Intent and Slot Classification DistilBERT Model Card

Model Overview

Joint Intent classification and Slot classification is a task of classifying an Intent and detecting all relevant Slots (Entities) for this Intent in a query. For example, in the query: What is the weather in Santa Clara tomorrow morning? , we would like to classify the query as a Weather Intent, and detect Santa Clara as a Location slot and 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. This is a fundamental step that is executed in any task-driven Conversational Assistant. The primary use case of this model is to jointly identify Intents and Entities in a given user query.

Model Architecture

This is a pretrained Distil Bert based model with 2 linear classifier heads on the top of it, one for classifying an intent of the query and another for classifying slots for each token of the query. This 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 for each word of the query it will classify it as one of the slots from the slot dictionary, including out of scope slot for all the remaining words in the query which does not fall in another slot category. Out 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 have queries releated to weather, smalltalk, map and POI domains.

List of the recognized Intents for this model:

weather.weather
weather.temperature, weather.temperature_yes_no
weather.rainfall, weather.rainfall_yes_no
weather.snow, weather.snow_yes_no
weather.humidity, weather.humidity_yes_no
weather.windspeed
weather.sunny
weather.cloudy
context.continue
navigation.startnavigation, navigation.startnavigationpoi
navigation.stopnavigation
navigation.navigationavoidhighways
navigation.istollsonroute
navigation.getspeedlimitonroute
navigation.getdistance, navigation.getdistancepoi
navigation.gettraveltime, navigation.gettraveltimepoi
navigation.getextrastoptime
navigation.geteta
navigation.showdirection, navigation.showdirectionpoi
navigation.showdirectionavoidhighways
navigation.showmap, navigation.showmappoi
navigation.getnumber
navigation.getrating
navigation.isclosed
nomatch
smalltalk.personality_hello
smalltalk.personality_nice_to_meet_you
smalltalk.personality_bot_age
smalltalk.personality_bots_owner
smalltalk.personality_bot_creator
smalltalk.personality_bot_is_happy
smalltalk.personality_what_bot_can_do
smalltalk.bot_personality_weather_interest
smalltalk.personality_bot_favorite_activity
smalltalk.personality_bot_name
smalltalk.personality_bot_challenge
smalltalk.personality_bot_location
smalltalk.personality_whats_going_on
smalltalk.personality_how_is_bot_doing
smalltalk.personality_can_bot_do_physical_activity
smalltalk.personality_bot_is_boring
smalltalk.personality_ask_me_question
smalltalk.personality_can_bot_do_action
smalltalk.personality_bot_gender
smalltalk.personality_bot_family
smalltalk.personality_what_does_bot_eat
smalltalk.personality_met_other_bot
smalltalk.personality_opinion_other_bot
smalltalk.personality_bot_love_life
smalltalk.personality_philosophical_question
smalltalk.personality_bot_user_comparision
smalltalk.personality_goodbye
smalltalk.personality_greet_user
smalltalk.personality_who_is_smarter
smalltalk.personality_opinion_on_ai
smalltalk.personality_is_user_beautiful
smalltalk.personality_ai_conquer_world
smalltalk.personality_bot_is_smart
smalltalk.personality_bot_available
smalltalk.personality_do_something_funny
smalltalk.personality_tell_me_a_joke
smalltalk.personality_sing_a_song
smalltalk.personality_bot_is_not_funny
smalltalk.personality_bot_reapeting_same_thing
smalltalk.personality_nice_talking_to_you
smalltalk.personality_happy_x_day
smalltalk.personality_thank_bot
smalltalk.personality_bot_is_fired
smalltalk.personality_goodwork_bot
smalltalk.personality_bot_is_useless
smalltalk.personality_user_dont_understand_bot
smalltalk.personality_user_apologize
smalltalk.personality_help_from_bot
smalltalk.personality_bot_affirmation
smalltalk.user_loves_bot
smalltalk.user_feel_emotion
smalltalk.bot_personality_language_bot_speak
smalltalk.bot_personality_in_free_time
smalltalk.bot_personality_about_bot

List of the recognized Entities:

