IntentAndSlotClassification Model Card

Model Overview

Joint Intent classification and Slot classification is a task of classifying an Intent\nand detecting all relevant Slots (Entities) for this Intent in a query.\nFor example, in the query: What is the weather in Santa Clara tomorrow morning?,\nwe would like to classify the query as a Weather Intent, and detect Santa Clara as a Location slot\nand tomorrow morning as a date_time slot.

Intended Use

Intents and Slots names are usually task specific and defined as labels in the training data.\nThis is a fundamental step that is executed in any task-driven Conversational Assistant.\nThe primary use case of this model is to jointly identify Intents and Entities in a given user query.

Model Architecture

This is a pretrained Bert based model with 2 linear classifier heads on the top of it,\none for classifying an intent of the query and another for classifying slots for each token of the query.\nThis model is trained with the combined loss function on the Intent and Slot classification task on the given dataset.

For each query the model will classify it as one the intents from the intent dictionary and\nfor each word of the query it will classify it as one of the slots from the slot dictionary, including out of scope slot\nfor all the remaining words in the query which does not fall in another slot category.\nOut of scope slot (O) is a part of slot dictionary that the model is trained on.

Training Data

We used a proprietary data set that was collected via Mechanical Turk to describe different queries in weather domain.

List of the recognized Intents for this model:

weather
temperature, Temperature_yes_no
rainfall, rainfall_yes_no
snow, snow_yes_no
humidity, humidity_yes_no
windspeed
sunny
cloudy
context

List of the recognized Entities:

O (out of scope)
weathertime
weatherplace
temperatureunit
current_location
wind_speed_unit
rainfallunit
snowunit
alert_type
weatherforecastdaily

Evaluation

Training dataset included 9500 queries related to the weather topic and about 100K total words in the queries (slots) and 2000 queries for testing.\nThe model was trained for 30 epochs after which it stopped giving improvements.\nWe got around 95% intent accuracy and 93% slot accuracy for the given dataset.

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 intent_slot_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 intent_slot_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 intent_slot_classification infer -e <experiment_spec> \\\n                                      -m <model_checkpoint> \n

References

The model architecture is based on the paper: https://arxiv.org/pdf/1902.10909.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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(deployable_v1.0)","nvidia/tlt-jarvis/intentslotclassification_weather_english_bert","key","nvidia/tlt-jarvis/models/intentslotclassification_weather_english_bert","resourceType","models","resourceId","intentslotclassification_weather_english_bert","shortDescription","Intent and Slot classification of the qeuries for the weather chat bot (trained on weather chat bot data).","IntentAndSlotClassification Model Card\n=========================================================\n\nModel Overview\n--------------\n\nJoint Intent classification and Slot classification is a task of classifying an Intent \nand detecting all relevant Slots (Entities) for this Intent in a query. \nFor example, in the query: `What is the weather in Santa Clara tomorrow morning?`, \nwe would like to classify the query as a `Weather` Intent, and detect `Santa Clara` as a `Location` slot \nand `tomorrow morning` as a `date_time` slot. \n\nIntended Use\n------------\n\nIntents and Slots names are usually task specific and defined as labels in the training data. \nThis is a fundamental step that is executed in any task-driven Conversational Assistant.\nThe primary use case of this model is to jointly identify Intents and Entities in a given user query.\n\nModel Architecture\n------------------\nThis is a pretrained Bert based model with 2 linear classifier heads on the top of it, \none for classifying an intent of the query and another for classifying slots for each token of the query.\nThis model is trained with the combined loss function on the Intent and Slot classification task on the given dataset.\n\nFor each query the model will classify it as one the intents from the intent dictionary and \nfor each word of the query it will classify it as one of the slots from the slot dictionary, including out of scope slot\nfor all the remaining words in the query which does not fall in another slot category.\nOut of scope slot (O) is a part of slot dictionary that the model is trained on. \n\n\nTraining Data\n-------------\n\nWe used a proprietary data set that was collected via Mechanical Turk to describe different queries in weather domain.\n\nList of the recognized Intents for this model:\n\n- weather\n- temperature, Temperature_yes_no\n- rainfall, rainfall_yes_no\n- snow, snow_yes_no\n- humidity, humidity_yes_no\n- windspeed\n- sunny\n- cloudy\n- context\n\nList of the recognized Entities:\n\n- O (out of scope)\n- weathertime\n- weatherplace\n- temperatureunit\n- current_location\n- wind_speed_unit\n- rainfallunit\n- snowunit\n- alert_type\n- weatherforecastdaily\n\n\nEvaluation\n----------\n\nTraining dataset included 9500 queries related to the weather topic and about 100K total words in the queries (slots) and 2000 queries for testing.\nThe model was trained for 30 epochs after which it stopped giving improvements.\nWe got around 95% intent accuracy and 93% slot accuracy for the given dataset.\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```\n!tlt intent_slot_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 intent_slot_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```\n!tlt intent_slot_classification infer -e \\\n -m \n```\n\nReferences\n----------\n- The model architecture is based on the paper: https://arxiv.org/pdf/1902.10909.pdf\n- Weather chat bot GTC 2020 keynote demo: https://www.youtube.com/watch?v=3ey76WVYkCI\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","Joint Intent and Slot Classification Bert",["D",1680637120521],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tlt-jarvis/models/intentslotclassification_weather_english_bert","rawImageUrl","https://raw.githubusercontent.com/kbojo/images/master/TLT-Jarvis.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[243,244,245,246,247],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_211":263,"_249":263,"_250":264,"_252":253},{"_211":261,"_249":261,"_250":262,"_252":253},{"_211":257,"_249":258,"_250":259,"_252":253},{"_211":255,"_249":255,"_250":256,"_252":253},{"_211":248,"_249":248,"_250":251,"_252":253},"Slot Tagging","value","filter",{"_254":253,"_249":248},"category","label","field","NLP",{"_254":253,"_249":255},"Natural Language Processing","uscs_natural_language_processing",{"_254":260,"_249":258},"application","Jarvis",{"_254":253,"_249":261},"Intent Classification",{"_254":253,"_249":263},{"_107":1243,"_109":1244},"ngcApps",[1196],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1177":1178,"_1179":1180,"_1181":1182,"_1183":1184,"_1185":276,"_139":300,"_1186":1187,"_1188":56,"_1189":87,"_1190":87,"_1191":1192,"_1193":87,"_1194":1195},"amplitude",{"_1174":1175,"_811":-7,"_1176":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMSUyMiUzQXRydWUlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1168":1169,"_1170":1171,"_1172":1173},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.10.0","defaultSearchSuggestions",{"_1051":170,"_1052":1053,"_1054":1055,"_1056":1057},"announcements",[],"consentState",{"_337":1041,"_1042":87,"_1043":31,"_1044":31,"_1045":1046},"consentConfig",{"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332,"_333":334,"_335":336,"_337":338},"showContactAdmin",1785184998208,[],[],[314,315,316,317,318],{"_45":60,"_47":71,"_49":68,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":68,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":68,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":50,"_51":52,"_53":54,"_55":56},{"_45":46,"_47":48,"_49":68,"_51":-5,"_53":-5,"_55":56},"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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