Training — unified schema
Collection
37 training sources, one strict schema: 15 general (13 repos), 15 domain, 7 RLHN (MS MARCO, NQ, HotpotQA, FEVER, SciDocs-RR, FiQA, ArguAna). • 36 items • Updated
Datasets:
Tasks:
Text Retrieval
Modalities:
Text
Formats:
parquet
Sub-tasks:
document-retrieval
Languages:
English
Size:
1M - 10M
License:
id stringlengths 20 20 | title stringclasses 1
value | text stringlengths 40 1.84k |
|---|---|---|
000428e97002589f0d32 | CREATE TABLE Parks (name VARCHAR(50), state VARCHAR(20), size_acres INT); INSERT INTO Parks (name, state, size_acres) VALUES ('ParkA', 'New York', 100), ('ParkB', 'New York', 200), ('ParkC', 'Pennsylvania', 150);
SELECT name, state, size_acres FROM Parks WHERE state IN ('New York', 'Pennsylvania'); | |
0004aa37df064fc473d2 | CREATE TABLE Orders (id INT, order_date DATE, menu_item VARCHAR(50), price DECIMAL(5,2), quantity INT);
SELECT SUM(price * quantity) FROM Orders WHERE menu_item IN (SELECT item_name FROM Menus WHERE restaurant_name = 'Seafood Shack' AND category = 'Seafood') AND MONTH(order_date) = 2 AND YEAR(order_date) = 2022; | |
0009b4ace28c1c636903 | CREATE TABLE freshwater_farms (id INT, name TEXT, location TEXT, species TEXT, biomass FLOAT); INSERT INTO freshwater_farms (id, name, location, species, biomass) VALUES (1, 'Farm A', 'USA', 'Tilapia', 5000.0), (2, 'Farm B', 'Canada', 'Salmon', 3000.0);
SELECT SUM(biomass) FROM freshwater_farms WHERE species IN ('Tilap... | |
000cb637c1ef5ce6ae9d | CREATE TABLE donations (donation_id INT, donation_amount DECIMAL(10,2), donation_date DATE, region VARCHAR(20)); INSERT INTO donations (donation_id, donation_amount, donation_date, region) VALUES (1, 500.00, '2020-01-01', 'northregion'), (2, 300.00, '2020-04-01', 'southregion'), (3, 700.00, '2020-07-01', 'northregion')... | |
000e6ac393a4426fbfa8 | CREATE TABLE ships (ship_id INT, ship_name VARCHAR(255), registration_date DATE); INSERT INTO ships VALUES (1, 'Sea Giant', '2010-03-23'), (2, 'Poseidon', '2012-09-08');
UPDATE ships SET registration_date = '2015-01-01' WHERE ship_name = 'Poseidon'; | |
000ef7f936f68fc789c3 | CREATE TABLE baseball_hits (player VARCHAR(50), team VARCHAR(50), homeruns INT); INSERT INTO baseball_hits (player, team, homeruns) VALUES ('Aaron Judge', 'New York Yankees', 30), ('Mike Trout', 'Los Angeles Angels', 25), ('Juan Soto', 'Washington Nationals', 20);
SELECT player, MAX(homeruns) FROM baseball_hits; | |
00100bd5e95757e6562a | CREATE TABLE aircraft_specs (id INT, model VARCHAR(50), max_passengers INT); INSERT INTO aircraft_specs (id, model, max_passengers) VALUES (1, 'Boeing 737', 215), (2, 'Airbus A320', 220), (3, 'Boeing 787', 335);
SELECT model, MAX(max_passengers) as max_passengers FROM aircraft_specs GROUP BY model; | |
0010ed0ddc567734c11e | CREATE TABLE artists (id INT, name VARCHAR(255), genre VARCHAR(255), home_country VARCHAR(255)); CREATE TABLE artist_concerts (artist_id INT, country VARCHAR(255), city VARCHAR(255)); INSERT INTO artists (id, name, genre, home_country) VALUES (1, 'Taylor Swift', 'Country Pop', 'United States'); INSERT INTO artist_conce... | |
00163b57a82d3ec7612f | CREATE TABLE MiningSites(id INT, name VARCHAR(30), location VARCHAR(30)); CREATE TABLE ResourceExtraction(site_id INT, date DATE, resources_extracted INT);
SELECT m.name, SUM(re.resources_extracted) FROM MiningSites m JOIN ResourceExtraction re ON m.id = re.site_id WHERE date >= DATE_SUB(CURRENT_DATE, INTERVAL 6 MONT... | |
001e683bf0f7f97f6967 | CREATE TABLE product (product_id INT, product_name VARCHAR(50), price DECIMAL(5,2), size VARCHAR(10)); INSERT INTO product (product_id, product_name, price, size) VALUES (1, 'Cotton T-Shirt', 25.99, 'S'), (2, 'Cotton T-Shirt', 25.99, 'M'), (3, 'Cotton T-Shirt', 25.99, 'L'), (4, 'Cotton T-Shirt', 25.99, 'XL');
SELECT si... | |
0024a46bdb323155a80b | CREATE TABLE WasteGeneration (year INT, region VARCHAR(50), material VARCHAR(50), volume FLOAT); INSERT INTO WasteGeneration (year, region, material, volume) VALUES (2020, 'North America', 'Electronic', 12000), (2020, 'Europe', 'Electronic', 15000), (2020, 'Asia', 'Electronic', 20000), (2020, 'South America', 'Electron... | |
0026ae2fa651b9bae8fd | CREATE TABLE EducationPrograms (program_id INT, program_name VARCHAR(50), location VARCHAR(50));
SELECT DISTINCT location FROM EducationPrograms WHERE program_name LIKE '%Community Education%'; | |
002a8bddd7a0cb81f44e | CREATE TABLE animal_species (id INT, name VARCHAR(20), habitat_id INT); INSERT INTO animal_species (id, name, habitat_id) VALUES (1, 'Lion', 2), (2, 'Elephant', 1), (3, 'Hippo', 3), (4, 'Tiger', 2), (5, 'Crane', 3), (6, 'Rhinoceros', 1), (7, 'Zebra', 2); CREATE TABLE habitats (id INT, type VARCHAR(20)); INSERT INTO hab... | |
