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gretel_sql:4414be351e3cb0325e4c01cfcd30bf7c54fa8936380626256464d49cf0967d7b | task:4bfe093341fa2e9148b3b3746378a75360e1ad1041add250e13ba8b29f731ce6 | 83018c64a584c8a72f9ef73d108c601f690a0a89d09cefb571b96fd3ea3239a6 | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 25 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | What is the total amount donated to each program? | CREATE TABLE Programs (ProgramID INT, ProgramName VARCHAR(255)); INSERT INTO Programs (ProgramID, ProgramName) VALUES (1, 'Education'), (2, 'Health'), (3, 'Environment'); CREATE TABLE DonorsPrograms (DonorID INT, ProgramID INT); INSERT INTO DonorsPrograms (DonorID, ProgramID) VALUES (1, 1), (2, 1), (3, 2), (4, 2), (5, ... | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT Programs.ProgramName, SUM(Donations.DonationAmount) AS TotalDonatedAmount FROM Programs INNER JOIN DonorsPrograms ON Programs.ProgramID = DonorsPrograms.ProgramID INNER JOIN Donations ON DonorsPrograms.DonorID = Donations.DonorID GROUP BY Programs.ProgramName; | {"domain":"nonprofit operations","domain_description":"Donation records, program outcomes, volunteer engagement metrics, budget reports, and community impact assessments.","id":25,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"The SQL ... | b8d2a0ae5ef1c7dd5edd627ad573a4aafdf3a31b91b9cd70013e13bd8393f078 | gretel_sql:test:25 | multiple_joins | intermediate | Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 24 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:4bfe093341fa2e9148b3b3746378a75360e1ad1041add250e13ba8b29f731ce6 | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:multiple_joins",
"sql_structure:selects=1;joins=2;statements=1;advanced=none",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:4aa73a02c9655084807c496d82864ca7f306db6c50f2c66817d67a263aa2e261 | task:5cc93490e8a9c5ef0211c436d805090ef927f4a7dc93663bf8ff93a987de2e9b | 0f5c27e213b43e19c4d48df757464d6cffefce0212a2dd897097aa5e642a813f | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 109 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | Which defense contracts have the highest total value, and what are their respective values? | CREATE TABLE Defense_Contracts (Contract_ID INT, Contract_Name VARCHAR(255), Agency VARCHAR(255), Value DECIMAL(18,2)); INSERT INTO Defense_Contracts (Contract_ID, Contract_Name, Agency, Value) VALUES (1, 'Contract A', 'DOD', 5000000), (2, 'Contract B', 'DOJ', 6000000), (3, 'Contract C', 'DOD', 7000000), (4, 'Contract ... | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT Contract_Name, Value FROM (SELECT Contract_Name, Value, ROW_NUMBER() OVER (ORDER BY Value DESC) as Rank FROM Defense_Contracts) as Ranked_Contracts WHERE Rank <= 3; | {"domain":"defense industry","domain_description":"Defense contract data, military equipment maintenance, threat intelligence metrics, and veteran employment stats.","id":109,"sql_complexity":"window functions","sql_complexity_description":"window functions (e.g., ROW_NUMBER, LEAD, LAG, RANk, NTILE, PERCENT_RANK, etc.)... | f9fcd425b721e4863a77910f8524b34f3e16da0574d8dc2efdb021f9953b69e2 | gretel_sql:test:109 | window functions | intermediate | Estimated from source sql_complexity='window functions'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 108 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:5cc93490e8a9c5ef0211c436d805090ef927f4a7dc93663bf8ff93a987de2e9b | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:window functions",
"sql_structure:selects=2;joins=0;statements=1;advanced=OVER",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:ac5f0ae715b67600dbf8ce5a233b447ec91698ea080464b3217e0c407055e66d | task:a72b7dc3bb4ac92b6d07577365bf38326217f46518a2b52fe09248e5c50dcd1f | 67988782d10d9406a2a2393df036571e86c130af3fe9ee728da96a9a7404b688 | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 114 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | What is the average weight of cargo handled by vessels in the 'Bulk Carrier' type at each port? | CREATE TABLE ports (id INT, name VARCHAR(50), location VARCHAR(50), un_code VARCHAR(10)); CREATE TABLE vessels (id INT, name VARCHAR(50), type VARCHAR(50), year_built INT, port_id INT); CREATE TABLE cargo (id INT, description VARCHAR(50), weight FLOAT, port_id INT, vessel_id INT); CREATE VIEW vessel_cargo AS SELECT v.n... | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT p.name AS port_name, AVG(vc.weight) AS avg_weight FROM ports p JOIN vessels v ON p.id = v.port_id JOIN vessel_cargo vc ON v.name = vc.vessel_name WHERE v.type = 'Bulk Carrier' GROUP BY p.name; | {"domain":"ocean shipping","domain_description":"Detailed records on cargo handling, fleet management, port operations, and regulatory compliance in ocean shipping.","id":114,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"This query ca... | cbc90ad494e107f7e99a3250f4fac61ed8b7cec945d93ac89348c6bfce39a51d | gretel_sql:test:114 | multiple_joins | intermediate | Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 113 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:a72b7dc3bb4ac92b6d07577365bf38326217f46518a2b52fe09248e5c50dcd1f | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:multiple_joins",
"sql_structure:selects=1;joins=2;statements=1;advanced=none",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:5a3983d437ea7313659783169e052c2fcc99ee5002db9768a5d4e16f9aacba4d | task:668d9ed3342193821fcf71aa112035d491236e82ba7a3a323df7ed6b554e99ae | ac036d0a37566ef33e478c11e478c09f9035bdb571b0abd7403dff2cf3cbb4a7 | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 221 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | Which mental health conditions were treated most frequently in Canada during 2022? | CREATE TABLE patients (id INT, country VARCHAR(255)); CREATE TABLE treatments (id INT, patient_id INT, treatment_date DATE); CREATE TABLE conditions (id INT, patient_id INT, condition VARCHAR(255)); INSERT INTO patients (id, country) VALUES (1, 'Canada'), (2, 'Canada'); INSERT INTO treatments (id, patient_id, treatment... | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT conditions.condition, COUNT(conditions.condition) AS count FROM conditions JOIN patients ON conditions.patient_id = patients.id JOIN treatments ON patients.id = treatments.patient_id WHERE patients.country = 'Canada' AND treatments.treatment_date >= '2022-01-01' AND treatments.treatment_date < '2023-01-01' GROUP... | {"domain":"mental health","domain_description":"In-depth data on mental health conditions, treatment approaches, patient outcomes, and public awareness campaigns in mental health.","id":221,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation"... | 1ade5c12ba44207339a049f5bedfe5c77f6a4b6cf22d53ace16a487a72389f68 | gretel_sql:test:221 | multiple_joins | intermediate | Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 220 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:668d9ed3342193821fcf71aa112035d491236e82ba7a3a323df7ed6b554e99ae | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:multiple_joins",
"sql_structure:selects=1;joins=2;statements=1;advanced=none",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:7e20e548f8a175e0b0b2a6519f174bb728f2f13d78123e4bb5365200d2eae019 | task:daf495e597b052523601e658cddeb5b2c335fb3edce926095df46755a04cf665 | be005e0c93da36ce0eab4e13780dbef8eb25b6bf0a3c5fbdd4f0cd4e6759c093 | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 230 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | Find the mobile subscribers with consecutive speed drops greater than 25% for the last 3 months, ordered by subscription IDs. | CREATE TABLE mobile_usage_detailed (subscriber_id INT, month INT, speed FLOAT); INSERT INTO mobile_usage_detailed (subscriber_id, month, speed) VALUES (1, 1, 100), (1, 2, 80), (1, 3, 70), (2, 1, 200), (2, 2, 180), (2, 3, 160), (3, 1, 150), (3, 2, 130), (3, 3, 110); | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT subscriber_id, speed, month FROM (SELECT subscriber_id, speed, month, LAG(speed, 1) OVER (PARTITION BY subscriber_id ORDER BY month) as prev_speed, LAG(speed, 2) OVER (PARTITION BY subscriber_id ORDER BY month) as prev_prev_speed FROM mobile_usage_detailed) t WHERE t.speed < 0.75 * t.prev_speed AND t.speed < 0.7... | {"domain":"telecommunications","domain_description":"Mobile and broadband subscriber data, network infrastructure investments, customer usage patterns, and regulatory compliance information.","id":230,"sql_complexity":"window functions","sql_complexity_description":"window functions (e.g., ROW_NUMBER, LEAD, LAG, RANk, ... | 112b3a088b4574579d30ffc11195b74bc894217dda47629d876a18ec9141a0bb | gretel_sql:test:230 | window functions | intermediate | Estimated from source sql_complexity='window functions'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 229 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:daf495e597b052523601e658cddeb5b2c335fb3edce926095df46755a04cf665 | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:window functions",
"sql_structure:selects=2;joins=0;statements=1;advanced=OVER",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:6bb8af4984d6034a3e9b32ff1346042eb58c39ad879aa32c4a6f5709edd08185 | task:a3871b1db697742e405cdd20bd7f55ca3f34b6bf88d9b3c67c38b2a05b9d1d6b | 8f64fc8cfb445395c52a2b3f77859683574cead87f8b1cb12dc18c687671528a | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 520 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | What is the total number of electric vehicles sold in 'California' in the 'sales' schema? | CREATE TABLE sales_regions (id INT, name VARCHAR(50)); CREATE TABLE sales (id INT, region_id INT, vehicle_count INT); CREATE TABLE vehicles (id INT, type VARCHAR(50)); INSERT INTO sales_regions VALUES (1, 'California'); INSERT INTO sales VALUES (1, 1, 5000); INSERT INTO vehicles VALUES (1, 'electric'); | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT SUM(sales.vehicle_count) FROM sales INNER JOIN sales_regions ON sales.region_id = sales_regions.id INNER JOIN vehicles ON sales.id = vehicles.id WHERE vehicles.type = 'electric' AND sales_regions.name = 'California'; | {"domain":"automotive","domain_description":"Vehicle safety testing results, autonomous driving research data, electric vehicle adoption statistics, and auto show information.","id":520,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"Fi... | 9d38bf9cb28f33e9e5ea04e6a96c8e8d545757cf665a2ed0abde4792de84157a | gretel_sql:test:520 | multiple_joins | intermediate | Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 519 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:a3871b1db697742e405cdd20bd7f55ca3f34b6bf88d9b3c67c38b2a05b9d1d6b | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:multiple_joins",
