Question Answering

The Question Answering task in NLP pertains to building a model which can answer questions posed in natural language. Many datasets (including SQuAD, the dataset we use in this notebook) pose this as a reading comprehension task i.e. given a question and a context, the goal is to predict the span within the context with a start and end position which indicates the answer to the question. For every word in the training dataset we predict:

likelihood this word is the start of the span
likelihood this word is the end of the span

The best place to get started with TAO Toolkit - Question Answering would be the TAO - Question Answering jupyter notebooks. This resource has two notebooks included.

Training: Sample workflow for training a question answering model and export the model to a .riva file
Deployment: Sample 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/questionanswering_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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Many datasets (including SQuAD, the dataset we use in this notebook) pose this as a reading comprehension task i.e. given a question and a context, the goal is to predict the span within the context with a start and end position which indicates the answer to the question. For every word in the training dataset we predict:\n\n* likelihood this word is the start of the span\n* likelihood this word is the end of the span\n\nThe best place to get started with TAO Toolkit - Question Answering would be the TAO - Question Answering jupyter notebooks. This resource has two notebooks included.\n\n1. Training: Sample workflow for training a question answering model and export the model to a `.riva` file\n2. Deployment: Sample 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/questionanswering_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)","Question Answering Notebook",["D",1680632423836],"/api/image/nvidia/tao/resources/questionanswering_notebook","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO-RIVA.png","bias","explainability","privacy","safetyAndSecurity",[],[266,267,268,269,270,271,272],[],[],[],{"_88":287,"_121":287,"_122":288,"_124":125},{"_88":127,"_121":127,"_122":286,"_124":125},{"_88":282,"_121":283,"_122":284,"_124":125},{"_88":278,"_121":279,"_122":280,"_124":125},{"_88":276,"_121":276,"_122":277,"_124":125},{"_88":274,"_121":274,"_122":275,"_124":125},{"_88":120,"_121":120,"_122":273,"_124":125},{"_126":125,"_121":120},"QuestionAnswering",{"_126":125,"_121":274},"QA",{"_126":125,"_121":276},"Natural Language Understanding","uscs_natural_language_understanding",{"_126":281,"_121":279},"application","Inference","soln_inference",{"_126":285,"_121":283},"technology",{"_126":125,"_121":127},"BERT",{"_126":125,"_121":287},{"_157":1266,"_159":1267},"ngcApps",[1219],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1200":1201,"_1202":1203,"_1204":1205,"_1206":1207,"_1208":300,"_189":326,"_1209":1210,"_1211":50,"_1212":134,"_1213":134,"_1214":1215,"_1216":134,"_1217":1218},"amplitude",{"_1197":1198,"_839":-7,"_1199":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1191":1192,"_1193":1194,"_1195":1196},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1789402911312,"1.15.0","defaultSearchSuggestions",{"_1079":406,"_1080":1081,"_1082":1083,"_1084":1085},"announcements",[],"consentState",{"_364":1069,"_1070":134,"_1071":26,"_1072":26,"_1073":1074},"consentConfig",{"_346":347,"_348":349,"_350":351,"_352":353,"_354":355,"_356":357,"_358":359,"_360":361,"_362":363,"_364":365},"showContactAdmin",1789402911313,[],[],[340,341,342,343,344,345],{"_39":54,"_41":63,"_43":62,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":61,"_43":62,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":59,"_43":62,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":55,"_43":62,"_45":-5,"_47":-5,"_49":50},{"_39":51,"_41":52,"_43":62,"_45":-5,"_47":-5,"_49":50},{"_39":40,"_41":42,"_43":62,"_45":-5,"_47":-5,"_49":50},"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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