TextClassification Model Card

Model Overview

Text classification model is useful for text classification problems such as sentiment analysis\nor domain detection for dialogue systems. Provided model here is trained to classify the given query into\n1 of 4 domains described below to use it as an initial step in the interactive weather chat bot, which was presented in GTC 2020 keynote.

Intended Use

Text Classification Model can be used for domain classification as the first step in the dialogue systems,\nto route query according to the appropriate domain.\nThis classification is a task specific according to the domains and examples provided in the training data.\nUsually in practical settings you need to take this model (pretrained Bert model) and train it on you own dataset.

Model Architecture

Our text classification model uses a pretrained BERT model (or other BERT-like models) followed by\na classification layer on the output of the first token ([CLS]).

Training Data

We used a proprietary data set that was collected via Mechanical Turk to describe large variety of queries\nthat fall in one of the next 4 domains:

weather (all weather related queries that triggered call to weather API)
meteorology (questions about meteorology topic that went for IR+QA route)
personality (questions about personality that went to chit chat route)
nomatch (all other queries that does not fall to any of other categories)

Evaluation

Training dataset included 2150 example of queries divided for 4 domains described above.\nWe got around 95% domain classification accuracy for this data.

How to Use This Model

These model checkpoints are intended to be used with Nvidia Riva.

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

References

Bert paper: https://arxiv.org/pdf/1810.04805.pdf
Weather chat bot GTC 2020 keynote demo: https://www.youtube.com/watch?v=3ey76WVYkCI

License

By downloading and using the models and resources packaged with Nvidia Riva, 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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Provided model here is trained to classify the given query into\n1 of 4 domains described below to use it as an initial step in the interactive weather chat bot, which was presented in GTC 2020 keynote.\n\n## Intended Use\n\nText Classification Model can be used for domain classification as the first step in the dialogue systems,\nto route query according to the appropriate domain.\nThis classification is a task specific according to the domains and examples provided in the training data.\nUsually in practical settings you need to take this model (pretrained Bert model) and train it on you own dataset.\n\n## Model Architecture\n\nOur text classification model uses a pretrained BERT model (or other BERT-like models) followed by\na classification layer on the output of the first token ([CLS]).\n\n## Training Data\n\nWe used a proprietary data set that was collected via Mechanical Turk to describe large variety of queries\nthat fall in one of the next 4 domains:\n\n- weather (all weather related queries that triggered call to weather API)\n- meteorology (questions about meteorology topic that went for IR+QA route)\n- personality (questions about personality that went to chit chat route)\n- nomatch (all other queries that does not fall to any of other categories)\n\n## Evaluation\n\nTraining dataset included 2150 example of queries divided for 4 domains described above.\nWe got around 95% domain classification accuracy for this data.\n\n## How to Use This Model\n\nThese model checkpoints are intended to be used with Nvidia Riva.\n\nNote: The model is encrypted and will only operate with the model load key `tlt-encode`.\n\n## References\n\n- Bert paper: https://arxiv.org/pdf/1810.04805.pdf\n- Weather chat bot GTC 2020 keynote demo: https://www.youtube.com/watch?v=3ey76WVYkCI\n\n## License\n\nBy downloading and using the models and resources packaged with Nvidia Riva, you would be accepting the terms of the Riva [license](https://developer.nvidia.com/riva/ga/license)\n\n## Suggested reading\n\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\n## Ethical AI\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.","RIVA Text Classification",["D",1680635769178],"deployable_v1.0","/api/image/nvidia/riva/models/domainclassification_english_bert","https://github.com/kbojo/images/raw/master/Nvidia-Centric-RIVA.png",440794733,"bias","explainability","privacy","safetyAndSecurity",[],[249,250,251,252],[],[],[],{"_91":257,"_127":257,"_128":258,"_130":131},{"_91":255,"_127":255,"_128":256,"_130":131},{"_91":136,"_127":136,"_128":254,"_130":131},{"_91":133,"_127":133,"_128":253,"_130":131},{"_132":135,"_127":133},{"_132":131,"_127":136},"Domain Classification",{"_132":131,"_127":255},"BERT",{"_132":131,"_127":257},{"_170":1237,"_172":1238},"ngcApps",[1190],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1171":1172,"_1173":1174,"_1175":1176,"_1177":1178,"_1179":270,"_202":296,"_1180":1181,"_1182":51,"_1183":213,"_1184":213,"_1185":1186,"_1187":213,"_1188":1189},"amplitude",{"_1168":1169,"_811":-7,"_1170":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1162":1163,"_1164":1165,"_1166":1167},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1789490231306,"1.15.0","defaultSearchSuggestions",{"_1051":378,"_1052":1053,"_1054":1055,"_1056":1057},"announcements",[],"consentState",{"_335":1041,"_1042":213,"_1043":26,"_1044":26,"_1045":1046},"consentConfig",{"_317":318,"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332,"_333":334,"_335":336},"showContactAdmin",1789490231307,[],[],[310,311,312,313,314,315],{"_40":55,"_42":66,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":63,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":56,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":316,"_50":51},4,"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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