"sql_structure:selects=1;joins=2;statements=1;advanced=none",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:87c4e12fe199ca347bf97c7e00082e4b70680965bef34d32f9654ae7ea2b33e9 | task:651a9a5de76a2827874f63c3aa44cfac2c2b54f44eda7c0fdf9ca8ecfd4e2bb8 | bab44501738294e19a13688cbababae07971f41a087ec3d4607480fcb6d33ab4 | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 526 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | What is the percentage of accidents for each aircraft model? | CREATE SCHEMA if not exists aerospace;CREATE TABLE if not exists aerospace.aircraft (id INT PRIMARY KEY, name VARCHAR(50), model VARCHAR(50), accidents INT); INSERT INTO aerospace.aircraft (id, name, model, accidents) VALUES (1, 'Boeing', '737', 3), (2, 'Boeing', '747', 2), (3, 'Airbus', 'A320', 6); | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT model, (SUM(accidents) OVER (PARTITION BY model) * 100.0 / (SELECT SUM(accidents) FROM aerospace.aircraft)) as accident_percentage FROM aerospace.aircraft; | {"domain":"aerospace","domain_description":"Aircraft manufacturing data, satellite deployment projects, flight safety records, and space exploration research.","id":526,"sql_complexity":"window functions","sql_complexity_description":"window functions (e.g., ROW_NUMBER, LEAD, LAG, RANk, NTILE, PERCENT_RANK, etc.) with ... | 38ab81904e9d975f69d0fa05ca547db4015474ec300712b5d55336f36abcbaf9 | gretel_sql:test:526 | window functions | intermediate | Estimated from source sql_complexity='window functions'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 525 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:651a9a5de76a2827874f63c3aa44cfac2c2b54f44eda7c0fdf9ca8ecfd4e2bb8 | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:window functions",
"sql_structure:selects=2;joins=0;statements=1;advanced=OVER",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:65cdf87508384e3bd00b6611e3293688bece5d4dcdc517cdb3d8539acd600c60 | task:cc75dc0b60e865f0c5430eacb08019bed52c3ef6a60b453a0b7dd5856be3e89a | 199f90e6943fe6c9fb106cf606f6027150300ef25d952cf88f84aff058ad05cc | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 530 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | What is the maximum feeding rate by feed type and farm size? | CREATE TABLE Feed ( id INT PRIMARY KEY, type VARCHAR(50) ); CREATE TABLE Farm ( id INT PRIMARY KEY, feed_id INT, size INT, FOREIGN KEY (feed_id) REFERENCES Feed(id) ); CREATE TABLE FeedingRate ( farm_id INT, feed_id INT, rate INT, FOREIGN KEY (farm_id) REFERENCES Farm(id), FOREIGN KEY (feed_id) REFERENCES Feed(id) ); | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT Feed.type, Farm.size, MAX(FeedingRate.rate) FROM Feed INNER JOIN FeedingRate ON Feed.id = FeedingRate.feed_id INNER JOIN Farm ON FeedingRate.farm_id = Farm.id GROUP BY Feed.type, Farm.size; | {"domain":"aquaculture","domain_description":"Aquatic farming data, fish stock management, ocean health metrics, and sustainable seafood trends.","id":530,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"The SQL query joins the Feed, Fee... | 1292890e22ff60e64aef70f7b4b88f852ef382ddbd049c8d67331dc343a48975 | gretel_sql:test:530 | multiple_joins | intermediate | Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 529 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:cc75dc0b60e865f0c5430eacb08019bed52c3ef6a60b453a0b7dd5856be3e89a | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:multiple_joins",
"sql_structure:selects=1;joins=2;statements=1;advanced=none",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:638fa07b6e6620e4ae8c7114ef15a6867b63cbb5fe0ba84ac8a902973e24397c | task:f0e68e38897af2d9b4357b3e6d6d88803b3a6b4a358a52c7947517ca280b9bd8 | f8fdfd74643d42004277a302f3d71f5a22dc70116726020732f517f2947af1dc | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 558 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | How many professional development courses were completed by teachers in the English department? | CREATE TABLE teachers (teacher_id INT, department_id INT, teacher_name VARCHAR(255)); INSERT INTO teachers VALUES (1, 1, 'Ms. Hernandez'); INSERT INTO teachers VALUES (2, 2, 'Mr. Johnson'); CREATE TABLE departments (department_id INT, department_name VARCHAR(255)); INSERT INTO departments VALUES (1, 'English'); INSERT ... | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT d.department_name, COUNT(c.course_id) FROM course_enrollment ce INNER JOIN teachers t ON ce.teacher_id = t.teacher_id INNER JOIN departments d ON t.department_id = d.department_id WHERE d.department_name = 'English'; | {"domain":"education","domain_description":"Education data on student mental health, teacher professional development, open pedagogy, and lifelong learning.","id":558,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"This query performs a... | 68e560f79fddabea2843cdd27372a25eb05682d3b88e8e22eab27cba4bb6672c | gretel_sql:test:558 | multiple_joins | intermediate | Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 557 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:f0e68e38897af2d9b4357b3e6d6d88803b3a6b4a358a52c7947517ca280b9bd8 | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:multiple_joins",
"sql_structure:selects=1;joins=2;statements=1;advanced=none",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:605eb1151f2bd663d78aa6d8914e4f1bbb6b3b94a4533f363f26b8eaf9a26853 | task:a1139ac892b43b22dcc4fcff41c14fdea6e7228520f879f048bed06ff6012a3c | 2341f0102a8336a0623162f960aeb4ff5f3b392216fee8894e6abc4f16e45828 | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 678 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | What is the average labor productivity by mine type in the past 12 months? | CREATE TABLE mine_labor_productivity (mine_type VARCHAR(255), productivity NUMERIC, measurement_date DATE); INSERT INTO mine_labor_productivity (mine_type, productivity, measurement_date) VALUES ('open_pit', 1234, '2021-08-01'), ('underground', 2345, '2021-08-01'), ('open_pit', 5432, '2021-07-01'), ('underground', 6789... | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT mine_type, AVG(productivity) as avg_productivity FROM (SELECT mine_type, productivity, measurement_date, ROW_NUMBER() OVER (PARTITION BY mine_type ORDER BY measurement_date DESC) as rn FROM mine_labor_productivity WHERE measurement_date >= DATEADD(month, -12, CURRENT_DATE)) t WHERE rn = 1 GROUP BY mine_type; | {"domain":"mining industry","domain_description":"Mineral extraction statistics, environmental impact assessments, labor productivity metrics, and geological survey information.","id":678,"sql_complexity":"window functions","sql_complexity_description":"window functions (e.g., ROW_NUMBER, LEAD, LAG, RANk, NTILE, PERCEN... | 587654e1d2bdf78b465061da0cf60f6d43f8b83a087923e2433c74c6335dd05a | gretel_sql:test:678 | window functions | intermediate | Estimated from source sql_complexity='window functions'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 677 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:a1139ac892b43b22dcc4fcff41c14fdea6e7228520f879f048bed06ff6012a3c | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:window functions",
"sql_structure:selects=2;joins=0;statements=1;advanced=OVER",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:1140fe7186b632d58ed096be6fcc60542e87a5d06d81be0050471bc91eeb2b54 | task:9ba448bbb897e7afcf0455f3aecc67154b6af21bdab77fb862718386fe8a75db | c5ccf013a7ed323d8af9f17fadd52a465d3eda3b76e42c9e20fc0ab35820f0e2 | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 682 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | What is the total number of articles written by each author in each region? | CREATE TABLE authors (id INT, name TEXT); CREATE TABLE regions (id INT, name TEXT); CREATE TABLE articles (id INT, title TEXT, content TEXT, author_id INT, region_id INT); INSERT INTO authors (id, name) VALUES (1, 'John Doe'), (2, 'Jane Smith'); INSERT INTO regions (id, name) VALUES (1, 'North'), (2, 'South'), (3, 'Eas... | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT authors.name, regions.name, COUNT(articles.id) FROM authors INNER JOIN articles ON authors.id = articles.author_id INNER JOIN regions ON regions.id = articles.region_id GROUP BY authors.name, regions.name; | {"domain":"journalism","domain_description":"News reporting data, audience demographics, media ethics information, and investigative journalism projects.","id":682,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"This SQL query retrieves... | 595530aa682c3b5c863053849e0c392fab3ca7cf706fce9f209e6ed737c60080 | gretel_sql:test:682 | multiple_joins | intermediate | Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 681 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:9ba448bbb897e7afcf0455f3aecc67154b6af21bdab77fb862718386fe8a75db | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:multiple_joins",
