1sf/bird-sql-train-with-schema huggingface dataset:
Database: college_completion
Tables: institution_details, institution_grads, state_sector_grads, state_sector_details
Columns (table index, column name): -1, *, 0, unitid, 0, chronname, 0, city, 0, state, 0, level, 0, control, 0, basic, 0, hbcu, 0, flagship, 0, long_x, 0, lat_y, 0, site, 0, student_count, 0, awards_per_value, 0, awards_per_state_value, 0, awards_per_natl_value, 0, exp_award_value, 0, exp_award_state_value, 0, exp_award_natl_value, 0, exp_award_percentile, 0, ft_pct, 0, fte_value, 0, fte_percentile, 0, med_sat_value, 0, med_sat_percentile, 0, aid_value, 0, aid_percentile, 0, endow_value, 0, endow_percentile, 0, grad_100_value, 0, grad_100_percentile, 0, grad_150_value, 0, grad_150_percentile, 0, pell_value, 0, pell_percentile, 0, retain_value, 0, retain_percentile, 0, ft_fac_value, 0, ft_fac_percentile, 0, vsa_year, 0, vsa_grad_after4_first, 0, vsa_grad_elsewhere_after4_first, 0, vsa_enroll_after4_first, 0, vsa_enroll_elsewhere_after4_first, 0, vsa_grad_after6_first, 0, vsa_grad_elsewhere_after6_first, 0, vsa_enroll_after6_first, 0, vsa_enroll_elsewhere_after6_first, 0, vsa_grad_after4_transfer, 0, vsa_grad_elsewhere_after4_transfer, 0, vsa_enroll_after4_transfer, 0, vsa_enroll_elsewhere_after4_transfer, 0, vsa_grad_after6_transfer, 0, vsa_grad_elsewhere_after6_transfer, 0, vsa_enroll_after6_transfer, 0, vsa_enroll_elsewhere_after6_transfer, 0, similar, 0, state_sector_ct, 0, carnegie_ct, 0, counted_pct, 0, nicknames, 0, cohort_size, 1, unitid, 1, year, 1, gender, 1, race, 1, cohort, 1, grad_cohort, 1, grad_100, 1, grad_150, 1, grad_100_rate, 1, grad_150_rate, 2, stateid, 2, state, 2, state_abbr, 2, control, 2, level, 2, year, 2, gender, 2, race, 2, cohort, 2, grad_cohort, 2, grad_100, 2, grad_150, 2, grad_100_rate, 2, grad_150_rate, 2, grad_cohort_ct, 3, stateid, 3, state, 3, state_post, 3, level, 3, control, 3, schools_count, 3, counted_pct, 3, awards_per_state_value, 3, awards_per_natl_value, 3, exp_award_state_value, 3, exp_award_natl_value, 3, state_appr_value, 3, state_appr_rank, 3, grad_rate_rank, 3, awards_per_rank
Question: Between 2011 to 2013, what is the average number of male Hispanic degree-seeking students at 2-year institutions who graduated within 150 percent of normal/expected time in United Education Institute-Huntington Park Campus?
