Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
intent-classification
Languages:
English
Size:
10K<n<100K
Tags:
License:
File size: 22,534 Bytes
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---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
paperswithcode_id: clinc150
pretty_name: CLINC150
dataset_info:
- config_name: small
features:
- name: text
dtype: string
- name: intent
dtype:
class_label:
names:
0: restaurant_reviews
1: nutrition_info
2: account_blocked
3: oil_change_how
4: time
5: weather
6: redeem_rewards
7: interest_rate
8: gas_type
9: accept_reservations
10: smart_home
11: user_name
12: report_lost_card
13: repeat
14: whisper_mode
15: what_are_your_hobbies
16: order
17: jump_start
18: schedule_meeting
19: meeting_schedule
20: freeze_account
21: what_song
22: meaning_of_life
23: restaurant_reservation
24: traffic
25: make_call
26: text
27: bill_balance
28: improve_credit_score
29: change_language
30: 'no'
31: measurement_conversion
32: timer
33: flip_coin
34: do_you_have_pets
35: balance
36: tell_joke
37: last_maintenance
38: exchange_rate
39: uber
40: car_rental
41: credit_limit
42: oos
43: shopping_list
44: expiration_date
45: routing
46: meal_suggestion
47: tire_change
48: todo_list
49: card_declined
50: rewards_balance
51: change_accent
52: vaccines
53: reminder_update
54: food_last
55: change_ai_name
56: bill_due
57: who_do_you_work_for
58: share_location
59: international_visa
60: calendar
61: translate
62: carry_on
63: book_flight
64: insurance_change
65: todo_list_update
66: timezone
67: cancel_reservation
68: transactions
69: credit_score
70: report_fraud
71: spending_history
72: directions
73: spelling
74: insurance
75: what_is_your_name
76: reminder
77: where_are_you_from
78: distance
79: payday
80: flight_status
81: find_phone
82: greeting
83: alarm
84: order_status
85: confirm_reservation
86: cook_time
87: damaged_card
88: reset_settings
89: pin_change
90: replacement_card_duration
91: new_card
92: roll_dice
93: income
94: taxes
95: date
96: who_made_you
97: pto_request
98: tire_pressure
99: how_old_are_you
100: rollover_401k
101: pto_request_status
102: how_busy
103: application_status
104: recipe
105: calendar_update
106: play_music
107: 'yes'
108: direct_deposit
109: credit_limit_change
110: gas
111: pay_bill
112: ingredients_list
113: lost_luggage
114: goodbye
115: what_can_i_ask_you
116: book_hotel
117: are_you_a_bot
118: next_song
119: change_speed
120: plug_type
121: maybe
122: w2
123: oil_change_when
124: thank_you
125: shopping_list_update
126: pto_balance
127: order_checks
128: travel_alert
129: fun_fact
130: sync_device
131: schedule_maintenance
132: apr
133: transfer
134: ingredient_substitution
135: calories
136: current_location
137: international_fees
138: calculator
139: definition
140: next_holiday
141: update_playlist
142: mpg
143: min_payment
144: change_user_name
145: restaurant_suggestion
146: travel_notification
147: cancel
148: pto_used
149: travel_suggestion
150: change_volume
splits:
- name: train
num_bytes: 394128
num_examples: 7600
- name: validation
num_bytes: 160302
num_examples: 3100
- name: test
num_bytes: 286970
num_examples: 5500
download_size: 1702451
dataset_size: 841400
- config_name: imbalanced
features:
- name: text
dtype: string
- name: intent
dtype:
class_label:
names:
0: restaurant_reviews
1: nutrition_info
2: account_blocked
3: oil_change_how
4: time
5: weather
6: redeem_rewards
7: interest_rate
8: gas_type
9: accept_reservations
10: smart_home
11: user_name
12: report_lost_card
13: repeat
14: whisper_mode
15: what_are_your_hobbies
16: order
17: jump_start
18: schedule_meeting
19: meeting_schedule
20: freeze_account
21: what_song
22: meaning_of_life
23: restaurant_reservation
24: traffic
25: make_call
26: text
27: bill_balance
28: improve_credit_score
29: change_language
30: 'no'
31: measurement_conversion