"sql_structure:selects=1;joins=2;statements=1;advanced=none",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:2cb817c0878d4ceca8e51cbe2251ddaab269ba243af0133f8df8b97c10542591 | task:9816b122d966001a0e963d05552c2c81c94d9e51bff1cfcb3eaae574f60afeaf | 9703fe06ce5ef646effd3caba6dbb2eeffc909cdec5569e5f71325d866cc4eb3 | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 735 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | Show the total assets under management (AUM) for each investment strategy. | CREATE TABLE investment_strategies (strategy_id INT, strategy VARCHAR(20)); INSERT INTO investment_strategies (strategy_id, strategy) VALUES (1, 'Conservative'), (2, 'Moderate'), (3, 'Aggressive'); CREATE TABLE client_strategy (client_id INT, strategy_id INT); INSERT INTO client_strategy (client_id, strategy_id) VALUES... | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT cs.strategy, SUM(value) AS total_aum FROM client_strategy cs JOIN clients c ON cs.client_id = c.client_id JOIN assets a ON c.client_id = a.client_id GROUP BY cs.strategy; | {"domain":"financial services","domain_description":"Detailed financial data including investment strategies, risk management, fraud detection, customer analytics, and regulatory compliance.","id":735,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_e... | c69a1cb177a81cee54cae23948e51d766b1dbc072cb1187ddbecb3bb3a8a5986 | gretel_sql:test:735 | multiple_joins | intermediate | Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 734 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:9816b122d966001a0e963d05552c2c81c94d9e51bff1cfcb3eaae574f60afeaf | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:multiple_joins",
"sql_structure:selects=1;joins=2;statements=1;advanced=none",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:7459d61c01da813f6ad9cbb9b4229d0cf7ba56e8dfa410d8e4a34583baa08390 | task:70f33e41fbcef91a27881f142726199b85e75bbb40417c18833e85b72ba12a31 | aaa07784f839ac7d7cfb0da7045e5af9beb07288fed3355722086d4b68ffae18 | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 746 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | Who has the highest number of wins as a coach for each team in a season? | CREATE TABLE Coach (CoachID int, CoachName varchar(50), TeamID int); CREATE TABLE Match (MatchID int, HomeTeamID int, AwayTeamID int, HomeTeamResult varchar(5)); INSERT INTO Coach (CoachID, CoachName, TeamID) VALUES (1, 'Jose Mourinho', 1), (2, 'Pep Guardiola', 1), (3, 'Jurgen Klopp', 2), (4, 'Mauricio Pochettino', 2)... | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT c.TeamID, c.CoachName, COUNT(CASE WHEN h.HomeTeamResult = 'Win' THEN 1 END) + COUNT(CASE WHEN a.HomeTeamResult = 'Win' THEN 1 END) AS Wins FROM Coach c LEFT JOIN Match h ON c.TeamID = h.HomeTeamID AND h.HomeTeamResult = 'Win' LEFT JOIN Match a ON c.TeamID = a.AwayTeamID AND a.HomeTeamResult = 'Win' GROUP BY c.Te... | {"domain":"sports","domain_description":"Extensive data on athlete performance, team management, fan engagement, facility operations, and event planning in sports.","id":746,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"We calculate t... | 63fc7c03b5b1ef1e6264594bd37f803866784b1511508639b707998eea5cb854 | gretel_sql:test:746 | multiple_joins | intermediate | Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 745 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:70f33e41fbcef91a27881f142726199b85e75bbb40417c18833e85b72ba12a31 | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:multiple_joins",
"sql_structure:selects=1;joins=2;statements=1;advanced=none",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:01d48c68838101488159909639607f2b3ef488591cb373e254b64b2df4aa9c31 | task:bc95ae27ab56e584f96e47115c558f8119bd5a8b666d2592292d0451cbaa4462 | 6063ff0c84448751a65d95d66772e00888e5ae9eda1e539a8d266a962c5728f8 | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 762 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | What is the name and location of the top 3 most donated heritage sites? | CREATE TABLE HeritageSites (SiteID int, Name varchar(100), Location varchar(100), TotalDonations decimal(10,2)); INSERT INTO HeritageSites (SiteID, Name, Location, TotalDonations) VALUES (1, 'Machu Picchu', 'Peru', 500000.00), (2, 'Great Wall', 'China', 700000.00), (3, 'Petra', 'Jordan', 600000.00); | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT Name, Location FROM (SELECT Name, Location, ROW_NUMBER() OVER (ORDER BY TotalDonations DESC) as rn FROM HeritageSites) t WHERE rn <= 3; | {"domain":"cultural preservation","domain_description":"In-depth data on heritage sites, traditional arts, language preservation, and community engagement in cultural preservation.","id":762,"sql_complexity":"window functions","sql_complexity_description":"window functions (e.g., ROW_NUMBER, LEAD, LAG, RANk, NTILE, PER... | dc12336decfa75a4d789ecda6789b4afbc64107f621a9e1c38da7b19cc8eb9ed | gretel_sql:test:762 | window functions | intermediate | Estimated from source sql_complexity='window functions'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 761 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:bc95ae27ab56e584f96e47115c558f8119bd5a8b666d2592292d0451cbaa4462 | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:window functions",
"sql_structure:selects=2;joins=0;statements=1;advanced=OVER",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:5a8aef0121a287c4b608a17e724ff816cd28d47ed00cd80665509e2f10a03707 | task:9aac2c920b3e6b48701b3a0fb8d316135bb4e5b060787e428ce3ac3eaf5a54e9 | 2c473044d22c2b9d5ab5eccfbf0b27573300dc08a81f65e2fba98229985c57f6 | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 784 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | List the top 2 countries with the highest water consumption in the current month. | CREATE TABLE water_consumption (country VARCHAR(255), consumption FLOAT, date DATE); INSERT INTO water_consumption (country, consumption, date) VALUES ('Brazil', 20000, '2022-05-01'); INSERT INTO water_consumption (country, consumption, date) VALUES ('Egypt', 25000, '2022-05-01'); | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT country, consumption FROM (SELECT country, consumption, ROW_NUMBER() OVER (ORDER BY consumption DESC) as rank FROM water_consumption WHERE date >= '2022-05-01' GROUP BY country, consumption) subquery WHERE rank <= 2; | {"domain":"water resources","domain_description":"Water usage metrics, drought impact assessments, wastewater treatment data, and water conservation initiatives.","id":784,"sql_complexity":"window functions","sql_complexity_description":"window functions (e.g., ROW_NUMBER, LEAD, LAG, RANk, NTILE, PERCENT_RANK, etc.) wi... | dd3f1a80e263ba18813c8c88581f6d56f165958216c1b8f5667969b39b4eadc3 | gretel_sql:test:784 | window functions | intermediate | Estimated from source sql_complexity='window functions'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 783 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:9aac2c920b3e6b48701b3a0fb8d316135bb4e5b060787e428ce3ac3eaf5a54e9 | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:window functions",
"sql_structure:selects=2;joins=0;statements=1;advanced=OVER",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:cf559e5c2bae52d4272c5eb38a7e2167eff926c33014044282561ff7c31bab38 | task:a38fc41269ee25b90f01c3a1d518e1f7d2ac103125ed9f6c2662086d957ce00c | 31b14284a2446b7f5887d374afa6710847564bf3eb09d99d233307779b8f9d5e | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 812 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | What is the average salary of 'engineer' workers in each factory? | CREATE TABLE factories (factory_id INT, factory_name VARCHAR(20)); INSERT INTO factories VALUES (1, 'Factory X'), (2, 'Factory Y'), (3, 'Factory Z'); CREATE TABLE roles (role_id INT, role_name VARCHAR(20)); INSERT INTO roles VALUES (1, 'engineer'), (2, 'manager'), (3, 'assistant'); CREATE TABLE workers (worker_id INT, ... | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT f.factory_name, AVG(salary) FROM workers w INNER JOIN factories f ON w.factory_id = f.factory_id INNER JOIN roles r ON w.role_id = r.role_id WHERE r.role_name = 'engineer' GROUP BY f.factory_name; | {"domain":"manufacturing","domain_description":"Detailed records on ethical manufacturing, circular economy, workforce development, and industry 4.0.","id":812,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"We join the 'workers', 'fact... | 32729a02f28933aa612841bfc8d1614eebf512675b185150d8c3e7dd81e6c641 | gretel_sql:test:812 | multiple_joins | intermediate | Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 811 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:a38fc41269ee25b90f01c3a1d518e1f7d2ac103125ed9f6c2662086d957ce00c | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:multiple_joins",
"sql_structure:selects=1;joins=2;statements=1;advanced=none",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:465e31d4932a5b02f83c2cf7b9f54f2a2196e23eb394402f6de197940f6ec0ee | task:e5574d8b9ccbfa0e6932d295d20b7579909c7d6875a9c85bb97894b516ff0538 | b032f9a26c6c0cedb5604b01ab4d101f953894fb7ce4811deec6cda5ec1ab148 | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 827 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | What is the average price of free-range eggs per store? | CREATE TABLE Stores (store_id INT, store_name VARCHAR(255)); CREATE TABLE Products (product_id INT, product_name VARCHAR(255), is_free_range BOOLEAN, price INT); CREATE TABLE Inventory (store_id INT, product_id INT, quantity INT); | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT s.store_name, AVG(p.price) as avg_price FROM Inventory i JOIN Stores s ON i.store_id = s.store_id JOIN Products p ON i.product_id = p.product_id WHERE p.is_free_range = TRUE AND p.product_category = 'egg' GROUP BY s.store_name; | {"domain":"food industry","domain_description":"Food safety records, nutrition data, supply chain transparency, and sustainable food trends.","id":827,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"This query joins the Stores, Products... | 9ab947c4d6bc9c13812d17392c9bff770e34780b806291d6e034a1a082700f0d | gretel_sql:test:827 | multiple_joins | intermediate | Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 826 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:e5574d8b9ccbfa0e6932d295d20b7579909c7d6875a9c85bb97894b516ff0538 | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:multiple_joins",