Evidence: between 2011 to 2013 refers to year BETWEEN '2011' AND '2013'; male refers to gender = 'M'; Hispanic refers to race = 'H'; number of degree-seeking students at 2-year institutions who graduated within 150 percent of normal/expected time refers to grad_150; United Education Institute-Huntington Park Campus refers to chronname = 'United Education Institute-Huntington Park Campus';
SQL: SELECT AVG(T2.grad_150) FROM institution_details AS T1 INNER JOIN institution_grads AS T2 ON T2.unitid = T1.unitid WHERE T1.chronname = 'United Education Institute-Huntington Park Campus' AND T2.year BETWEEN 2011 AND 2013 AND T2.gender = 'M' AND T2.race = 'H'
Database: college_completion
Tables: institution_details, institution_grads, state_sector_grads, state_sector_details
Columns (table index, column name): -1, *, 0, unitid, 0, chronname, 0, city, 0, state, 0, level, 0, control, 0, basic, 0, hbcu, 0, flagship, 0, long_x, 0, lat_y, 0, site, 0, student_count, 0, awards_per_value, 0, awards_per_state_value, 0, awards_per_natl_value, 0, exp_award_value, 0, exp_award_state_value, 0, exp_award_natl_value, 0, exp_award_percentile, 0, ft_pct, 0, fte_value, 0, fte_percentile, 0, med_sat_value, 0, med_sat_percentile, 0, aid_value, 0, aid_percentile, 0, endow_value, 0, endow_percentile, 0, grad_100_value, 0, grad_100_percentile, 0, grad_150_value, 0, grad_150_percentile, 0, pell_value, 0, pell_percentile, 0, retain_value, 0, retain_percentile, 0, ft_fac_value, 0, ft_fac_percentile, 0, vsa_year, 0, vsa_grad_after4_first, 0, vsa_grad_elsewhere_after4_first, 0, vsa_enroll_after4_first, 0, vsa_enroll_elsewhere_after4_first, 0, vsa_grad_after6_first, 0, vsa_grad_elsewhere_after6_first, 0, vsa_enroll_after6_first, 0, vsa_enroll_elsewhere_after6_first, 0, vsa_grad_after4_transfer, 0, vsa_grad_elsewhere_after4_transfer, 0, vsa_enroll_after4_transfer, 0, vsa_enroll_elsewhere_after4_transfer, 0, vsa_grad_after6_transfer, 0, vsa_grad_elsewhere_after6_transfer, 0, vsa_enroll_after6_transfer, 0, vsa_enroll_elsewhere_after6_transfer, 0, similar, 0, state_sector_ct, 0, carnegie_ct, 0, counted_pct, 0, nicknames, 0, cohort_size, 1, unitid, 1, year, 1, gender, 1, race, 1, cohort, 1, grad_cohort, 1, grad_100, 1, grad_150, 1, grad_100_rate, 1, grad_150_rate, 2, stateid, 2, state, 2, state_abbr, 2, control, 2, level, 2, year, 2, gender, 2, race, 2, cohort, 2, grad_cohort, 2, grad_100, 2, grad_150, 2, grad_100_rate, 2, grad_150_rate, 2, grad_cohort_ct, 3, stateid, 3, state, 3, state_post, 3, level, 3, control, 3, schools_count, 3, counted_pct, 3, awards_per_state_value, 3, awards_per_natl_value, 3, exp_award_state_value, 3, exp_award_natl_value, 3, state_appr_value, 3, state_appr_rank, 3, grad_rate_rank, 3, awards_per_rank
Question: What is the name of the school with the highest difference in the average completion rate for the national in which it belongs? Indicate the state appropriations to higher education in fiscal year 2011 per resident to which the school belongs.
