NemoCurator Prompt Task and Complexity Classifier

Model Overview

\n $\"image\"$ \n

This is a multi-headed model which classifies English text prompts across task types and complexity dimensions. Tasks are classified across 11 common categories. Complexity is evaluated across 6 dimensions and ensembled to create an overall complexity score. Further information on the taxonomies can be found below.

This model is ready for commercial use.

Task types:

Open QA: A question where the response is based on general knowledge
Closed QA: A question where the response is based on text/data provided with the prompt
Summarization
Text Generation
Code Generation
Chatbot
Classification
Rewrite
Brainstorming
Extraction
Other

Complexity dimensions:

Overall Complexity Score: The weighted sum of the complexity dimensions. Calculated as 0.35*CreativityScore + 0.25*ReasoningScore + 0.15*ConstraintScore + 0.15*DomainKnowledgeScore + 0.05*ContextualKnowledgeScore + 0.05*NumberOfFewShots
Creativity: The level of creativity needed to respond to a prompt. Score range of 0-1, with a higher score indicating more creativity.
Reasoning: The extent of logical or cognitive effort required to respond to a prompt. Score range of 0-1, with a higher score indicating more reasoning
Contextual Knowledge: The background information necessary to respond to a prompt. Score range of 0-1, with a higher score indicating more contextual knowledge required outside of prompt.
Domain Knowledge: The amount of specialized knowledge or expertise within a specific subject area needed to respond to a prompt. Score range of 0-1, with a higher score indicating more domain knowledge is required.
Constraints: The number of constraints or conditions provided with the prompt. Score range of 0-1, with a higher score indicating more constraints in the prompt.
Number of Few Shots: The number of examples provided with the prompt. Score range of 0-n, with a higher score indicating more examples provided in the prompt.

License

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

Model Architecture

The model architecture uses a DeBERTa backbone and incorporates multiple classification heads, each dedicated to a task categorization or complexity dimension. This approach enables the training of a unified network, allowing it to predict simultaneously during inference. Deberta-v3-base can theoretically handle up to 12k tokens, but default context length is set at 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/Numeric Classifications
Output Format: String & Numeric
Output Parameters: 1D
Other Properties Related to Output: None

Examples

Prompt: Write a mystery set in a small town where an everyday object goes missing, causing a ripple of curiosity and suspicion. Follow the investigation and reveal the surprising truth behind the disappearance.\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\">

Task	Complexity	Creativity	Reasoning	Contextual Knowledge	Domain Knowledge	Constraints	# of Few Shots
Text Generation	0.472	0.867	0.056	0.048	0.226	0.785	0

Prompt: Antibiotics are a type of medication used to treat bacterial infections. They work by either killing the bacteria or preventing them from reproducing, allowing the body’s immune system to fight off the infection. Antibiotics are usually taken orally in the form of pills, capsules, or liquid solutions, or sometimes administered intravenously. They are not effective against viral infections, and using them inappropriately can lead to antibiotic resistance. Explain the above in one sentence.\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\">

Task	Complexity	Creativity	Reasoning	Contextual Knowledge	Domain Knowledge	Constraints	# of Few Shots
Summarization	0.133	0.003	0.014	0.003	0.644	0.211	0

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

Model Version

NemoCurator Prompt Task and Complexity Classifier v1.1

Training, Testing, and Evaluation Datasets

Training Data

4024 English prompts with task distribution outlined below
Prompts were annotated by humans according to task and complexity taxonomies

Task distribution:

\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\">

Task	Count
Open QA	1214
Closed QA	786
Text Generation	480
Chatbot	448
Classification	267
Summarization	230
Code Generation	185
Rewrite	169
Other	104
Brainstorming	81
Extraction	60
Total	4024

Evaluation

For evaluation, Top-1 accuracy metric was used, which involves matching the category with the highest probability to the expected answer. Additionally, n-fold cross-validation was used to produce n different values for this metric to verify the consistency of the results. The table below displays the average of the top-1 accuracy values for the N folds calculated for each complexity dimension separately.