"sql_structure:selects=1;joins=2;statements=1;advanced=none",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:5c30989fbb666c1be52f42e17ae1853c362200c2c002627ee8914eca4859c8fa | task:14368e576c6c19a25ef9a785dfa5cb7a3c23b2244503755d1b4eed6cbad9a939 | ea117b6e90849d900c0ffc580e759d7119323088f4f77bd78e4d83848387abf1 | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 895 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | How many volunteers with 'Medical' skills were assigned before a volunteer with 'Engineering' skills? | CREATE TABLE volunteers_ext (id INT, name VARCHAR(50), age INT, gender VARCHAR(10), skill VARCHAR(50), assignment_date DATE, end_date DATE); INSERT INTO volunteers_ext (id, name, age, gender, skill, assignment_date, end_date) VALUES (1, 'David', 25, 'Male', 'Medical', '2022-06-01', '2022-09-30'), (2, 'Emma', 30, 'Femal... | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT COUNT(*) FROM (SELECT skill, assignment_date, LAG(skill) OVER (ORDER BY assignment_date) AS prev_skill FROM volunteers_ext WHERE skill = 'Medical') t WHERE prev_skill = 'Engineering'; | {"domain":"humanitarian aid","domain_description":"Extensive data on disaster response, refugee support, community development, and advocacy in humanitarian aid.","id":895,"sql_complexity":"window functions","sql_complexity_description":"window functions (e.g., ROW_NUMBER, LEAD, LAG, RANk, NTILE, PERCENT_RANK, etc.) wi... | b4316fb2f1c4cbf3f49ec9be60740aec4eec75ef6cdb72203c6c1c3cbe66c59a | gretel_sql:test:895 | window functions | intermediate | Estimated from source sql_complexity='window functions'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 894 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:14368e576c6c19a25ef9a785dfa5cb7a3c23b2244503755d1b4eed6cbad9a939 | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:window functions",
"sql_structure:selects=2;joins=0;statements=1;advanced=OVER",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:e59724c100af36653e60a79bf51e03365c936fa2e34be9f3358770f744c647a0 | task:192a1ff1c67b2582f4c2bfbfe2b8a6d4e3361b1ee963fabc1f0bb516ff43483f | 9b1643b005f623c0e0cfb0687a7fb79c34fc2fc9ca0b79c7cea94cfe46b32d50 | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 926 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | What is the average age of players who play VR games and their total spending on games? | CREATE TABLE players (id INT, name VARCHAR(50), age INT, country VARCHAR(50)); INSERT INTO players (id, name, age, country) VALUES (1, 'John Doe', 25, 'USA'), (2, 'Jane Smith', 30, 'Canada'); CREATE TABLE games (id INT, name VARCHAR(50), type VARCHAR(50), price DECIMAL(5,2)); INSERT INTO games (id, name, type, price) V... | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT AVG(players.age), SUM(games.price) FROM players INNER JOIN player_games ON players.id = player_games.player_id INNER JOIN games ON player_games.game_id = games.id WHERE games.type = 'VR'; | {"domain":"gaming technology","domain_description":"Player demographics, game design data, esports event information, and virtual reality technology adoption.","id":926,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"First, we join the ... | ae7f531a5f3bcddad69b00e3a3047e65715ec65e2d71f77ed893bcc4a73dcf0b | gretel_sql:test:926 | multiple_joins | intermediate | Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 925 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:192a1ff1c67b2582f4c2bfbfe2b8a6d4e3361b1ee963fabc1f0bb516ff43483f | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:multiple_joins",
"sql_structure:selects=1;joins=2;statements=1;advanced=none",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:66f2d9803d8a04db0de3ff55a25b95ab916be000a2c54667d4c4c704de5a640b | task:f35523c79d36444f3d64cd5403cdfa72c5c0c4530c2d32d186ec143f3f70dae0 | 4eb7871388f934fc562f451545b5fd3c1213a1c386738efd15926f514e332632 | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 959 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | What is the total salary paid to construction workers who worked on sustainable building projects in Washington? | CREATE TABLE ConstructionLaborStatistics (id INT, name VARCHAR(50), job VARCHAR(50), salary INT); INSERT INTO ConstructionLaborStatistics VALUES (1, 'Charles Doe', 'Carpenter', 50000); INSERT INTO ConstructionLaborStatistics VALUES (2, 'Diana Smith', 'Electrician', 60000); CREATE TABLE BuildingTypes (id INT, building_t... | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT SUM(cls.salary) FROM ConstructionLaborStatistics cls JOIN WorkerBuildings wb ON cls.id = wb.worker_id JOIN BuildingTypes bt ON wb.building_id = bt.id WHERE bt.building_type = 'Sustainable' AND state = 'Washington'; | {"domain":"construction","domain_description":"Building permit data, construction labor statistics, project timeline information, and sustainable building practices.","id":959,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"We find the ... | 46e6728bd1a85e027a69d5fbe73a362e9a4489b37fee9efb6110ea8af635a8d6 | gretel_sql:test:959 | multiple_joins | intermediate | Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 958 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:f35523c79d36444f3d64cd5403cdfa72c5c0c4530c2d32d186ec143f3f70dae0 | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:multiple_joins",
"sql_structure:selects=1;joins=2;statements=1;advanced=none",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:c2c8897bca9564cfd28a876124d3931163624130d248f21cfb497c72edefc231 | task:e7b4b5f2110113e5ecdac39ac67ff02aacbca58c5363f23bd1725f7dd2776d6b | 64847034182786e5eabd96e3518f7a07e67c7aaaf5ecf761d788eb10781dd8e8 | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 978 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | What is the production rate for the well with the highest production rate? | CREATE TABLE wells (well_id INT, well_type VARCHAR(10), location VARCHAR(20), production_rate FLOAT); INSERT INTO wells (well_id, well_type, location, production_rate) VALUES (1, 'offshore', 'Gulf of Mexico', 1000), (2, 'onshore', 'Texas', 800), (3, 'offshore', 'North Sea', 1200); | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT production_rate FROM (SELECT well_id, well_type, location, production_rate, ROW_NUMBER() OVER (ORDER BY production_rate DESC) rn FROM wells) t WHERE rn = 1; | {"domain":"oil and gas","domain_description":"Exploration data, production figures, infrastructure development, and market trends.","id":978,"sql_complexity":"window functions","sql_complexity_description":"window functions (e.g., ROW_NUMBER, LEAD, LAG, RANk, NTILE, PERCENT_RANK, etc.) with partitioning and ordering","... | 2a5cfa63434192e8899a6d24c32b5e41eb96e71716ba44dab4f8f5c7946f172e | gretel_sql:test:978 | window functions | intermediate | Estimated from source sql_complexity='window functions'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 977 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:e7b4b5f2110113e5ecdac39ac67ff02aacbca58c5363f23bd1725f7dd2776d6b | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:window functions",
"sql_structure:selects=2;joins=0;statements=1;advanced=OVER",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:57dd8407cbca173b558f00ef01f253e505f13cdf433ee055ae0e5513271eba2b | task:54457ae6a4f1c948787157d5269384e546075af95d8e601a04c1b46621dcab9b | e76a2d0a6305cc0f378b6f90f7fee120d8128a87c44970567c59aecb3ccbf083 | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 1009 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | Find the number of IoT sensors installed in each farm that use Sprinkler irrigation. | CREATE TABLE IoT_Sensors (id INT, sensor_type VARCHAR(50), Farm_id INT); INSERT INTO IoT_Sensors (id, sensor_type, Farm_id) VALUES (1, 'Soil Moisture', 1), (2, 'Temperature', 1), (3, 'Humidity', 2); CREATE TABLE Irrigation (id INT, Farm_id INT, irrigation_type VARCHAR(50), duration INT); INSERT INTO Irrigation (id, Far... | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT f.id, COUNT(s.id) FROM Farmers f JOIN Irrigation i ON f.id = i.Farm_id JOIN IoT_Sensors s ON f.id = s.Farm_id WHERE i.irrigation_type = 'Sprinkler' GROUP BY f.id; | {"domain":"precision agriculture","domain_description":"Precision farming data, satellite imagery analysis, IoT sensor metrics, and agricultural automation trends.","id":1009,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"This query jo... | 18e0c6a04e73d57937e51b8f42dce495bada1fcce20ed6453d850d566f9d2c13 | gretel_sql:test:1009 | multiple_joins | intermediate | Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 1,008 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:54457ae6a4f1c948787157d5269384e546075af95d8e601a04c1b46621dcab9b | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:multiple_joins",
"sql_structure:selects=1;joins=2;statements=1;advanced=none",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:a3a6f3a5ff71f557ad8867b5153fb65fd983b8aceba08e05ee2c1141f7b135b5 | task:f979f8ef3f1bfee4bc4e9af1863d72ea3c95c88873dc82faf56a9a6cc0bc2e44 | b5f416338ad2926d1bdffdfb727cf11827a3ed3e1588d73aed0ea6e2ab062e19 | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 1033 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | What is the average dissolved oxygen level for each species in our fish farms? | CREATE TABLE fish_farms (id INT, name TEXT, location TEXT, water_type TEXT); INSERT INTO fish_farms (id, name, location, water_type) VALUES (1, 'Farm A', 'Seattle', 'Saltwater'); INSERT INTO fish_farms (id, name, location, water_type) VALUES (2, 'Farm B', 'Portland', 'Freshwater'); CREATE TABLE fish_species (id INT, na... | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT fs.name AS species_name, AVG(av.dissolved_oxygen) AS avg_dissolved_oxygen FROM fish_inventory fi JOIN fish_farms ff ON fi.fish_farm_id = ff.id JOIN fish_species fs ON fi.fish_species_id = fs.id JOIN (SELECT fish_species_id, AVG(dissolved_oxygen) AS dissolved_oxygen FROM water_quality GROUP BY fish_species_id) av... | {"domain":"aquaculture","domain_description":"Aquatic farming data, fish stock management, ocean health metrics, and sustainable seafood trends.","id":1033,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"This query joins the fish_farms,... | 887ab0c78c632b519f1071f7119b01202ba110c11be6cb56df2fa497de06fbcf | gretel_sql:test:1033 | multiple_joins | intermediate | Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 1,032 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:f979f8ef3f1bfee4bc4e9af1863d72ea3c95c88873dc82faf56a9a6cc0bc2e44 | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:multiple_joins",
