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 TLT - Intent and Slot Classification would be the TLT - 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 .ejrvs file
Deployment: Workflow to consume the .ejrvs file and deploy it to Jarvis.

If you are a seasoned Conversation AI developer we recommend installing TLT and referring to the TLT 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 TLT inside a virtual environment. The steps to do the same are as follows

\nsource /bin/activate\npip install jupyter notebook # If you need to run the notebooks\n\">

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

TLT is 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-tlt\n

To download the jupyter notebook please:

Download the samples using the ngc cli with the following command

ngc registry resource download-version \"nvidia/tlt-jarvis/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 TLT Conversational AI, you would be accepting the terms of the Jarvis 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 TLT - Intent and Slot Classification would be the TLT - 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 `.ejrvs` file\n2. Deployment: Workflow to consume the `.ejrvs` file and deploy it to Jarvis.\n\nIf you are a seasoned Conversation AI developer we recommend installing TLT and referring to the [TLT documentation](https://docs.nvidia.com/TLT/tlt-user-guide/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 TLT 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\nTLT is 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-tlt\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/tlt-jarvis/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 TLT Conversational AI, you would be accepting the terms of the Jarvis [license](https://developer.nvidia.com/ngc/nvidia-jarvis-license)","displayName","Intent Slot Classification Notebook",["D",1680632773336],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tlt-jarvis/resources/intentslotclassification_notebook","rawImageUrl","https://raw.githubusercontent.com/kbojo/images/master/TLT-Jarvis.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[246,247,248,249,250,251,252,253,254],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_214":282,"_256":282,"_257":283,"_259":260},{"_214":279,"_256":280,"_257":281,"_259":260},{"_214":277,"_256":277,"_257":278,"_259":260},{"_214":273,"_256":274,"_257":275,"_259":260},{"_214":271,"_256":271,"_257":272,"_259":260},{"_214":268,"_256":269,"_257":270,"_259":260},{"_214":264,"_256":265,"_257":266,"_259":260},{"_214":262,"_256":262,"_257":263,"_259":260},{"_214":255,"_256":255,"_257":258,"_259":260},"Transfer Learning","value","filter",{"_261":260,"_256":255},"category","label","field","Slot Tagging",{"_261":260,"_256":262},"Natural Language Understanding","uscs_natural_language_understanding",{"_261":267,"_256":265},"application","Natural Language Processing","uscs_natural_language_processing",{"_261":267,"_256":269},"Intent Detection",{"_261":260,"_256":271},"Inference","soln_inference",{"_261":276,"_256":274},"technology","Finetuning",{"_261":260,"_256":277},"Conversational AI","soln_conversational_ai",{"_261":276,"_256":280},"BERT",{"_261":260,"_256":282},{"_113":1264,"_115":1265},"ngcApps",[1217],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1198":1199,"_1200":1201,"_1202":1203,"_1204":1205,"_1206":295,"_144":319,"_1207":1208,"_1209":57,"_1210":92,"_1211":92,"_1212":1213,"_1214":92,"_1215":1216},"amplitude",{"_1195":1196,"_832":-7,"_1197":32},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMSUyMiUzQXRydWUlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1189":1190,"_1191":1192,"_1193":1194},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.10.0","defaultSearchSuggestions",{"_1072":399,"_1073":1074,"_1075":1076,"_1077":1078},"announcements",[],"consentState",{"_356":1062,"_1063":92,"_1064":32,"_1065":32,"_1066":1067},"consentConfig",{"_338":339,"_340":341,"_342":343,"_344":345,"_346":347,"_348":349,"_350":351,"_352":353,"_354":355,"_356":357},"showContactAdmin",1785250177225,[],[],[333,334,335,336,337],{"_46":60,"_48":70,"_50":108,"_52":-5,"_54":-5,"_56":57},{"_46":60,"_48":68,"_50":108,"_52":-5,"_54":-5,"_56":57},{"_46":60,"_48":61,"_50":108,"_52":-5,"_54":-5,"_56":57},{"_46":58,"_48":59,"_50":108,"_52":-5,"_54":-5,"_56":57},{"_46":47,"_48":49,"_50":108,"_52":-5,"_54":-5,"_56":57},"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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Name	Size	Updated	Actions
intent-slot-classification-deployment.ipynbView Notebook	13.82 KB	July 28, 2021 UTC	curl wget Download
intent-slot-classification-training.ipynbView Notebook	27.25 KB	July 28, 2021 UTC	curl wget Download