1sf/bird-sql-train-with-schema huggingface dataset:
Database: talkingdata
Tables: app_all, app_events, app_events_relevant, app_labels, events, events_relevant, gender_age, gender_age_test, gender_age_train, label_categories, phone_brand_device_model2, sample_submission
Columns (table index, column name): -1, *, 0, app_id, 1, event_id, 1, app_id, 1, is_installed, 1, is_active, 2, event_id, 2, app_id, 2, is_installed, 2, is_active, 3, app_id, 3, label_id, 4, event_id, 4, device_id, 4, timestamp, 4, longitude, 4, latitude, 5, event_id, 5, device_id, 5, timestamp, 5, longitude, 5, latitude, 6, device_id, 6, gender, 6, age, 6, group, 7, device_id, 8, device_id, 8, gender, 8, age, 8, group, 9, label_id, 9, category, 10, device_id, 10, phone_brand, 10, device_model, 11, device_id, 11, F23-, 11, F24-26, 11, F27-28, 11, F29-32, 11, F33-42, 11, F43+, 11, M22-, 11, M23-26, 11, M27-28, 11, M29-31, 11, M32-38, 11, M39+
Question: What are the behavior categories that user number -9222198347540750000 belongs to?
Evidence: behavior categories refers to category; user number refers to app_id; app_id = -9222198347540750000;
SQL: SELECT T3.category FROM app_all AS T1 INNER JOIN app_labels AS T2 ON T1.app_id = T2.app_id INNER JOIN label_categories AS T3 ON T2.label_id = T3.label_id WHERE T1.app_id = -9222198347540750000
Database: talkingdata
Tables: app_all, app_events, app_events_relevant, app_labels, events, events_relevant, gender_age, gender_age_test, gender_age_train, label_categories, phone_brand_device_model2, sample_submission
Columns (table index, column name): -1, *, 0, app_id, 1, event_id, 1, app_id, 1, is_installed, 1, is_active, 2, event_id, 2, app_id, 2, is_installed, 2, is_active, 3, app_id, 3, label_id, 4, event_id, 4, device_id, 4, timestamp, 4, longitude, 4, latitude, 5, event_id, 5, device_id, 5, timestamp, 5, longitude, 5, latitude, 6, device_id, 6, gender, 6, age, 6, group, 7, device_id, 8, device_id, 8, gender, 8, age, 8, group, 9, label_id, 9, category, 10, device_id, 10, phone_brand, 10, device_model, 11, device_id, 11, F23-, 11, F24-26, 11, F27-28, 11, F29-32, 11, F33-42, 11, F43+, 11, M22-, 11, M23-26, 11, M27-28, 11, M29-31, 11, M32-38, 11, M39+
Question: Please provide the age group of any LG Nexus 4 device users.
Evidence: age group refers to `group`; LG Nexus 4 refers to phone_brand = 'LG' AND device_model = 'Nexus 4';
SQL: SELECT T1.`group` FROM gender_age AS T1 INNER JOIN phone_brand_device_model2 AS T2 ON T1.device_id = T2.device_id WHERE T2.phone_brand = 'LG' AND T2.device_model = 'Nexus 4'
Database: talkingdata
Tables: app_all, app_events, app_events_relevant, app_labels, events, events_relevant, gender_age, gender_age_test, gender_age_train, label_categories, phone_brand_device_model2, sample_submission
Columns (table index, column name): -1, *, 0, app_id, 1, event_id, 1, app_id, 1, is_installed, 1, is_active, 2, event_id, 2, app_id, 2, is_installed, 2, is_active, 3, app_id, 3, label_id, 4, event_id, 4, device_id, 4, timestamp, 4, longitude, 4, latitude, 5, event_id, 5, device_id, 5, timestamp, 5, longitude, 5, latitude, 6, device_id, 6, gender, 6, age, 6, group, 7, device_id, 8, device_id, 8, gender, 8, age, 8, group, 9, label_id, 9, category, 10, device_id, 10, phone_brand, 10, device_model, 11, device_id, 11, F23-, 11, F24-26, 11, F27-28, 11, F29-32, 11, F33-42, 11, F43+, 11, M22-, 11, M23-26, 11, M27-28, 11, M29-31, 11, M32-38, 11, M39+
Question: Please provide the gender of at least one user who owns an HTC Desire 826 device.
