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 the Transfer Learning Toolkit (TLT).\nIn order to use these checkpoints, there should be a specification file (.yaml) that specifies hyperparameters, datasets for training and evaluation, and any other information needed for the experiment.\nFor more information on the experiment spec files for each use case, please refer to the Transfer Learning Toolkit User Guide.

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

To fine-tune from a model checkpoint (.tlt), use the following command (<experiment_spec> parameter should be a valid path to the file that specifies the fine-tuning hyperparameters,\nthe dataset to fine-tune on, the dataset to evaluate on, epochs number):

\\\n -m \\\n -g \n\n\">

!tlt text_classification finetune -e <experiment_spec> \\\n                                  -m <model_checkpoint> \\\n                                  -g <num_gpus>\n\n

To evaluate an existing dataset using a model checkpoint (.tlt), use the following command\n(<experiment_spec> parameter should be a valid path to the file that specifies the dataset\nthat is being evaluated):

\\\n -m \n\">

!tlt text_classification evaluate -e <experiment_spec> \\\n                                  -m <model_checkpoint> \n

To evaluate a model checkpoint (.tlt) on a set of query examples, use the following command\n(<experiment_spec> parameter should be a valid path to the file that specifies list of queries to test):

\\\n -m \n\">

!tlt text_classification infer -e <experiment_spec> \\\n                               -m <model_checkpoint> \n

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 TLT Conversational AI, you would be accepting the terms of the Jarvis 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\nIntended Use\n------------\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\nModel 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\n\nTraining Data\n-------------\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\n\nEvaluation\n----------\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\nHow to Use This Model\n---------------------\nThese model checkpoints are intended to be used with the Transfer Learning Toolkit (TLT).\nIn order to use these checkpoints, there should be a specification file (.yaml) that specifies hyperparameters, datasets for training and evaluation, and any other information needed for the experiment.\nFor more information on the experiment spec files for each use case, please refer to the [Transfer Learning Toolkit User Guide](https://docs.nvidia.com/TLT/tlt-user-guide/text/overview.html).\n\nNote: The model is encrypted and will only operate with the model load key `tlt-encode`.\n\n- To fine-tune from a model checkpoint (.tlt), use the following command (`` parameter should be a valid path to the file that specifies the fine-tuning hyperparameters, \nthe dataset to fine-tune on, the dataset to evaluate on, epochs number):\n\n```sh\n!tlt text_classification finetune -e \\\n -m \\\n -g \n\n```\n\n- To evaluate an existing dataset using a model checkpoint (.tlt), use the following command\n(`` parameter should be a valid path to the file that specifies the dataset \nthat is being evaluated):\n\n```\n!tlt text_classification evaluate -e \\\n -m \n```\n\n- To evaluate a model checkpoint (.tlt) on a set of query examples, use the following command\n(`` parameter should be a valid path to the file that specifies list of queries to test):\n\n```sh\n!tlt text_classification infer -e \\\n -m \n```\n\nReferences\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\nLicense \n-------\n\nBy downloading and using the models and resources packaged with TLT Conversational AI, you would be accepting the terms of the Jarvis [license](https://developer.nvidia.com/ngc/nvidia-jarvis-license)\n\nSuggested reading\n-----------------\n\n- More information on about Transfer learning toolkit and pre-trained models can be found at the [NVIDIA Developer Zone](https://developer.nvidia.com/transfer-learning-toolkit)\n- Read the [TLT Getting Started]([TLT getting Started](https://docs.nvidia.com/tlt/tlt-user-guide/text/tlt_quick_start_guide.html) guide and [release notes](https://docs.nvidia.com/TLT/tlt-user-guide/text/release_notes.html)\n- If you have any questions or feedback, please refer to the discussions on [TLT Developer Forums](https://devtalk.nvidia.com/default/board/417/transfer-learning-toolkit/)\n- Deploy your model for Production using Jarvis. Learn more about [Jarvis framework](https://docs.nvidia.com/deeplearning/jarvis/user-guide/docs/index.html)\n\nEthical AI\n----------\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.","displayName","Domain Classification English Bert",["D",1680638411630],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/tlt-jarvis/models/domainclassification_english_bert","rawImageUrl","https://raw.githubusercontent.com/kbojo/images/master/TLT-Jarvis.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[253,254,255,256,257],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_222":273,"_259":273,"_260":274,"_262":263},{"_222":271,"_259":271,"_260":272,"_262":263},{"_222":269,"_259":269,"_260":270,"_262":263},{"_222":265,"_259":266,"_260":267,"_262":263},{"_222":258,"_259":258,"_260":261,"_262":263},"NLP","value","filter",{"_264":263,"_259":258},"category","label","field","Natural Language Processing","uscs_natural_language_processing",{"_264":268,"_259":266},"application","Jarvis",{"_264":263,"_259":269},"Domain Classification",{"_264":263,"_259":271},"BERT",{"_264":263,"_259":273},{"_130":1448,"_132":1449},"ngcApps",[1401],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1382":1383,"_1384":1385,"_1386":1387,"_1388":1389,"_1390":286,"_161":310,"_1391":1392,"_1393":59,"_1394":109,"_1395":109,"_1396":1397,"_1398":109,"_1399":1400},"amplitude",{"_1379":1380,"_983":-7,"_1381":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1373":1374,"_1375":1376,"_1377":1378},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1258":192,"_1259":1260,"_1261":1262,"_1263":1264},"announcements",[],"consentState",{"_348":1248,"_1249":109,"_1250":35,"_1251":35,"_1252":1253},"consentConfig",{"_330":331,"_332":333,"_334":335,"_336":337,"_338":339,"_340":341,"_342":343,"_344":345,"_346":347,"_348":349},"showContactAdmin",1784287762046,[],[],[324,325,326,327,328],{"_49":63,"_51":74,"_53":125,"_55":-5,"_57":-5,"_58":59},{"_49":63,"_51":70,"_53":125,"_55":-5,"_57":-5,"_58":59},{"_49":63,"_51":64,"_53":125,"_55":-5,"_57":-5,"_58":59},{"_49":60,"_51":61,"_53":125,"_55":-5,"_57":-5,"_58":59},{"_49":50,"_51":52,"_53":54,"_55":56,"_57":329,"_58":59},6,"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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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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