32: timer
33: flip_coin
34: do_you_have_pets
35: balance
36: tell_joke
37: last_maintenance
38: exchange_rate
39: uber
40: car_rental
41: credit_limit
42: oos
43: shopping_list
44: expiration_date
45: routing
46: meal_suggestion
47: tire_change
48: todo_list
49: card_declined
50: rewards_balance
51: change_accent
52: vaccines
53: reminder_update
54: food_last
55: change_ai_name
56: bill_due
57: who_do_you_work_for
58: share_location
59: international_visa
60: calendar
61: translate
62: carry_on
63: book_flight
64: insurance_change
65: todo_list_update
66: timezone
67: cancel_reservation
68: transactions
69: credit_score
70: report_fraud
71: spending_history
72: directions
73: spelling
74: insurance
75: what_is_your_name
76: reminder
77: where_are_you_from
78: distance
79: payday
80: flight_status
81: find_phone
82: greeting
83: alarm
84: order_status
85: confirm_reservation
86: cook_time
87: damaged_card
88: reset_settings
89: pin_change
90: replacement_card_duration
91: new_card
92: roll_dice
93: income
94: taxes
95: date
96: who_made_you
97: pto_request
98: tire_pressure
99: how_old_are_you
100: rollover_401k
101: pto_request_status
102: how_busy
103: application_status
104: recipe
105: calendar_update
106: play_music
107: 'yes'
108: direct_deposit
109: credit_limit_change
110: gas
111: pay_bill
112: ingredients_list
113: lost_luggage
114: goodbye
115: what_can_i_ask_you
116: book_hotel
117: are_you_a_bot
118: next_song
119: change_speed
120: plug_type
121: maybe
122: w2
123: oil_change_when
124: thank_you
125: shopping_list_update
126: pto_balance
127: order_checks
128: travel_alert
129: fun_fact
130: sync_device
131: schedule_maintenance
132: apr
133: transfer
134: ingredient_substitution
135: calories
136: current_location
137: international_fees
138: calculator
139: definition
140: next_holiday
141: update_playlist
142: mpg
143: min_payment
144: change_user_name
145: restaurant_suggestion
146: travel_notification
147: cancel
148: pto_used
149: travel_suggestion
150: change_volume
splits:
- name: train
num_bytes: 546909
num_examples: 10625
- name: validation
num_bytes: 160302
num_examples: 3100
- name: test
num_bytes: 286970
num_examples: 5500
download_size: 2016773
dataset_size: 994181
- config_name: plus
features:
- name: text
dtype: string
- name: intent
dtype:
class_label:
names:
0: restaurant_reviews
1: nutrition_info
2: account_blocked
3: oil_change_how
4: time
5: weather
6: redeem_rewards
7: interest_rate
8: gas_type
9: accept_reservations
10: smart_home
11: user_name
12: report_lost_card
13: repeat
14: whisper_mode
15: what_are_your_hobbies
16: order
17: jump_start
18: schedule_meeting
19: meeting_schedule
20: freeze_account
21: what_song
22: meaning_of_life
23: restaurant_reservation
24: traffic
25: make_call
26: text
27: bill_balance
28: improve_credit_score
29: change_language
30: 'no'
31: measurement_conversion
32: timer
33: flip_coin
34: do_you_have_pets
35: balance
36: tell_joke
37: last_maintenance
38: exchange_rate
39: uber
40: car_rental
41: credit_limit
42: oos
43: shopping_list
44: expiration_date
45: routing
46: meal_suggestion
47: tire_change
48: todo_list
49: card_declined
50: rewards_balance
51: change_accent
52: vaccines
53: reminder_update
54: food_last
55: change_ai_name
56: bill_due
57: who_do_you_work_for
58: share_location
59: international_visa
60: calendar
61: translate
62: carry_on
63: book_flight
64: insurance_change
65: todo_list_update
66: timezone
67: cancel_reservation
68: transactions
69: credit_score
70: report_fraud
71: spending_history
72: directions
73: spelling
74: insurance
75: what_is_your_name
76: reminder
77: where_are_you_from
78: distance
79: payday
80: flight_status
81: find_phone
82: greeting
83: alarm
84: order_status
85: confirm_reservation
86: cook_time
87: damaged_card
88: reset_settings
89: pin_change
