Question Answering Squad2.0 Bert Large Uncased Model Card

Model Overview

This is a checkpoint for Bert Large Uncased model for Question Answering trained on the question answering dataset SQuADv2.0\nThe model was trained for 2 epochs, with O1/16 bit mixed precision and batch size of 12 on 2 V100 GPUs.\nThe model exact match (EM) score is 77.16% and an F1 score of 80.22%.

Model Architecture

BERT, or Bidirectional Encoder Representations from Transformers, is a neural approach to pre-train language representations\nwhich obtains near state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks,\nincluding SQuAD Question Answering dataset. Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset,\nconsisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question\nis a segment of text, or span, from the corresponding reading passage.\nUnlike SQuADv1.1, SQuADv2.0 can contain questions that are unanswerable.

Apart from the BERT architecture this model also includes a question answering model head, which is stacked on top of BERT.\nThis question answering head is a token classifier and is, more specifically, a single fully connected layer.

Intended Use

This model can be used as a part of the chat bot or semantic search system where it enables to find answers in the document to the factual questions\nTo extend this for collections of the documents it is usually combined with Information retrieval systems on the first step,\nwhich return a small number of documents or passages that may contain the answer and will go as an input to the question answering model.

The model returns a concrete span of the answer in the content. If you want to create a conversational full sentence answer,\nyou can either use some simple heuristics like, returning the full sentence where the answer is found. Or train another gnerative model,\nthat can take the question and the answer span and generate a full sentence answer.

This QA model is trained on Squad dataset but it may also work on any new content without a need for retraining.\nBut if you are working with specific technical language like for example medical one it may help first to fine tune\nthe basic language model on this type of a content.

Training Data

The Question Answering model was trained on Squad2.0 dataset which combines the 100,000 questions in SQuAD1.1\nwith over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones.\nTo do well on SQuAD2.0, systems must not only answer questions when possible,\nbut also determine when no answer is supported by the paragraph and abstain from answering.

SQuAD 1.1, the previous version of the SQuAD dataset, contains 100,000+ question-answer pairs on 500+ articles\nfrom Wikipedia.

Training works as follows: The user provides training and evaluation data in text form in JSON format. This data is parsed by scripts in NeMo and converted into model input.\nThe input sequence is a concatenation of a tokenized query and its according reading passage.\nThe question answering head predicts for each token in the reading passage or context if it is the start or end\nof the answer span. The model is trained using cross entropy loss.

Evaluation Metrics

With Extractive QA models, when the answer span is returned by the model, accuracy evaluation\nuses two metrics: The exact match (EM) and F1 score of the returned answer spans compared\nto the right answers. The overall EM and F1 scores are computed for a model by averaging\nthe individual example scores.

Exact match: If the answer span is exactly equal to the correct one, it returns 1;\notherwise, it returns 0. When assessing against a negative example (SQuAD 2.0), if the model\npredicts any text at all, it automatically receives a 0 for that example.
F1: The F1 score is a common metric for classification problems and widely used in QA.\nIt is appropriate when we care equally about precision and recall. In this case, it's computed\nover the individual words in the prediction against those in the True Answer. The number of\nshared words between the prediction and the truth is the basis of the F1 score: Precision is the\nratio of the number of shared words to the total number of words in the prediction, and recall is\nthe ratio of the number of shared words to the total number of words in the ground truth.\nF1 = 2 * (precision * recall) / (precision + recall)

How to Use This Model

These model checkpoints are intended to be used with the Train Adapt Optimize (TAO) Toolkit.\nIn order to use these checkpoints, there should be a specification file (.yaml) that specifies hyperparameters, datasets for training and evaluation, and any other information needed for the experiment.\nFor more information on the experiment spec files for each use case, please refer to the TAO Toolkit User Guide.

Note: The model is encrypted and will only operate with the model load key tao-encode.

