Added dataset information in clinic oos dataset card (#4751)

* Added Dataset information in Clinic oos card

* Added Field and Instance Information

* Added Label List in Data Fields

* Updated Table Caption

* Update datasets/clinc_oos/README.md

* Update README.md

Co-authored-by: Quentin Lhoest <[email protected]>

Commit from https://github.com/huggingface/datasets/commit/f9713d2e23813142a02f1b0e965095f528785cff

README.md

@@ -21,7 +21,7 @@ paperswithcode_id: clinc150
 pretty_name: CLINC150
 ---
 
-# Dataset Card for [Dataset Name]
+# Dataset Card for CLINC150
 
 ## Table of Contents
 - [Dataset Description](#dataset-description)
@@ -52,34 +52,210 @@ pretty_name: CLINC150
 - **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:**
+- **Leaderboard:** [PapersWithCode](https://paperswithcode.com/sota/text-classification-on-clinc-oos)
 - **Point of Contact:**
 
 ### Dataset Summary
 
-[More Information Needed]
+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
 
-[More Information Needed]
+- `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
 
-[More Information Needed]
+English
 
 ## Dataset Structure
 
 ### Data Instances
 
-[More Information Needed]
+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
 
-[More Information Needed]
+- 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
 
-[More Information Needed]
+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
 
@@ -136,8 +312,25 @@ pretty_name: CLINC150
 [More Information Needed]
 
 ### Citation Information
-
-[More Information Needed]
+```
+@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.