Evidence: HTC Desire 826 refers to phone_brand = 'HTC' AND device_model = 'Desire 826';
SQL: SELECT T1.gender FROM gender_age AS T1 INNER JOIN phone_brand_device_model2 AS T2 ON T1.device_id = T2.device_id WHERE T2.device_model = 'Desire 826' AND T2.phone_brand = 'HTC'
Database: talkingdata
Tables: app_all, app_events, app_events_relevant, app_labels, events, events_relevant, gender_age, gender_age_test, gender_age_train, label_categories, phone_brand_device_model2, sample_submission
Columns (table index, column name): -1, *, 0, app_id, 1, event_id, 1, app_id, 1, is_installed, 1, is_active, 2, event_id, 2, app_id, 2, is_installed, 2, is_active, 3, app_id, 3, label_id, 4, event_id, 4, device_id, 4, timestamp, 4, longitude, 4, latitude, 5, event_id, 5, device_id, 5, timestamp, 5, longitude, 5, latitude, 6, device_id, 6, gender, 6, age, 6, group, 7, device_id, 8, device_id, 8, gender, 8, age, 8, group, 9, label_id, 9, category, 10, device_id, 10, phone_brand, 10, device_model, 11, device_id, 11, F23-, 11, F24-26, 11, F27-28, 11, F29-32, 11, F33-42, 11, F43+, 11, M22-, 11, M23-26, 11, M27-28, 11, M29-31, 11, M32-38, 11, M39+
Question: What is the percentage of users who are in the same behavior category as "Academic Information"?
Evidence: percentage = MULTIPLY(DIVIDE(SUM(category = 'Academic Information'), COUNT(app_id)), 1.0); behavior category refers to category; category = 'Academic Information';
SQL: SELECT SUM(IIF(T1.category = 'Academic Information', 1.0, 0)) / COUNT(T2.app_id) AS per FROM label_categories AS T1 INNER JOIN app_labels AS T2 ON T1.label_id = T2.label_id
Database: talkingdata
Tables: app_all, app_events, app_events_relevant, app_labels, events, events_relevant, gender_age, gender_age_test, gender_age_train, label_categories, phone_brand_device_model2, sample_submission
Columns (table index, column name): -1, *, 0, app_id, 1, event_id, 1, app_id, 1, is_installed, 1, is_active, 2, event_id, 2, app_id, 2, is_installed, 2, is_active, 3, app_id, 3, label_id, 4, event_id, 4, device_id, 4, timestamp, 4, longitude, 4, latitude, 5, event_id, 5, device_id, 5, timestamp, 5, longitude, 5, latitude, 6, device_id, 6, gender, 6, age, 6, group, 7, device_id, 8, device_id, 8, gender, 8, age, 8, group, 9, label_id, 9, category, 10, device_id, 10, phone_brand, 10, device_model, 11, device_id, 11, F23-, 11, F24-26, 11, F27-28, 11, F29-32, 11, F33-42, 11, F43+, 11, M22-, 11, M23-26, 11, M27-28, 11, M29-31, 11, M32-38, 11, M39+
Question: What is the percentage of device users in the F27-28 age group who experienced an event on the 3rd of May 2016?
Evidence: percentage = MULTIPLY(DIVIDE(SUM(`group` = 'F27-28'), COUNT(device_id)), 1.0); on the 3rd of May 2016 refers to timestamp = '2016-05-03%';
SQL: SELECT SUM(IIF(T1.`group` = 'F27-28', 1, 0)) / COUNT(T1.device_id) AS per FROM gender_age AS T1 INNER JOIN events_relevant AS T2 ON T1.device_id = T2.device_id WHERE SUBSTR(T2.timestamp, 1, 10) = '2016-05-03'
Database: talkingdata
Tables: app_all, app_events, app_events_relevant, app_labels, events, events_relevant, gender_age, gender_age_test, gender_age_train, label_categories, phone_brand_device_model2, sample_submission
Columns (table index, column name): -1, *, 0, app_id, 1, event_id, 1, app_id, 1, is_installed, 1, is_active, 2, event_id, 2, app_id, 2, is_installed, 2, is_active, 3, app_id, 3, label_id, 4, event_id, 4, device_id, 4, timestamp, 4, longitude, 4, latitude, 5, event_id, 5, device_id, 5, timestamp, 5, longitude, 5, latitude, 6, device_id, 6, gender, 6, age, 6, group, 7, device_id, 8, device_id, 8, gender, 8, age, 8, group, 9, label_id, 9, category, 10, device_id, 10, phone_brand, 10, device_model, 11, device_id, 11, F23-, 11, F24-26, 11, F27-28, 11, F29-32, 11, F33-42, 11, F43+, 11, M22-, 11, M23-26, 11, M27-28, 11, M29-31, 11, M32-38, 11, M39+
Question: How many OPPO devices are there?