"sql_structure:selects=2;joins=3;statements=1;advanced=none",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:f9b9d0cbd6b8f356f1fdb0252b797cfb5cdefc97d635773b95d85c2688076955 | task:418483412367aad348001ae1e866b409432f34c35a911db3427878c1b44ea09e | 647fc6fd9339004874dcc92dff31fca841adc36ed2dd74f99df831b6177ea05d | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 1126 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | How many innovations have been made in the African region since 2016? | CREATE TABLE region (id INT, region VARCHAR(50)); INSERT INTO region (id, region) VALUES (1, 'North America'); INSERT INTO region (id, region) VALUES (2, 'Europe'); INSERT INTO region (id, region) VALUES (3, 'Africa'); CREATE TABLE innovation_region (id INT, innovation_id INT, region_id INT); INSERT INTO innovation_reg... | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT COUNT(*) FROM innovation i INNER JOIN innovation_region ir ON i.id = ir.innovation_id INNER JOIN region r ON ir.region_id = r.id WHERE r.region = 'Africa' AND i.year >= 2016; | {"domain":"startups","domain_description":"Company founding data, funding records, diversity metrics, and innovation trends.","id":1126,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"This query joins the innovation, innovation_region, ... | e583dfd8a9b9149ee7b6055b732bfb8cb6e5a343d36d60b42ce759b947dc3ef2 | gretel_sql:test:1126 | multiple_joins | intermediate | Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 1,125 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:418483412367aad348001ae1e866b409432f34c35a911db3427878c1b44ea09e | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:multiple_joins",
"sql_structure:selects=1;joins=2;statements=1;advanced=none",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:c69e12dbc5c450a9368a5b805663e84ea5abaa96216b9e58c14daa243739ab5d | task:dc854f72eecca7b38213372fe5935fcae76b70f22149574bae5d2ca90ad6efd1 | cb5f90d914b6709023a07b1e55e3f7b3166b063f4fa034702bfeb357a361dc8c | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 1178 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | List the booking dates and hotel names for all OTA bookings where the hotel has implemented at least one AI-powered solution. | CREATE TABLE otas (ota_id INT, booking_date DATE, hotel_id INT); CREATE TABLE hotels (hotel_id INT, hotel_name TEXT, region TEXT); CREATE TABLE ai_solutions (solution_id INT, hotel_id INT, implemented_date DATE); INSERT INTO hotels (hotel_id, hotel_name, region) VALUES (1, 'Beach Retreat', 'Americas'); INSERT INTO ai_s... | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT otas.booking_date, hotels.hotel_name FROM otas INNER JOIN hotels ON otas.hotel_id = hotels.hotel_id INNER JOIN ai_solutions ON hotels.hotel_id = ai_solutions.hotel_id GROUP BY otas.booking_date, hotels.hotel_name HAVING COUNT(DISTINCT ai_solutions.solution_id) >= 1; | {"domain":"hospitality technology","domain_description":"Hotel tech adoption metrics, online travel agency data, virtual tour engagement stats, and hospitality AI trends.","id":1178,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"This S... | cd33d91e040fb59fb6292d32bc51f67aa98f3b7ada08a6a7032bdeb02f805161 | gretel_sql:test:1178 | multiple_joins | intermediate | Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 1,177 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:dc854f72eecca7b38213372fe5935fcae76b70f22149574bae5d2ca90ad6efd1 | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:multiple_joins",
"sql_structure:selects=1;joins=2;statements=1;advanced=none",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:8091ef2ad6bfd5f179cf52d7d9c5784b9752f35a18d8f92632ddfd3e7bcaadb2 | task:b71d9e9e836386899ba8fab23e0e852d6ef781e10c294c226d79df57b32a5b48 | e05fc109dd77313ecbe78a07430ab46389c6cd1b4f8e158edbcb3499b83784e7 | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 1187 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | Who are the top 3 authors with the highest number of articles published in The Guardian? | CREATE TABLE authors (id INT, name VARCHAR(100), publisher VARCHAR(50)); CREATE TABLE articles_authors (article_id INT, author_id INT); INSERT INTO authors (id, name, publisher) VALUES (1, 'Author1', 'The Guardian'), (2, 'Author2', 'The Guardian'), (3, 'Author3', 'The Guardian'); INSERT INTO articles_authors (article_i... | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT a.name, COUNT(aa.article_id) AS articles_count FROM authors a JOIN articles_authors aa ON a.id = aa.author_id JOIN articles ar ON aa.article_id = ar.id WHERE ar.publisher = 'The Guardian' GROUP BY a.name ORDER BY articles_count DESC LIMIT 3; | {"domain":"media","domain_description":"Media data on content diversity, media literacy, disinformation detection, and media representation.","id":1187,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"Find the top 3 authors with the high... | 79566f440acc5280d1fe46233ad6ac4a5057421fb1ba103e44a2974000552a8d | gretel_sql:test:1187 | multiple_joins | intermediate | Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 1,186 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:b71d9e9e836386899ba8fab23e0e852d6ef781e10c294c226d79df57b32a5b48 | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:multiple_joins",
"sql_structure:selects=1;joins=2;statements=1;advanced=none",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:5ca9ec89e334c46262dc057ae5b3672513875e655f83e66402db9b6776e583a3 | task:939eb10a689dc7ee5910af3e2631fc6fe324d187ca4b02e46aeb86cdde707339 | fc1477d1e3b668dd55c4e4c79eac8415f9a19fce4d3f35ba6dbb9b08a67ab976 | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 1286 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | Identify the top 3 countries with the highest percentage of attendees | CREATE TABLE attendee_info (attendee_id INT, country VARCHAR(20)); INSERT INTO attendee_info (attendee_id, country) VALUES (1, 'USA'), (2, 'Canada'), (3, 'Mexico'), (4, 'USA'), (5, 'Brazil'), (6, 'USA'); | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT country, (COUNT(attendee_id) OVER (PARTITION BY country) * 100.0 / (SELECT COUNT(attendee_id) FROM attendee_info)) AS percentage FROM attendee_info GROUP BY country ORDER BY percentage DESC LIMIT 3; | {"domain":"arts and culture","domain_description":"Audience demographics, event attendance, program impact, and funding sources.","id":1286,"sql_complexity":"window functions","sql_complexity_description":"window functions (e.g., ROW_NUMBER, LEAD, LAG, RANk, NTILE, PERCENT_RANK, etc.) with partitioning and ordering","s... | a3b27dcf054f2409ca3ef0b1252c1db16b374ee71f2b21f75eb45921ee5e4d6a | gretel_sql:test:1286 | window functions | intermediate | Estimated from source sql_complexity='window functions'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 1,285 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:939eb10a689dc7ee5910af3e2631fc6fe324d187ca4b02e46aeb86cdde707339 | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:window functions",
"sql_structure:selects=2;joins=0;statements=1;advanced=OVER",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:8b6199f7be509bfdb02b69909153a02dbada923919ff778c44fc721fdfbc08e4 | task:dd65ae24ccfeaf9deb50355ab01bd74336df6c7793e18da45ca0a997029cb989 | 029152ac5a4f73586513e210e1df3b68b88ad52b6db08a2fc1a4e85033d4646b | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 1301 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | What is the total quantity of corn sold by farmers in 'Summerfield'? | CREATE TABLE farmers (id INT, name VARCHAR(50), location VARCHAR(50), crops VARCHAR(50)); CREATE TABLE crops (id INT, name VARCHAR(50), yield INT); CREATE TABLE sales (id INT, farmer_id INT, crop_name VARCHAR(50), quantity INT, price DECIMAL(5,2)); INSERT INTO farmers VALUES (1, 'Jane Doe', 'Summerfield', 'Corn'); INSE... | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT SUM(quantity) FROM sales INNER JOIN farmers ON sales.farmer_id = farmers.id INNER JOIN crops ON sales.crop_name = crops.name WHERE farmers.location = 'Summerfield' AND crops.name = 'Corn'; | {"domain":"agriculture","domain_description":"Comprehensive data on agroecology, food justice, indigenous food systems, and urban agriculture.","id":1301,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"This query performs an inner join ... | 86c05ef3c9666eccccdbc317eef1dc90fe593d5303f2cc9469be06b5f4ef38c1 | gretel_sql:test:1301 | multiple_joins | intermediate | Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 1,300 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:dd65ae24ccfeaf9deb50355ab01bd74336df6c7793e18da45ca0a997029cb989 | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:multiple_joins",
"sql_structure:selects=1;joins=2;statements=1;advanced=none",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:87e28bf7631d6664e4f85539ca2bb5b6fd275199a3b872fbe5cdd24a2b15cb76 | task:3339f470b71c58870339ba40b989859de73c49ab732efa8ee0a6a5c9a079ec6c | c136c6516cc57d182ac637418f3cb3abeef5347f144dec6ee9e50ca3c9b3dfd6 | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 1303 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | What is the average total value of transactions for the top 3 digital assets in the 'Binance Smart Chain' network? | CREATE TABLE binance_transactions (asset_name VARCHAR(20), network VARCHAR(20), transactions_value FLOAT); INSERT INTO binance_transactions (asset_name, network, transactions_value) VALUES ('BNB', 'Binance Smart Chain', 200000), ('ETH', 'Binance Smart Chain', 300000), ('CAKE', 'Binance Smart Chain', 400000); | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT asset_name, network, AVG(transactions_value) FROM binance_transactions WHERE network = 'Binance Smart Chain' AND asset_name IN (SELECT asset_name FROM (SELECT asset_name, ROW_NUMBER() OVER (ORDER BY transactions_value DESC) as rn FROM binance_transactions WHERE network = 'Binance Smart Chain') x WHERE rn <= 3) G... | {"domain":"blockchain","domain_description":"Comprehensive data on smart contracts, decentralized applications, digital assets, and regulatory frameworks in blockchain.","id":1303,"sql_complexity":"window functions","sql_complexity_description":"window functions (e.g., ROW_NUMBER, LEAD, LAG, RANk, NTILE, PERCENT_RANK, ... | 1435f1b77e5a316065c917773514494a3324c255d991599c04e32fa67f4f0af8 | gretel_sql:test:1303 | window functions | intermediate | Estimated from source sql_complexity='window functions'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 1,302 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:3339f470b71c58870339ba40b989859de73c49ab732efa8ee0a6a5c9a079ec6c | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:window functions",