002c7f9a9832e01dbfd1 | CREATE TABLE Raw_Materials (raw_material_code TEXT, raw_material_name TEXT, quantity INTEGER); INSERT INTO Raw_Materials (raw_material_code, raw_material_name, quantity) VALUES ('M123', 'Hydrochloric Acid', 500), ('M234', 'Sodium Hydroxide', 800), ('M345', 'Acetic Acid', 300), ('M456', 'B302', 1000); CREATE TABLE Produ... | |
002f2e4940e99297f931 | CREATE TABLE drills (id SERIAL PRIMARY KEY, department VARCHAR(255), timestamp TIMESTAMP); INSERT INTO drills (department, timestamp) VALUES ('Police', '2020-03-01 10:00:00'), ('Fire', '2020-03-01 14:00:00'), ('Police', '2020-06-15 16:00:00'), ('Fire', '2020-06-15 18:00:00');
SELECT COUNT(id) as total_drills FROM drill... | |
002f7f71d66100bdd5cc | CREATE TABLE pollution_control_initiatives (id INT, name TEXT, location TEXT, year INT); INSERT INTO pollution_control_initiatives (id, name, location, year) VALUES (1, 'Ocean Plastic Reduction Project', 'Southern Ocean', 2016), (2, 'Coral Reef Protection Program', 'Southern Ocean', 2017), (3, 'Marine Life Restoration ... | |
00306bed7e9fea8e4d34 | CREATE TABLE Research_Grants (Grant_ID INT, Grant_Title VARCHAR(100), Start_Date DATE, End_Date DATE, Grant_Amount DECIMAL(10, 2), Grant_Status VARCHAR(20));
UPDATE Research_Grants SET Grant_Status = 'Closed' WHERE End_Date < CURDATE(); | |
0030872e32b29a957e56 | CREATE TABLE investments (client_id INT, investment_strategy VARCHAR(50), age INT, investment_date DATE); INSERT INTO investments (client_id, investment_strategy, age, investment_date) VALUES (1, 'Stocks', 35, '2022-01-10'), (2, 'Bonds', 45, '2022-01-15'), (3, 'Stocks', 32, '2022-02-01');
SELECT investment_strategy, CO... | |
0031f216503f65a17e09 | CREATE TABLE vessels (vessel_id INT, vessel_name VARCHAR(50), flag_state VARCHAR(50)); CREATE TABLE voyages (voyage_id INT, vessel_id INT, voyage_start_date DATE, voyage_end_date DATE); INSERT INTO vessels VALUES (1, 'MSC Maya', 'Panama'); INSERT INTO voyages VALUES (1, 1, '2021-07-01', '2021-07-15'); INSERT INTO voyag... | |
00340ab2971867f15dc8 | CREATE TABLE world_cities (city VARCHAR(50), population INT); INSERT INTO world_cities (city, population) VALUES ('New York', 8500000), ('Los Angeles', 4000000), ('Tokyo', 9000000), ('Sydney', 5000000), ('Berlin', 3500000);
SELECT city FROM world_cities WHERE population > 6000000; | |
00351186d2caebe94fa7 | CREATE TABLE Donors (DonorID int, DonorName text, Country text); INSERT INTO Donors (DonorID, DonorName, Country) VALUES (1, 'John Doe', 'USA'), (2, 'Jane Smith', 'Canada'); CREATE TABLE Donations (DonationID int, DonorID int, DonationAmount numeric); INSERT INTO Donations (DonationID, DonorID, DonationAmount) VALUES (... | |
003687c7dd56c7a0d840 | CREATE TABLE interactions (user_id INT, post_id INT); CREATE TABLE posts (id INT, content TEXT);
SELECT DISTINCT u.username FROM users u LEFT JOIN interactions i ON u.id = i.user_id LEFT JOIN posts p ON i.post_id = p.id WHERE p.content NOT LIKE '%sustainability%'; | |
0037960bf279555b73ce | CREATE TABLE revenue (restaurant_id INT, revenue_date DATE, total_revenue DECIMAL(10,2));
UPDATE revenue SET total_revenue = 5000.00 WHERE restaurant_id = 789 AND revenue_date = '2022-02-15'; | |
00390789798e9af27dd1 | CREATE TABLE concerts (id INT, city VARCHAR(255), revenue FLOAT); INSERT INTO concerts (id, city, revenue) VALUES (1, 'New York', 50000.0), (2, 'Los Angeles', 70000.0);
SELECT SUM(revenue) FROM concerts WHERE city = 'New York'; | |
00415dd7069652aed2c3 | CREATE TABLE farmers (id INT, name TEXT, country TEXT, year INT, corn_yield FLOAT);
SELECT country, year, AVG(corn_yield) FROM farmers GROUP BY country, year; | |
00456f40d522b90e76e5 | CREATE TABLE UnionM(member_id INT, join_date DATE, salary INT); INSERT INTO UnionM(member_id, join_date, salary) VALUES(13001, '2017-01-01', 50000), (13002, '2016-12-31', 55000), (13003, '2018-01-01', 45000);
SELECT COUNT(*), AVG(salary) FROM UnionM WHERE YEAR(join_date) > 2016; | |
004cd191d555cb9a7f66 | CREATE TABLE ads (ad_id INT, user_id INT, country VARCHAR(50)); INSERT INTO ads (ad_id, user_id, country) VALUES (1, 101, 'USA'), (2, 102, 'Canada'), (3, 101, 'USA'), (4, 103, 'Canada');
SELECT COUNT(DISTINCT ad_id) FROM ads WHERE country IN ('USA', 'Canada'); | |
004d4542ad42cab27ddb | CREATE TABLE renewable_energy_projects (id INT, name VARCHAR(100), country VARCHAR(50), capacity_mw FLOAT); INSERT INTO renewable_energy_projects (id, name, country, capacity_mw) VALUES (1, 'Project 1', 'India', 30.5), (2, 'Project 2', 'India', 15.2);
SELECT name, MIN(capacity_mw) FROM renewable_energy_projects WHERE c... | |