Evidence: OPPO devices refers to phone_brand = 'OPPO';
SQL: SELECT COUNT(device_id) FROM phone_brand_device_model2 WHERE phone_brand = 'OPPO'
Database: talkingdata
Tables: app_all, app_events, app_events_relevant, app_labels, events, events_relevant, gender_age, gender_age_test, gender_age_train, label_categories, phone_brand_device_model2, sample_submission
Columns (table index, column name): -1, *, 0, app_id, 1, event_id, 1, app_id, 1, is_installed, 1, is_active, 2, event_id, 2, app_id, 2, is_installed, 2, is_active, 3, app_id, 3, label_id, 4, event_id, 4, device_id, 4, timestamp, 4, longitude, 4, latitude, 5, event_id, 5, device_id, 5, timestamp, 5, longitude, 5, latitude, 6, device_id, 6, gender, 6, age, 6, group, 7, device_id, 8, device_id, 8, gender, 8, age, 8, group, 9, label_id, 9, category, 10, device_id, 10, phone_brand, 10, device_model, 11, device_id, 11, F23-, 11, F24-26, 11, F27-28, 11, F29-32, 11, F33-42, 11, F43+, 11, M22-, 11, M23-26, 11, M27-28, 11, M29-31, 11, M32-38, 11, M39+
Question: What is the most common age group among all device users?
Evidence: most common age group refers to MAX(COUNT(`group`));
SQL: SELECT T.num FROM ( SELECT `group`, COUNT(`group`) AS num FROM gender_age GROUP BY `group` ) T
Database: talkingdata
Tables: app_all, app_events, app_events_relevant, app_labels, events, events_relevant, gender_age, gender_age_test, gender_age_train, label_categories, phone_brand_device_model2, sample_submission
Columns (table index, column name): -1, *, 0, app_id, 1, event_id, 1, app_id, 1, is_installed, 1, is_active, 2, event_id, 2, app_id, 2, is_installed, 2, is_active, 3, app_id, 3, label_id, 4, event_id, 4, device_id, 4, timestamp, 4, longitude, 4, latitude, 5, event_id, 5, device_id, 5, timestamp, 5, longitude, 5, latitude, 6, device_id, 6, gender, 6, age, 6, group, 7, device_id, 8, device_id, 8, gender, 8, age, 8, group, 9, label_id, 9, category, 10, device_id, 10, phone_brand, 10, device_model, 11, device_id, 11, F23-, 11, F24-26, 11, F27-28, 11, F29-32, 11, F33-42, 11, F43+, 11, M22-, 11, M23-26, 11, M27-28, 11, M29-31, 11, M32-38, 11, M39+
Question: How many events does the device "4069764298338760000" have?
SQL: SELECT COUNT(event_id) FROM events WHERE device_id = 4069764298338760000
Database: talkingdata
Tables: app_all, app_events, app_events_relevant, app_labels, events, events_relevant, gender_age, gender_age_test, gender_age_train, label_categories, phone_brand_device_model2, sample_submission
Columns (table index, column name): -1, *, 0, app_id, 1, event_id, 1, app_id, 1, is_installed, 1, is_active, 2, event_id, 2, app_id, 2, is_installed, 2, is_active, 3, app_id, 3, label_id, 4, event_id, 4, device_id, 4, timestamp, 4, longitude, 4, latitude, 5, event_id, 5, device_id, 5, timestamp, 5, longitude, 5, latitude, 6, device_id, 6, gender, 6, age, 6, group, 7, device_id, 8, device_id, 8, gender, 8, age, 8, group, 9, label_id, 9, category, 10, device_id, 10, phone_brand, 10, device_model, 11, device_id, 11, F23-, 11, F24-26, 11, F27-28, 11, F29-32, 11, F33-42, 11, F43+, 11, M22-, 11, M23-26, 11, M27-28, 11, M29-31, 11, M32-38, 11, M39+
Question: How many of the apps belong in the "Equity Fund" category?