90: replacement_card_duration
91: new_card
92: roll_dice
93: income
94: taxes
95: date
96: who_made_you
97: pto_request
98: tire_pressure
99: how_old_are_you
100: rollover_401k
101: pto_request_status
102: how_busy
103: application_status
104: recipe
105: calendar_update
106: play_music
107: 'yes'
108: direct_deposit
109: credit_limit_change
110: gas
111: pay_bill
112: ingredients_list
113: lost_luggage
114: goodbye
115: what_can_i_ask_you
116: book_hotel
117: are_you_a_bot
118: next_song
119: change_speed
120: plug_type
121: maybe
122: w2
123: oil_change_when
124: thank_you
125: shopping_list_update
126: pto_balance
127: order_checks
128: travel_alert
129: fun_fact
130: sync_device
131: schedule_maintenance
132: apr
133: transfer
134: ingredient_substitution
135: calories
136: current_location
137: international_fees
138: calculator
139: definition
140: next_holiday
141: update_playlist
142: mpg
143: min_payment
144: change_user_name
145: restaurant_suggestion
146: travel_notification
147: cancel
148: pto_used
149: travel_suggestion
150: change_volume
splits:
- name: train
num_bytes: 791255
num_examples: 15250
- name: validation
num_bytes: 160302
num_examples: 3100
- name: test
num_bytes: 286970
num_examples: 5500
download_size: 2509789
dataset_size: 1238527
---
# Dataset Card for CLINC150
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Github](https://github.com/clinc/oos-eval/)
- **Repository:** [Github](https://github.com/clinc/oos-eval/)
- **Paper:** [Aclweb](https://www.aclweb.org/anthology/D19-1131)
- **Leaderboard:** [PapersWithCode](https://paperswithcode.com/sota/text-classification-on-clinc-oos)
- **Point of Contact:**
### Dataset Summary
Task-oriented dialog systems need to know when a query falls outside their range of supported intents, but current text classification corpora only define label sets that cover every example. We introduce a new dataset that includes queries that are out-of-scope (OOS), i.e., queries that do not fall into any of the system's supported intents. This poses a new challenge because models cannot assume that every query at inference time belongs to a system-supported intent class. Our dataset also covers 150 intent classes over 10 domains, capturing the breadth that a production task-oriented agent must handle. It offers a way of more rigorously and realistically benchmarking text classification in task-driven dialog systems.
### Supported Tasks and Leaderboards
- `intent-classification`: This dataset is for evaluating the performance of intent classification systems in the presence of "out-of-scope" queries, i.e., queries that do not fall into any of the system-supported intent classes. The dataset includes both in-scope and out-of-scope data. [here](https://paperswithcode.com/sota/text-classification-on-clinc-oos).
### Languages
English
## Dataset Structure
### Data Instances
A sample from the training set is provided below:
```
{
'text' : 'can you walk me through setting up direct deposits to my bank of internet savings account',
'label' : 108
}
```
### Data Fields
- text : Textual data
- label : 150 intent classes over 10 domains, the dataset contains one label for 'out-of-scope' intent.
The Label Id to Label Name map is mentioned in the table below:
| **Label Id** | **Label name** |
|--- |--- |
| 0 | restaurant_reviews |
| 1 | nutrition_info |
| 2 | account_blocked |
| 3 | oil_change_how |
| 4 | time |
| 5 | weather |
| 6 | redeem_rewards |
| 7 | interest_rate |
| 8 | gas_type |
| 9 | accept_reservations |
| 10 | smart_home |
| 11 | user_name |
| 12 | report_lost_card |
| 13 | repeat |
| 14 | whisper_mode |
| 15 | what_are_your_hobbies |
| 16 | order |
| 17 | jump_start |
| 18 | schedule_meeting |
| 19 | meeting_schedule |
| 20 | freeze_account |
| 21 | what_song |
| 22 | meaning_of_life |
| 23 | restaurant_reservation |
| 24 | traffic |