004db483d1419c172c0b | CREATE TABLE community_development (id INT, location TEXT, year INT, completed BOOLEAN); INSERT INTO community_development (id, location, year, completed) VALUES (1, 'Asia', 2020, TRUE), (2, 'Africa', 2019, FALSE);
SELECT COUNT(*) FROM community_development WHERE year = 2020 AND completed = FALSE; | |
0052329d6380b01a46dd | CREATE TABLE DefenseDiplomacy (id INT PRIMARY KEY, event VARCHAR(100), country VARCHAR(50), year INT, participants INT); INSERT INTO DefenseDiplomacy (id, event, country, year, participants) VALUES (3, 'Joint Naval Exercise', 'Canada', 2019, 20);
SELECT COUNT(*) FROM DefenseDiplomacy WHERE country LIKE '%North America%... | |
0053c868e5838d42404c | CREATE TABLE garment_colors (id INT, garment_id INT, color VARCHAR(20)); INSERT INTO garment_colors (id, garment_id, color) VALUES (1, 301, 'red'), (2, 302, 'blue'), (3, 303, 'black'), (4, 304, 'red'), (5, 305, 'green');
SELECT color, COUNT(*) as count FROM garment_colors gc JOIN sales s ON gc.garment_id = s.garment_id... | |
0055cf92b26fa59d19a4 | CREATE TABLE indigenous_crops (id INT, crop_name VARCHAR(50), area_ha INT); INSERT INTO indigenous_crops (id, crop_name, area_ha) VALUES (1, 'Potatoes', 400), (2, 'Quinoa', 300), (3, 'Amaranth', 200);
SELECT crop_name, SUM(area_ha * 0.01) as area_sq_km FROM indigenous_crops GROUP BY crop_name; | |
005a0fae40c8e67a309f | CREATE TABLE warehouses (id INT, name VARCHAR(50), location VARCHAR(50)); INSERT INTO warehouses (id, name, location) VALUES (1, 'Warehouse A', 'City A'), (2, 'Warehouse B', 'City B'), (3, 'Warehouse C', 'City C'); CREATE TABLE inventory (id INT, warehouse_id INT, item_type VARCHAR(50), quantity INT); INSERT INTO inven... | |
005d605d58b398f6c64c | CREATE TABLE Donors (DonorID INT, DonorName VARCHAR(50), DonationAmount DECIMAL(10,2), CauseID INT, FirstDonationDate DATE, Country VARCHAR(50));CREATE TABLE Causes (CauseID INT, CauseName VARCHAR(50), Focus VARCHAR(50));
SELECT D.Country, COUNT(DISTINCT D.DonorID) FROM Donors D JOIN Causes C ON D.CauseID = C.CauseID W... | |
0060a6aecbddc2af60c0 | CREATE TABLE sales (drug_name TEXT, quarter TEXT, year INTEGER, revenue INTEGER); INSERT INTO sales (drug_name, quarter, year, revenue) VALUES ('DrugA', 'Q1', 2022, 500000);
SELECT revenue FROM sales WHERE drug_name = 'DrugA' AND quarter = 'Q1' AND year = 2022; | |
00610a8957f5c0a7e81c | CREATE TABLE Naval_Ships (id INT, country VARCHAR(50), type VARCHAR(50), acquisition_date DATE, maintenance_cost FLOAT);
SELECT AVG(maintenance_cost/MONTHS_BETWEEN(acquisition_date, CURRENT_DATE)) FROM Naval_Ships WHERE country = 'Canada'; | |
00610dc815d36457c393 | CREATE TABLE arctic_weather (date DATE, temperature FLOAT); INSERT INTO arctic_weather (date, temperature) VALUES ('2021-01-01', -10.0), ('2021-02-01', -15.0), ('2021-03-01', -5.0);
SELECT AVG(temperature) FROM arctic_weather WHERE EXTRACT(YEAR FROM date) = 2021; | |
00610ee2f707e85790e5 | CREATE TABLE community_health_workers (id INT, worker_name VARCHAR(50), gender VARCHAR(50)); CREATE TABLE cultural_competency_training (id INT, community_health_worker_id INT, hours INT); INSERT INTO community_health_workers (id, worker_name, gender) VALUES (1, 'Jamal', 'Male'), (2, 'Sophia', 'Female'), (3, 'Alex', 'No... | |
00612fd794eac4b7d295 | CREATE TABLE military_bases (base_id INT, state VARCHAR(255), name VARCHAR(255)); INSERT INTO military_bases (base_id, state, name) VALUES (1, 'California', 'Base Alpha'), (2, 'Texas', 'Base Bravo');
SELECT state, COUNT(state) FROM military_bases GROUP BY state; | |
0061cd1b0909c3a735df | CREATE TABLE riders (rider_id INT, rider_name TEXT); CREATE TABLE trips (trip_id INT, rider_id INT, trip_date DATE);
SELECT r.rider_name, COUNT(t.trip_id) as total_trips FROM riders r INNER JOIN trips t ON r.rider_id = t.rider_id WHERE t.trip_date >= DATEADD(month, -1, GETDATE()) GROUP BY r.rider_name; | |
006219333eb13de863d0 | CREATE TABLE investments (id INT, sector VARCHAR(255), risk_assessment_score INT); INSERT INTO investments (id, sector, risk_assessment_score) VALUES (1, 'Healthcare', 70), (2, 'Healthcare', 80), (3, 'Technology', 60);
SELECT COUNT(*) FROM investments WHERE sector = 'Healthcare' AND risk_assessment_score > 75; | |
0064da9217287d828d6d | CREATE TABLE HeritageSites (id INT, name VARCHAR(255), location VARCHAR(255), focus_area VARCHAR(255), year_established INT, UNIQUE(id));
SELECT (COUNT(HeritageSites.id) * 100.0 / (SELECT COUNT(*) FROM HeritageSites WHERE HeritageSites.location = 'Africa')) as pct, AVG(HeritageSites.year_established) as avg_year FROM H... | |
0064e8d4b72dc28781b3 | CREATE TABLE social_media_users_latam (user_id INT, signup_date DATE, country VARCHAR(50));
SELECT signup_date, COUNT(*) as new_users FROM social_media_users_latam WHERE signup_date >= DATE_SUB(CURDATE(), INTERVAL 1 MONTH) GROUP BY signup_date; | |
