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Model Overview

This is an uncased question answering model with a BERT Base 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 Base 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 base 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 75.04%

F1\t\t\t\t78.08%

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_bertbase\")\n

Inference

python [NEMO_GIT_FOLDER]/examples/nlp/question_answering/question_answering_squad.py do_training=false pretrained_model=qa_squadv2.0_bertbase 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 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_bert) is a pre-trained Riva question answering model with a BERT Base encoder finetuned on dataset SQuADv2.0.\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 base uncased checkpoint](https://ngc.nvidia.com/catalog/models/nvidia:bertbaseuncasedfornemo).\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 75.04%\n \n F1\t\t\t\t78.08%\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_bertbase\")\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_bertbase 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 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. 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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.",[605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625],[570,571,572,573,574],{"_373":465,"_206":465,"_167":168,"_346":596},{"_373":431,"_206":431,"_167":168,"_346":591},{"_373":584,"_206":584,"_167":168,"_346":585},{"_373":580,"_206":580,"_167":168,"_346":581},{"_373":575,"_206":575,"_167":168,"_346":576},"nvcrm.my.site.com",[577],{"_78":578,"_379":575,"_380":31,"_167":579},"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",[582],{"_78":583,"_379":580,"_380":31,"_167":414},"edge","github.com",[586,587],{"_78":590,"_379":584,"_380":31,"_167":589},{"_78":588,"_379":584,"_380":31,"_167":589},"logged_in","This domain is owned by Github. 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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. 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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.","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. 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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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Usually associated with managing sessions on load balanced servers, to ensure user requests are routed consistently to the correct server. The common root is BIGipServer most commonly followed by a domain name, usually the one that it is hosted on, but not always.","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 one year lifespan and contains no personal information.","default","

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