Evidence: name of the school refers to chronname; highest difference in the average completion rate for the national in which it belongs = MAX(SUBTRACT(awards_per_value, awards_per_natl_value)); state appropriations to higher education in fiscal year 2011 per resident to which the school belongs refers to state_appr_value;
SQL: SELECT T1.chronname, T2.state_appr_value FROM institution_details AS T1 INNER JOIN state_sector_details AS T2 ON T2.state = T1.state ORDER BY T1.awards_per_value - T2.awards_per_natl_value DESC LIMIT 1
Database: public_review_platform
Tables: Attributes, Categories, Compliments, Days, Years, Business_Attributes, Business_Categories, Business_Hours, Checkins, Elite, Reviews, Tips, Users_Compliments, Business, Users
Columns (table index, column name): -1, *, 0, attribute_id, 0, attribute_name, 1, category_id, 1, category_name, 2, compliment_id, 2, compliment_type, 3, day_id, 3, day_of_week, 4, year_id, 4, actual_year, 5, attribute_id, 5, business_id, 5, attribute_value, 6, business_id, 6, category_id, 7, business_id, 7, day_id, 7, opening_time, 7, closing_time, 8, business_id, 8, day_id, 8, label_time_0, 8, label_time_1, 8, label_time_2, 8, label_time_3, 8, label_time_4, 8, label_time_5, 8, label_time_6, 8, label_time_7, 8, label_time_8, 8, label_time_9, 8, label_time_10, 8, label_time_11, 8, label_time_12, 8, label_time_13, 8, label_time_14, 8, label_time_15, 8, label_time_16, 8, label_time_17, 8, label_time_18, 8, label_time_19, 8, label_time_20, 8, label_time_21, 8, label_time_22, 8, label_time_23, 9, user_id, 9, year_id, 10, business_id, 10, user_id, 10, review_stars, 10, review_votes_funny, 10, review_votes_useful, 10, review_votes_cool, 10, review_length, 11, business_id, 11, user_id, 11, likes, 11, tip_length, 12, compliment_id, 12, user_id, 12, number_of_compliments, 13, business_id, 13, active, 13, city, 13, state, 13, stars, 13, review_count, 14, user_id, 14, user_yelping_since_year, 14, user_average_stars, 14, user_votes_funny, 14, user_votes_useful, 14, user_votes_cool, 14, user_review_count, 14, user_fans
Question: How many Yelp businesses are there in 'AZ' with less than "3" stars?
Evidence: AZ refers to state = 'AZ'; stars < 3;
SQL: SELECT COUNT(business_id) FROM Business WHERE state LIKE 'AZ' AND stars < 3
Database: public_review_platform
Tables: Attributes, Categories, Compliments, Days, Years, Business_Attributes, Business_Categories, Business_Hours, Checkins, Elite, Reviews, Tips, Users_Compliments, Business, Users
Columns (table index, column name): -1, *, 0, attribute_id, 0, attribute_name, 1, category_id, 1, category_name, 2, compliment_id, 2, compliment_type, 3, day_id, 3, day_of_week, 4, year_id, 4, actual_year, 5, attribute_id, 5, business_id, 5, attribute_value, 6, business_id, 6, category_id, 7, business_id, 7, day_id, 7, opening_time, 7, closing_time, 8, business_id, 8, day_id, 8, label_time_0, 8, label_time_1, 8, label_time_2, 8, label_time_3, 8, label_time_4, 8, label_time_5, 8, label_time_6, 8, label_time_7, 8, label_time_8, 8, label_time_9, 8, label_time_10, 8, label_time_11, 8, label_time_12, 8, label_time_13, 8, label_time_14, 8, label_time_15, 8, label_time_16, 8, label_time_17, 8, label_time_18, 8, label_time_19, 8, label_time_20, 8, label_time_21, 8, label_time_22, 8, label_time_23, 9, user_id, 9, year_id, 10, business_id, 10, user_id, 10, review_stars, 10, review_votes_funny, 10, review_votes_useful, 10, review_votes_cool, 10, review_length, 11, business_id, 11, user_id, 11, likes, 11, tip_length, 12, compliment_id, 12, user_id, 12, number_of_compliments, 13, business_id, 13, active, 13, city, 13, state, 13, stars, 13, review_count, 14, user_id, 14, user_yelping_since_year, 14, user_average_stars, 14, user_votes_funny, 14, user_votes_useful, 14, user_votes_cool, 14, user_review_count, 14, user_fans
Question: What is the quantity of the closed or not running Yelp Businesses in 'AZ'?