SQL: SELECT COUNT(T1.app_id) FROM app_labels AS T1 INNER JOIN label_categories AS T2 ON T1.label_id = T2.label_id WHERE T2.category = 'Equity Fund'
Database: talkingdata
Tables: app_all, app_events, app_events_relevant, app_labels, events, events_relevant, gender_age, gender_age_test, gender_age_train, label_categories, phone_brand_device_model2, sample_submission
Columns (table index, column name): -1, *, 0, app_id, 1, event_id, 1, app_id, 1, is_installed, 1, is_active, 2, event_id, 2, app_id, 2, is_installed, 2, is_active, 3, app_id, 3, label_id, 4, event_id, 4, device_id, 4, timestamp, 4, longitude, 4, latitude, 5, event_id, 5, device_id, 5, timestamp, 5, longitude, 5, latitude, 6, device_id, 6, gender, 6, age, 6, group, 7, device_id, 8, device_id, 8, gender, 8, age, 8, group, 9, label_id, 9, category, 10, device_id, 10, phone_brand, 10, device_model, 11, device_id, 11, F23-, 11, F24-26, 11, F27-28, 11, F29-32, 11, F33-42, 11, F43+, 11, M22-, 11, M23-26, 11, M27-28, 11, M29-31, 11, M32-38, 11, M39+
Question: List all females aged 24 to 26 devices' locations.
Evidence: females refers to gender = 'F'; aged 24 to 26 refers to `group` = 'F24-26';
SQL: SELECT T2.longitude, T2.latitude FROM gender_age AS T1 INNER JOIN events_relevant AS T2 ON T1.device_id = T2.device_id WHERE T1.`group` = 'F24-26' AND T1.gender = 'F'
Database: talkingdata
Tables: app_all, app_events, app_events_relevant, app_labels, events, events_relevant, gender_age, gender_age_test, gender_age_train, label_categories, phone_brand_device_model2, sample_submission
Columns (table index, column name): -1, *, 0, app_id, 1, event_id, 1, app_id, 1, is_installed, 1, is_active, 2, event_id, 2, app_id, 2, is_installed, 2, is_active, 3, app_id, 3, label_id, 4, event_id, 4, device_id, 4, timestamp, 4, longitude, 4, latitude, 5, event_id, 5, device_id, 5, timestamp, 5, longitude, 5, latitude, 6, device_id, 6, gender, 6, age, 6, group, 7, device_id, 8, device_id, 8, gender, 8, age, 8, group, 9, label_id, 9, category, 10, device_id, 10, phone_brand, 10, device_model, 11, device_id, 11, F23-, 11, F24-26, 11, F27-28, 11, F29-32, 11, F33-42, 11, F43+, 11, M22-, 11, M23-26, 11, M27-28, 11, M29-31, 11, M32-38, 11, M39+
Question: How many male users have a Galaxy Note 3?
Evidence: male refers to gender = 'M'; Galaxy Note 3 refers to device_model = 'Galaxy Note 3';
SQL: SELECT COUNT(T1.device_id) FROM gender_age AS T1 INNER JOIN phone_brand_device_model2 AS T2 ON T1.device_id = T2.device_id WHERE T2.device_model = 'Galaxy Note 3' AND T1.gender = 'M'
Database: talkingdata
Tables: app_all, app_events, app_events_relevant, app_labels, events, events_relevant, gender_age, gender_age_test, gender_age_train, label_categories, phone_brand_device_model2, sample_submission
Columns (table index, column name): -1, *, 0, app_id, 1, event_id, 1, app_id, 1, is_installed, 1, is_active, 2, event_id, 2, app_id, 2, is_installed, 2, is_active, 3, app_id, 3, label_id, 4, event_id, 4, device_id, 4, timestamp, 4, longitude, 4, latitude, 5, event_id, 5, device_id, 5, timestamp, 5, longitude, 5, latitude, 6, device_id, 6, gender, 6, age, 6, group, 7, device_id, 8, device_id, 8, gender, 8, age, 8, group, 9, label_id, 9, category, 10, device_id, 10, phone_brand, 10, device_model, 11, device_id, 11, F23-, 11, F24-26, 11, F27-28, 11, F29-32, 11, F33-42, 11, F43+, 11, M22-, 11, M23-26, 11, M27-28, 11, M29-31, 11, M32-38, 11, M39+
Question: List all the devices' brands and models of events on 5/7/2016 at 6:03:22 AM.
