Model Overview

This is an uncased question answering model with a BERT Large encoder finetuned on dataset SQuADv2.0 [1].\nWith Question Answering, or Reading Comprehension, given a question and a passage of content (context) that may contain an answer for the question, the model predicts the span within the text with a start and end position indicating the answer to the question.

Trained or fine-tuned NeMo models (with the file extenstion .nemo) can be converted to Riva models (with the file extension .riva) and then deployed. Here is a pre-trained Riva question answering model with a BERT Large encoder finetuned on dataset SQuADv2.0.

Model Architecture

The current version of the question answering model The model is based on the architecture presented in \"BERT: Pre-training of Deep Bidirectional Transformers for\nLanguage Understanding\" paper [2]. In this particular instance, the model has 24 Transformer blocks. On top of that it is using a span prediction head, that is equivalent to token classification with 2 classes: one for the start of the span and one for the end of the span. All model parameters are jointly fine-tuned on the downstream task. More specifically, an\ninput text is fed to the BERT encoder model, and the output states are further fed to the span prediction.

Training

The model was trained with NeMo BERT large uncased checkpoint.

Dataset

The model was trained on SQuADv2.0 [1] corpus for question answering. For datasets like SQuAD 2.0, this model supports cases when the answer is not contained in the content.

Performance

Evaluation on the SQuAD2.0 dev set:

Exact Match 80.22%

F1\t\t\t\t83.05%

How to use this model

The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.

Automatically load the model from NGC

import nemo\nimport nemo.collections.nlp as nemo_nlp\nmodel = nemo_nlp.models.question_answering.qa_model.QAModel.from_pretrained(model_name=\"qa_squadv2.0_bertlarge\")\n

Inference

python [NEMO_GIT_FOLDER]/examples/nlp/question_answering/question_answering_squad.py do_training=false pretrained_model=qa_squadv2.0_bertlarge model.validation_ds.file=[SOURCE_FILE] model.dataset.version_2_with_negative=true model.dataset.do_lower_case=true\n

Input

The model takes a Json file as input that follows the SQuAD format.

Output

The model outputs a JSON file as output for prediction and n-Best list.

Limitations

The length of the input text is currently constrained by the maximum sequence length of the uncased encoder model, which is 512 tokens after tokenization.

References

[1] https://rajpurkar.github.io/SQuAD-explorer/

[2] https://arxiv.org/pdf/1810.04805.pdf

[3] NVIDIA NeMo Toolkit

Licence

License to use this model is covered by the NGC TERMS OF USE unless another License/Terms Of Use/EULA is clearly specified. By downloading the public and release version of the model, you accept the terms and conditions of the NGC TERMS OF USE.

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[Here](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/questionanswering_squad_english_bertlarge) is a pre-trained Riva question answering model with a BERT Large encoder finetuned on dataset SQuADv2.0.\n\n\n## Model Architecture\n\nThe current version of the question answering model The model is based on the architecture presented in \"BERT: Pre-training of Deep Bidirectional Transformers for\nLanguage Understanding\" paper [2]. In this particular instance, the model has 24 Transformer blocks. On top of that it is using a span prediction head, that is equivalent to token classification with 2 classes: one for the start of the span and one for the end of the span. All model parameters are jointly fine-tuned on the downstream task. More specifically, an \ninput text is fed to the BERT encoder model, and the output states are further fed to the span prediction.\n\n# Training\n\nThe model was trained with [NeMo BERT large uncased checkpoint](https://ngc.nvidia.com/catalog/models/nvidia:bertlargeuncasedfornemo).\n\n\n### Dataset\n\nThe model was trained on SQuADv2.0 [1] corpus for question answering. For datasets like SQuAD 2.0, this model supports cases when the answer is not contained in the content.\n\n## Performance\n\nEvaluation on the SQuAD2.0 dev set:\n \n Exact Match 80.22%\n \n F1\t\t\t\t83.05%\n\n\n## How to use this model\n\nThe model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.\n\n### Automatically load the model from NGC\n```python\nimport nemo\nimport nemo.collections.nlp as nemo_nlp\nmodel = nemo_nlp.models.question_answering.qa_model.QAModel.from_pretrained(model_name=\"qa_squadv2.0_bertlarge\")\n```\n\n### Inference\n```bash\npython [NEMO_GIT_FOLDER]/examples/nlp/question_answering/question_answering_squad.py do_training=false pretrained_model=qa_squadv2.0_bertlarge model.validation_ds.file=[SOURCE_FILE] model.dataset.version_2_with_negative=true model.dataset.do_lower_case=true\n```\n### Input\n\nThe model takes a Json file as input that follows the SQuAD format.\n\n### Output\n\nThe model outputs a JSON file as output for prediction and n-Best list.\n\n## Limitations\n\nThe length of the input text is currently constrained by the maximum sequence length of the uncased encoder model, which is 512 tokens after tokenization. \n\n## References\n\n[1] https://rajpurkar.github.io/SQuAD-explorer/\n\n[2] https://arxiv.org/pdf/1810.04805.pdf\n\n[3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)\n\n\n## Licence\n\nLicense to use this model is covered by the NGC [TERMS OF USE](https://ngc.nvidia.com/legal/terms) unless another License/Terms Of Use/EULA is clearly specified. By downloading the public and release version of the model, you accept the terms and conditions of the NGC [TERMS OF USE](https://ngc.nvidia.com/legal/terms).","displayName","QA squadv2.0 Bertlarge",["D",1680637720054],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/qa_squadv2_0_bertlarge","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[239,240,241,242,243],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_207":259,"_245":259,"_246":260,"_248":249},{"_207":257,"_245":257,"_246":258,"_248":249},{"_207":255,"_245":255,"_246":256,"_248":249},{"_207":251,"_245":252,"_246":253,"_248":249},{"_207":244,"_245":244,"_246":247,"_248":249},"squadv2.0","value","filter",{"_250":249,"_245":244},"category","label","field","Question Answering","uscs_question_answering",{"_250":254,"_245":252},"application","PytorchLightning",{"_250":249,"_245":255},"NLP",{"_250":249,"_245":257},"Bertlarge",{"_250":249,"_245":259},{"_108":1435,"_110":1436},"ngcApps",[1388],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1369":1370,"_1371":1372,"_1373":1374,"_1375":1376,"_1377":272,"_139":296,"_1378":1379,"_1380":56,"_1381":87,"_1382":87,"_1383":1384,"_1385":87,"_1386":1387},"amplitude",{"_1366":1367,"_968":-7,"_1368":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1360":1361,"_1362":1363,"_1364":1365},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1243":169,"_1244":1245,"_1246":1247,"_1248":1249},"announcements",[],"consentState",{"_333":1233,"_1234":87,"_1235":31,"_1236":31,"_1237":1238},"consentConfig",{"_315":316,"_317":318,"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332,"_333":334},"showContactAdmin",1784010722381,[],[],[310,311,312,313,314],{"_45":60,"_47":71,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":103,"_51":-5,"_53":-5,"_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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It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","AMP_MKTG_91bf70adfe","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",4,[606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626],[571,572,573,574,575],{"_373":465,"_207":465,"_168":169,"_347":597},{"_373":431,"_207":431,"_168":169,"_347":592},{"_373":585,"_207":585,"_168":169,"_347":586},{"_373":581,"_207":581,"_168":169,"_347":582},{"_373":576,"_207":576,"_168":169,"_347":577},"nvcrm.my.site.com",[578],{"_79":579,"_379":576,"_380":31,"_168":580},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[583],{"_79":584,"_379":581,"_380":31,"_168":414},"edge","github.com",[587,588],{"_79":591,"_379":585,"_380":31,"_168":590},{"_79":589,"_379":585,"_380":31,"_168":590},"logged_in","This domain is owned by Github. 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This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","research.nvidia.com","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","s_pltp","This cookie is typically associated with Adobe Analytics (formerly SiteCatalyst). It is used to identify the previous link clicked within the site, which helps in analyzing site usage patterns.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","This cookie name is used as a long-lived unique identifier of the visitor on the website. 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