Evidence: closed or not running refers to active = 'False'; AZ refers to state = 'AZ';
SQL: SELECT COUNT(business_id) FROM Business WHERE state LIKE 'AZ' AND active LIKE 'False'
Database: public_review_platform
Tables: Attributes, Categories, Compliments, Days, Years, Business_Attributes, Business_Categories, Business_Hours, Checkins, Elite, Reviews, Tips, Users_Compliments, Business, Users
Columns (table index, column name): -1, *, 0, attribute_id, 0, attribute_name, 1, category_id, 1, category_name, 2, compliment_id, 2, compliment_type, 3, day_id, 3, day_of_week, 4, year_id, 4, actual_year, 5, attribute_id, 5, business_id, 5, attribute_value, 6, business_id, 6, category_id, 7, business_id, 7, day_id, 7, opening_time, 7, closing_time, 8, business_id, 8, day_id, 8, label_time_0, 8, label_time_1, 8, label_time_2, 8, label_time_3, 8, label_time_4, 8, label_time_5, 8, label_time_6, 8, label_time_7, 8, label_time_8, 8, label_time_9, 8, label_time_10, 8, label_time_11, 8, label_time_12, 8, label_time_13, 8, label_time_14, 8, label_time_15, 8, label_time_16, 8, label_time_17, 8, label_time_18, 8, label_time_19, 8, label_time_20, 8, label_time_21, 8, label_time_22, 8, label_time_23, 9, user_id, 9, year_id, 10, business_id, 10, user_id, 10, review_stars, 10, review_votes_funny, 10, review_votes_useful, 10, review_votes_cool, 10, review_length, 11, business_id, 11, user_id, 11, likes, 11, tip_length, 12, compliment_id, 12, user_id, 12, number_of_compliments, 13, business_id, 13, active, 13, city, 13, state, 13, stars, 13, review_count, 14, user_id, 14, user_yelping_since_year, 14, user_average_stars, 14, user_votes_funny, 14, user_votes_useful, 14, user_votes_cool, 14, user_review_count, 14, user_fans
Question: How many long reviews does user No. 36139 give for the Yelp businesses?
Evidence: long reviews refers to review_length = 'long'; user No. refers to user_id;
SQL: SELECT COUNT(review_length) FROM Reviews WHERE user_id = 36139 AND review_length LIKE 'long'
Database: public_review_platform
Tables: Attributes, Categories, Compliments, Days, Years, Business_Attributes, Business_Categories, Business_Hours, Checkins, Elite, Reviews, Tips, Users_Compliments, Business, Users
Columns (table index, column name): -1, *, 0, attribute_id, 0, attribute_name, 1, category_id, 1, category_name, 2, compliment_id, 2, compliment_type, 3, day_id, 3, day_of_week, 4, year_id, 4, actual_year, 5, attribute_id, 5, business_id, 5, attribute_value, 6, business_id, 6, category_id, 7, business_id, 7, day_id, 7, opening_time, 7, closing_time, 8, business_id, 8, day_id, 8, label_time_0, 8, label_time_1, 8, label_time_2, 8, label_time_3, 8, label_time_4, 8, label_time_5, 8, label_time_6, 8, label_time_7, 8, label_time_8, 8, label_time_9, 8, label_time_10, 8, label_time_11, 8, label_time_12, 8, label_time_13, 8, label_time_14, 8, label_time_15, 8, label_time_16, 8, label_time_17, 8, label_time_18, 8, label_time_19, 8, label_time_20, 8, label_time_21, 8, label_time_22, 8, label_time_23, 9, user_id, 9, year_id, 10, business_id, 10, user_id, 10, review_stars, 10, review_votes_funny, 10, review_votes_useful, 10, review_votes_cool, 10, review_length, 11, business_id, 11, user_id, 11, likes, 11, tip_length, 12, compliment_id, 12, user_id, 12, number_of_compliments, 13, business_id, 13, active, 13, city, 13, state, 13, stars, 13, review_count, 14, user_id, 14, user_yelping_since_year, 14, user_average_stars, 14, user_votes_funny, 14, user_votes_useful, 14, user_votes_cool, 14, user_review_count, 14, user_fans
Question: How many users have "uber" number of fans?
