Train Question-Answering Models using NVIDIA NeMO

This resource contains a Jupyter Notebook that walks though a streamlined approach to training, evaluating, and testing models for Question Answering (QA) tasks, leveraging NVIDIA’s NeMo Framework. Within this notebook, users can explore three QA model types: BERT-like models for Extractive Question Answering, Sequence-to-Sequence (S2S) models like T5/BART for Generative Question Answering, and GPT-like models for advanced generative responses. This adaptation focuses on practical application, guiding users through the process of generating answers from given contexts and queries, with detailed explanations on how to use NeMo.

Deploy now

To streamline your experience and jump directly into a GPU-accelerated environment with this notebook and NeMo pre-installed, click the badge below. Our 1-click deploys are powered by Brev.dev.

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Getting started

Use the 1-click deploy link above to set up a machine with NeMO installed. Once the VM is ready, use the Access Notebook button to enter the Jupyter Lab instance

Models

For this notebook, we use two types of question-answering paradigms and three different models:

Extractive QA\n
- BERT-like model
\n
Generative QA\n
- Sequence-to-Sequence (T5/BART-like) models
- GPT-like models
\n

We will be using the SQuAD dataset to showcase the training and inference. We train, test, and deploy all three models for inference and evaluate performance of all three architectures.

NeMO

NVIDIA NeMo Framework is a generative AI framework built for researchers and pytorch developers working on large language models (LLMs), multimodal models (MM), automatic speech recognition (ASR), and text-to-speech synthesis (TTS). NeMO provides a scalable framework to easily design, implement, and scale new AI models using existing pre-trained models and a simple API for configuration.

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Within this notebook, users can explore three QA model types: BERT-like models for Extractive Question Answering, Sequence-to-Sequence (S2S) models like T5/BART for Generative Question Answering, and GPT-like models for advanced generative responses. This adaptation focuses on practical application, guiding users through the process of generating answers from given contexts and queries, with detailed explanations on how to use NeMo.\n\n## Deploy now\nTo streamline your experience and jump directly into a GPU-accelerated environment with this notebook and NeMo pre-installed, click the badge below. Our 1-click deploys are powered by [Brev.dev](https://brev.dev/).\n\n[![ Click here to deploy.](https://uohmivykqgnnbiouffke.supabase.co/storage/v1/object/public/landingpage/brevdeploynavy.svg)](https://console.brev.dev/notebook/question_answer_nemo)\n\n## Getting started \nUse the 1-click deploy link above to set up a machine with NeMO installed. Once the VM is ready, use the Access Notebook button to enter the Jupyter Lab instance\n\n### Models\nFor this notebook, we use two types of question-answering paradigms and three different models: \n- **Extractive QA**\n - BERT-like model\n- **Generative QA**\n - Sequence-to-Sequence (T5/BART-like) models\n - GPT-like models\n\nWe will be using the SQuAD dataset to showcase the training and inference. We train, test, and deploy all three models for inference and evaluate performance of all three architectures. \n\n### NeMO\nNVIDIA NeMo Framework is a generative AI framework built for researchers and pytorch developers working on large language models (LLMs), multimodal models (MM), automatic speech recognition (ASR), and text-to-speech synthesis (TTS). NeMO provides a scalable framework to easily design, implement, and scale new AI models using existing pre-trained models and a simple API for configuration.","displayName","Quick Deploy Question_Answering Models Using Brev.dev",["D",1712703600598],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/csp_launcher/resources/question_answering","rawImageUrl","https://developer.download.nvidia.com/images/brev-nvidia-dark.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[238,239,240,241,242,243,244,245,246,247],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_206":281,"_249":282,"_250":283,"_252":253},{"_206":278,"_249":279,"_250":280,"_252":253},{"_206":275,"_249":276,"_250":277,"_252":253},{"_206":271,"_249":272,"_250":273,"_252":253},{"_206":267,"_249":268,"_250":269,"_252":253},{"_206":265,"_249":265,"_250":266,"_252":253},{"_206":262,"_249":263,"_250":264,"_252":253},{"_206":259,"_249":259,"_250":260,"_252":253},{"_206":255,"_249":256,"_250":257,"_252":253},{"_206":248,"_249":248,"_250":251,"_252":253},"Tutorial","value","filter",{"_254":73,"_249":248},"category","label","field","Question Answering","uscs_question_answering",{"_254":258,"_249":256},"application","NeMo",{"_254":261,"_249":259},"framework","Natural Language Processing","uscs_natural_language_processing",{"_254":258,"_249":263},"Jupyter Notebook",{"_254":73,"_249":265},"DL","soln_dl",{"_254":270,"_249":268},"technology","Deep Learning Examples","pltfm_deep_learning_examples",{"_254":274,"_249":272},"platform","Conversational AI","soln_conversational_ai",{"_254":270,"_249":276},"Automatic Speech Recognition","uscs_automatic_speech_recognition",{"_254":258,"_249":279},"AI","soln_ai",{"_254":270,"_249":282},{"_107":1263,"_109":1264},"ngcApps",[1216],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1197":1198,"_1199":1200,"_1201":1202,"_1203":1204,"_1205":295,"_138":319,"_1206":1207,"_1208":56,"_1209":86,"_1210":86,"_1211":1212,"_1213":86,"_1214":1215},"amplitude",{"_1194":1195,"_831":-7,"_1196":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMSUyMiUzQXRydWUlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1188":1189,"_1190":1191,"_1192":1193},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.10.0","defaultSearchSuggestions",{"_1071":182,"_1072":1073,"_1074":1075,"_1076":1077},"announcements",[],"consentState",{"_356":1061,"_1062":31,"_1063":31,"_1064":86,"_1065":1066},"consentConfig",{"_338":339,"_340":341,"_342":343,"_344":345,"_346":347,"_348":349,"_350":351,"_352":353,"_354":355,"_356":357},"showContactAdmin",1785065488613,[],[],[333,334,335,336,337],{"_45":59,"_47":70,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":68,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":60,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":50,"_51":52,"_53":54,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56},"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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