\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\">

	Task Accuracy	Creative Accuracy	Reasoning Accuracy	Contextual Accuracy	FewShots Accuracy	Domain Accuracy	Constraint Accuracy
Average of 10 Folds	0.981	0.996	0.997	0.981	0.979	0.937	0.991

Inference

Engine: PyTorch
Test Hardware: A10G

References

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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Tasks are classified across 11 common categories. Complexity is evaluated across 6 dimensions and ensembled to create an overall complexity score. Further information on the taxonomies can be found below.\n\nThis model is ready for commercial use.\n\n**Task types:**\n* Open QA: A question where the response is based on general knowledge\n* Closed QA: A question where the response is based on text/data provided with the prompt\n* Summarization\n* Text Generation\n* Code Generation\n* Chatbot\n* Classification\n* Rewrite\n* Brainstorming\n* Extraction\n* Other\n\n**Complexity dimensions:**\n* Overall Complexity Score: The weighted sum of the complexity dimensions. Calculated as 0.35\\*CreativityScore + 0.25\\*ReasoningScore + 0.15\\*ConstraintScore + 0.15\\*DomainKnowledgeScore + 0.05\\*ContextualKnowledgeScore + 0.05\\*NumberOfFewShots\n* Creativity: The level of creativity needed to respond to a prompt. Score range of 0-1, with a higher score indicating more creativity.\n* Reasoning: The extent of logical or cognitive effort required to respond to a prompt. Score range of 0-1, with a higher score indicating more reasoning\n* Contextual Knowledge: The background information necessary to respond to a prompt. Score range of 0-1, with a higher score indicating more contextual knowledge required outside of prompt.\n* Domain Knowledge: The amount of specialized knowledge or expertise within a specific subject area needed to respond to a prompt. Score range of 0-1, with a higher score indicating more domain knowledge is required.\n* Constraints: The number of constraints or conditions provided with the prompt. Score range of 0-1, with a higher score indicating more constraints in the prompt.\n* Number of Few Shots: The number of examples provided with the prompt. Score range of 0-n, with a higher score indicating more examples provided in the prompt.\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# Model Architecture\nThe model architecture uses a DeBERTa backbone and incorporates multiple classification heads, each dedicated to a task categorization or complexity dimension. This approach enables the training of a unified network, allowing it to predict simultaneously during inference. Deberta-v3-base can theoretically handle up to 12k tokens, but default context length is set at 512 tokens.\n\n# How to Use in NVIDIA NeMo Curator\n[NeMo Curator](https://developer.nvidia.com/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.\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/Numeric Classifications\n* Output Format: String & Numeric\n* Output Parameters: 1D\n* Other Properties Related to Output: None\n\n## Examples\n\n```\nPrompt: Write a mystery set in a small town where an everyday object goes missing, causing a ripple of curiosity and suspicion. Follow the investigation and reveal the surprising truth behind the disappearance.\n```\n\n| Task | Complexity | Creativity | Reasoning | Contextual Knowledge | Domain Knowledge | Constraints | # of Few Shots |\n|------------------|------------|------------|-----------|-----------------------|------------------|-------------|----------------|\n| Text Generation | 0.472 | 0.867 | 0.056 | 0.048 | 0.226 | 0.785 | 0 |\n\n```\nPrompt: Antibiotics are a type of medication used to treat bacterial infections. They work by either killing the bacteria or preventing them from reproducing, allowing the body’s immune system to fight off the infection. Antibiotics are usually taken orally in the form of pills, capsules, or liquid solutions, or sometimes administered intravenously. They are not effective against viral infections, and using them inappropriately can lead to antibiotic resistance. Explain the above in one sentence.