Evidence: uber number of fans refers to user_fans = 'uber';
SQL: SELECT COUNT(user_id) FROM Users WHERE user_fans LIKE 'Uber'
Database: public_review_platform
Tables: Attributes, Categories, Compliments, Days, Years, Business_Attributes, Business_Categories, Business_Hours, Checkins, Elite, Reviews, Tips, Users_Compliments, Business, Users
Columns (table index, column name): -1, *, 0, attribute_id, 0, attribute_name, 1, category_id, 1, category_name, 2, compliment_id, 2, compliment_type, 3, day_id, 3, day_of_week, 4, year_id, 4, actual_year, 5, attribute_id, 5, business_id, 5, attribute_value, 6, business_id, 6, category_id, 7, business_id, 7, day_id, 7, opening_time, 7, closing_time, 8, business_id, 8, day_id, 8, label_time_0, 8, label_time_1, 8, label_time_2, 8, label_time_3, 8, label_time_4, 8, label_time_5, 8, label_time_6, 8, label_time_7, 8, label_time_8, 8, label_time_9, 8, label_time_10, 8, label_time_11, 8, label_time_12, 8, label_time_13, 8, label_time_14, 8, label_time_15, 8, label_time_16, 8, label_time_17, 8, label_time_18, 8, label_time_19, 8, label_time_20, 8, label_time_21, 8, label_time_22, 8, label_time_23, 9, user_id, 9, year_id, 10, business_id, 10, user_id, 10, review_stars, 10, review_votes_funny, 10, review_votes_useful, 10, review_votes_cool, 10, review_length, 11, business_id, 11, user_id, 11, likes, 11, tip_length, 12, compliment_id, 12, user_id, 12, number_of_compliments, 13, business_id, 13, active, 13, city, 13, state, 13, stars, 13, review_count, 14, user_id, 14, user_yelping_since_year, 14, user_average_stars, 14, user_votes_funny, 14, user_votes_useful, 14, user_votes_cool, 14, user_review_count, 14, user_fans
Question: How many Yelp businesses are opened 24 hours?
Evidence: open 24 hours refers to attribute_name = 'Open 24 Hours' AND attribute_value = 'true';
SQL: SELECT COUNT(T2.business_id) FROM Attributes AS T1 INNER JOIN Business_Attributes AS T2 ON T1.attribute_id = T2.attribute_id WHERE T1.attribute_name LIKE 'Open 24 Hours' AND T2.attribute_value LIKE 'TRUE'
Database: public_review_platform
Tables: Attributes, Categories, Compliments, Days, Years, Business_Attributes, Business_Categories, Business_Hours, Checkins, Elite, Reviews, Tips, Users_Compliments, Business, Users
Columns (table index, column name): -1, *, 0, attribute_id, 0, attribute_name, 1, category_id, 1, category_name, 2, compliment_id, 2, compliment_type, 3, day_id, 3, day_of_week, 4, year_id, 4, actual_year, 5, attribute_id, 5, business_id, 5, attribute_value, 6, business_id, 6, category_id, 7, business_id, 7, day_id, 7, opening_time, 7, closing_time, 8, business_id, 8, day_id, 8, label_time_0, 8, label_time_1, 8, label_time_2, 8, label_time_3, 8, label_time_4, 8, label_time_5, 8, label_time_6, 8, label_time_7, 8, label_time_8, 8, label_time_9, 8, label_time_10, 8, label_time_11, 8, label_time_12, 8, label_time_13, 8, label_time_14, 8, label_time_15, 8, label_time_16, 8, label_time_17, 8, label_time_18, 8, label_time_19, 8, label_time_20, 8, label_time_21, 8, label_time_22, 8, label_time_23, 9, user_id, 9, year_id, 10, business_id, 10, user_id, 10, review_stars, 10, review_votes_funny, 10, review_votes_useful, 10, review_votes_cool, 10, review_length, 11, business_id, 11, user_id, 11, likes, 11, tip_length, 12, compliment_id, 12, user_id, 12, number_of_compliments, 13, business_id, 13, active, 13, city, 13, state, 13, stars, 13, review_count, 14, user_id, 14, user_yelping_since_year, 14, user_average_stars, 14, user_votes_funny, 14, user_votes_useful, 14, user_votes_cool, 14, user_review_count, 14, user_fans
Question: What kind of "wi-fi" does Yelp business No."10172" have?
