteins for testing.

The GB1 activity dataset contains experimental binding affinities of GB1 protein variants with variation at four sites (V39, D40, G41 and V54) measured in a binding assay, with 6289 proteins for training, 699 proteins for validation, and 1745 proteins for testing.

Inference:

Engine: BioNeMo, NeMo
\nTest Hardware:

Ampere
Hopper
Volta

Accuracy Benchmarks

The accuracy of ESM2-nv was measured using the Fitness Landscape Inference for Proteins (FLIP) benchmarks {cite:p}dallago2021flip, which involve training a downstream task on top of the ESM2-nv model and measuring some characteristic:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\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\">

FLIP type	Dataset	Dataset Split for Measurement	Metric	650m	3b
Secondary Structure	sequences.fasta	test	accuracy (%)	85.2	85.7
GB1	two_vs_rest.fasta	test	rmse (score)	1.29	1.28
Residue Conservation	sequences.fasta	test	accuracy (%)	33.2	34.5
Protein Melting Point (Meltome)	mixed_split.fasta	test	rmse (°C)	7.19	6.68

License

ESM-2nv is as provided under Apache 2.0

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\n\n### Third-Party Community Consideration\n\nThis model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case [1]; see link to [Hugging Face Model Card for ESM-2 650M model](https://huggingface.co/facebook/esm2_t36_650M_UR50D).\n\n### References:\n\n[1] Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y. and dos Santos Costa, A., 2023. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, 379(6637), pp.1123-1130.\n[2] \"UniProt: the universal protein knowledgebase in 2021.\" Nucleic acids research 49, no. D1 (2021): D480-D489.\n[3] Devlin, J., Chang, M.W., Lee, K. and Toutanova, K., 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.\n
\n\n### Model Architecture:\n\n**Architecture Type:** BERT
\n**Network Architecture:** ESM-2
\n\n### Input:\n\n**Input Type(s):** Text (Protein Sequences)
\n**Input Parameters:** 1D
\n**Other Properties Related to Input:** Protein sequence represented as a string of canonical amino acids, of maximum length 1022. Longer sequences are automatically truncated to this length.
\n\n### Output:\n\n**Output Type(s):** Text (Protein Sequences)
\n**Output Parameters:** 1D
\n**Other Properties Related to Output:** Numeric vector with one float-point value corresponding to an embedding for each amino acid in the input protein sequence. Maximum output length is 1022 embeddings - one embedding vector per amino acid.
\n\n### Software Integration:\n\n**Runtime Engine(s):**\n* BioNeMo, NeMo 1.2
\n\n**Supported Hardware Microarchitecture Compatibility:**
\n* Ampere
\n* Hopper
\n* Volta
\n\n**Preferred/Supported Operating System(s):**
\n* Linux
\n\n### Model Version(s):\n\nesm2nv_650M_converted.nemo, version 1.0
\n\n## Training & Evaluation:\n\n### Training Dataset:\n\n**Links:** \n* [UniRef50](https://ftp.uniprot.org/pub/databases/uniprot/uniref/uniref50)
\n* [UniRef90](https://ftp.uniprot.org/pub/databases/uniprot/uniref/uniref90)
\n\n**Data Collection Method by dataset**
\n* Human
\n\n**Properties:** UniRef50 (release 04/2021) was used for training [2]. The representative sequence for each UniRef50 cluster was selected, resulting in 49,874,565 protein sequences. The sequences were randomly split with 249,372 sequences in validation and 49,625,193 in training. All training sequences that matched a validation sequence with 50% sequence identity were removed from the train set, resulting in 49,425,807 train sequences. A sampling dataset of UniRef90 sequences was created based on any UniRef90 representatives and cluster members that had complete sequences available from UniRef90 or UniRef100, and filtered to UniRef90 sequences for clusters that corresponded to the UniRef50 train set. The UniRef90 dataset was combined with the filtered UniRef50 training dataset to create the sampling fasta file. A mapping file was created to enable rapid replacement of UniRef50 sequences with a sequence sampled uniformly from the corresponding records in the sampling fasta file during each training update. The UniRef50 training fasta was sorted in the order of occurrence of records in column 1 of the mapping file. The UniRef90+UniRef50 sampling fasta file was sorted in the order of occurrence of records in column 2 of the mapping file. Protein sequences longer than 1024 amino acids were cropped to 1022 from sequence start [3].