\n```\n\n| Task | Complexity | Creativity | Reasoning | Contextual Knowledge | Domain Knowledge | Constraints | # of Few Shots |\n|-----------------|------------|------------|-----------|-----------------------|------------------|-------------|----------------|\n| Summarization | 0.133 | 0.003 | 0.014 | 0.003 | 0.644 | 0.211 | 0 |\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# Model Version\nNemoCurator Prompt Task and Complexity Classifier v1.1\n\n# Training, Testing, and Evaluation Datasets\n## Training Data\n* 4024 English prompts with task distribution outlined below\n* Prompts were annotated by humans according to task and complexity taxonomies\n\nTask distribution:\n| Task | Count |\n|------------------|-------|\n| Open QA | 1214 |\n| Closed QA | 786 |\n| Text Generation | 480 |\n| Chatbot | 448 |\n| Classification | 267 |\n| Summarization | 230 |\n| Code Generation | 185 |\n| Rewrite | 169 |\n| Other | 104 |\n| Brainstorming | 81 |\n| Extraction | 60 |\n| Total | 4024 |\n\n## Evaluation\nFor evaluation, Top-1 accuracy metric was used, which involves matching the category with the highest probability to the expected answer. Additionally, n-fold cross-validation was used to produce n different values for this metric to verify the consistency of the results. The table below displays the average of the top-1 accuracy values for the N folds calculated for each complexity dimension separately.\n\n| | Task Accuracy | Creative Accuracy | Reasoning Accuracy | Contextual Accuracy | FewShots Accuracy | Domain Accuracy | Constraint Accuracy |\n|-|------------------|-------------------|--------------------|---------------------|-------------------|-----------------|---------------------|\n| Average of 10 Folds | 0.981 | 0.996 | 0.997 | 0.981 | 0.979 | 0.937 | 0.991 |\n\n# Inference\n* Engine: PyTorch\n* Test Hardware: A10G\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* [Training language models to follow instructions with human feedback](https://arxiv.org/pdf/2203.02155)\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 Prompt Task and Complexity Classifier",["D",1740706070937],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/prompt-task-and-complexity-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",[252,253,254,255,256],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_220":274,"_258":275,"_259":276,"_261":262},{"_220":271,"_258":271,"_259":272,"_261":262},{"_220":267,"_258":268,"_259":269,"_261":262},{"_220":264,"_258":264,"_259":265,"_261":262},{"_220":257,"_258":257,"_259":260,"_261":262},"NSPECT-M81T-FE3Z","value","filter",{"_263":262,"_258":257},"category","label","field","NeMo",{"_263":266,"_258":264},"framework","Natural Language Processing","uscs_natural_language_processing",{"_263":270,"_258":268},"application","English",{"_263":273,"_258":271},"language","Annotation","uscs_annotation",{"_263":270,"_258":275},{"_130":1449,"_132":1450},"ngcApps",[1402],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1384":1385,"_1386":1387,"_1388":1389,"_1390":1391,"_1392":288,"_157":312,"_1393":1394,"_1382":59,"_1395":103,"_1396":103,"_1397":1398,"_1399":103,"_1400":1401},"amplitude",{"_1380":1381,"_1382":59,"_984":-7,"_1383":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBZmFsc2UlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1374":1375,"_1376":1377,"_1378":1379},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.4.0","defaultSearchSuggestions",{"_1259":237,"_1260":1261,"_1262":1263,"_1264":1265},"announcements",[],"consentState",{"_350":1249,"_1250":35,"_1251":35,"_1252":103,"_1253":1254},"consentConfig",{"_332":333,"_334":335,"_336":337,"_338":339,"_340":341,"_342":343,"_344":345,"_346":347,"_348":349,"_350":351},"showContactAdmin",1783080756308,[],[],[326,327,328,329,330],{"_49":62,"_51":73,"_53":68,"_55":-5,"_57":-5,"_58":59},{"_49":62,"_51":69,"_53":68,"_55":-5,"_57":-5,"_58":59},{"_49":62,"_51":63,"_53":68,"_55":-5,"_57":-5,"_58":59},{"_49":60,"_51":61,"_53":54,"_55":56,"_57":331,"_58":59},{"_49":50,"_51":52,"_53":68,"_55":-5,"_57":-5,"_58":59},4,"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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This function remembers how far the user has scrolled on the current session across the website's sub-pages.","dmn_chk_-xxxxxxxxxxxx","com","Posthog Analytics","_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.","_cs_id","blogs.nvidia.com","_session_id","resources.nvidia.com","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.","_hjSessionUser_xxxxxx","Hotjar cookie that is set when a user first lands on a page with the Hotjar script. It is used to persist the Hotjar User ID, unique to that site on the browser. 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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_biz_pendingA","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. 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