Named Entity Recognition

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.

For example, in a sentence: Mary lives in Santa Clara and works at NVIDIA, we should detect that Mary is a person, Santa Clara is a location and NVIDIA is a company.

The best place to get started with TLT - NER would be the TLT - NER jupyter notebooks enclosed with this sample. This resource has two notebooks included.

Training: Sample workflow for training a named entity recognition model and export the model to a .ejrvs file
Deployment: Sample 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/tokenclassification_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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This resource has two notebooks included.\n\n1. Training: Sample workflow for training a named entity recognition model and export the model to a `.ejrvs` file\n2. Deployment: Sample 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/tokenclassification_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","Token Classification Notebook",["D",1680631906243],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/tlt-jarvis/resources/tokenclassification_notebook","rawImageUrl","https://raw.githubusercontent.com/kbojo/images/master/TLT-Jarvis.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[279,280,281,282,283,284,285,286,287],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_248":315,"_289":315,"_290":316,"_292":89},{"_248":312,"_289":313,"_290":314,"_292":89},{"_248":310,"_289":310,"_290":311,"_292":89},{"_248":306,"_289":307,"_290":308,"_292":89},{"_248":303,"_289":304,"_290":305,"_292":89},{"_248":300,"_289":301,"_290":302,"_292":89},{"_248":296,"_289":297,"_290":298,"_292":89},{"_248":294,"_289":294,"_290":295,"_292":89},{"_248":288,"_289":288,"_290":291,"_292":89},"Transfer Learning","value","filter",{"_293":89,"_289":288},"category","field","Token Classification",{"_293":89,"_289":294},"Natural Language Understanding","uscs_natural_language_understanding",{"_293":299,"_289":297},"application","Natural Language Processing","uscs_natural_language_processing",{"_293":299,"_289":301},"Named Entity Recognition","uscs_named_entity_recognition",{"_293":299,"_289":304},"Inference","soln_inference",{"_293":309,"_289":307},"technology","Finetuning",{"_293":89,"_289":310},"Conversational AI","soln_conversational_ai",{"_293":309,"_289":313},"BERT",{"_293":89,"_289":315},{"_159":1296,"_161":1297},"ngcApps",[1249],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1230":1231,"_1232":1233,"_1234":1235,"_1236":1237,"_1238":328,"_111":352,"_1239":1240,"_1241":60,"_1242":92,"_1243":92,"_1244":1245,"_1246":92,"_1247":1248},"amplitude",{"_1227":1228,"_865":-7,"_1229":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMSUyMiUzQXRydWUlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1221":1222,"_1223":1224,"_1225":1226},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.10.0","defaultSearchSuggestions",{"_1105":432,"_1106":1107,"_1108":1109,"_1110":1111},"announcements",[],"consentState",{"_389":1095,"_1096":92,"_1097":35,"_1098":35,"_1099":1100},"consentConfig",{"_371":372,"_373":374,"_375":376,"_377":378,"_379":380,"_381":382,"_383":384,"_385":386,"_387":388,"_389":390},"showContactAdmin",1785076112632,[],[],[366,367,368,369,370],{"_49":64,"_51":75,"_53":154,"_55":-5,"_57":-5,"_59":60},{"_49":64,"_51":71,"_53":154,"_55":-5,"_57":-5,"_59":60},{"_49":64,"_51":65,"_53":154,"_55":-5,"_57":-5,"_59":60},{"_49":61,"_51":62,"_53":154,"_55":-5,"_57":-5,"_59":60},{"_49":50,"_51":52,"_53":154,"_55":-5,"_57":-5,"_59":60},"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
v1.1	2	38.69 KB	June 9, 2021 UTC
v1.0Selected	2	38.69 KB	February 12, 2021 UTC