0064fc1c7465d26a4e3c | CREATE TABLE indiafarms (country VARCHAR(20), disease_outbreak BOOLEAN, year INTEGER); INSERT INTO indiafarms (country, disease_outbreak, year) VALUES ('India', true, 2021), ('India', false, 2020), ('India', false, 2019), ('India', false, 2018), ('India', false, 2017);
SELECT COUNT(*) FROM indiafarms WHERE country = 'I... | |
0069fccecec18dbc75c4 | CREATE TABLE agricultural_innovation (innovation_id INT, innovation_type VARCHAR(255), success_rate DECIMAL(5,2), implementation_date DATE);
SELECT innovation_type, MIN(success_rate) FROM agricultural_innovation WHERE implementation_date >= DATE_SUB(CURRENT_DATE, INTERVAL 2 YEAR) GROUP BY innovation_type; | |
006d4a40210bfde18e8e | CREATE TABLE posts (post_id INT, user_id INT, post_date DATE); CREATE TABLE users (user_id INT, name VARCHAR(255), region VARCHAR(255)); INSERT INTO posts (post_id, user_id, post_date) VALUES (1, 1, '2021-03-01'); INSERT INTO users (user_id, name, region) VALUES (1, 'Maria', 'Europe');
SELECT users.name, COUNT(*) as po... | |
006de6b9ea5414a6a617 | CREATE TABLE patients (id INT, age INT, has_insurance BOOLEAN, has_diabetes BOOLEAN); INSERT INTO patients (id, age, has_insurance, has_diabetes) VALUES (1, 55, false, true), (2, 45, true, false); CREATE TABLE locations (id INT, region VARCHAR, is_rural BOOLEAN); INSERT INTO locations (id, region, is_rural) VALUES (1, ... | |
006f7a20f72e693dd659 | CREATE TABLE midwest_conservation (region VARCHAR(20), conservation_amount FLOAT); INSERT INTO midwest_conservation (region, conservation_amount) VALUES ('Central', 120), ('Eastern', 150), ('Western', 180);
SELECT SUM(conservation_amount) FROM midwest_conservation; | |
00700274f48c0b34d912 | CREATE TABLE users (id INT, name VARCHAR(50), country VARCHAR(50)); INSERT INTO users (id, name, country) VALUES (1, 'Eve', 'Brazil'), (2, 'Frank', 'Mexico'), (3, 'Grace', 'Jamaica'); CREATE TABLE assets (id INT, user_id INT, name VARCHAR(50), value DECIMAL(10, 2)); INSERT INTO assets (id, user_id, name, value) VALUES ... | |
007360eca710da057c22 | CREATE TABLE Warehouse (name varchar(20), quarter int, year int, pallets_handled int); INSERT INTO Warehouse (name, quarter, year, pallets_handled) VALUES ('Warehouse A', 1, 2022, 500), ('Warehouse B', 1, 2022, 800);
SELECT name, MAX(pallets_handled) FROM Warehouse WHERE quarter = 1 AND year = 2022 GROUP BY name; | |
007699a633b7f0af264a | CREATE TABLE community_development (id INT, initiative_name VARCHAR(255), region VARCHAR(255), investment FLOAT, completion_year INT); INSERT INTO community_development (id, initiative_name, region, investment, completion_year) VALUES (1, 'Sustainable Irrigation', 'Indus Plains', 100000, 2018);
UPDATE community_develop... | |
007f341e0ba63f40a074 | CREATE TABLE humanitarian_assistance (org_name VARCHAR(255), mission_id INT);
SELECT org_name, COUNT(*) FROM humanitarian_assistance GROUP BY org_name HAVING COUNT(*) > 10; | |
007f7034ed17a075f6e2 | CREATE TABLE warehouses (id INT, name TEXT, region TEXT); INSERT INTO warehouses (id, name, region) VALUES (1, 'Boston Warehouse', 'east'), (2, 'Atlanta Warehouse', 'east'); CREATE TABLE packages (id INT, warehouse_id INT, weight FLOAT, state TEXT); INSERT INTO packages (id, warehouse_id, weight, state) VALUES (1, 1, 1... | |
0083e1f886997b23b012 | CREATE TABLE decentralized_exchanges_polygon (exchange_name TEXT, transaction_volume INTEGER, transaction_date DATE);
SELECT exchange_name, SUM(transaction_volume) FROM decentralized_exchanges_polygon WHERE transaction_date >= DATEADD(week, -1, GETDATE()) GROUP BY exchange_name; | |
0086c07edf2711d781c4 | CREATE TABLE Training (EmployeeID INT, Salary DECIMAL(10, 2), LeadershipTraining BOOLEAN, Position VARCHAR(20)); INSERT INTO Training (EmployeeID, Salary, LeadershipTraining, Position) VALUES (1, 90000.00, TRUE, 'Manager'), (2, 80000.00, FALSE, 'Manager'), (3, 70000.00, TRUE, 'Individual Contributor');
SELECT SUM(Salar... | |
0088d60efbb4dfeafa8a | CREATE TABLE mobile_subscribers (subscriber_id INT, country VARCHAR(50), age INT);
SELECT country, CASE WHEN age >= 18 AND age <= 24 THEN '18-24' WHEN age >= 25 AND age <= 34 THEN '25-34' WHEN age >= 35 AND age <= 44 THEN '35-44' WHEN age >= 45 AND age <= 54 THEN '45-54' WHEN age >= 55 THEN '55+' END AS age_group, COUN... | |
0089575fe0ee5fabad79 | CREATE TABLE subscriber_data (subscriber_id INT, subscriber_type VARCHAR(20), tech_type VARCHAR(20)); INSERT INTO subscriber_data (subscriber_id, subscriber_type, tech_type) VALUES (1, 'Regular', '4G'), (2, 'Test', '3G'), (3, 'Regular', '5G');
SELECT subscriber_type, COUNT(*) as total_subscribers FROM subscriber_data W... | |