O (out of scope)
weathertime
weatherplace
temperatureunit
windspeedunit
rainfallunit
snowunit
weatherforecastdaily
season_rain
season_cold
season_sunny
season_spring
cuisinetype
poiplace
poisortcriteria
destinationplace
sourceplace
speedunit
distanceunit
distance
navigationmethod
unknown_location
bot_age
bot_birthday
hobby
favorite_color
favorite_food
favorite_animal
greettime.morning
greettime.night
greettime.evening
smalltalk.festival
smalltalk.occassion

Evaluation

Misty model is trained on dataset of multiple domains weather, poi, smalltalk and nomatch to identify outlier queries. This model is trained on over 20000 unique queries from various domain/intents for 50 epochs. It's performance is evaluated on an held out set of around 3500 unique queries. On the evaluation set it has show f1 score of 97.59 and f1 of 99.69 in slot identification.

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

The model architecture is based on the paper: https://arxiv.org/pdf/1910.01108.pdf

License

By downloading and using the models and resources packaged with 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. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that 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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For example, in the query: **What is the weather in Santa Clara tomorrow morning?** , we would like to classify the query as a **Weather** Intent, and detect **Santa Clara** as a **Location** slot and **tomorrow morning** as a **date_time** slot.\n\n#### Intended Use\n\nIntents and Slots names are usually task specific and defined as labels in the training data. This is a fundamental step that is executed in any task-driven Conversational Assistant. The primary use case of this model is to jointly identify Intents and Entities in a given user query.\n\n#### Model Architecture\n\nThis is a pretrained Distil Bert based model with 2 linear classifier heads on the top of it, one for classifying an intent of the query and another for classifying slots for each token of the query. This 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 for each word of the query it will classify it as one of the slots from the slot dictionary, including out of scope slot for all the remaining words in the query which does not fall in another slot category. Out of scope slot (O) is a part of slot dictionary that the model is trained on.\n\n#### Training Data\n\nWe used a proprietary data set that have queries releated to weather, smalltalk, map and POI domains.\n\nList of the recognized Intents for this model:\n\n- weather.weather\n- weather.temperature, weather.temperature_yes_no\n- weather.rainfall, weather.rainfall_yes_no\n- weather.snow, weather.snow_yes_no\n- weather.humidity, weather.humidity_yes_no\n- weather.windspeed\n- weather.sunny\n- weather.cloudy\n- context.continue\n- navigation.startnavigation, navigation.startnavigationpoi\n- navigation.stopnavigation\n- navigation.navigationavoidhighways\n- navigation.istollsonroute\n- navigation.getspeedlimitonroute\n- navigation.getdistance, navigation.getdistancepoi\n- navigation.gettraveltime, navigation.gettraveltimepoi\n- navigation.getextrastoptime\n- navigation.geteta\n- navigation.showdirection, navigation.showdirectionpoi\n- navigation.showdirectionavoidhighways\n- navigation.showmap, navigation.showmappoi\n- navigation.getnumber\n- navigation.getrating\n- navigation.isclosed\n- nomatch\n- smalltalk.personality_hello\n- smalltalk.personality_nice_to_meet_you\n- smalltalk.personality_bot_age\n- smalltalk.personality_bots_owner\n- smalltalk.personality_bot_creator\n- smalltalk.personality_bot_is_happy\n- smalltalk.personality_what_bot_can_do\n- smalltalk.bot_personality_weather_interest\n- smalltalk.personality_bot_favorite_activity\n- smalltalk.personality_bot_name\n- smalltalk.personality_bot_challenge\n- smalltalk.personality_bot_location\n- smalltalk.personality_whats_going_on\n- smalltalk.personality_how_is_bot_doing\n- smalltalk.personality_can_bot_do_physical_activity\n- smalltalk.personality_bot_is_boring\n- smalltalk.personality_ask_me_question\n- smalltalk.personality_can_bot_do_action\n- smalltalk.personality_bot_gender\n- smalltalk.personality_bot_family\n- smalltalk.personality_what_does_bot_eat\n- smalltalk.personality_met_other_bot\n- smalltalk.personality_opinion_other_bot\n- smalltalk.personality_bot_love_life\n- smalltalk.personality_philosophical_question\n- smalltalk.personality_bot_user_comparision\n- smalltalk.personality_goodbye\n- smalltalk.personality_greet_user\n- smalltalk.personality_who_is_smarter\n- smalltalk.personality_opinion_on_ai\n- smalltalk.personality_is_user_beautiful\n- smalltalk.personality_ai_conquer_world\n- smalltalk.personality_bot_is_smart\n- smalltalk.personality_bot_available\n- smalltalk.personality_do_something_funny\n- smalltalk.personality_tell_me_a_joke\n- smalltalk.personality_sing_a_song\n- smalltalk.personality_bot_is_not_funny\n- smalltalk.personality_bot_reapeting_same_thing\n- smalltalk.personality_nice_talking_to_you\n- smalltalk.personality_happy_x_day\n- smalltalk.personality_thank_bot\n- smalltalk.personality_bot_is_fired\n- smalltalk.personality_goodwork_bot\n- smalltalk.personality_bot_is_useless\n- smalltalk.personality_user_dont_understand_bot\n- smalltalk.personality_user_apologize\n- smalltalk.personality_help_from_bot\n- smalltalk.personality_bot_affirmation\n- smalltalk.user_loves_bot\n- smalltalk.user_feel_emotion\n- smalltalk.bot_personality_language_bot_speak\n- smalltalk.bot_personality_in_free_time\n- smalltalk.bot_personality_about_bot\n\nList of the recognized Entities:\n\n- O (out of scope)\n- weathertime\n- weatherplace\n- temperatureunit\n- windspeedunit\n- rainfallunit\n- snowunit\n- weatherforecastdaily\n- season_rain\n- season_cold\n- season_sunny\n- season_spring\n- cuisinetype\n- poiplace\n- poisortcriteria\n- destinationplace\n- sourceplace\n- speedunit\n- distanceunit\n- distance\n- navigationmethod\n- unknown_location\n- bot_age\n- bot_birthday\n- hobby\n- favorite_color\n- favorite_food\n- favorite_animal\n- greettime.morning\n- greettime.night\n- greettime.evening\n- smalltalk.festival\n- smalltalk.occassion\n\n#### Evaluation\n\nMisty model is trained on dataset of multiple domains weather, poi, smalltalk and nomatch to identify outlier queries. This model is trained on over 20000 unique queries from various domain/intents for 50 epochs. It's performance is evaluated on an held out set of around 3500 unique queries. On the evaluation set it has show f1 score of 97.59 and f1 of 99.69 in slot identification.\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\nThe model architecture is based on the paper: https://arxiv.org/pdf/1910.01108.pdf\n\n#### License\n\nBy downloading and using the models and resources packaged with Riva, you would be accepting the terms of the Riva [license](https://developer.nvidia.com/riva/license)\n\n#### Suggested reading\n\nDeploy 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. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that 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 Intent Slot",["D",1680635788536],"deployable_v1.0","/api/image/nvidia/riva/models/intentslotclassification_misty_english_distilbert","https://github.com/kbojo/images/raw/master/Nvidia-Centric-RIVA.png",266351975,"bias","explainability","privacy","safetyAndSecurity",[],[250,251,252,253],[],[],[],{"_93":258,"_129":258,"_130":259,"_132":133},{"_93":256,"_129":256,"_130":257,"_132":133},{"_93":138,"_129":138,"_130":255,"_132":133},{"_93":135,"_129":135,"_130":254,"_132":133},{"_134":137,"_129":135},{"_134":133,"_129":138},"Intent and slot classification",{"_134":133,"_129":256},"BERT",{"_134":133,"_129":258},{"_172":1234,"_174":1235},"ngcApps",[1187],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1168":1169,"_1170":1171,"_1172":1173,"_1174":1175,"_1176":271,"_204":297,"_1177":1178,"_1179":51,"_1180":215,"_1181":215,"_1182":1183,"_1184":215,"_1185":1186},"amplitude",{"_1165":1166,"_811":-7,"_1167":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1159":1160,"_1161":1162,"_1163":1164},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787523996501,"1.12.0","defaultSearchSuggestions",{"_1051":378,"_1052":1053,"_1054":1055,"_1056":1057},"announcements",[],"consentState",{"_335":1041,"_1042":26,"_1043":26,"_1044":215,"_1045":1046},"consentConfig",{"_317":318,"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332,"_333":334,"_335":336},"showContactAdmin",1787523996502,[],[],[311,312,313,314,315,316],{"_40":55,"_42":67,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":64,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":56,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":45,"_46":47,"_48":54,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":49,"_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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