NemoCurator Multilingual Domain Classifier

Model Overview

This is a multilingual text classification model that can enable data annotation, creation of domain-specific blends and the addition of metadata tags. The model classifies documents into one of 26 domain classes:

'Adult', 'Arts_and_Entertainment', 'Autos_and_Vehicles', 'Beauty_and_Fitness', 'Books_and_Literature', 'Business_and_Industrial', 'Computers_and_Electronics', 'Finance', 'Food_and_Drink', 'Games', 'Health', 'Hobbies_and_Leisure', 'Home_and_Garden', 'Internet_and_Telecom', 'Jobs_and_Education', 'Law_and_Government', 'News', 'Online_Communities', 'People_and_Society', 'Pets_and_Animals', 'Real_Estate', 'Science', 'Sensitive_Subjects', 'Shopping', 'Sports', 'Travel_and_Transportation'\n

It supports 52 languages (English and 51 other languages):

\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\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\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\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\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\">

Code	Language Name
ar	Arabic
az	Azerbaijani
bg	Bulgarian
bn	Bengali
ca	Catalan
cs	Czech
da	Danish
de	German
el	Greek
es	Spanish
et	Estonian
fa	Persian
fi	Finnish
fr	French
gl	Galician
he	Hebrew
hi	Hindi
hr	Croatian
hu	Hungarian
hy	Armenian
id	Indonesian
is	Icelandic
it	Italian
ka	Georgian
kk	Kazakh
kn	Kannada
ko	Korean
lt	Lithuanian
lv	Latvian
mk	Macedonian
ml	Malayalam
mr	Marathi
ne	Nepali
nl	Dutch
no	Norwegian
pl	Polish
pt	Portuguese
ro	Romanian
ru	Russian
sk	Slovak
sl	Slovenian
sq	Albanian
sr	Serbian
sv	Swedish
ta	Tamil
tr	Turkish
uk	Ukrainian
ur	Urdu
vi	Vietnamese
ja	Japanese
zh	Chinese

License

This model is released under the NVIDIA Open Model License Agreement.

References

DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
DeBERTa: Decoding-enhanced BERT with Disentangled Attention

Model Architecture

The model architecture is Deberta V3 Base
Context length is 512 tokens

How To Use in NVIDIA NeMo Curator

NeMo Curator improves generative AI model accuracy by processing text, image, and video data at scale for training and customization. It also provides pre-built pipelines for generating synthetic data to customize and evaluate generative AI systems.

The inference code for this model is available through the NeMo Curator GitHub repository. Check out this example notebook to get started.

Input & Output

Input

Input Type: Text
Input Format: String
Input Parameters: 1D
Other Properties Related to Input: Token Limit of 512 tokens

Output

Output Type: Text Classifications
Output Format: String
Output Parameters: 1D
Other Properties Related to Output: None

The model takes one or several paragraphs of text as input. Example input:

最年少受賞者はエイドリアン・ブロディの29歳、最年少候補者はジャッキー・クーパーの9歳。最年長受賞者、最年長候補者は、アンソニー・ホプキンスの83歳。\n最多受賞者は3回受賞のダニエル・デイ＝ルイス。2回受賞経験者はスペンサー・トレイシー、フレドリック・マーチ、ゲイリー・クーパー、ダスティン・ホフマン、トム・ハンクス、ジャック・ニコルソン（助演男優賞も1回受賞している）、ショーン・ペン、アンソニー・ホプキンスの8人。なお、マーロン・ブランドも2度受賞したが、2度目の受賞を拒否している。最多候補者はスペンサー・トレイシー、ローレンス・オリヴィエの9回。\n死後に受賞したのはピーター・フィンチが唯一。ほか、ジェームズ・ディーン、スペンサー・トレイシー、マッシモ・トロイージ、チャドウィック・ボーズマンが死後にノミネートされ、うち2回死後にノミネートされたのはディーンのみである。\n非白人（黒人）で初めて受賞したのはシドニー・ポワチエであり、英語以外の演技で受賞したのはロベルト・ベニーニである。\n

The model outputs one of the 26 domain classes as the predicted domain for each input sample. Example output:

Arts_and_Entertainment \n

Software Integration

Runtime Engine: Python 3.10 and NeMo Curator
Supported Hardware Microarchitecture Compatibility: NVIDIA GPU, Volta™ or higher (compute capability 7.0+), CUDA 12 (or above)
Preferred/Supported Operating System(s): Ubuntu 22.04/20.04

Training, Testing, and Evaluation Dataset

Training data

1 million Common Crawl samples, labeled using Google Cloud’s Natural Language API
500k Wikipedia articles, curated using Wikipedia-API

Training steps

Translate the English training data into 51 other languages. Each sample has 52 copies.
During training, randomly pick one of the 52 copies for each sample.
During validation, evaluate the model on validation set 52 times, to get the validation score for each language.

Evaluation

Metric: PR-AUC

PR-AUC by language:\n $\"pr_auc_by_language\"$

Inference

Engine: PyTorch
Test Hardware: V100

Ethical Considerations

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Please report security vulnerabilities or NVIDIA AI Concerns here.