008a324fb93f1f8d884b | CREATE TABLE Judgments (JudgmentID INT, CaseID INT, JudgmentDate DATE, Judgment VARCHAR(20)); INSERT INTO Judgments (JudgmentID, CaseID, JudgmentDate, Judgment) VALUES (1, 1, '2022-01-05', 'Favorable');
SELECT COUNT(*) FROM Judgments WHERE Judgment = 'Favorable' AND MONTH(JudgmentDate) = 1 AND YEAR(JudgmentDate) = 2022... | |
008a531ed6d9f72bdcd2 | CREATE TABLE sales (sale_id INT, sale_date DATE, vendor_id INT, product_category VARCHAR(50)); INSERT INTO sales (sale_id, sale_date, vendor_id, product_category) VALUES (1, '2022-01-01', 1, 'Eco-friendly'), (2, '2022-02-01', 2, 'Conventional'), (3, '2022-03-01', 3, 'Eco-friendly');
SELECT COUNT(*) FROM sales WHERE pro... | |
008a860852c37694ca32 | CREATE TABLE articles (title VARCHAR(255), author VARCHAR(255), date DATE, topic VARCHAR(255));
DELETE FROM articles WHERE author = 'Frank' AND date < '2022-01-01'; | |
0096f7b9dc19d7fc9a49 | CREATE TABLE districts (id INT, name VARCHAR(255)); INSERT INTO districts (id, name) VALUES (1, 'School District 1'), (2, 'School District 2'); CREATE TABLE schools (id INT, name VARCHAR(255), district_id INT); INSERT INTO schools (id, name, district_id) VALUES (1, 'School 1', 1), (2, 'School 2', 1), (3, 'School 3', 2)... | |
00979307e23eb2d2cfae | CREATE TABLE warehouse_inventory (id INT, item_name VARCHAR(255), quantity INT, warehouse_location VARCHAR(50), restock_date DATE);
INSERT INTO warehouse_inventory (id, item_name, quantity, warehouse_location, restock_date) VALUES (1, 'laptop', 1000, 'Houston', '2022-03-01'); | |
009875554246db0fad17 | CREATE TABLE transactions (id INT, tx_hash VARCHAR(50), tx_type VARCHAR(10), block_height INT, tx_time TIMESTAMP); INSERT INTO transactions (id, tx_hash, tx_type, block_height, tx_time) VALUES (1, '0x123...', 'transfer', 1000000, '2021-01-01 00:00:00'), (2, '0x456...', 'deploy', 1000001, '2021-01-02 00:00:00');
SELECT ... | |
009a42889d664b06c34e | CREATE TABLE energy_generation (id INT, state TEXT, source TEXT, generation FLOAT); INSERT INTO energy_generation (id, state, source, generation) VALUES (1, 'California', 'solar', 12345.6), (2, 'Texas', 'wind', 23456.7);
SELECT SUM(generation) FROM energy_generation WHERE state = 'California' AND source = 'solar'; | |
009caabb35430a2eb375 | CREATE TABLE if not exists countries (country_id INT, name TEXT); INSERT INTO countries (country_id, name) VALUES (1, 'USA'); CREATE TABLE if not exists cities (city_id INT, name TEXT, country_id INT, population INT); INSERT INTO cities (city_id, name, country_id, population) VALUES (1, 'Barcelona', 1, 1600000); CREATE... | |
009df5bdaa1489037014 | CREATE TABLE intelligence_operations (id INT, operation TEXT, region TEXT, operation_date DATE); INSERT INTO intelligence_operations (id, operation, region, operation_date) VALUES (1, 'Operation Red Falcon', 'Asia-Pacific', '2021-07-08'), (2, 'Operation Iron Fist', 'Asia-Pacific', '2021-08-12'), (3, 'Operation Black Sw... | |
00a052e6a3ae7da88d03 | CREATE TABLE factories (factory_id INT, location VARCHAR(255), has_ethical_labor BOOLEAN); INSERT INTO factories (factory_id, location, has_ethical_labor) VALUES (1, 'New York', TRUE), (2, 'Los Angeles', FALSE); CREATE TABLE workers (worker_id INT, factory_id INT); INSERT INTO workers (worker_id, factory_id) VALUES (1,... | |
00a06a57d09072c19ec9 | CREATE TABLE accommodations (id INT, name TEXT, region TEXT, cost FLOAT); INSERT INTO accommodations (id, name, region, cost) VALUES (1, 'Wheelchair Ramp', 'Asian', 120000.00), (2, 'Sign Language Interpreter', 'Asian', 60000.00);
DELETE FROM accommodations WHERE cost > 100000 AND region = 'Asian'; | |
00a2bff6f3924946642b | CREATE TABLE circular_economy (country VARCHAR(255), initiatives INT); INSERT INTO circular_economy (country, initiatives) VALUES ('Italy', 30), ('Spain', 25), ('Germany', 40), ('France', 35), ('Sweden', 20);
SELECT country, initiatives FROM circular_economy ORDER BY initiatives DESC; | |
00a2ff141c2d7d8f4967 | CREATE TABLE player_scores (player_id INT, score INT); INSERT INTO player_scores (player_id, score) VALUES (1, 600), (2, 300), (3, 700);
WITH low_scores AS (DELETE FROM player_scores WHERE score < 500 RETURNING *) SELECT * FROM low_scores; | |
00a58928bdfefa1c0735 | CREATE TABLE Concerts (id INT, genre VARCHAR(20), price DECIMAL(5,2)); INSERT INTO Concerts (id, genre, price) VALUES (1, 'Latin', 180.00), (2, 'Reggae', 130.00), (3, 'Latin', 220.00);
SELECT MAX(price) FROM Concerts WHERE genre = 'Latin' AND date BETWEEN '2022-10-01' AND '2022-10-31'; | |
00a6781db15723e0bb01 | CREATE TABLE world_cities (city_name VARCHAR(50), country_name VARCHAR(50), population BIGINT, gdp_of_country BIGINT);
SELECT city_name FROM world_cities WHERE population > 1000000 AND gdp_of_country = (SELECT gdp FROM world_gdp WHERE country_name = world_cities.country_name) AND gdp > 1000000000000; | |