"sql_structure:selects=3;joins=0;statements=1;advanced=OVER",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:4cded537725dc5ab510341218ef158624d6801f127e0e912c1b581a1a396cf46 | task:f817c483a186b0c73ce39caa88db2575b17606e7e4845a17c6fd7ccc13acab63 | 50d7ad974cb3ec769e37adeafb1ff97c1fd59ed1a9d7dabfcd0a6cb6b7195069 | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 1316 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | List the ports and their average cargo weight for company "HarborLink" in Q2 2017 and Q3 2017. | CREATE TABLE company (id INT, name VARCHAR(255)); INSERT INTO company (id, name) VALUES (1, 'HarborLink'); CREATE TABLE port (id INT, name VARCHAR(255)); CREATE TABLE cargo (id INT, port_id INT, company_id INT, weight INT, quarter INT); INSERT INTO port (id, name) VALUES (1, 'PortA'), (2, 'PortB'), (3, 'PortC'); INSERT... | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT port.name, AVG(cargo.weight) FROM port INNER JOIN cargo ON port.id = cargo.port_id AND cargo.quarter IN (2, 3) INNER JOIN company ON cargo.company_id = company.id WHERE company.name = 'HarborLink' GROUP BY port.name; | {"domain":"ocean shipping","domain_description":"Detailed records on cargo handling, fleet management, port operations, and regulatory compliance in ocean shipping.","id":1316,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"This query c... | bbedad588f3a3452596d0c589c32a4ff72bc43fbeff7e4dfd24821c77866a9c0 | gretel_sql:test:1316 | multiple_joins | intermediate | Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 1,315 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:f817c483a186b0c73ce39caa88db2575b17606e7e4845a17c6fd7ccc13acab63 | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:multiple_joins",
"sql_structure:selects=1;joins=2;statements=1;advanced=none",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:762a41d3d49035e6cdbda94976579a01299870ba0632587b6a5fa07369ca151c | task:cb9973e895539e119850c9d0aa0f408b8d18a46b81d99ae55ea5b246f03b7253 | a0bc3b8291877c9331c13e939f06610a929cea7e3b75ee1b38dedc1715d32ad3 | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 1321 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | List the top 2 countries with the highest average artifact weight, along with the year and total weight of those artifacts. | CREATE TABLE ExcavationSites (SiteID INT, Country VARCHAR(50), Year INT, ArtifactWeight FLOAT); INSERT INTO ExcavationSites (SiteID, Country, Year, ArtifactWeight) VALUES (1, 'USA', 2020, 23.5), (2, 'Mexico', 2020, 14.2), (3, 'USA', 2019, 34.8), (4, 'Canada', 2019, 45.6), (5, 'Canada', 2019, 56.7); | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT Country, Year, SUM(ArtifactWeight) AS TotalWeight, AVG(ArtifactWeight) OVER (PARTITION BY Country) AS AvgWeight FROM (SELECT Country, Year, ArtifactWeight, ROW_NUMBER() OVER (PARTITION BY Country ORDER BY ArtifactWeight DESC) rn FROM ExcavationSites) x WHERE rn <= 2 GROUP BY Country, Year; | {"domain":"archeology","domain_description":"Detailed records on excavation sites, artifact analysis, historical context, and public outreach in archeology.","id":1321,"sql_complexity":"window functions","sql_complexity_description":"window functions (e.g., ROW_NUMBER, LEAD, LAG, RANk, NTILE, PERCENT_RANK, etc.) with p... | c696ef06f37dac90b076553b40e0c07a4a76d6aafa30e18a01af9122c2737db7 | gretel_sql:test:1321 | window functions | intermediate | Estimated from source sql_complexity='window functions'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 1,320 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:cb9973e895539e119850c9d0aa0f408b8d18a46b81d99ae55ea5b246f03b7253 | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:window functions",
"sql_structure:selects=2;joins=0;statements=1;advanced=OVER",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:e3ecd99a7bf2bd67b72a76d54c988e28c13ee5c918433fc1b23459c1acc48625 | task:edc83842f8fafbd94b772ba78b614443a1ffe8907d292b2509d09048c19e99a6 | e598fa238ed773468230da7058f1d7206f032a4700f66ebaec9338da3cf8801b | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 1367 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | Calculate the percentage of vessels in each ocean basin that have outdated engine technology. | CREATE TABLE fleet_information (id INT, vessel_name VARCHAR(255), ocean_basin VARCHAR(255), engine_technology DATE); INSERT INTO fleet_information (id, vessel_name, ocean_basin, engine_technology) VALUES (1, 'Ocean Titan', 'Atlantic', '2000-01-01'), (2, 'Sea Explorer', 'Pacific', '2010-01-01'); | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT ocean_basin, PERCENTAGE_RANK() OVER (ORDER BY outdated_engine_count) FROM (SELECT ocean_basin, COUNT(*) FILTER (WHERE engine_technology < '2010-01-01') AS outdated_engine_count FROM fleet_information GROUP BY ocean_basin); | {"domain":"oceanography","domain_description":"Marine life research data, ocean floor mapping projects, pollution control initiatives, and maritime law compliance.","id":1367,"sql_complexity":"window functions","sql_complexity_description":"window functions (e.g., ROW_NUMBER, LEAD, LAG, RANk, NTILE, PERCENT_RANK, etc.)... | c87e5117c200a782cabc93bf714049908f20ffa77eff37127faf7d58b5c2467b | gretel_sql:test:1367 | window functions | intermediate | Estimated from source sql_complexity='window functions'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 1,366 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:edc83842f8fafbd94b772ba78b614443a1ffe8907d292b2509d09048c19e99a6 | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:window functions",
"sql_structure:selects=2;joins=0;statements=1;advanced=OVER",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:6a3cdfe57773484f1b9a61de4b9f559623407a67c90d1949985030e3c1f7631d | task:fc94554297817190d67058846e324366315728d4159d79425cf9ccee7d970a7e | 8c7748ec80232bf76d6e89a057bddb591677b21f415a773de80288f583b5927d | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 1510 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | What is the total number of visitors from African countries who attended events in 2021? | CREATE TABLE events (event_id INT, event_name VARCHAR(50), event_year INT, location VARCHAR(50)); INSERT INTO events (event_id, event_name, event_year, location) VALUES (1, 'Music Festival', 2021, 'Nigeria'), (2, 'Art Exhibition', 2022, 'Egypt'), (3, 'Theater Performance', 2021, 'South Africa'); CREATE TABLE countries ... | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT COUNT(*) FROM attendees JOIN events ON attendees.event_id = events.event_id JOIN countries ON attendees.country_id = countries.country_id WHERE events.event_year = 2021 AND countries.continent = 'Africa'; | {"domain":"arts and culture","domain_description":"Audience demographics, event attendance, program impact, and funding sources.","id":1510,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"First, we join the attendees, events, and countr... | 061f9414ff6fa38423e0203736d47a4b0a4f8dbc180b7088fb672d799f2fce71 | gretel_sql:test:1510 | multiple_joins | intermediate | Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 1,509 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:fc94554297817190d67058846e324366315728d4159d79425cf9ccee7d970a7e | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:multiple_joins",
"sql_structure:selects=1;joins=2;statements=1;advanced=none",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:de6d37a92c3ff155f2d04d585285955980637b82bef19a0b350ca72c3347b1b2 | task:6a323d2babdc9b885be5d156e2f009b6ce8e0c26ea3a6b36c1b4f23326e939be | a07a6258eb5cd19a0234ac344221d4fe0c7624bd7538fd4239fe552877964167 | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 1515 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | What is the average age of clients who lost cases in the 'personal injury' category? | CREATE TABLE Cases (CaseID int, ClientID int, Category varchar(50)); INSERT INTO Cases (CaseID, ClientID, Category) VALUES (701, 7, 'Personal Injury'); CREATE TABLE Clients (ClientID int, Age int, Gender varchar(10)); INSERT INTO Clients (ClientID, Age, Gender) VALUES (7, 45, 'Male'); CREATE TABLE CaseOutcomes (CaseID ... | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT AVG(C.Age) as AvgAge FROM Clients C INNER JOIN Cases CA ON C.ClientID = CA.ClientID INNER JOIN CaseOutcomes CO ON CA.CaseID = CO.CaseID WHERE CA.Category = 'Personal Injury' AND CO.Outcome = 'Lost'; | {"domain":"legal services","domain_description":"Case outcomes, legal precedents, attorney performance metrics, client demographics, and billing information.","id":1515,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"This query finds th... | 45e914721c988f5409960a8d80da4230fce881d47ee66a0c493a3d7c2a1fb4ac | gretel_sql:test:1515 | multiple_joins | intermediate | Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 1,514 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:6a323d2babdc9b885be5d156e2f009b6ce8e0c26ea3a6b36c1b4f23326e939be | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:multiple_joins",