Evidence: kind of wi-fi refers to attribute_value where attribute_name = 'Wi-Fi'; business No. refers to business_id;
SQL: SELECT T2.attribute_value FROM Attributes AS T1 INNER JOIN Business_Attributes AS T2 ON T1.attribute_id = T2.attribute_id WHERE T2.business_id = 10172 AND T1.attribute_name LIKE 'wi-fi'
Database: public_review_platform
Tables: Attributes, Categories, Compliments, Days, Years, Business_Attributes, Business_Categories, Business_Hours, Checkins, Elite, Reviews, Tips, Users_Compliments, Business, Users
Columns (table index, column name): -1, *, 0, attribute_id, 0, attribute_name, 1, category_id, 1, category_name, 2, compliment_id, 2, compliment_type, 3, day_id, 3, day_of_week, 4, year_id, 4, actual_year, 5, attribute_id, 5, business_id, 5, attribute_value, 6, business_id, 6, category_id, 7, business_id, 7, day_id, 7, opening_time, 7, closing_time, 8, business_id, 8, day_id, 8, label_time_0, 8, label_time_1, 8, label_time_2, 8, label_time_3, 8, label_time_4, 8, label_time_5, 8, label_time_6, 8, label_time_7, 8, label_time_8, 8, label_time_9, 8, label_time_10, 8, label_time_11, 8, label_time_12, 8, label_time_13, 8, label_time_14, 8, label_time_15, 8, label_time_16, 8, label_time_17, 8, label_time_18, 8, label_time_19, 8, label_time_20, 8, label_time_21, 8, label_time_22, 8, label_time_23, 9, user_id, 9, year_id, 10, business_id, 10, user_id, 10, review_stars, 10, review_votes_funny, 10, review_votes_useful, 10, review_votes_cool, 10, review_length, 11, business_id, 11, user_id, 11, likes, 11, tip_length, 12, compliment_id, 12, user_id, 12, number_of_compliments, 13, business_id, 13, active, 13, city, 13, state, 13, stars, 13, review_count, 14, user_id, 14, user_yelping_since_year, 14, user_average_stars, 14, user_votes_funny, 14, user_votes_useful, 14, user_votes_cool, 14, user_review_count, 14, user_fans
Question: How many "bars" are there in the Yelp business?
Evidence: bars refers to category_name = 'Bars';
SQL: SELECT COUNT(T1.category_id) FROM Categories AS T1 INNER JOIN Business_Categories AS T2 ON T1.category_id = T2.category_id WHERE T1.category_name LIKE 'Bars'
Database: public_review_platform
Tables: Attributes, Categories, Compliments, Days, Years, Business_Attributes, Business_Categories, Business_Hours, Checkins, Elite, Reviews, Tips, Users_Compliments, Business, Users
Columns (table index, column name): -1, *, 0, attribute_id, 0, attribute_name, 1, category_id, 1, category_name, 2, compliment_id, 2, compliment_type, 3, day_id, 3, day_of_week, 4, year_id, 4, actual_year, 5, attribute_id, 5, business_id, 5, attribute_value, 6, business_id, 6, category_id, 7, business_id, 7, day_id, 7, opening_time, 7, closing_time, 8, business_id, 8, day_id, 8, label_time_0, 8, label_time_1, 8, label_time_2, 8, label_time_3, 8, label_time_4, 8, label_time_5, 8, label_time_6, 8, label_time_7, 8, label_time_8, 8, label_time_9, 8, label_time_10, 8, label_time_11, 8, label_time_12, 8, label_time_13, 8, label_time_14, 8, label_time_15, 8, label_time_16, 8, label_time_17, 8, label_time_18, 8, label_time_19, 8, label_time_20, 8, label_time_21, 8, label_time_22, 8, label_time_23, 9, user_id, 9, year_id, 10, business_id, 10, user_id, 10, review_stars, 10, review_votes_funny, 10, review_votes_useful, 10, review_votes_cool, 10, review_length, 11, business_id, 11, user_id, 11, likes, 11, tip_length, 12, compliment_id, 12, user_id, 12, number_of_compliments, 13, business_id, 13, active, 13, city, 13, state, 13, stars, 13, review_count, 14, user_id, 14, user_yelping_since_year, 14, user_average_stars, 14, user_votes_funny, 14, user_votes_useful, 14, user_votes_cool, 14, user_review_count, 14, user_fans
Question: How many more "buffets" than "gyms" in Yelp business?