00a6b83b6f0244940c91 | CREATE TABLE articles (id INT, title VARCHAR(100), topic VARCHAR(50));
SELECT topic, COUNT(*) AS num_articles FROM articles GROUP BY topic; | |
00a75e2d8fcb53cef9c1 | CREATE TABLE urban_gardens (id INT, region VARCHAR(10), crop VARCHAR(20), yield INT);
SELECT crop, yield FROM urban_gardens WHERE region = '02'; | |
00ab2eddfcad4ef86037 | CREATE TABLE LunarMissions (MissionName TEXT, LaunchCountry TEXT, LaunchDate DATE);CREATE VIEW US_China_Lunar_Missions AS SELECT * FROM LunarMissions WHERE LaunchCountry IN ('United States', 'China');
SELECT MissionName, LaunchDate FROM US_China_Lunar_Missions; | |
00ad8e25ce106ed35df1 | CREATE TABLE Countries (Country_ID INT, Country_Name VARCHAR(255)); INSERT INTO Countries (Country_ID, Country_Name) VALUES (1, 'United States'), (2, 'Mexico'), (3, 'Canada'), (4, 'Brazil'), (5, 'Argentina'); CREATE TABLE Visitor_Origins (Visitor_ID INT, Country_ID INT);
SELECT c.Country_Name, COUNT(v.Visitor_ID) AS Vi... | |
00ae618486fa5b855a3e | CREATE TABLE fabrics (id INT, name VARCHAR(50), is_sustainable BOOLEAN, price DECIMAL(5,2)); INSERT INTO fabrics (id, name, is_sustainable, price) VALUES (1, 'Organic Cotton', TRUE, 10.99), (2, 'Recycled Polyester', TRUE, 12.49), (3, 'Conventional Cotton', FALSE, 8.99);
SELECT AVG(price) FROM fabrics WHERE is_sustainab... | |
00b2d35ac4c0712d09fe | CREATE TABLE drug_sales (drug_name VARCHAR(255), sales INT); INSERT INTO drug_sales (drug_name, sales) VALUES ('DrugA', 5000000), ('DrugB', 7000000), ('DrugC', 8000000); CREATE TABLE drug_approval (drug_name VARCHAR(255), approval_year INT); INSERT INTO drug_approval (drug_name, approval_year) VALUES ('DrugA', 2019), (... | |
00b3357e4a5c7c2a5f3a | CREATE TABLE region_customers (region VARCHAR(50), customer_type VARCHAR(20), customer_id INT); INSERT INTO region_customers (region, customer_type, customer_id) VALUES ('Chicago', 'broadband', 1), ('Chicago', 'broadband', 2), ('Chicago', 'mobile', 3), ('New York', 'broadband', 4), ('New York', 'mobile', 5), ('Los Ange... | |
00b6b7e375b256aa2aef | CREATE SCHEMA if not exists genetics;CREATE TABLE if not exists genetics.project_timeline (id INT, project_id INT, phase VARCHAR(50), start_date DATE, end_date DATE); INSERT INTO genetics.project_timeline (id, project_id, phase, start_date, end_date) VALUES (1, 1, 'Planning', '2021-01-01', '2021-06-30'), (2, 1, 'Execut... | |
00b9f84f2fe47cf6cf8e | CREATE TABLE Farm (FarmID int, FarmName varchar(50), WaterTemperature numeric, WaterSalinity numeric); INSERT INTO Farm (FarmID, FarmName, WaterTemperature, WaterSalinity) VALUES (1, 'Farm A', 15, 20); INSERT INTO Farm (FarmID, FarmName, WaterTemperature, WaterSalinity) VALUES (2, 'Farm B', 18, 32); INSERT INTO Farm (F... | |
00c5609aa494c9212fcc | CREATE TABLE programs (program_id INT, program_name TEXT, manager_name TEXT); INSERT INTO programs VALUES (1, 'Education', 'Alice Johnson'), (2, 'Health', 'Bob Brown');
SELECT * FROM programs; | |
00c75b2d038b4bbf6474 | CREATE TABLE PeacekeepingOperations (id INT, country VARCHAR(50), operation_count INT, year INT); INSERT INTO PeacekeepingOperations (id, country, operation_count, year) VALUES (1, 'Australia', 2, 2016), (2, 'New Zealand', 1, 2016), (3, 'Australia', 3, 2017), (4, 'New Zealand', 2, 2017), (5, 'Australia', 4, 2018), (6, ... | |
00c88ef5945ff19bd510 | CREATE TABLE Player (PlayerID INT, Name VARCHAR(50), Country VARCHAR(50), Score INT);
SELECT AVG(Score) FROM Player WHERE Country IN ('China', 'India', 'Japan', 'South Korea', 'Indonesia'); | |
00cc0e7200f70ce04492 | CREATE TABLE VesselTypes (id INT, vessel_type VARCHAR(50)); CREATE TABLE IncidentLog (id INT, vessel_id INT, incident_type VARCHAR(50), time TIMESTAMP);
SELECT vt.vessel_type, COUNT(il.id) FROM VesselTypes vt JOIN IncidentLog il ON vt.id = il.vessel_id GROUP BY vt.vessel_type; | |
00cc7f595e0d3f440466 | CREATE TABLE wellbeing (id INT, region VARCHAR(20), gender VARCHAR(10), score DECIMAL(3,1)); INSERT INTO wellbeing (id, region, gender, score) VALUES (1, 'South America', 'Female', 75.5), (2, 'Southeast Asia', 'Male', 80.0), (3, 'Asia-Pacific', 'Female', 72.0);
SELECT MAX(score) FROM wellbeing WHERE region IN ('South A... | |
00cdb99aa3b1b8edd1cf | CREATE TABLE menus (id INT, name VARCHAR(255)); CREATE TABLE menu_items (id INT, name VARCHAR(255), menu_id INT); INSERT INTO menus (id, name) VALUES (1, 'Breakfast'), (2, 'Lunch'), (3, 'Dinner'); INSERT INTO menu_items (id, name, menu_id) VALUES (1, 'Pancakes', 1), (2, 'Salad', 2), (3, 'Pizza', 3);
SELECT mi.name FROM... | |
