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Model Overview

The TokenClassification Model in TAO supports Named Entity Recognition (NER) and other token-level classification tasks, as long as the data follows the format specified below. This model card will focus on the NER task.

Named entity recognition (NER), also referred to as entity chunking, identification or extraction, is the task of detecting and classifying key information (entities) in text. In other words, a NER model takes a piece of text as input and for each word in the text, the model identifies a category the word belongs to.\nFor example, in a sentence: Mary lives in Santa Clara and works at NVIDIA, the model should detect that Mary is a person, Santa Clara is a location and NVIDIA is a company.

Trained or fine-tuned NeMo models (with the file extenstion .nemo) can be converted to Riva models (with the file extension .riva) and then deployed. Here is a pre-trained Riva model for NER with BERT in English.

Model Architecture

The current version of the Named Entity Recognition model consists of the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model [1] followed by a token classification head. All model parameters are jointly fine-tuned on the downstream task. More specifically, an\ninput text is fed to the BERT model, and then the [CLS] representation of the text sequence is passed to the classification layer(s).

Training

The model was trained with NeMo BERT base uncased checkpoint.

Dataset

The model was trained on GMB (Groningen Meaning Bank) corpus for entity recognition.\nThe GMB dataset is a fairly large corpus with a lot of annotations. Note, that GMB is not completely human annotated and it’s not considered 100% correct. The data is labeled using the IOB format (short for inside, outside, beginning).\nThe following classes appear in the dataset:

LOC = Geographical Entity
ORG = Organization
PER = Person
GPE = Geopolitical Entity
TIME = Time indicator
ART = Artifact
EVE = Event
NAT = Natural Phenomenon

For this model, the classes ART, EVE, and NAT were combined into a MISC class due to small number of examples for these classes.

Pre-processed data that was used for training and evaluation could be found here.

Performance

Each word in the input sequence could be split into one or more tokens, as a result, there are two possible ways of the model evaluation:

marking the whole entity as a single label
perform evaluation on the sub token level

Here, the first approach was applied, and the predictions for the first token of the input were used to label the whole word.\nDue to high class unbalancing, the suggested metric for this model is F1 score (with macro averaging).

Evaluation on the GMB dataset dev set:

                            precision    recall  f1-score   support\n          O (label id: 0)     0.9913    0.9917    0.9915    131141\n      B-GPE (label id: 1)     0.9574    0.9420    0.9496      2362\n      B-LOC (label id: 2)     0.8402    0.9037    0.8708      5346\n     B-MISC (label id: 3)     0.4124    0.3077    0.3524       130\n      B-ORG (label id: 4)     0.7732    0.6805    0.7239      2980\n      B-PER (label id: 5)     0.8335    0.8510    0.8422      2577\n     B-TIME (label id: 6)     0.9176    0.9133    0.9154      2975\n      I-GPE (label id: 7)     0.8889    0.3478    0.5000        23\n      I-LOC (label id: 8)     0.7782    0.7835    0.7808      1030\n     I-MISC (label id: 9)     0.3036    0.2267    0.2595        75\n     I-ORG (label id: 10)     0.7712    0.7466    0.7587      2384\n     I-PER (label id: 11)     0.8710    0.8820    0.8765      2687\n    I-TIME (label id: 12)     0.8255    0.8273    0.8264       938\n    \n                 accuracy                         0.9689    154648\n                macro avg     0.7818    0.7234    0.7421    154648\n             weighted avg     0.9685    0.9689    0.9686    154648\n

How to use this model

The model is available for use in the NeMo toolkit [2], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.

Input

The model takes text as input.

Output

The model outputs category labels for each token in the input text.

Automatically load the model from NGC

import nemo\nimport nemo.collections.nlp as nemo_nlp\nmodel = nemo_nlp.models.TokenClassificationModel.from_pretrained(model_name=\"ner_en_bert\")\nmodel.add_predictions(['we bought four shirts from the nvidia gear store in santa clara.', 'NVIDIA is a company.'])\n

Limitations

The length of the input text is currently constrained by the maximum sequence length of the BERT base uncased model, which is 512 tokens after tokenization. The punctuation model supports commas, periods and question marks.

References

[1] Devlin, Jacob, et al. \"Bert: Pre-training of deep bidirectional transformers for language understanding.\" arXiv preprint arXiv:1810.04805 (2018).