"sql_structure:selects=1;joins=2;statements=1;advanced=none",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:ef79c30a0789531703b8eddb68073b5375da80666becd02149f23ff2c0031af4 | task:26fc01a2e492f7de5be58c73ca71b71bf70c516071ede4b4acea04ff6992a246 | a3622d5b31cb16418d3e1e521bfd13eb14ee998344830a548993cc6fd511c039 | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 1531 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | How many transactions were made in each region for the 'Credit Cards' product type? | CREATE TABLE regions (id INT, region_name VARCHAR(50)); INSERT INTO regions (id, region_name) VALUES (1, 'Northeast'), (2, 'Southeast'); CREATE TABLE transactions (region_id INT, product_type_id INT, transaction_count INT); INSERT INTO transactions (region_id, product_type_id, transaction_count) VALUES (1, 1, 20), (1, ... | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT r.region_name, p.product_type, SUM(t.transaction_count) as total_transactions FROM regions r JOIN transactions t ON r.id = t.region_id JOIN product_types p ON t.product_type_id = p.id WHERE p.product_type = 'Credit Cards' GROUP BY r.region_name, p.product_type; | {"domain":"financial services","domain_description":"Detailed financial data including investment strategies, risk management, fraud detection, customer analytics, and regulatory compliance.","id":1531,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_... | 0d900449eb2a652885116c659921c5b31cb24ea676531ef7b767aa33ea3be555 | gretel_sql:test:1531 | multiple_joins | intermediate | Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 1,530 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:26fc01a2e492f7de5be58c73ca71b71bf70c516071ede4b4acea04ff6992a246 | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:multiple_joins",
"sql_structure:selects=1;joins=2;statements=1;advanced=none",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:a03e79bd8209d499ff19973e14231b79b4cb1545a9d064ad5a0d971e4cb433fc | task:f7b1bf27656cb27292cf90eb7fef0ee661651d5c35ae8011ea319b3c49a38ae0 | 781fcdffd244553d8701b9d474f72b3dae1f423888bc4cbf721b38ee331358eb | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 1562 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | What is the total CO2 emission of each menu item, considering its ingredients and their origin? | CREATE TABLE menu_items (menu_id INT, name VARCHAR(50), co2_emission FLOAT); CREATE TABLE ingredients (ingredient_id INT, name VARCHAR(50), origin VARCHAR(50), co2_emission_per_kg FLOAT); CREATE TABLE recipe (menu_id INT, ingredient_id INT, quantity FLOAT); | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT m.name, SUM(i.co2_emission_per_kg * r.quantity) as total_co2_emission FROM menu_items m JOIN recipe r ON m.menu_id = r.menu_id JOIN ingredients i ON r.ingredient_id = i.ingredient_id GROUP BY m.menu_id; | {"domain":"food services","domain_description":"Menu engineering, customer preferences, inventory management, and sustainability initiatives.","id":1562,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"The SQL query joins the menu_items,... | 1875055bfcec78d42960e73e1923895715a1fa5cf6b4cae67be64e711363c2d5 | gretel_sql:test:1562 | multiple_joins | intermediate | Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 1,561 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:f7b1bf27656cb27292cf90eb7fef0ee661651d5c35ae8011ea319b3c49a38ae0 | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:multiple_joins",
"sql_structure:selects=1;joins=2;statements=1;advanced=none",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:17792feb0a5139588e62172c1cc3bb87e05d71b8d776d64aa740dabcfca85719 | task:7a2c93c498aa9fdb7b1071cd589756b310e45021cedd69eed22e6bb1e28c4a80 | 4538afbbbc45752bc61243c62d346e948e662f07752ca6e98c59afd84ef322d4 | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 1584 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | What are the names of the vessels with the highest average speed that arrived in Busan? | CREATE TABLE VesselArrivals (ID INT, VesselName VARCHAR(50), ArrivalPort VARCHAR(50), ArrivalDate DATE, AverageSpeed DECIMAL(5,2)); INSERT INTO VesselArrivals (ID, VesselName, ArrivalPort, ArrivalDate, AverageSpeed) VALUES (1, 'Test Vessel 1', 'Busan', '2022-01-01', 20.0), (2, 'Test Vessel 2', 'Busan', '2022-01-02', 18... | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT VesselName FROM (SELECT VesselName, ROW_NUMBER() OVER (ORDER BY AverageSpeed DESC) AS rn FROM VesselArrivals WHERE ArrivalPort = 'Busan') t WHERE rn = 1; | {"domain":"maritime","domain_description":"Vessel performance data, cargo tracking, safety records, and regulatory compliance.","id":1584,"sql_complexity":"window functions","sql_complexity_description":"window functions (e.g., ROW_NUMBER, LEAD, LAG, RANk, NTILE, PERCENT_RANK, etc.) with partitioning and ordering","sql... | 9122ca8044d6a310183df14adb53a14832637af95426a3b45ddf0793998b4e5e | gretel_sql:test:1584 | window functions | intermediate | Estimated from source sql_complexity='window functions'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 1,583 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:7a2c93c498aa9fdb7b1071cd589756b310e45021cedd69eed22e6bb1e28c4a80 | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:window functions",
"sql_structure:selects=2;joins=0;statements=1;advanced=OVER",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:e328b528b1500e06131037295b1af701c8e1421c8caf806677f5f55e13b239cb | task:d99418893fc67eab0e4011e5aad6940506d35ae6fff3405b5b08e8d3313cd2a4 | 556ad253c9b5b9759d9a323fb0e22c66f04ee6c1f7373200051e5d83834d771a | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 1610 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | What is the number of primary care physicians per capita in each state of the United States? | CREATE TABLE us_states (id INT, name VARCHAR(255)); CREATE TABLE primary_care_physicians (id INT, state_id INT, count INT); CREATE TABLE population (id INT, state_id INT, total_population INT); INSERT INTO us_states (id, name) VALUES (1, 'Alabama'), (2, 'Alaska'), (3, 'Arizona'), (4, 'Arkansas'), (5, 'California'); | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT s.name, pc.count / p.total_population AS physicians_per_capita FROM primary_care_physicians pc JOIN us_states s ON pc.state_id = s.id JOIN population p ON pc.state_id = p.state_id; | {"domain":"public health","domain_description":"Community health statistics, infectious disease tracking data, healthcare access metrics, and public health policy analysis.","id":1610,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"This... | e8a5afbee2ce4c58702ebfadfd9e358a70490e21eaeaf43c480eb738b2b47993 | gretel_sql:test:1610 | multiple_joins | intermediate | Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 1,609 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:d99418893fc67eab0e4011e5aad6940506d35ae6fff3405b5b08e8d3313cd2a4 | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:multiple_joins",
"sql_structure:selects=1;joins=2;statements=1;advanced=none",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:04eb006484af290c371699dbd4c5c4dc8954750e1c41efad7a57d89e158d076b | task:fbaca957d2329d4181b84f5712d9d34a05ed8780473579ad60ded5acaa1a500d | ef9a5e5718b0f1d4f4abe5d1e1c9682c5b719cb8092d6816fb9ad416607caa52 | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 1669 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | Which countries have the highest and lowest ocean acidity levels? | CREATE TABLE ocean_acidity (country TEXT, avg_ph REAL); INSERT INTO ocean_acidity (country, avg_ph) VALUES ('United States', 7.8), ('Canada', 7.6), ('Mexico', 7.9); | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT country, avg_ph FROM (SELECT country, avg_ph, ROW_NUMBER() OVER (ORDER BY avg_ph DESC) as rnk FROM ocean_acidity) subq WHERE rnk = 1 OR rnk = (SELECT COUNT(*) FROM ocean_acidity) ORDER BY avg_ph; | {"domain":"oceans","domain_description":"Ocean data on marine conservation, ocean acidification, deep-sea exploration, and maritime safety.","id":1669,"sql_complexity":"window functions","sql_complexity_description":"window functions (e.g., ROW_NUMBER, LEAD, LAG, RANk, NTILE, PERCENT_RANK, etc.) with partitioning and o... | 4f013a3cdceba239d4648392d0c92cbe6d0d21ceac5864c026e423f894450e39 | gretel_sql:test:1669 | window functions | intermediate | Estimated from source sql_complexity='window functions'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 1,668 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:fbaca957d2329d4181b84f5712d9d34a05ed8780473579ad60ded5acaa1a500d | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:window functions",
"sql_structure:selects=3;joins=0;statements=1;advanced=OVER",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:c0e478e277636d4ded47904f433733506cfa4cb5de5a26d9a5980e2af4d6804d | task:1cbd92181b981f0d989be02ec431a7c59a3efc3aa3ada1a26370b30542ee757a | b362c64856db8b1a86006db6e086da870e4a79ba72cb4bb88ff89994a38e60f2 | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 1672 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | Identify the top 3 countries with the highest number of players who have adopted virtual reality technology. | CREATE TABLE CountryData (Country VARCHAR(50), Population INT, VRAdopters INT); INSERT INTO CountryData (Country, Population, VRAdopters) VALUES ('USA', 331002651, 50000), ('China', 1439323776, 25000), ('Canada', 37410003), ('India', 1380004385, 10000); | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT Country, VRAdopters FROM (SELECT Country, VRAdopters, ROW_NUMBER() OVER (ORDER BY VRAdopters DESC) AS RN FROM CountryData JOIN (SELECT PlayerID, VRDevice FROM VRAdoption GROUP BY PlayerID, VRDevice) VR ON CountryData.Country = VR.PlayerCountry) T WHERE RN <= 3 | {"domain":"gaming technology","domain_description":"Player demographics, game design data, esports event information, and virtual reality technology adoption.","id":1672,"sql_complexity":"window functions","sql_complexity_description":"window functions (e.g., ROW_NUMBER, LEAD, LAG, RANk, NTILE, PERCENT_RANK, etc.) with... | 3068b9cd7ccf2b89247cb76954700b5f80b5bf7d549feefcb041ddeee45de519 | gretel_sql:test:1672 | window functions | intermediate | Estimated from source sql_complexity='window functions'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 1,671 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:1cbd92181b981f0d989be02ec431a7c59a3efc3aa3ada1a26370b30542ee757a | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:window functions",
