Referensi:
kecil
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:clinc_oos/small')
- Keterangan :
This dataset is for evaluating the performance of intent classification systems in the
presence of "out-of-scope" queries. By "out-of-scope", we mean queries that do not fall
into any of the system-supported intent classes. Most datasets include only data that is
"in-scope". Our dataset includes both in-scope and out-of-scope data. You might also know
the term "out-of-scope" by other terms, including "out-of-domain" or "out-of-distribution".
Small, in which there are only 50 training queries per each in-scope intent
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
| Membelah | Contoh |
|---|---|
'test' | 5500 |
'train' | 7600 |
'validation' | 3100 |
- Fitur :
{
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"intent": {
"num_classes": 151,
"names": [
"restaurant_reviews",
"nutrition_info",
"account_blocked",
"oil_change_how",
"time",
"weather",
"redeem_rewards",
"interest_rate",
"gas_type",
"accept_reservations",
"smart_home",
"user_name",
"report_lost_card",
"repeat",
"whisper_mode",
"what_are_your_hobbies",
"order",
"jump_start",
"schedule_meeting",
"meeting_schedule",
"freeze_account",
"what_song",
"meaning_of_life",
"restaurant_reservation",
"traffic",
"make_call",
"text",
"bill_balance",
"improve_credit_score",
"change_language",
"no",
"measurement_conversion",
"timer",
"flip_coin",
"do_you_have_pets",
"balance",
"tell_joke",
"last_maintenance",
"exchange_rate",
"uber",
"car_rental",
"credit_limit",
"oos",
"shopping_list",
"expiration_date",
"routing",
"meal_suggestion",
"tire_change",
"todo_list",
"card_declined",
"rewards_balance",
"change_accent",
"vaccines",
"reminder_update",
"food_last",
"change_ai_name",
"bill_due",
"who_do_you_work_for",
"share_location",
"international_visa",
"calendar",
"translate",
"carry_on",
"book_flight",
"insurance_change",
"todo_list_update",
"timezone",
"cancel_reservation",
"transactions",
"credit_score",
"report_fraud",
"spending_history",
"directions",
"spelling",
"insurance",
"what_is_your_name",
"reminder",
"where_are_you_from",
"distance",
"payday",
"flight_status",
"find_phone",
"greeting",
"alarm",
"order_status",
"confirm_reservation",
"cook_time",
"damaged_card",
"reset_settings",
"pin_change",
"replacement_card_duration",
"new_card",
"roll_dice",
"income",
"taxes",
"date",
"who_made_you",
"pto_request",
"tire_pressure",
"how_old_are_you",
"rollover_401k",
"pto_request_status",
"how_busy",
"application_status",
"recipe",
"calendar_update",
"play_music",
"yes",
"direct_deposit",
"credit_limit_change",
"gas",
"pay_bill",
"ingredients_list",
"lost_luggage",
"goodbye",
"what_can_i_ask_you",
"book_hotel",
"are_you_a_bot",
"next_song",
"change_speed",
"plug_type",
"maybe",
"w2",
"oil_change_when",
"thank_you",
"shopping_list_update",
"pto_balance",
"order_checks",
"travel_alert",
"fun_fact",
"sync_device",
"schedule_maintenance",
"apr",
"transfer",
"ingredient_substitution",
"calories",
"current_location",
"international_fees",
"calculator",
"definition",
"next_holiday",
"update_playlist",
"mpg",
"min_payment",
"change_user_name",
"restaurant_suggestion",
"travel_notification",
"cancel",
"pto_used",
"travel_suggestion",
"change_volume"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
}
}
tidak seimbang
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:clinc_oos/imbalanced')
- Keterangan :
This dataset is for evaluating the performance of intent classification systems in the
presence of "out-of-scope" queries. By "out-of-scope", we mean queries that do not fall
into any of the system-supported intent classes. Most datasets include only data that is
"in-scope". Our dataset includes both in-scope and out-of-scope data. You might also know
the term "out-of-scope" by other terms, including "out-of-domain" or "out-of-distribution".
Imbalanced, in which intents have either 25, 50, 75, or 100 training queries.