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The model classifies documents into one of 26 domain classes:\n\n```\n'Adult', 'Arts_and_Entertainment', 'Autos_and_Vehicles', 'Beauty_and_Fitness', 'Books_and_Literature', 'Business_and_Industrial', 'Computers_and_Electronics', 'Finance', 'Food_and_Drink', 'Games', 'Health', 'Hobbies_and_Leisure', 'Home_and_Garden', 'Internet_and_Telecom', 'Jobs_and_Education', 'Law_and_Government', 'News', 'Online_Communities', 'People_and_Society', 'Pets_and_Animals', 'Real_Estate', 'Science', 'Sensitive_Subjects', 'Shopping', 'Sports', 'Travel_and_Transportation'\n```\n\nIt supports 52 languages (English and 51 other languages):\n| Code | Language Name |\n|------|----------------|\n| ar | Arabic |\n| az | Azerbaijani |\n| bg | Bulgarian |\n| bn | Bengali |\n| ca | Catalan |\n| cs | Czech |\n| da | Danish |\n| de | German |\n| el | Greek |\n| es | Spanish |\n| et | Estonian |\n| fa | Persian |\n| fi | Finnish |\n| fr | French |\n| gl | Galician |\n| he | Hebrew |\n| hi | Hindi |\n| hr | Croatian |\n| hu | Hungarian |\n| hy | Armenian |\n| id | Indonesian |\n| is | Icelandic |\n| it | Italian |\n| ka | Georgian |\n| kk | Kazakh |\n| kn | Kannada |\n| ko | Korean |\n| lt | Lithuanian |\n| lv | Latvian |\n| mk | Macedonian |\n| ml | Malayalam |\n| mr | Marathi |\n| ne | Nepali |\n| nl | Dutch |\n| no | Norwegian |\n| pl | Polish |\n| pt | Portuguese |\n| ro | Romanian |\n| ru | Russian |\n| sk | Slovak |\n| sl | Slovenian |\n| sq | Albanian |\n| sr | Serbian |\n| sv | Swedish |\n| ta | Tamil |\n| tr | Turkish |\n| uk | Ukrainian |\n| ur | Urdu |\n| vi | Vietnamese |\n| ja | Japanese |\n| zh | Chinese |\n\n# License\nThis model is released under the [NVIDIA Open Model License Agreement](https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf).\n\n# References\n- DeBERTaV3: [Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://arxiv.org/abs/2111.09543)\n- DeBERTa: [Decoding-enhanced BERT with Disentangled Attention](https://github.com/microsoft/DeBERTa)\n\n# Model Architecture\n- The model architecture is Deberta V3 Base \n- Context length is 512 tokens\n\n# How To Use in NVIDIA NeMo Curator\nNeMo Curator improves generative AI model accuracy by processing text, image, and video data at scale for training and customization. It also provides pre-built pipelines for generating synthetic data to customize and evaluate generative AI systems.\n\nThe inference code for this model is available through the NeMo Curator GitHub repository. Check out this [example notebook](https://github.com/NVIDIA/NeMo-Curator/tree/main/tutorials/distributed_data_classification) to get started.\n\n# Input & Output\n## Input\n- Input Type: Text\n- Input Format: String\n- Input Parameters: 1D \n- Other Properties Related to Input: Token Limit of 512 tokens\n\n## Output\n- Output Type: Text Classifications\n- Output Format: String \n- Output Parameters: 1D \n- Other Properties Related to Output: None\n\nThe model takes one or several paragraphs of text as input. Example input:\n```\n最年少受賞者はエイドリアン・ブロディの29歳、最年少候補者はジャッキー・クーパーの9歳。最年長受賞者、最年長候補者は、アンソニー・ホプキンスの83歳。\n最多受賞者は3回受賞のダニエル・デイ＝ルイス。2回受賞経験者はスペンサー・トレイシー、フレドリック・マーチ、ゲイリー・クーパー、ダスティン・ホフマン、トム・ハンクス、ジャック・ニコルソン（助演男優賞も1回受賞している）、ショーン・ペン、アンソニー・ホプキンスの8人。なお、マーロン・ブランドも2度受賞したが、2度目の受賞を拒否している。最多候補者はスペンサー・トレイシー、ローレンス・オリヴィエの9回。\n死後に受賞したのはピーター・フィンチが唯一。ほか、ジェームズ・ディーン、スペンサー・トレイシー、マッシモ・トロイージ、チャドウィック・ボーズマンが死後にノミネートされ、うち2回死後にノミネートされたのはディーンのみである。\n非白人（黒人）で初めて受賞したのはシドニー・ポワチエであり、英語以外の演技で受賞したのはロベルト・ベニーニである。\n```\n\nThe model outputs one of the 26 domain classes as the predicted domain for each input sample. Example output:\n```\nArts_and_Entertainment \n```\n\n# Software Integration\n- Runtime Engine: Python 3.10 and NeMo Curator\n- Supported Hardware Microarchitecture Compatibility: NVIDIA GPU, Volta™ or higher (compute capability 7.0+), CUDA 12 (or above)\n- Preferred/Supported Operating System(s): Ubuntu 22.04/20.04\n\n# Training, Testing, and Evaluation Dataset\n## Training data\n- 1 million Common Crawl samples, labeled using Google Cloud’s Natural Language [API](https://cloud.google.com/natural-language/docs/classifying-text)\n- 500k Wikipedia articles, curated using [Wikipedia-API](https://pypi.org/project/Wikipedia-API/)\n\n## Training steps\n- Translate the English training data into 51 other languages. Each sample has 52 copies.