[2] NVIDIA NeMo Toolkit

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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This model card will focus on the NER task.\n\nNamed entity recognition (NER), also referred to as entity chunking, identification or extraction, is the task of detecting and classifying key information (entities) in text. In other words, a NER model takes a piece of text as input and for each word in the text, the model identifies a category the word belongs to.\nFor example, in a sentence: `Mary lives in Santa Clara and works at NVIDIA`, the model should detect that `Mary` is a person, `Santa Clara` is a location and `NVIDIA` is a company.\n\nTrained or fine-tuned NeMo models (with the file extenstion `.nemo`) can be converted to Riva models (with the file extension `.riva`) and then deployed. [Here](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/namedentityrecognition_english_bert) is a pre-trained Riva model for NER with BERT in English.\n\n## Model Architecture\n\nThe current version of the Named Entity Recognition model consists of the pre-trained [Bidirectional Encoder Representations from Transformers (BERT)](https://arxiv.org/pdf/1810.04805.pdf) model [1] followed by a token classification head. All model parameters are jointly fine-tuned on the downstream task. More specifically, an \ninput text is fed to the BERT model, and then the [CLS] representation of the text sequence is passed to the classification layer(s).\n\n## Training\n\nThe model was trained with [NeMo BERT base uncased checkpoint](https://ngc.nvidia.com/catalog/models/nvidia:bertbaseuncasedfornemo).\n\n### Dataset\n\nThe model was trained on [GMB (Groningen Meaning Bank)](http://www.let.rug.nl/bjerva/gmb/about.php) corpus for entity recognition.\nThe GMB dataset is a fairly large corpus with a lot of annotations. Note, that GMB is not completely human annotated and it’s not considered 100% correct. The data is labeled using the [IOB format](https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)) (short for inside, outside, beginning).\nThe following classes appear in the dataset:\n\n- LOC = Geographical Entity\n- ORG = Organization\n- PER = Person\n- GPE = Geopolitical Entity\n- TIME = Time indicator\n- ART = Artifact\n- EVE = Event\n- NAT = Natural Phenomenon\n\nFor this model, the classes ART, EVE, and NAT were combined into a MISC class due to small number of examples for these classes.\n\nPre-processed data that was used for training and evaluation could be found [here](https://dldata-public.s3.us-east-2.amazonaws.com/gmb_v_2.2.0_clean.zip).\n\n## Performance\n\nEach word in the input sequence could be split into one or more tokens, as a result, there are two possible ways of the model evaluation: \n- marking the whole entity as a single label\n- perform evaluation on the sub token level\n\nHere, the first approach was applied, and the predictions for the first token of the input were used to label the whole word. \nDue to high class unbalancing, the suggested metric for this model is F1 score (with macro averaging).\n\n\nEvaluation on the GMB dataset dev set:\n \n precision recall f1-score support\n O (label id: 0) 0.9913 0.9917 0.9915 131141\n B-GPE (label id: 1) 0.9574 0.9420 0.9496 2362\n B-LOC (label id: 2) 0.8402 0.9037 0.8708 5346\n B-MISC (label id: 3) 0.4124 0.3077 0.3524 130\n B-ORG (label id: 4) 0.7732 0.6805 0.7239 2980\n B-PER (label id: 5) 0.8335 0.8510 0.8422 2577\n B-TIME (label id: 6) 0.9176 0.9133 0.9154 2975\n I-GPE (label id: 7) 0.8889 0.3478 0.5000 23\n I-LOC (label id: 8) 0.7782 0.7835 0.7808 1030\n I-MISC (label id: 9) 0.3036 0.2267 0.2595 75\n I-ORG (label id: 10) 0.7712 0.7466 0.7587 2384\n I-PER (label id: 11) 0.8710 0.8820 0.8765 2687\n I-TIME (label id: 12) 0.8255 0.8273 0.8264 938\n \n accuracy 0.9689 154648\n macro avg 0.7818 0.7234 0.7421 154648\n weighted avg 0.9685 0.9689 0.9686 154648\n\n\n## How to use this model\n\nThe model is available for use in the NeMo toolkit [2], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.\n\n### Input\n\nThe model takes text as input.\n\n### Output\n\nThe model outputs category labels for each token in the input text.\n\nAutomatically load the model from NGC\n-------------------------------------\n\n\timport nemo\n\timport nemo.collections.nlp as nemo_nlp\n\tmodel = nemo_nlp.models.TokenClassificationModel.from_pretrained(model_name=\"ner_en_bert\")\n model.add_predictions(['we bought four shirts from the nvidia gear store in santa clara.', 'NVIDIA is a company.'])\n\n## Limitations\n\nThe length of the input text is currently constrained by the maximum sequence length of the BERT base uncased model, which is 512 tokens after tokenization. The punctuation model supports commas, periods and question marks.\n\n## References\n\n[1] [Devlin, Jacob, et al. \"Bert: Pre-training of deep bidirectional transformers for language understanding.