Evidence: devices' brands refers to phone_brand; models refers to device_model; on 5/7/2016 at 6:03:22 AM refers to timestamp = '2016-05-07 06:03:22';
SQL: SELECT T1.phone_brand, T1.device_model FROM phone_brand_device_model2 AS T1 INNER JOIN events_relevant AS T2 ON T1.device_id = T2.device_id WHERE T2.timestamp = '2016-05-07 06:03:22'
Database: talkingdata
Tables: app_all, app_events, app_events_relevant, app_labels, events, events_relevant, gender_age, gender_age_test, gender_age_train, label_categories, phone_brand_device_model2, sample_submission
Columns (table index, column name): -1, *, 0, app_id, 1, event_id, 1, app_id, 1, is_installed, 1, is_active, 2, event_id, 2, app_id, 2, is_installed, 2, is_active, 3, app_id, 3, label_id, 4, event_id, 4, device_id, 4, timestamp, 4, longitude, 4, latitude, 5, event_id, 5, device_id, 5, timestamp, 5, longitude, 5, latitude, 6, device_id, 6, gender, 6, age, 6, group, 7, device_id, 8, device_id, 8, gender, 8, age, 8, group, 9, label_id, 9, category, 10, device_id, 10, phone_brand, 10, device_model, 11, device_id, 11, F23-, 11, F24-26, 11, F27-28, 11, F29-32, 11, F33-42, 11, F43+, 11, M22-, 11, M23-26, 11, M27-28, 11, M29-31, 11, M32-38, 11, M39+
Question: Which brand is most common among people in their twenties?
Evidence: brand refers to phone_brand; brand that is most common refers to MAX(COUNT(phone_brand)); twenties refers to age BETWEEN 20 AND 30;
SQL: SELECT T.phone_brand FROM ( SELECT T2.phone_brand, COUNT(T2.phone_brand) AS num FROM gender_age AS T1 INNER JOIN phone_brand_device_model2 AS T2 ON T1.device_id = T2.device_id WHERE T1.age BETWEEN 20 AND 30 GROUP BY T2.phone_brand ) AS T ORDER BY T.num DESC LIMIT 1
Database: talkingdata
Tables: app_all, app_events, app_events_relevant, app_labels, events, events_relevant, gender_age, gender_age_test, gender_age_train, label_categories, phone_brand_device_model2, sample_submission
Columns (table index, column name): -1, *, 0, app_id, 1, event_id, 1, app_id, 1, is_installed, 1, is_active, 2, event_id, 2, app_id, 2, is_installed, 2, is_active, 3, app_id, 3, label_id, 4, event_id, 4, device_id, 4, timestamp, 4, longitude, 4, latitude, 5, event_id, 5, device_id, 5, timestamp, 5, longitude, 5, latitude, 6, device_id, 6, gender, 6, age, 6, group, 7, device_id, 8, device_id, 8, gender, 8, age, 8, group, 9, label_id, 9, category, 10, device_id, 10, phone_brand, 10, device_model, 11, device_id, 11, F23-, 11, F24-26, 11, F27-28, 11, F29-32, 11, F33-42, 11, F43+, 11, M22-, 11, M23-26, 11, M27-28, 11, M29-31, 11, M32-38, 11, M39+
Question: What percentage of vivo devices belong to users with no information?