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
| Membelah | Contoh |
|---|---|
'test' | 5500 |
'train' | 10625 |
'validation' | 3100 |
- Fitur :
{
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"intent": {
"num_classes": 151,
"names": [
"restaurant_reviews",
"nutrition_info",
"account_blocked",
"oil_change_how",
"time",
"weather",
"redeem_rewards",
"interest_rate",
"gas_type",
"accept_reservations",
"smart_home",
"user_name",
"report_lost_card",
"repeat",
"whisper_mode",
"what_are_your_hobbies",
"order",
"jump_start",
"schedule_meeting",
"meeting_schedule",
"freeze_account",
"what_song",
"meaning_of_life",
"restaurant_reservation",
"traffic",
"make_call",
"text",
"bill_balance",
"improve_credit_score",
"change_language",
"no",
"measurement_conversion",
"timer",
"flip_coin",
"do_you_have_pets",
"balance",
"tell_joke",
"last_maintenance",
"exchange_rate",
"uber",
"car_rental",
"credit_limit",
"oos",
"shopping_list",
"expiration_date",
"routing",
"meal_suggestion",
"tire_change",
"todo_list",
"card_declined",
"rewards_balance",
"change_accent",
"vaccines",
"reminder_update",
"food_last",
"change_ai_name",
"bill_due",
"who_do_you_work_for",
"share_location",
"international_visa",
"calendar",
"translate",
"carry_on",
"book_flight",
"insurance_change",
"todo_list_update",
"timezone",
"cancel_reservation",
"transactions",
"credit_score",
"report_fraud",
"spending_history",
"directions",
"spelling",
"insurance",
"what_is_your_name",
"reminder",
"where_are_you_from",
"distance",
"payday",
"flight_status",
"find_phone",
"greeting",
"alarm",
"order_status",
"confirm_reservation",
"cook_time",
"damaged_card",
"reset_settings",
"pin_change",
"replacement_card_duration",
"new_card",
"roll_dice",
"income",
"taxes",
"date",
"who_made_you",
"pto_request",
"tire_pressure",
"how_old_are_you",
"rollover_401k",
"pto_request_status",
"how_busy",
"application_status",
"recipe",
"calendar_update",
"play_music",
"yes",
"direct_deposit",
"credit_limit_change",
"gas",
"pay_bill",
"ingredients_list",
"lost_luggage",
"goodbye",
"what_can_i_ask_you",
"book_hotel",
"are_you_a_bot",
"next_song",
"change_speed",
"plug_type",
"maybe",
"w2",
"oil_change_when",
"thank_you",
"shopping_list_update",
"pto_balance",
"order_checks",
"travel_alert",
"fun_fact",
"sync_device",
"schedule_maintenance",
"apr",
"transfer",
"ingredient_substitution",
"calories",
"current_location",
"international_fees",
"calculator",
"definition",
"next_holiday",
"update_playlist",
"mpg",
"min_payment",
"change_user_name",
"restaurant_suggestion",
"travel_notification",
"cancel",
"pto_used",
"travel_suggestion",
"change_volume"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
}
}
plus
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:clinc_oos/plus')
- Keterangan :
This dataset is for evaluating the performance of intent classification systems in the
presence of "out-of-scope" queries. By "out-of-scope", we mean queries that do not fall
into any of the system-supported intent classes. Most datasets include only data that is
"in-scope". Our dataset includes both in-scope and out-of-scope data. You might also know
the term "out-of-scope" by other terms, including "out-of-domain" or "out-of-distribution".
OOS+, in which there are 250 out-of-scope training examples, rather than 100.
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 1.0.0
- Perpecahan :
| Membelah | Contoh |
|---|---|
'test' | 5500 |
'train' | 15250 |
'validation' | 3100 |
- Fitur :
{
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"intent": {
"num_classes": 151,
"names": [
"restaurant_reviews",
"nutrition_info",
"account_blocked",
"oil_change_how",
"time",
"weather",
"redeem_rewards",
"interest_rate",
"gas_type",
"accept_reservations",
"smart_home",
"user_name",
"report_lost_card",
"repeat",
"whisper_mode",
"what_are_your_hobbies",
"order",
"jump_start",
"schedule_meeting",
"meeting_schedule",
"freeze_account",
"what_song",
"meaning_of_life",
"restaurant_reservation",
"traffic",
"make_call",
"text",
"bill_balance",
"improve_credit_score",
"change_language",
"no",
"measurement_conversion",
"timer",
"flip_coin",
"do_you_have_pets",
"balance",
"tell_joke",
"last_maintenance",
"exchange_rate",
"uber",
"car_rental",
"credit_limit",
"oos",
"shopping_list",
"expiration_date",
"routing",
"meal_suggestion",
"tire_change",
"todo_list",
"card_declined",
"rewards_balance",
"change_accent",
"vaccines",
"reminder_update",
"food_last",
"change_ai_name",
"bill_due",
"who_do_you_work_for",
"share_location",
"international_visa",
"calendar",
"translate",
"carry_on",
"book_flight",
"insurance_change",
"todo_list_update",
"timezone",
"cancel_reservation",
"transactions",
"credit_score",
"report_fraud",
"spending_history",
"directions",
"spelling",
"insurance",
"what_is_your_name",
"reminder",
"where_are_you_from",
"distance",
"payday",
"flight_status",
"find_phone",
"greeting",
"alarm",
"order_status",
"confirm_reservation",
"cook_time",
"damaged_card",
"reset_settings",
"pin_change",
"replacement_card_duration",
"new_card",
"roll_dice",
"income",
"taxes",
"date",
"who_made_you",
"pto_request",
"tire_pressure",
"how_old_are_you",
"rollover_401k",
"pto_request_status",
"how_busy",
"application_status",
"recipe",
"calendar_update",
"play_music",
"yes",
"direct_deposit",
"credit_limit_change",
"gas",
"pay_bill",
"ingredients_list",
"lost_luggage",
"goodbye",
"what_can_i_ask_you",
"book_hotel",
"are_you_a_bot",
"next_song",
"change_speed",
"plug_type",
"maybe",
"w2",
"oil_change_when",
"thank_you",
"shopping_list_update",
"pto_balance",
"order_checks",
"travel_alert",
"fun_fact",
"sync_device",
"schedule_maintenance",
"apr",
"transfer",
"ingredient_substitution",
"calories",
"current_location",
"international_fees",
"calculator",
"definition",
"next_holiday",
"update_playlist",
"mpg",
"min_payment",
"change_user_name",
"restaurant_suggestion",
"travel_notification",
"cancel",
"pto_used",
"travel_suggestion",
"change_volume"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
}
}