\n- During training, randomly pick one of the 52 copies for each sample.\n- During validation, evaluate the model on validation set 52 times, to get the validation score for each language.\n\n## Evaluation\n- Metric: PR-AUC\n\nPR-AUC by language:\n $\"pr_auc_by_language\"$ \n\n# Inference\n- Engine: PyTorch\n- Test Hardware: V100\n\n# Ethical Considerations\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.\n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability).","displayName","NemoCurator Multilingual Domain Classifier",["D",1738603610576],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/multilingual-domain-classifier","rawImageUrl","https://huggingface.co/nvidia/quality-classifier-deberta/resolve/main/llm-blog-data-curator-2847806-1920x1080.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_209":299,"_259":300,"_260":301,"_262":263},{"_209":297,"_259":297,"_260":298,"_262":263},{"_209":295,"_259":295,"_260":296,"_262":263},{"_209":293,"_259":293,"_260":294,"_262":263},{"_209":291,"_259":291,"_260":292,"_262":263},{"_209":289,"_259":289,"_260":290,"_262":263},{"_209":287,"_259":287,"_260":288,"_262":263},{"_209":285,"_259":285,"_260":286,"_262":263},{"_209":283,"_259":283,"_260":284,"_262":263},{"_209":281,"_259":281,"_260":282,"_262":263},{"_209":279,"_259":279,"_260":280,"_262":263},{"_209":275,"_259":276,"_260":277,"_262":263},{"_209":272,"_259":272,"_260":273,"_262":263},{"_209":270,"_259":270,"_260":271,"_262":263},{"_209":268,"_259":268,"_260":269,"_262":263},{"_209":266,"_259":266,"_260":267,"_262":263},{"_209":258,"_259":258,"_260":261,"_262":263},"Spanish","value","filter",{"_264":265,"_259":258},"category","label","field","language","Russian",{"_264":265,"_259":266},"Portugese",{"_264":265,"_259":268},"NSPECT-HKTP-T2YW",{"_264":263,"_259":270},"NeMo",{"_264":274,"_259":272},"framework","Natural Language Processing","uscs_natural_language_processing",{"_264":278,"_259":276},"application","Mandarin",{"_264":265,"_259":279},"Korean",{"_264":265,"_259":281},"Japanese",{"_264":265,"_259":283},"Italian",{"_264":265,"_259":285},"Hindi",{"_264":265,"_259":287},"German",{"_264":265,"_259":289},"French",{"_264":265,"_259":291},"English",{"_264":265,"_259":293},"Dutch",{"_264":265,"_259":295},"Arabic",{"_264":265,"_259":297},"Annotation","uscs_annotation",{"_264":278,"_259":300},{"_120":1475,"_122":1476},"ngcApps",[1428],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1409":1410,"_1411":1412,"_1413":1414,"_1415":1416,"_1417":313,"_151":337,"_1418":1419,"_1420":59,"_1421":89,"_1422":89,"_1423":1424,"_1425":89,"_1426":1427},"amplitude",{"_1406":1407,"_1009":-7,"_1408":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1400":1401,"_1402":1403,"_1404":1405},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1284":226,"_1285":1286,"_1287":1288,"_1289":1290},"announcements",[],"consentState",{"_375":1274,"_1275":89,"_1276":34,"_1277":34,"_1278":1279},"consentConfig",{"_357":358,"_359":360,"_361":362,"_363":364,"_365":366,"_367":368,"_369":370,"_371":372,"_373":374,"_375":376},"showContactAdmin",1784221346114,[],[],[351,352,353,354,355,356],{"_48":63,"_50":74,"_52":115,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":115,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":68,"_52":115,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":115,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":115,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":53,"_54":55,"_56":57,"_58":59},"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 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,[648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668],[613,614,615,616,617],{"_415":507,"_209":507,"_180":226,"_389":639},{"_415":473,"_209":473,"_180":226,"_389":634},{"_415":627,"_209":627,"_180":226,"_389":628},{"_415":623,"_209":623,"_180":226,"_389":624},{"_415":618,"_209":618,"_180":226,"_389":619},"nvcrm.my.site.com",[620],{"_78":621,"_421":618,"_422":34,"_180":622},"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",[625],{"_78":626,"_421":623,"_422":34,"_180":456},"edge","github.com",[629,630],{"_78":633,"_421":627,"_422":34,"_180":632},{"_78":631,"_421":627,"_422":34,"_180":632},"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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