To fine-tune from a model checkpoint (.tlt), use the following command (`` parameter should be a valid path to the file that specifies the fine-tuning hyperparameters,\nthe dataset to fine-tune on, the dataset to evaluate on, epochs number):

!tao question_answering finetune -e \\\n -m \\\n -g \n

To evaluate an existing dataset using a model checkpoint (.tlt), use the following command\n(`` parameter should be a valid path to the file that specifies the dataset\nthat is being evaluated)

!tao question_answering evaluate -e \\\n -m \n

References

Squad dataset: https://rajpurkar.github.io/SQuAD-explorer/

License

By downloading and using the models and resources packaged with TAO Conversational AI, you would be accepting the terms of the Riva license

Ethical AI

NVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications.\nConsider potential algorithmic bias when choosing or creating the models being deployed.\nWork with the model’s developer to ensure that it meets the requirements for the relevant industry and use case;\nthat the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.

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Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, \nconsisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question \nis a segment of text, or span, from the corresponding reading passage. \nUnlike SQuADv1.1, SQuADv2.0 can contain questions that are unanswerable.\n\nApart from the BERT architecture this model also includes a question answering model head, which is stacked on top of BERT.\nThis question answering head is a token classifier and is, more specifically, a single fully connected layer.\n\nIntended Use\n------------\n\nThis model can be used as a part of the chat bot or semantic search system where it enables to find answers in the document to the factual questions\nTo extend this for collections of the documents it is usually combined with Information retrieval systems on the first step,\nwhich return a small number of documents or passages that may contain the answer and will go as an input to the question answering model.\n\nThe model returns a concrete span of the answer in the content. If you want to create a conversational full sentence answer,\nyou can either use some simple heuristics like, returning the full sentence where the answer is found. Or train another gnerative model,\nthat can take the question and the answer span and generate a full sentence answer.\n\nThis QA model is trained on Squad dataset but it may also work on any new content without a need for retraining.\nBut if you are working with specific technical language like for example medical one it may help first to fine tune \nthe basic language model on this type of a content.\n\nTraining Data\n-------------\n\nThe Question Answering model was trained on Squad2.0 dataset which combines the 100,000 questions in SQuAD1.1 \nwith over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. \nTo do well on SQuAD2.0, systems must not only answer questions when possible, \nbut also determine when no answer is supported by the paragraph and abstain from answering.\n\nSQuAD 1.1, the previous version of the SQuAD dataset, contains 100,000+ question-answer pairs on 500+ articles\nfrom Wikipedia.\n\nTraining works as follows: The user provides training and evaluation data in text form in JSON format. This data is parsed by scripts in NeMo and converted into model input. \nThe input sequence is a concatenation of a tokenized query and its according reading passage. \nThe question answering head predicts for each token in the reading passage or context if it is the start or end \nof the answer span. The model is trained using cross entropy loss.\n\nEvaluation Metrics\n------------------\n\nWith Extractive QA models, when the answer span is returned by the model, accuracy evaluation\nuses two metrics: The exact match (EM) and F1 score of the returned answer spans compared\nto the right answers. The overall EM and F1 scores are computed for a model by averaging\nthe individual example scores.\n\n* `Exact match`: If the answer span is exactly equal to the correct one, it returns 1;\n otherwise, it returns 0. When assessing against a negative example (SQuAD 2.0), if the model\n predicts any text at all, it automatically receives a 0 for that example.\n* `F1`: The F1 score is a common metric for classification problems and widely used in QA.\n It is appropriate when we care equally about precision and recall. In this case, it's computed\n over the individual words in the prediction against those in the True Answer. The number of\n shared words between the prediction and the truth is the basis of the F1 score: Precision is the\n ratio of the number of shared words to the total number of words in the prediction, and recall is\n the ratio of the number of shared words to the total number of words in the ground truth.