00d6da4910e5486851fd | CREATE TABLE CustomersRegion (CustomerID INT, CustomerName VARCHAR(255), Region VARCHAR(50), TotalFreightCharges DECIMAL(10, 2)); INSERT INTO CustomersRegion (CustomerID, CustomerName, Region, TotalFreightCharges) VALUES (1, 'ABC Corp', 'East', 5000.00), (2, 'XYZ Inc', 'West', 7000.00), (3, 'LMN Ltd', 'East', 6000.00),... | |
00d737c98f6d5d0530f9 | CREATE TABLE company_departments (dept_name TEXT, avg_salary NUMERIC); INSERT INTO company_departments (dept_name, avg_salary) VALUES ('automation', 42000.00);
DELETE FROM company_departments WHERE dept_name = 'automation'; | |
00ddc791e03b804f1b92 | CREATE TABLE warehouses (id INT, name TEXT, region TEXT); INSERT INTO warehouses (id, name, region) VALUES (1, 'Chicago Warehouse', 'north'), (2, 'Dallas Warehouse', 'south'); CREATE TABLE packages (id INT, warehouse_id INT, weight FLOAT, state TEXT); INSERT INTO packages (id, warehouse_id, weight, state) VALUES (1, 1,... | |
00ddefc7d37a89f42cba | CREATE TABLE accessibility_improvements (location VARCHAR(20), budget DECIMAL); INSERT INTO accessibility_improvements (location, budget) VALUES ('Rural', 50000.00); INSERT INTO accessibility_improvements (location, budget) VALUES ('Rural', 75000.00); INSERT INTO accessibility_improvements (location, budget) VALUES ('R... | |
00e118a23d4aa95a89fb | CREATE TABLE trend_forecasting(id INT PRIMARY KEY, region VARCHAR(50), product_category VARCHAR(50), forecast_date DATE, forecast_units INT); CREATE TABLE garment_manufacturing(id INT PRIMARY KEY, garment_id INT, country VARCHAR(50), material VARCHAR(50), manufacturing_date DATE, quantity INT);
SELECT t.product_categor... | |
00e1512dcbc5cdacb2d9 | CREATE TABLE fines (fine_id INT, violation_type VARCHAR(20), fine INT, collection_date DATE); INSERT INTO fines (fine_id, violation_type, fine, collection_date) VALUES (1, 'Speeding', 200, '2022-01-15'), (2, 'Parking', 50, '2022-01-17'), (3, 'Speeding', 100, '2022-01-18');
SELECT violation_type, SUM(fine) as total_fine... | |
00e4fcaafc62059bc52f | CREATE TABLE Sites_Over_Time (Id INT, Year INT, Mineral VARCHAR(50), Type VARCHAR(50)); INSERT INTO Sites_Over_Time (Id, Year, Mineral, Type) VALUES (1, 2020, 'Gold', 'underground'); INSERT INTO Sites_Over_Time (Id, Year, Mineral, Type) VALUES (2, 2021, 'Silver', 'open-pit');
SELECT Year, Mineral, COUNT(*) as Number_of... | |
00e589f6ef9a667fd1e3 | CREATE TABLE Aerospace_Sales (id INT, corporation VARCHAR(20), customer VARCHAR(20), quantity INT, equipment VARCHAR(20)); INSERT INTO Aerospace_Sales (id, corporation, customer, quantity, equipment) VALUES (1, 'Aerospace Corp', 'European Union', 15, 'Aircraft');
SELECT SUM(quantity) FROM Aerospace_Sales WHERE corporat... | |
00e848f756cb81215dda | CREATE TABLE RestaurantRevenue(restaurant_id INT, revenue DECIMAL(10,2), revenue_date DATE); CREATE TABLE RestaurantInfo(restaurant_id INT, food_safety_score INT);
SELECT SUM(revenue) FROM RestaurantRevenue R JOIN RestaurantInfo I ON R.restaurant_id = I.restaurant_id WHERE R.revenue_date BETWEEN '2022-05-01' AND '2022-... | |
00eb4b92bbec9d486428 | CREATE TABLE renewable_energy_projects (project_id INT, name VARCHAR(100), capacity INT, technology VARCHAR(50)); INSERT INTO renewable_energy_projects (project_id, name, capacity, technology) VALUES (1, 'Wind Farm', 300, 'Wind');
SELECT SUM(capacity) as total_capacity FROM renewable_energy_projects WHERE country = 'Ca... | |
00f268bc6abbbcde0fc3 | CREATE TABLE sectors (sector_id INT, sector_name VARCHAR(20)); CREATE TABLE companies (company_id INT, company_name VARCHAR(30), sector_id INT, esg_rating FLOAT);
SELECT AVG(c.esg_rating) FROM companies c INNER JOIN sectors s ON c.sector_id = s.sector_id WHERE s.sector_name = 'technology'; | |
00f2e1b08117305db3c0 | CREATE TABLE tree_types (id INT, name VARCHAR(255)); INSERT INTO tree_types (id, name) VALUES (1, 'Deciduous'), (2, 'Evergreen'); CREATE TABLE trees (id INT, biomass INT, tree_type_id INT); INSERT INTO trees (id, biomass, tree_type_id) VALUES (1, 1000, 1), (2, 800, 2); CREATE TABLE wildlife_habitats (id INT, name VARCH... | |
00f57b6e49f3b8d58d5f | CREATE TABLE patients (id INT, age INT, gender TEXT, state TEXT, disease TEXT); INSERT INTO patients (id, age, gender, state, disease) VALUES (1, 18, 'Female', 'Australia', 'Chlamydia'); INSERT INTO patients (id, age, gender, state, disease) VALUES (2, 30, 'Male', 'Australia', 'Chlamydia');
SELECT MIN(age) FROM patient... |
End of preview. Expand in Data Studio
Gretel Synthetic Text-to-SQL — Training, unified schema
A seeded sample of gretelai/synthetic_text_to_sql, made into retrieval training pairs and reshaped into the strict schema shared by every dataset in this collection. One of the 15 domain sources (code, medical, science, finance, legal) added to the collection's general sources.
