1sf/bird-sql-train-with-schema huggingface dataset:
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 businesses that are registered in the database can be attributed to 'Good for Kids'?
Evidence: can be attributed to 'Good for Kids' refers to attribute_name = 'Good for Kids' 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 = 'Good for Kids' AND T2.attribute_value = '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: Identify the most popular and appealing active business in Gilbert based on users' reviews.
Evidence: most popular and appealing refers to review_count = 'High' and max(stars); active business refers to active = 'true'; in Gilbert refers to city = 'Gilbert'
SQL: SELECT business_id FROM Business WHERE city = 'Gilbert' AND active = 'true' AND review_count = 'High' ORDER BY stars 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: Find the 5-star business in Ahwatukee, AZ and identify it's business category.
Evidence: 5-star refers to stars = 5; in Ahwatukee refers to city = 'Ahwatukee'; business category refers to category_name
SQL: SELECT T1.business_id, T3.category_name FROM Business AS T1 INNER JOIN Business_Categories AS T2 ON T1.business_id = T2.business_id INNER JOIN Categories AS T3 ON T2.category_id = T3.category_id WHERE T1.city = 'Ahwatukee' AND T1.stars = 5
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: Among all closed businesses in Avondale, AZ what percent have obtained a 'wonderful experience' rating of the business.
Evidence: closed business refers to active = 'false'; in Avondale refers to city = 'Avondale'; 'wonderful experience' rating refers to stars > 3; percentage = divide(count(business_id where stars > 3), count(business_id))*100%
SQL: SELECT CAST(SUM(CASE WHEN stars > 3 THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(stars) FROM Business WHERE city = 'Avondale' AND active = '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: Identify the user who has been yelping since 2004. Is he or she an Yelp Elite member?
Evidence: has been yelping since 2004 refers to user_yelping_since_year = 2004
SQL: SELECT DISTINCT T2.user_id FROM Users AS T1 INNER JOIN Elite AS T2 ON T1.user_id = T2.user_id WHERE T1.user_yelping_since_year = 2004
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: Identify the percent of long reviews among all 5-star reviews given to businesses by the Yelp users.
Evidence: percentage = divide(count(business_id where review_length = 'Long' and review_stars = 5), count(business_id)) * 100%; long reviews refers to review_length = 'Long'; 5-star review refers to review_stars = 5
SQL: SELECT CAST(SUM(CASE WHEN review_length = 'Long' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(review_length) FROM Reviews WHERE review_stars = 5
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: Among all the users with the average ratings of at least 4 and above of all reviews, calculate the percent that have no fans or followers.
Evidence: average ratings of at least 4 refers to user_average_stars > = 4; no fans or followers refers to user_fans = 'None'; percentage = divide(count(user_id where user_average_stars > = 4 and user_fans = 'None'), sum(user_id where user_average_stars > = 4))*100%
SQL: SELECT CAST(SUM(CASE WHEN user_fans = 'None' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(user_id) FROM Users WHERE user_average_stars >= 4
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 short tips were left for the business with ID no.2?
Evidence: short tip refers to tip_length = 'Short'; business category refers to category_name
SQL: SELECT COUNT(business_id) FROM Tips WHERE business_id = 2 AND tip_length = 'Short'
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: Find the Yelp user with the average 5-star rating of all reviews who has been yelping the longest.
Evidence: Yelp user refers to user_id; average 5-star rating refers to user_average_stars = 5; yelping the longest refers to min(user_yelping_since_year)
SQL: SELECT user_id FROM Users WHERE user_average_stars = 5 ORDER BY user_yelping_since_year ASC 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: Identify the operating hours of businesses in Black Canyon City with review count greater than average.
Evidence: operating hours refers to opening_time closing_time on day_id; in Black Canyon City refers to city = 'Black Canyon City'; greater than average refers to review_count > AVG(T1.review_count)
SQL: SELECT T2.opening_time, T2.closing_time FROM Business AS T1 INNER JOIN Business_Hours AS T2 ON T1.business_id = T2.business_id INNER JOIN Days AS T3 ON T2.day_id = T3.day_id WHERE T1.city = 'Black Canyon City' GROUP BY t2.business_id HAVING T1.review_count > AVG(T1.review_count)
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: Among all the users who received the high number of compliments, what percent received the 'cute' type of compliment.
Evidence: high number of compliments refers to number_of_compliments = 'High'; percentage = divide(count(user_id where compliment_type = 'cute'), count(user_id))*100%
SQL: SELECT CAST(SUM(CASE WHEN T1.compliment_type = 'cute' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(T2.user_id) FROM Compliments AS T1 INNER JOIN Users_Compliments AS T2 ON T1.compliment_id = T2.compliment_id WHERE T2.number_of_compliments = 'High'
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: Mention the number of businesses that have no any attribute.
Evidence: have no attribute refers to attribute_value in( 'none', 'no', 'false')
SQL: SELECT COUNT(business_id) FROM Business_Attributes WHERE attribute_value IN ('none', 'no', 'false')