\n `F1 = 2 * (precision * recall) / (precision + recall)`\n\nHow to Use This Model\n---------------------\n\nThese model checkpoints are intended to be used with the Train Adapt Optimize (TAO) Toolkit.\nIn order to use these checkpoints, there should be a specification file (.yaml) that specifies hyperparameters, datasets for training and evaluation, and any other information needed for the experiment.\nFor more information on the experiment spec files for each use case, please refer to the [TAO Toolkit User Guide](https://docs.nvidia.com/tao/tao-toolkit/text/overview.html).\n\nNote: The model is encrypted and will only operate with the model load key `tao-encode`.\n\n- To fine-tune from a model checkpoint (.tlt), use the following command (`` parameter should be a valid path to the file that specifies the fine-tuning hyperparameters, \nthe dataset to fine-tune on, the dataset to evaluate on, epochs number):\n\n```sh\n!tao question_answering finetune -e \\\n -m \\\n -g \n```\n\n- To evaluate an existing dataset using a model checkpoint (.tlt), use the following command\n(`` parameter should be a valid path to the file that specifies the dataset \nthat is being evaluated)\n\n```sh\n!tao question_answering evaluate -e \\\n -m \n```\n\nReferences\n----------\n\n- Squad dataset: https://rajpurkar.github.io/SQuAD-explorer/\n\nLicense\n-------\n\nBy downloading and using the models and resources packaged with TAO Conversational AI, you would be accepting the terms of the Riva [license](https://developer.nvidia.com/riva/ga/license)\n\nSuggested reading\n-----------------\n\n- More information on about TAO Toolkit and pre-trained models can be found at the [NVIDIA Developer Zone](https://developer.nvidia.com/tao-toolkit)\n- Read the [TAO Toolkit getting Started](https://docs.nvidia.com/tao/tao-toolkit/text/tao_quick_start_guide.html) guide and [release notes](https://docs.nvidia.com/tao/tao-toolkit/text/release_notes.html)\n- If you have any questions or feedback, please refer to the discussions on [TAO Toolkit Developer Forums](https://forums.developer.nvidia.com/c/accelerated-computing/intelligent-video-analytics/tao-toolkit/17)\n- Deploy your model for Production using Riva. Learn more about [Riva framework](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/index.html)\n\nEthical AI\n----------\n\nNVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications.\nConsider potential algorithmic bias when choosing or creating the models being deployed.\nWork with the model’s developer to ensure that it meets the requirements for the relevant industry and use case;\nthat the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.","displayName","Question Answering SQUAD2.0 Bert - Large",["D",1696600312133],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/tao/models/questionanswering_squad_english_bertlarge","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO-RIVA.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[253,254,255,256,257],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_222":274,"_259":274,"_260":275,"_262":263},{"_222":272,"_259":272,"_260":273,"_262":263},{"_222":268,"_259":269,"_260":270,"_262":263},{"_222":265,"_259":265,"_260":266,"_262":263},{"_222":258,"_259":258,"_260":261,"_262":263},"SQUAD2.0","value","filter",{"_264":263,"_259":258},"category","label","field","Riva",{"_264":267,"_259":265},"framework","Question Answering","uscs_question_answering",{"_264":271,"_259":269},"application","NLP",{"_264":263,"_259":272},"BERT Large",{"_264":263,"_259":274},{"_130":1448,"_132":1449},"ngcApps",[1401],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1382":1383,"_1384":1385,"_1386":1387,"_1388":1389,"_1390":287,"_161":311,"_1391":1392,"_1393":60,"_1394":109,"_1395":109,"_1396":1397,"_1398":109,"_1399":1400},"amplitude",{"_1379":1380,"_983":-7,"_1381":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1373":1374,"_1375":1376,"_1377":1378},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1258":192,"_1259":1260,"_1261":1262,"_1263":1264},"announcements",[],"consentState",{"_348":1248,"_1249":109,"_1250":35,"_1251":35,"_1252":1253},"consentConfig",{"_330":331,"_332":333,"_334":335,"_336":337,"_338":339,"_340":341,"_342":343,"_344":345,"_346":347,"_348":349},"showContactAdmin",1784466543904,[],[],[325,326,327,328,329],{"_49":63,"_51":74,"_53":125,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":70,"_53":125,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":64,"_53":125,"_55":-5,"_57":-5,"_59":60},{"_49":61,"_51":62,"_53":125,"_55":-5,"_57":-5,"_59":60},{"_49":50,"_51":52,"_53":125,"_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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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_biz_pendingA","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. 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