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Model Overview

This is an uncased question answering model with a BERT Large encoder finetuned on dataset SQuADv1.1 [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.

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 SQuADv1.1 [1] corpus for question answering. For datasets like SQuAD 1.1, this model only supports cases where the answer is contained in the context.

Performance

Evaluation on the SQuAD1.1 dev set:

Exact Match 85.44%

F1\t\t\t\t92.06%

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_squadv1.1_bertlarge\")\n

Inference

python [NEMO_GIT_FOLDER]/examples/nlp/question_answering/question_answering_squad.py do_training=false pretrained_model=qa_squadv1.1_bertlarge model.validation_ds.file=[SOURCE_FILE] 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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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\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 SQuADv1.1 [1] corpus for question answering. For datasets like SQuAD 1.1, this model only supports cases where the answer is contained in the context.\n\n## Performance\n\nEvaluation on the SQuAD1.1 dev set:\n \n Exact Match 85.44%\n \n F1\t\t\t\t92.06%\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\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_squadv1.1_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_squadv1.1_bertlarge model.validation_ds.file=[SOURCE_FILE] 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. 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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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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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