Evidence: buffets refers to category_name = 'Buffets'; gyms refers to category_name = 'Gyms'; difference = SUBTRACT(SUM(category_name = 'Buffets'), SUM(category_name = 'Gyms'));
SQL: SELECT SUM(CASE WHEN T1.category_name LIKE 'Buffets' THEN 1 ELSE 0 END) - SUM(CASE WHEN T1.category_name LIKE 'Gyms' THEN 1 ELSE 0 END) FROM Categories AS T1 INNER JOIN Business_Categories AS T2 ON T1.category_id = T2.category_id
Database: public_review_platform
Tables: Attributes, Categories, Compliments, Days, Years, Business_Attributes, Business_Categories, Business_Hours, Checkins, Elite, Reviews, Tips, Users_Compliments, Business, Users
Columns (table index, column name): -1, *, 0, attribute_id, 0, attribute_name, 1, category_id, 1, category_name, 2, compliment_id, 2, compliment_type, 3, day_id, 3, day_of_week, 4, year_id, 4, actual_year, 5, attribute_id, 5, business_id, 5, attribute_value, 6, business_id, 6, category_id, 7, business_id, 7, day_id, 7, opening_time, 7, closing_time, 8, business_id, 8, day_id, 8, label_time_0, 8, label_time_1, 8, label_time_2, 8, label_time_3, 8, label_time_4, 8, label_time_5, 8, label_time_6, 8, label_time_7, 8, label_time_8, 8, label_time_9, 8, label_time_10, 8, label_time_11, 8, label_time_12, 8, label_time_13, 8, label_time_14, 8, label_time_15, 8, label_time_16, 8, label_time_17, 8, label_time_18, 8, label_time_19, 8, label_time_20, 8, label_time_21, 8, label_time_22, 8, label_time_23, 9, user_id, 9, year_id, 10, business_id, 10, user_id, 10, review_stars, 10, review_votes_funny, 10, review_votes_useful, 10, review_votes_cool, 10, review_length, 11, business_id, 11, user_id, 11, likes, 11, tip_length, 12, compliment_id, 12, user_id, 12, number_of_compliments, 13, business_id, 13, active, 13, city, 13, state, 13, stars, 13, review_count, 14, user_id, 14, user_yelping_since_year, 14, user_average_stars, 14, user_votes_funny, 14, user_votes_useful, 14, user_votes_cool, 14, user_review_count, 14, user_fans
Question: What business category is the Yelp business which got the most 5 star reviews in?
Evidence: business category refers to category_name; most 5 star reviews refers to MAX(COUNT(category_name WHERE star_reviews = 5));
SQL: SELECT T1.category_name FROM Categories AS T1 INNER JOIN Business_Categories AS T2 ON T1.category_id = T2.category_id INNER JOIN Business AS T3 ON T2.business_id = T3.business_id INNER JOIN Reviews AS T4 ON T3.business_id = T4.business_id WHERE T4.review_stars = 5 GROUP BY T1.category_name ORDER BY COUNT(T1.category_name) DESC LIMIT 1