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",[],[265,266,267,268,269,270,271],[],[],[],{"_91":286,"_124":286,"_125":287,"_127":128},{"_91":130,"_124":130,"_125":285,"_127":128},{"_91":281,"_124":282,"_125":283,"_127":128},{"_91":277,"_124":278,"_125":279,"_127":128},{"_91":275,"_124":275,"_125":276,"_127":128},{"_91":273,"_124":273,"_125":274,"_127":128},{"_91":123,"_124":123,"_125":272,"_127":128},{"_129":128,"_124":123},"QuestionAnswering",{"_129":128,"_124":273},"QA",{"_129":128,"_124":275},"Natural Language Understanding","uscs_natural_language_understanding",{"_129":280,"_124":278},"application","Inference","soln_inference",{"_129":284,"_124":282},"technology",{"_129":128,"_124":130},"BERT",{"_129":128,"_124":286},{"_158":1457,"_160":1458},"ngcApps",[1410],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1391":1392,"_1393":1394,"_1395":1396,"_1397":1398,"_1399":299,"_190":323,"_1400":1401,"_1402":51,"_1403":137,"_1404":137,"_1405":1406,"_1407":137,"_1408":1409},"amplitude",{"_1388":1389,"_995":-7,"_1390":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1382":1383,"_1384":1385,"_1386":1387},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1270":403,"_1271":1272,"_1273":1274,"_1275":1276},"announcements",[],"consentState",{"_361":1260,"_1261":137,"_1262":26,"_1263":26,"_1264":1265},"consentConfig",{"_343":344,"_345":346,"_347":348,"_349":350,"_351":352,"_353":354,"_355":356,"_357":358,"_359":360,"_361":362},"showContactAdmin",1784099243242,[],[],[337,338,339,340,341],{"_40":55,"_42":65,"_44":153,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":64,"_44":153,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":153,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":153,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":342,"_50":51},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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