Evidence: percentage = MULTIPLY(DIVIDE(SUM(gender = NULL and age = NULL and `group` = NULL), COUNT(device_id)), 1.0); vivo devices refers to phone_brand = 'vivo'; no information refers to gender = NULL AND age = NULL AND `group` = NULL;
SQL: SELECT SUM(IIF(T1.gender IS NULL AND T1.age IS NULL AND T1.`group` IS NULL, 1, 0)) / COUNT(T1.device_id) AS per FROM gender_age AS T1 INNER JOIN phone_brand_device_model2 AS T2 ON T1.device_id = T2.device_id WHERE T2.phone_brand = 'vivo'
Database: law_episode
Tables: Episode, Keyword, Person, Award, Credit, Vote
Columns (table index, column name): -1, *, 0, episode_id, 0, series, 0, season, 0, episode, 0, number_in_series, 0, title, 0, summary, 0, air_date, 0, episode_image, 0, rating, 0, votes, 1, episode_id, 1, keyword, 2, person_id, 2, name, 2, birthdate, 2, birth_name, 2, birth_place, 2, birth_region, 2, birth_country, 2, height_meters, 2, nickname, 3, award_id, 3, organization, 3, year, 3, award_category, 3, award, 3, series, 3, episode_id, 3, person_id, 3, role, 3, result, 4, episode_id, 4, person_id, 4, category, 4, role, 4, credited, 5, episode_id, 5, stars, 5, votes, 5, percent
Question: Please list all the keywords of the episode "Refuge: Part 1".
Evidence: episode "Refuge: Part 1" refers to title = 'Refuge: Part 1'
SQL: SELECT T2.keyword FROM Episode AS T1 INNER JOIN Keyword AS T2 ON T1.episode_id = T2.episode_id WHERE T1.title = 'Refuge: Part 1'
Database: law_episode
Tables: Episode, Keyword, Person, Award, Credit, Vote
Columns (table index, column name): -1, *, 0, episode_id, 0, series, 0, season, 0, episode, 0, number_in_series, 0, title, 0, summary, 0, air_date, 0, episode_image, 0, rating, 0, votes, 1, episode_id, 1, keyword, 2, person_id, 2, name, 2, birthdate, 2, birth_name, 2, birth_place, 2, birth_region, 2, birth_country, 2, height_meters, 2, nickname, 3, award_id, 3, organization, 3, year, 3, award_category, 3, award, 3, series, 3, episode_id, 3, person_id, 3, role, 3, result, 4, episode_id, 4, person_id, 4, category, 4, role, 4, credited, 5, episode_id, 5, stars, 5, votes, 5, percent
Question: How many keywords are there for season 9, episode 23 of law_and_order?
SQL: SELECT COUNT(T2.keyword) FROM Episode AS T1 INNER JOIN Keyword AS T2 ON T1.episode_id = T2.episode_id WHERE T1.season = 9 AND T1.episode = 23
Database: law_episode
Tables: Episode, Keyword, Person, Award, Credit, Vote
Columns (table index, column name): -1, *, 0, episode_id, 0, series, 0, season, 0, episode, 0, number_in_series, 0, title, 0, summary, 0, air_date, 0, episode_image, 0, rating, 0, votes, 1, episode_id, 1, keyword, 2, person_id, 2, name, 2, birthdate, 2, birth_name, 2, birth_place, 2, birth_region, 2, birth_country, 2, height_meters, 2, nickname, 3, award_id, 3, organization, 3, year, 3, award_category, 3, award, 3, series, 3, episode_id, 3, person_id, 3, role, 3, result, 4, episode_id, 4, person_id, 4, category, 4, role, 4, credited, 5, episode_id, 5, stars, 5, votes, 5, percent
Question: What is the title of the episode with the keyword "laundering money"?
Evidence: keyword "laundering money" refers to keyword = 'laundering money'
SQL: SELECT T1.title FROM Episode AS T1 INNER JOIN Keyword AS T2 ON T1.episode_id = T2.episode_id WHERE T2.keyword = 'laundering money'
Database: law_episode
Tables: Episode, Keyword, Person, Award, Credit, Vote
Columns (table index, column name): -1, *, 0, episode_id, 0, series, 0, season, 0, episode, 0, number_in_series, 0, title, 0, summary, 0, air_date, 0, episode_image, 0, rating, 0, votes, 1, episode_id, 1, keyword, 2, person_id, 2, name, 2, birthdate, 2, birth_name, 2, birth_place, 2, birth_region, 2, birth_country, 2, height_meters, 2, nickname, 3, award_id, 3, organization, 3, year, 3, award_category, 3, award, 3, series, 3, episode_id, 3, person_id, 3, role, 3, result, 4, episode_id, 4, person_id, 4, category, 4, role, 4, credited, 5, episode_id, 5, stars, 5, votes, 5, percent
Question: Please list all the keywords for the episodes with a rating of over 8.