| Source | gretelai/synthetic_text_to_sql @ 740ab236e645 |
| Task | question → schema and SQL |
| Domain · languages | code · eng |
| Queries / documents / qrels | 30,000 / 29,987 / 30,000 |
| Qrels per query | min 1 · mean 1.0 · max 1 |
| Score values | 2 ×30,000 (2: the first positive, 1: any other) |
| Layout | queries · corpus · qrels · hard-negatives · teacher-scores, split train |
| Splits | corpus: train · hard-negatives: train · qrels: train · queries: train · teacher-scores: train |
| Hard negatives | sources: dense · 2,970,047 rows |
| Teacher scores | none yet (0 rows): jinaai/jina-reranker-v3.5 scores come next |
| Ids | sha1(text)[:20]; identical texts collapse to one document / query |
| License | apache-2.0 |
Schema
| config | columns | rules |
|---|---|---|
queries |
id: string, text: string |
ids unique and non-empty; every query has ≥ 1 qrel |
corpus |
id: string, title: string, text: string |
title is always present ("" when the source has none) |
qrels |
query-id: string, corpus-id: string, score: int32 |
referential integrity to both tables; no duplicate pairs; no floats |
hard-negatives |
query-id: string, corpus-id: string, rank: int32, source: string |
one row per negative; (query-id, corpus-id, source) unique; never a labelled positive of the same query |
teacher-scores |
query-id: string, corpus-id: string, teacher: string, score: float32 |
one row per scored pair (positives included); a row means scored — never a placeholder |
Files are Parquet, sorted by id, zstd-compressed, sharded at 500 MB. Every rule above is checked before publishing; provenance.json records the source revision, what changed, and the output file hashes.
What changed from the source
- sampled: a seeded random sample (seed 1) of up to 30,000 pairs
- reshaped: the question (
sql_prompt) is the query; the document is the schema (sql_context), a newline and the SQL (sql) - decontaminated (exact): a pair was dropped when its normalised query equals any evaluation query, or a positive equals a document of a test or dev corpus; a repeated query keeps its first pair
- decontaminated (near-duplicates): 0 passages and 0 queries that nearly copy a text of an evaluation set (word 13-grams for passages, 8-grams for queries; at least half shared with one text of the 23 test corpora (BEIR, RTEB, LitSearch) and the 3 dev corpora) were removed, and with them 0 queries in total
- text: leading and trailing whitespace stripped; otherwise as converted above
- ids re-keyed to
sha1(text)[:20]: 13 documents and 0 queries collapsed into identical texts - added a
titlecolumn filled with""(the source has none)
Hard negatives and teacher scores
Filled by the collection's annotation pipeline (annotation=jina35). Interim: the candidates are final, the teacher scores are still to come.
- Candidates: dense retrieval with
jinaai/jina-embeddings-v5-text-smallover this corpus to depth 1,000; 100 candidates per query drawn from the rank windows 1–30 (30), 31–100 (30), 101–300 (20), 301–1000 (20), the query's labelled positives excluded.rankis the dense rank;sourceisdensefor a mined row anddatasetfor a negative the source labels itself. - Teacher scores: none yet.
teacher-scoresholds 0 rows until thejinaai/jina-reranker-v3.5scores (listwise, as in the other repositories) are filled in;datasetscannot return a 0-example split, so read that file withpyarrow/pandasmeanwhile. The candidates stay.
| configs | queries | hard negatives | teacher scores |
|---|---|---|---|
hard-negatives · teacher-scores |
30,000 (all) | 2,970,047 (2,970,047 dense) | 0 |
Load it
from datasets import load_dataset
queries = load_dataset("Hyukkyu/train-text2sql", "queries", split="train")
corpus = load_dataset("Hyukkyu/train-text2sql", "corpus", split="train")
qrels = load_dataset("Hyukkyu/train-text2sql", "qrels", split="train")
negatives = load_dataset("Hyukkyu/train-text2sql", "hard-negatives", split="train")
scores = load_dataset("Hyukkyu/train-text2sql", "teacher-scores", split="train")
License and attribution
The data is redistributed under the source's terms — apache-2.0. All credit belongs to the original authors; see the source repository (https://huggingface.co/datasets/gretelai/synthetic_text_to_sql). This repository is an independent repackaging.
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