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. 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).","QA squadv2.0 Bertbase",["D",1680637798242],"/api/image/nvidia/nemo/models/qa_squadv2_0_bertbase","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","bias","explainability","privacy","safetyAndSecurity",[],[276,277,278,279,280],[],[],[],{"_91":291,"_128":291,"_129":292,"_131":132},{"_91":289,"_128":289,"_129":290,"_131":132},{"_91":287,"_128":287,"_129":288,"_131":132},{"_91":283,"_128":284,"_129":285,"_131":132},{"_91":281,"_128":281,"_129":282,"_131":132},"squadv2.0",{"_133":132,"_128":281},"Question Answering","uscs_question_answering",{"_133":286,"_128":284},"application","PyTorchLightning",{"_133":132,"_128":287},"NLP",{"_133":132,"_128":289},"Bertbase",{"_133":132,"_128":291},{"_170":1463,"_172":1464},"ngcApps",[1416],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1397":1398,"_1399":1400,"_1401":1402,"_1403":1404,"_1405":304,"_201":328,"_1406":1407,"_1408":51,"_1409":149,"_1410":149,"_1411":1412,"_1413":149,"_1414":1415},"amplitude",{"_1394":1395,"_1000":-7,"_1396":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1388":1389,"_1390":1391,"_1392":1393},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1275":229,"_1276":1277,"_1278":1279,"_1280":1281},"announcements",[],"consentState",{"_365":1265,"_1266":149,"_1267":26,"_1268":26,"_1269":1270},"consentConfig",{"_347":348,"_349":350,"_351":352,"_353":354,"_355":356,"_357":358,"_359":360,"_361":362,"_363":364,"_365":366},"showContactAdmin",1783955653438,[],[],[342,343,344,345,346],{"_40":55,"_42":66,"_44":165,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":64,"_44":165,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":165,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":165,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":54,"_50":51},"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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