Evidence: a rating of over 8 refers to rating > 8
SQL: SELECT T2.keyword FROM Episode AS T1 INNER JOIN Keyword AS T2 ON T1.episode_id = T2.episode_id WHERE T1.rating > 8
Database: law_episode
Tables: Episode, Keyword, Person, Award, Credit, Vote
Columns (table index, column name): -1, *, 0, episode_id, 0, series, 0, season, 0, episode, 0, number_in_series, 0, title, 0, summary, 0, air_date, 0, episode_image, 0, rating, 0, votes, 1, episode_id, 1, keyword, 2, person_id, 2, name, 2, birthdate, 2, birth_name, 2, birth_place, 2, birth_region, 2, birth_country, 2, height_meters, 2, nickname, 3, award_id, 3, organization, 3, year, 3, award_category, 3, award, 3, series, 3, episode_id, 3, person_id, 3, role, 3, result, 4, episode_id, 4, person_id, 4, category, 4, role, 4, credited, 5, episode_id, 5, stars, 5, votes, 5, percent
Question: How many 10-star votes were given to the episode titled "Cherished"?
Evidence: 10-star vote refers to stars = 10; titled "Cherished" refers to title = 'Cherished'
SQL: SELECT T2.votes FROM Episode AS T1 INNER JOIN Vote AS T2 ON T1.episode_id = T2.episode_id WHERE T1.title = 'Cherished' AND T2.stars = 10
Database: law_episode
Tables: Episode, Keyword, Person, Award, Credit, Vote
Columns (table index, column name): -1, *, 0, episode_id, 0, series, 0, season, 0, episode, 0, number_in_series, 0, title, 0, summary, 0, air_date, 0, episode_image, 0, rating, 0, votes, 1, episode_id, 1, keyword, 2, person_id, 2, name, 2, birthdate, 2, birth_name, 2, birth_place, 2, birth_region, 2, birth_country, 2, height_meters, 2, nickname, 3, award_id, 3, organization, 3, year, 3, award_category, 3, award, 3, series, 3, episode_id, 3, person_id, 3, role, 3, result, 4, episode_id, 4, person_id, 4, category, 4, role, 4, credited, 5, episode_id, 5, stars, 5, votes, 5, percent
Question: How many votes did the episode titled "Cherished" get in total?
Evidence: titled "Cherished" refers to title = 'Cherished'
SQL: SELECT SUM(T2.votes) FROM Episode AS T1 INNER JOIN Vote AS T2 ON T1.episode_id = T2.episode_id WHERE T1.title = 'Cherished'
Database: law_episode
Tables: Episode, Keyword, Person, Award, Credit, Vote
Columns (table index, column name): -1, *, 0, episode_id, 0, series, 0, season, 0, episode, 0, number_in_series, 0, title, 0, summary, 0, air_date, 0, episode_image, 0, rating, 0, votes, 1, episode_id, 1, keyword, 2, person_id, 2, name, 2, birthdate, 2, birth_name, 2, birth_place, 2, birth_region, 2, birth_country, 2, height_meters, 2, nickname, 3, award_id, 3, organization, 3, year, 3, award_category, 3, award, 3, series, 3, episode_id, 3, person_id, 3, role, 3, result, 4, episode_id, 4, person_id, 4, category, 4, role, 4, credited, 5, episode_id, 5, stars, 5, votes, 5, percent
Question: What is the title of the episode that got the most 10-star votes?
Evidence: the most refers to max(votes); 10-star refers to stars = 10
SQL: SELECT T1.title FROM Episode AS T1 INNER JOIN Vote AS T2 ON T1.episode_id = T2.episode_id WHERE T2.stars = 10 ORDER BY T2.votes DESC LIMIT 1