\n\nUnlike ESM-2 pre-training data, the curated pre-training dataset provided with ESM-2nv release contains hits for de novo proteins, since sequences in UniRef100, UniRef90, and UniRef50 with high sequence similarity to a non-public 81 de novo proteins [1] are not filtered.
\n\n### Evaluation Dataset:\n\n**Link:** [FLIP – secondary structure, conservation,subcellular localization, meltome, GB1 activity](http://data.bioembeddings.com/public/FLIP/fasta/)
\n**Data Collection Method by dataset**
\n* Hybrid: Human & Automated
\n\n**Labeling Method by dataset**
\n* Hybrid: Human & Automated
\n\n**Properties:**\nThe FLIP datasets evaluate the performance of the model on five specific downstream tasks for proteins. It provides pre-defined splits for fine-tuning a pretrained model using task-specific train and validation examples, and subsequently evaluating it on a task-specific test split.\n\nThe secondary structure FLIP dataset contains experimental secondary structures, with 9712 proteins for model finetuning, 1080 proteins for validation, and 648 proteins for testing.\n\nConservation dataset contains conservation scores of the residues of protein sequences with 9392 proteins for training, 555 proteins for validation, and 519 proteins for testing.\n\nSubcellular localization dataset contains protein subcellular locations with 9503 proteins for training, 1678 proteins for validation, and 2768 proteins for testing.\n\nMeltome dataset contains experimental melting temperatures for proteins, with 22335 proteins for training, 2482 proteins for validation, and 3134 proteins for testing.\n\nThe GB1 activity dataset contains experimental binding affinities of GB1 protein variants with variation at four sites (V39, D40, G41 and V54) measured in a binding assay, with 6289 proteins for training, 699 proteins for validation, and 1745 proteins for testing.
\n\n### Inference:\n\n**Engine:** BioNeMo, NeMo
\n**Test Hardware:**
\n* Ampere
\n* Hopper
\n* Volta
\n\n#### Accuracy Benchmarks\n\nThe accuracy of ESM2-nv was measured using the Fitness Landscape Inference for Proteins (FLIP) benchmarks {cite:p}`dallago2021flip`, which involve training a downstream task on top of the ESM2-nv model and measuring some characteristic:\n\n| FLIP type | Dataset | Dataset Split for Measurement | Metric | 650m | 3b |\n|---------------------------------|-------------------|-------------------------------|---------------------|-------|------|\n| Secondary Structure | sequences.fasta | test | accuracy (%) | 85.2 | 85.7 |\n| GB1 | two_vs_rest.fasta | test | rmse (score) | 1.29 | 1.28 |\n| Residue Conservation | sequences.fasta | test | accuracy (%) | 33.2 | 34.5 |\n| Protein Melting Point (Meltome) | mixed_split.fasta | test | rmse (°C) | 7.19 | 6.68 |\n\n### License\n\nESM-2nv is as provided under Apache 2.0","displayName","ESM-2nv 8M",["D",1737486133611],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/clara/models/esm2nv8m","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Clara.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[243,244,245,246,247,248,249,250],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_210":275,"_252":276,"_253":277,"_255":256},{"_210":271,"_252":272,"_253":273,"_255":256},{"_210":267,"_252":268,"_253":269,"_255":256},{"_210":265,"_252":265,"_253":266,"_255":256},{"_210":263,"_252":263,"_253":264,"_255":256},{"_210":261,"_252":261,"_253":262,"_255":256},{"_210":259,"_252":259,"_253":260,"_255":256},{"_210":251,"_252":251,"_253":254,"_255":256},"PyTorch with NeMo","value","filter",{"_257":258,"_252":251},"category","label","field","framework","PyTorch",{"_257":258,"_252":259},"NSPECT-EPER-HUN5",{"_257":256,"_252":261},"NeMo",{"_257":258,"_252":263},"Megatron-LM",{"_257":258,"_252":265},"Healthcare","indus_healthcare",{"_257":270,"_252":268},"industry","DL","soln_dl",{"_257":274,"_252":272},"technology","Clara","pltfm_clara",{"_257":278,"_252":276},"platform",{"_106":1453,"_108":1454},"ngcApps",[1406],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1387":1388,"_1389":1390,"_1391":1392,"_1393":1394,"_1395":290,"_138":314,"_1396":1397,"_1398":56,"_1399":87,"_1400":87,"_1401":1402,"_1403":87,"_1404":1405},"amplitude",{"_1384":1385,"_987":-7,"_1386":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1378":1379,"_1380":1381,"_1382":1383},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1262":228,"_1263":1264,"_1265":1266,"_1267":1268},"announcements",[],"consentState",{"_353":1252,"_1253":87,"_1254":31,"_1255":31,"_1256":1257},"consentConfig",{"_335":336,"_337":338,"_339":340,"_341":342,"_343":344,"_345":346,"_347":348,"_349":350,"_351":352,"_353":354},"showContactAdmin",1784094607228,[],[],[328,329,330,331,332,333,334],{"_45":60,"_47":71,"_49":62,"_51":63,"_53":72,"_55":56},{"_45":60,"_47":69,"_49":62,"_51":63,"_53":70,"_55":56},{"_45":60,"_47":67,"_49":62,"_51":63,"_53":161,"_55":56},{"_45":60,"_47":65,"_49":62,"_51":63,"_53":66,"_55":56},{"_45":60,"_47":61,"_49":62,"_51":63,"_53":64,"_55":56},{"_45":57,"_47":58,"_49":50,"_51":52,"_53":59,"_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. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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For more information about our privacy practices, please see our Privacy Policy.","mainText","Cookie Settings","alertNoticeText",{"_1244":1245,"_1246":1247,"_1248":1249,"_1250":1251},"bannerAcceptLabel","Accept All","bannerRejectLabel","Reject Optional","bannerSettingsLabel","Manage Settings","preferenceSaveLabel","Save Choices","preferenceRejectLabel","Decline All","alwaysActiveText","Always Active","groups",[355,356,357,358,359],{"_360":896,"_79":897,"_168":898,"_364":87,"_365":16,"_367":899,"_369":900},{"_360":686,"_79":687,"_168":688,"_364":31,"_365":54,"_367":689,"_369":690},{"_360":586,"_79":587,"_168":588,"_364":31,"_365":59,"_367":589,"_369":590},{"_360":371,"_79":372,"_168":373,"_364":31,"_365":374,"_367":375,"_369":376},{"_360":361,"_79":362,"_168":363,"_364":31,"_365":366,"_367":368,"_369":370},"optanonGroupId","C0005","Social Media Cookies","These cookies are set by a range of social media services that we have added to the site to enable you to share our content with your friends and networks. They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools.","alwaysActive","order",11,"cookies",[],"hosts",[],"C0004","Advertising Cookies","These cookies record your visit to our websites, the pages you have visited and the links you have followed to influence the advertisements that you see on other websites. These cookies and the information they collect may be managed by other companies, including our advertising partners, and may be used to build a profile of your interests and show you relevant advertising on other sites. We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538],[377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393],{"_394":500,"_210":500,"_168":228,"_367":501},{"_394":495,"_210":495,"_168":228,"_367":496},{"_394":491,"_210":491,"_168":228,"_367":492},{"_394":486,"_210":486,"_168":228,"_367":487},{"_394":481,"_210":481,"_168":228,"_367":482},{"_394":469,"_210":469,"_168":228,"_367":470},{"_394":457,"_210":457,"_168":228,"_367":458},{"_394":452,"_210":452,"_168":228,"_367":453},{"_394":441,"_210":441,"_168":228,"_367":442},{"_394":436,"_210":436,"_168":228,"_367":437},{"_394":431,"_210":431,"_168":228,"_367":432},{"_394":426,"_210":426,"_168":228,"_367":427},{"_394":421,"_210":421,"_168":228,"_367":422},{"_394":416,"_210":416,"_168":228,"_367":417},{"_394":411,"_210":411,"_168":228,"_367":412},{"_394":404,"_210":404,"_168":228,"_367":405},{"_394":395,"_210":395,"_168":228,"_367":396},"hostName","amazon-adsystem.com",[397,398],{"_79":403,"_400":395,"_401":31,"_168":402},{"_79":399,"_400":395,"_401":31,"_168":402},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[406,407],{"_79":410,"_400":404,"_401":31,"_168":409},{"_79":408,"_400":404,"_401":31,"_168":409},"IDE","This domain is owned by Doubleclick (Google). 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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",[459,460,461,462],{"_79":468,"_400":457,"_401":31,"_168":464},{"_79":466,"_400":457,"_401":31,"_168":467},{"_79":465,"_400":457,"_401":31,"_168":464},{"_79":463,"_400":457,"_401":31,"_168":464},"personalization_id","This domain is owned by Twitter. The main business activity is: Social Networking 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.",[626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646],[591,592,593,594,595],{"_394":486,"_210":486,"_168":228,"_367":617},{"_394":452,"_210":452,"_168":228,"_367":612},{"_394":605,"_210":605,"_168":228,"_367":606},{"_394":601,"_210":601,"_168":228,"_367":602},{"_394":596,"_210":596,"_168":228,"_367":597},"nvcrm.my.site.com",[598],{"_79":599,"_400":596,"_401":31,"_168":600},"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",[603],{"_79":604,"_400":601,"_401":31,"_168":435},"edge","github.com",[607,608],{"_79":611,"_400":605,"_401":31,"_168":610},{"_79":609,"_400":605,"_401":31,"_168":610},"logged_in","This domain is owned by Github. 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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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