Intent Slot Classification

Understanding the intent in natural language (Intent Classification) and extracting values of pertinent attributes or specific pieces of information from a sentence (Slot Filling) are two essential tasks in Natural Language Understanding (NLU). For example:

In the query: What is the weather in Santa Clara tomorrow morning? we would like to classify the query as a weather Intent, and detect Santa Clara as a location slot and tomorrow morning as a date_time slot. Intents and Slots names are usually task specific and defined as labels in the training data. This is a fundamental step that is executed in any task-driven Conversational Assistant.

Recent research has shown the proficiency of BERT models in this task. TLT provides the capability to train a BERT model and perform inference for both intent detection and slot filling together.

The best place to get started with TAO Toolkit - Intent and Slot Classification would be the TAO - Intent and Slot Classification jupyter notebook. This resource has two notebooks included.

Training: Workflow for training an intent slot classification model and export the model to a .riva file
Deployment: Workflow to consume the .riva file and deploy it to Riva.

If you are a seasoned Conversation AI developer we recommend installing TAO and referring to the TAO documentation for usage information.

Pre-Requisites

Please make sure to install the following before proceeding further:

python 3.6.9
docker-ce > 19.03.5
docker-API 1.40
nvidia-container-toolkit > 1.3.0-1
nvidia-container-runtime > 3.4.0-1
nvidia-docker2 > 2.5.0-1
nvidia-driver >= 455.23

Note: A compatible NVIDIA GPU would be required.

Installation

We recommend that you install TAO Toolkit inside a virtual environment. The steps to do the same are as follows

virtualenv -p python3 \nsource /bin/activate\npip install jupyter notebook # If you need to run the notebooks\n

TAO Toolkit is a python package that is hosted in nvidia python package index. You may install by using python’s package manager, pip.

pip install nvidia-pyindex\npip install nvidia-tao\n

To download the jupyter notebook please:

Download the samples using the ngc cli with the following command

ngc registry resource download-version \"nvidia/tao/intentslotclassification_notebook:v1.0\"\n

Instantiate the jupyter notebook server

jupyter notebook --ip 0.0.0.0 --allow-root --port 8888\n

License

By downloading and using the models and resources packaged with TAO Toolkit Conversational AI, you would be accepting the terms of the Riva license

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For example:\n\nIn the query: What is the weather in Santa Clara tomorrow morning? we would like to classify the query as a weather Intent, and detect Santa Clara as a location slot and tomorrow morning as a date_time slot. Intents and Slots names are usually task specific and defined as labels in the training data. This is a fundamental step that is executed in any task-driven Conversational Assistant.\n\nRecent research has shown the proficiency of BERT models in this task. TLT provides the capability to train a BERT model and perform inference for both intent detection and slot filling together.\n\nThe best place to get started with TAO Toolkit - Intent and Slot Classification would be the TAO - Intent and Slot Classification jupyter notebook. This resource has two notebooks included.\n\n1. Training: Workflow for training an intent slot classification model and export the model to a `.riva` file\n2. Deployment: Workflow to consume the `.riva` file and deploy it to Riva.\n\nIf you are a seasoned Conversation AI developer we recommend installing TAO and referring to the [TAO documentation](https://docs.nvidia.com/tao/tao-toolkit/text/overview.html) for usage information.\n\n## Pre-Requisites\n\nPlease make sure to install the following before proceeding further:\n\n* python 3.6.9\n* docker-ce > 19.03.5\n* docker-API 1.40\n* nvidia-container-toolkit > 1.3.0-1\n* nvidia-container-runtime > 3.4.0-1\n* nvidia-docker2 > 2.5.0-1\n* nvidia-driver >= 455.23\n\n*Note: A compatible NVIDIA GPU would be required.*\n\n## Installation\n\nWe recommend that you install TAO Toolkit inside a virtual environment. The steps to do the same are as follows \n\n```sh\nvirtualenv -p python3 \nsource /bin/activate\npip install jupyter notebook # If you need to run the notebooks\n```\n\nTAO Toolkit is a python package that is hosted in nvidia python package index. You may install by using python’s package manager, pip.\n\n```sh\npip install nvidia-pyindex\npip install nvidia-tao\n```\n\nTo download the jupyter notebook please:\n\n1. Download the samples using the ngc cli with the following command\n\n```sh\nngc registry resource download-version \"nvidia/tao/intentslotclassification_notebook:v1.0\"\n```\n\n2. Instantiate the jupyter notebook server\n\n```sh\njupyter notebook --ip 0.0.0.0 --allow-root --port 8888\n```\n\n## License\n\nBy downloading and using the models and resources packaged with TAO Toolkit Conversational AI, you would be accepting the terms of the Riva [license](https://developer.nvidia.com/riva/ga/license)","displayName","Intent Slot Classification Notebook",["D",1680632776085],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/tao/resources/intentslotclassification_notebook","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO-RIVA.png","canGuestDownload","latestVersionSizeInBytes",13111,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[275,276,277,278,279,280,281],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_243":302,"_283":302,"_284":303,"_286":87},{"_243":300,"_283":300,"_284":301,"_286":87},{"_243":296,"_283":297,"_284":298,"_286":87},{"_243":294,"_283":294,"_284":295,"_286":87},{"_243":290,"_283":291,"_284":292,"_286":87},{"_243":288,"_283":288,"_284":289,"_286":87},{"_243":282,"_283":282,"_284":285,"_286":87},"Transfer Learning","value","filter",{"_287":87,"_283":282},"category","field","Slot Tagging",{"_287":87,"_283":288},"Natural Language Understanding","uscs_natural_language_understanding",{"_287":293,"_283":291},"application","Intent Detection",{"_287":87,"_283":294},"Inference","soln_inference",{"_287":299,"_283":297},"technology","Finetuning",{"_287":87,"_283":300},"BERT",{"_287":87,"_283":302},{"_156":1287,"_158":1288},"ngcApps",[1240],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1221":1222,"_1223":1224,"_1225":1226,"_1227":1228,"_1229":315,"_109":343,"_1230":1231,"_1232":59,"_1233":90,"_1234":90,"_1235":1236,"_1237":90,"_1238":1239},"amplitude",{"_1218":1219,"_857":-7,"_1220":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1212":1213,"_1214":1215,"_1216":1217},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1786142462481,"1.11.0","defaultSearchSuggestions",{"_1097":424,"_1098":1099,"_1100":1101,"_1102":1103},"announcements",[],"consentState",{"_381":1087,"_1088":90,"_1089":35,"_1090":35,"_1091":1092},"consentConfig",{"_363":364,"_365":366,"_367":368,"_369":370,"_371":372,"_373":374,"_375":376,"_377":378,"_379":380,"_381":382},"showContactAdmin",1786142462482,[],[],[357,358,359,360,361,362],{"_49":63,"_51":73,"_53":151,"_55":-5,"_57":-5,"_58":59},{"_49":63,"_51":70,"_53":151,"_55":-5,"_57":-5,"_58":59},{"_49":63,"_51":68,"_53":151,"_55":-5,"_57":-5,"_58":59},{"_49":63,"_51":64,"_53":151,"_55":-5,"_57":-5,"_58":59},{"_49":60,"_51":61,"_53":151,"_55":-5,"_57":-5,"_58":59},{"_49":50,"_51":52,"_53":151,"_55":-5,"_57":-5,"_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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	Files	Compressed Size		Download
v1.3Selected	2	40.08 KB	November 23, 2021 UTC
v1.2	2	40.08 KB	August 20, 2021 UTC