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 TLT - Question Answering would be the TLT - 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 .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/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 TLT Conversational AI, you would be accepting the terms of the Jarvis 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 TLT - Question Answering would be the TLT - 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 `.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/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 TLT Conversational AI, you would be accepting the terms of the Jarvis [license](https://developer.nvidia.com/ngc/nvidia-jarvis-license)","Question Answering Notebook",["D",1680632215305],"/api/image/nvidia/tlt-jarvis/resources/questionanswering_notebook","https://raw.githubusercontent.com/kbojo/images/master/TLT-Jarvis.png","bias","explainability","privacy","safetyAndSecurity",[],[304,305,306,307,308,309,310,311,312],[],[],[],{"_92":146,"_130":146,"_131":333,"_133":134},{"_92":330,"_130":331,"_131":332,"_133":134},{"_92":144,"_130":144,"_131":329,"_133":134},{"_92":325,"_130":326,"_131":327,"_133":134},{"_92":322,"_130":323,"_131":324,"_133":134},{"_92":318,"_130":319,"_131":320,"_133":134},{"_92":316,"_130":316,"_131":317,"_133":134},{"_92":314,"_130":314,"_131":315,"_133":134},{"_92":136,"_130":136,"_131":313,"_133":134},{"_135":134,"_130":136},"QuestionAnswering",{"_135":134,"_130":314},"QA",{"_135":134,"_130":316},"Natural Language Understanding","uscs_natural_language_understanding",{"_135":321,"_130":319},"application","Natural Language Processing","uscs_natural_language_processing",{"_135":321,"_130":323},"Inference","soln_inference",{"_135":328,"_130":326},"technology",{"_135":134,"_130":144},"Conversational AI","soln_conversational_ai",{"_135":328,"_130":331},{"_135":134,"_130":146},{"_195":1311,"_197":1312},"ngcApps",[1264],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1245":1246,"_1247":1248,"_1249":1250,"_1251":1252,"_1253":345,"_226":369,"_1254":1255,"_1256":51,"_1257":174,"_1258":174,"_1259":1260,"_1261":174,"_1262":1263},"amplitude",{"_1242":1243,"_883":-7,"_1244":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMSUyMiUzQXRydWUlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1236":1237,"_1238":1239,"_1240":1241},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.10.0","defaultSearchSuggestions",{"_1123":450,"_1124":1125,"_1126":1127,"_1128":1129},"announcements",[],"consentState",{"_407":1113,"_1114":174,"_1115":26,"_1116":26,"_1117":1118},"consentConfig",{"_389":390,"_391":392,"_393":394,"_395":396,"_397":398,"_399":400,"_401":402,"_403":404,"_405":406,"_407":408},"showContactAdmin",1785068615215,[],[],[383,384,385,386,387,388],{"_40":55,"_42":65,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":63,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":56,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":61,"_46":-5,"_48":-5,"_50":51},"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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