| 25 | make_call |
| 26 | text |
| 27 | bill_balance |
| 28 | improve_credit_score |
| 29 | change_language |
| 30 | no |
| 31 | measurement_conversion |
| 32 | timer |
| 33 | flip_coin |
| 34 | do_you_have_pets |
| 35 | balance |
| 36 | tell_joke |
| 37 | last_maintenance |
| 38 | exchange_rate |
| 39 | uber |
| 40 | car_rental |
| 41 | credit_limit |
| 42 | oos |
| 43 | shopping_list |
| 44 | expiration_date |
| 45 | routing |
| 46 | meal_suggestion |
| 47 | tire_change |
| 48 | todo_list |
| 49 | card_declined |
| 50 | rewards_balance |
| 51 | change_accent |
| 52 | vaccines |
| 53 | reminder_update |
| 54 | food_last |
| 55 | change_ai_name |
| 56 | bill_due |
| 57 | who_do_you_work_for |
| 58 | share_location |
| 59 | international_visa |
| 60 | calendar |
| 61 | translate |
| 62 | carry_on |
| 63 | book_flight |
| 64 | insurance_change |
| 65 | todo_list_update |
| 66 | timezone |
| 67 | cancel_reservation |
| 68 | transactions |
| 69 | credit_score |
| 70 | report_fraud |
| 71 | spending_history |
| 72 | directions |
| 73 | spelling |
| 74 | insurance |
| 75 | what_is_your_name |
| 76 | reminder |
| 77 | where_are_you_from |
| 78 | distance |
| 79 | payday |
| 80 | flight_status |
| 81 | find_phone |
| 82 | greeting |
| 83 | alarm |
| 84 | order_status |
| 85 | confirm_reservation |
| 86 | cook_time |
| 87 | damaged_card |
| 88 | reset_settings |
| 89 | pin_change |
| 90 | replacement_card_duration |
| 91 | new_card |
| 92 | roll_dice |
| 93 | income |
| 94 | taxes |
| 95 | date |
| 96 | who_made_you |
| 97 | pto_request |
| 98 | tire_pressure |
| 99 | how_old_are_you |
| 100 | rollover_401k |
| 101 | pto_request_status |
| 102 | how_busy |
| 103 | application_status |
| 104 | recipe |
| 105 | calendar_update |
| 106 | play_music |
| 107 | yes |
| 108 | direct_deposit |
| 109 | credit_limit_change |
| 110 | gas |
| 111 | pay_bill |
| 112 | ingredients_list |
| 113 | lost_luggage |
| 114 | goodbye |
| 115 | what_can_i_ask_you |
| 116 | book_hotel |
| 117 | are_you_a_bot |
| 118 | next_song |
| 119 | change_speed |
| 120 | plug_type |
| 121 | maybe |
| 122 | w2 |
| 123 | oil_change_when |
| 124 | thank_you |
| 125 | shopping_list_update |
| 126 | pto_balance |
| 127 | order_checks |
| 128 | travel_alert |
| 129 | fun_fact |
| 130 | sync_device |
| 131 | schedule_maintenance |
| 132 | apr |
| 133 | transfer |
| 134 | ingredient_substitution |
| 135 | calories |
| 136 | current_location |
| 137 | international_fees |
| 138 | calculator |
| 139 | definition |
| 140 | next_holiday |
| 141 | update_playlist |
| 142 | mpg |
| 143 | min_payment |
| 144 | change_user_name |
| 145 | restaurant_suggestion |
| 146 | travel_notification |
| 147 | cancel |
| 148 | pto_used |
| 149 | travel_suggestion |
| 150 | change_volume |
### Data Splits
The dataset comes in different subsets:
- `small` : Small, in which there are only 50 training queries per each in-scope intent
- `imbalanced` : Imbalanced, in which intents have either 25, 50, 75, or 100 training queries.
- `plus`: OOS+, in which there are 250 out-of-scope training examples, rather than 100.
| name |train|validation|test|
|----------|----:|---------:|---:|
|small|7600| 3100| 5500 |
|imbalanced|10625| 3100| 5500|
|plus|15250| 3100| 5500|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{larson-etal-2019-evaluation,
title = "An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction",
author = "Larson, Stefan and
Mahendran, Anish and
Peper, Joseph J. and
Clarke, Christopher and
Lee, Andrew and
Hill, Parker and
Kummerfeld, Jonathan K. and
Leach, Kevin and
Laurenzano, Michael A. and
Tang, Lingjia and
Mars, Jason",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
year = "2019",
url = "https://www.aclweb.org/anthology/D19-1131"
}
```
### Contributions
Thanks to [@sumanthd17](https://github.com/sumanthd17) for adding this dataset. |