\" arXiv preprint arXiv:1810.04805 (2018).](https://arxiv.org/pdf/1810.04805.pdf)\n\n[2] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)\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","NER En Bert",["D",1680638543710],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/ner_en_bert","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[242,243,244,245],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_210":257,"_247":257,"_248":258,"_250":251},{"_210":255,"_247":255,"_248":256,"_250":251},{"_210":253,"_247":253,"_248":254,"_250":251},{"_210":246,"_247":246,"_248":249,"_250":251},"PytorchLightning","value","filter",{"_252":251,"_247":246},"category","label","field","NER",{"_252":251,"_247":253},"En",{"_252":251,"_247":255},"Bert",{"_252":251,"_247":257},{"_107":1434,"_109":1435},"ngcApps",[1387],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1368":1369,"_1370":1371,"_1372":1373,"_1374":1375,"_1376":270,"_139":294,"_1377":1378,"_1379":55,"_1380":86,"_1381":86,"_1382":1383,"_1384":86,"_1385":1386},"amplitude",{"_1365":1366,"_968":-7,"_1367":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1359":1360,"_1361":1362,"_1363":1364},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1243":375,"_1244":1245,"_1246":1247,"_1248":1249},"announcements",[],"consentState",{"_332":1233,"_1234":86,"_1235":30,"_1236":30,"_1237":1238},"consentConfig",{"_314":315,"_316":317,"_318":319,"_320":321,"_322":323,"_324":325,"_326":327,"_328":329,"_330":331,"_332":333},"showContactAdmin",1784133778647,[],[],[308,309,310,311,312,313],{"_44":59,"_46":70,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":68,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":66,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":60,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":45,"_46":47,"_48":102,"_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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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,[484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518],[356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372],{"_373":480,"_210":480,"_168":375,"_346":481},{"_373":475,"_210":475,"_168":375,"_346":476},{"_373":471,"_210":471,"_168":375,"_346":472},{"_373":466,"_210":466,"_168":375,"_346":467},{"_373":461,"_210":461,"_168":375,"_346":462},{"_373":449,"_210":449,"_168":375,"_346":450},{"_373":437,"_210":437,"_168":375,"_346":438},{"_373":432,"_210":432,"_168":375,"_346":433},{"_373":421,"_210":421,"_168":375,"_346":422},{"_373":416,"_210":416,"_168":375,"_346":417},{"_373":411,"_210":411,"_168":375,"_346":412},{"_373":406,"_210":406,"_168":375,"_346":407},{"_373":401,"_210":401,"_168":375,"_346":402},{"_373":396,"_210":396,"_168":375,"_346":397},{"_373":391,"_210":391,"_168":375,"_346":392},{"_373":384,"_210":384,"_168":375,"_346":385},{"_373":374,"_210":374,"_168":375,"_346":376},"hostName","amazon-adsystem.com","",[377,378],{"_78":383,"_380":374,"_381":30,"_168":382},{"_78":379,"_380":374,"_381":30,"_168":382},"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",[386,387],{"_78":390,"_380":384,"_381":30,"_168":389},{"_78":388,"_380":384,"_381":30,"_168":389},"IDE","This domain is owned by Doubleclick (Google). 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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",[413],{"_78":414,"_380":411,"_381":30,"_168":415},"sp","Testing","google.com",[418],{"_78":419,"_380":416,"_381":30,"_168":420},"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",[423,424,425,426],{"_78":431,"_380":421,"_381":30,"_168":428},{"_78":430,"_380":421,"_381":30,"_168":428},{"_78":429,"_380":421,"_381":30,"_168":428},{"_78":427,"_380":421,"_381":30,"_168":428},"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",[434],{"_78":435,"_380":432,"_381":30,"_168":436},"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",[439,440,441,442],{"_78":448,"_380":437,"_381":30,"_168":444},{"_78":446,"_380":437,"_381":30,"_168":447},{"_78":445,"_380":437,"_381":30,"_168":444},{"_78":443,"_380":437,"_381":30,"_168":444},"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",[451,452,453,454],{"_78":460,"_380":449,"_381":30,"_168":458},{"_78":459,"_380":449,"_381":30,"_168":458},{"_78":457,"_380":449,"_381":30,"_168":458},{"_78":455,"_380":449,"_381":30,"_168":456},"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",[463],{"_78":464,"_380":461,"_381":30,"_168":465},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[468],{"_78":469,"_380":466,"_381":30,"_168":470},"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",[473],{"_78":474,"_380":471,"_381":30,"_168":415},"R","6sc.co",[477],{"_78":478,"_380":475,"_381":30,"_168":479},"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.",4,[607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627],[572,573,574,575,576],{"_373":466,"_210":466,"_168":375,"_346":598},{"_373":432,"_210":432,"_168":375,"_346":593},{"_373":586,"_210":586,"_168":375,"_346":587},{"_373":582,"_210":582,"_168":375,"_346":583},{"_373":577,"_210":577,"_168":375,"_346":578},"nvcrm.my.site.com",[579],{"_78":580,"_380":577,"_381":30,"_168":581},"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",[584],{"_78":585,"_380":582,"_381":30,"_168":415},"edge","github.com",[588,589],{"_78":592,"_380":586,"_381":30,"_168":591},{"_78":590,"_380":586,"_381":30,"_168":591},"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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