"sql_structure:selects=3;joins=1;statements=1;advanced=OVER",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:e6d059f342cfb9d93e11ad20293db21f723c31571a68cd472283aad509d429a7 | task:fd78f98f90969e07e25286eef9e2323a4edc8a80b1c7effc65a247928fa03caf | 1ad1ade9e9912e3fe9761be4c2cbd7bcfe918b3ebff5006af68afe8f8eaedd63 | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 1751 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | What is the maximum consecutive number of days with a financial wellbeing score below 60 for each customer? | CREATE TABLE customer_scores (customer_id INT, score_date DATE, financial_wellbeing_score INT); INSERT INTO customer_scores (customer_id, score_date, financial_wellbeing_score) VALUES (1, '2021-01-01', 65), (1, '2021-01-02', 60), (1, '2021-01-03', 55), (1, '2021-01-04', 60), (2, '2021-01-01', 70), (2, '2021-01-02', 75)... | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT customer_id, MAX(consecutive_below_60) FROM (SELECT customer_id, score_date, financial_wellbeing_score, COUNT(*) FILTER (WHERE financial_wellbeing_score < 60) OVER (PARTITION BY customer_id ORDER BY score_date ROWS BETWEEN UNBOUNDED PRECEDING AND 1 PRECEDING) AS consecutive_below_60 FROM customer_scores) subquer... | {"domain":"finance","domain_description":"Financial data on Shariah-compliant finance, socially responsible lending, financial capability, and financial wellbeing.","id":1751,"sql_complexity":"window functions","sql_complexity_description":"window functions (e.g., ROW_NUMBER, LEAD, LAG, RANk, NTILE, PERCENT_RANK, etc.)... | d1919d3b018e586512741aa56bc453592d712354910f360d2c0a55fab447abb4 | gretel_sql:test:1751 | window functions | intermediate | Estimated from source sql_complexity='window functions'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 1,750 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:fd78f98f90969e07e25286eef9e2323a4edc8a80b1c7effc65a247928fa03caf | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:window functions",
"sql_structure:selects=2;joins=0;statements=1;advanced=OVER",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured | ||||
gretel_sql:cde37366c9a77951ab03d4af9e2e0a8cb805cf3a54c0f932e333137b1870538e | task:fcaa3dc216b5f2edc188454a81e45b073278a6294b32f28ab7636896e40ccd40 | e8d13bf1b649c99ca3737287e6957bb2fefdd4c1374842a512c0fa1c234b11c3 | gretel_sql | gretelai/synthetic_text_to_sql | 740ab236e64503fba51be1101df7a1be83bf455d | synthetic_text_to_sql_test.snappy.parquet | 1763 | test | https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/resolve/740ab236e64503fba51be1101df7a1be83bf455d/synthetic_text_to_sql_test.snappy.parquet | apache-2.0 | Gretel Synthetic Text-to-SQL dataset creators; source card and author citation preserved in the download manifest. | proposed | What is the total food and beverage revenue last month for hotels in 'Bangkok'? | CREATE TABLE revenue (hotel_id INT, revenue_source VARCHAR(50), revenue INT, revenue_date DATE); INSERT INTO revenue (hotel_id, revenue_source, revenue, revenue_date) VALUES (5, 'Room revenue', 12000, '2022-03-01'), (5, 'Food and beverage', 4000, '2022-03-02'), (5, 'Other revenue', 1000, '2022-03-03'); CREATE TABLE hot... | database_business_operations | database_business_operations.database_query_analysis | heuristic_unreviewed | Mapped from source sql_task_type='analytics and reporting'; no individual SQL execution or semantic review. | en | none | SELECT SUM(revenue) FROM revenue JOIN hotels ON revenue.hotel_id = hotels.hotel_id JOIN dates ON revenue.revenue_date = dates.date WHERE hotels.city = 'Bangkok' AND revenue_source = 'Food and beverage' AND dates.date >= DATEADD(month, -1, GETDATE()); | {"domain":"hospitality technology","domain_description":"Hotel tech adoption metrics, online travel agency data, virtual tour engagement stats, and hospitality AI trends.","id":1763,"sql_complexity":"multiple_joins","sql_complexity_description":"two or more joins (specify inner, outer, cross)","sql_explanation":"The SQ... | 7523c6adc7cb6d6bbfe300c5712a1a2b08b05b868db61795f894fcb1ef13b685 | gretel_sql:test:1763 | multiple_joins | intermediate | Estimated from source sql_complexity='multiple_joins'; query feature labels are only proxies for difficulty, not measured solver performance. No empirical solver calibration was performed. | heuristic | needs_difficulty_calibration | not_measured | analysis_only_unvalidated | primary | task_annotation | 1,762 | [
"difficulty_not_calibrated",
"not_individually_reviewed",
"reference_not_independently_verified",
"reference_or_constraint_only_no_rubric",
"response_diversity_not_validated",
"source_evaluation_split",
"sql_dialect_and_result_equivalence_need_review",
"sql_not_executed",
"synthetic_reference"
] | false | true | false | task:fcaa3dc216b5f2edc188454a81e45b073278a6294b32f28ab7636896e40ccd40 | 1 | 1 | [
"gretel_sql"
] | held_out | curated_candidate | [] | core_candidate | [
"source_sql_complexity:multiple_joins",
"sql_structure:selects=1;joins=2;statements=1;advanced=none",
"combined_relational_operations_require_calibration"
] | structural_heuristic | Qwen3.6-27B | heuristic_not_model_measured |
Rubric Diversity Tasks
The default qwen_core configuration contains 1,296,719 curated task groups after screening 108,713 foundational tasks into the separate foundational configuration. The tasks configuration retains all 1,405,432 curated task groups.
Difficulty is labeled for a Qwen3.6-27B curriculum using transparent source and structural priors. No new Qwen3.6-27B success rates were measured. core_candidate means an explicit nonfoundational source/structure signal; uncalibrated means the available evidence does not justify a difficulty claim. Both remain in the default view. The default view is a difficulty-screened research inventory; held-out source lineages remain labeled and must still be separated for evaluation.
A curated inventory of 1,405,432 task groups, with 2,444,946 source annotation records retained across 20 source configurations. All source releases are revision-pinned where upstream provenance is known.
The tasks configuration contains one representative eligible annotation per exact prompt/context group. Environment tasks additionally include source and upstream environment identity in their task key. This is a public research inventory for rubric discovery. It does not contain newly accepted rubric training targets or certify that each task is solvable.
The audit quarantined 538,621 annotations. 9,742 eligible task groups have held-out source lineage and are marked source_split_status=held_out. No model inference, empirical difficulty calibration, or full task-environment execution was performed.
from datasets import load_dataset
tasks = load_dataset("asingh15/rubric-diversity-tasks", "qwen_core", split="collection", streaming=True)
example = next(iter(tasks))
# Full source annotations, including alternate representations:
source = load_dataset("asingh15/rubric-diversity-tasks", "gsm8k", split="collection")
Configurations
qwen_core(default): curated task groups excluding the foundational screen; includes explicitly labeled uncalibrated tasks.foundational: basic/easy task candidates retained as controls and for calibration.tasks: one representative per eligible task group. Prefer native rubrics, then supplied references and verifiers; alternate references remain in source configurations.quarantine: all excluded annotations, with explicit reasons. These also remain in their source configurations.Source configurations: every normalized annotation from the selected complete upstream releases, without privacy redaction. Source response expansions and alternate representations are preserved.
All configurations use the neutral split name collection. Original split names remain in source_split; they have not been relabeled as training data. source_split_status is held_out, source_train, or unspecified. Held-out protection propagates to exact shared prompts and declared upstream task identities until stable. It is a conservative overlap check, not a comprehensive benchmark decontamination guarantee.
Curation
Every shard is checked for schema, row counts, SHA-256, source coordinates, record-ID uniqueness, prompt/task fingerprints, JSON fields, and reference/verifier flags. Source adapters audit extraction and preserve references separately from task-visible inputs. Missing prompts, unresolved upstream provenance, source-invalid problems/solutions/rubrics, known defective references, external-image dependencies, unavailable internal-registry environments and records without reference evidence are excluded from the default task view. See curation_reasons, quality_flags, and audit/quality_report.json.
Deduplication is exact and preserves case, code indentation and internal whitespace. It normalizes Unicode NFC, line endings and surrounding whitespace only. Task counts are exact-input groups, not claims of semantically distinct problems. The per-source annotations retain all grading perspectives, responses and representations.
Fields
prompt,context: task input, with source roles/tools retained where applicable.rubric_markdown,rubric_json: source rubric material; absence does not trigger an invented generic rubric.reference_solution,reference_answer,verifier_json: source evidence, not independently certified outcomes.source_repo,source_revision,source_file,source_row,source_row_id,source_url,source_record_sha256: provenance.task_id,prompt_context_id,record_id,annotation_count,eligible_annotation_count,group_sources: stable identities and exact grouping. Source configuration rows use the same task key as the default view.family,leaf,taxonomy_status,taxonomy_reason: provisional taxonomy. Unassigned tasks remain unassigned.source_difficulty,estimated_difficulty,difficulty_basis,difficulty_confidence,empirical_difficulty: source labels/heuristics are distinct from unmeasured solver difficulty.curation_status,curation_reasons,source_split_status,quality_flags,validation_status: eligibility and limitations.curated_candidatemeans structurally admitted for research, not accepted for training.
Source coverage
| Configuration | Annotations | Eligible annotations | Quarantined |
|---|---|---|---|
| auto_rubric | 38,459 | 35,408 | 3,051 |
| deepresearch_bench_ii | 132 | 132 | 0 |
| facet_terminal | 6,020 | 6,020 | 0 |
| gretel_sql | 105,851 | 105,851 | 0 |
| gsm8k | 17,584 | 17,584 | 0 |
| helpsteer3 | 132,937 | 0 | 132,937 |
| junkai | 13,045 | 11,076 | 1,969 |
| mbpp | 1,401 | 1,399 | 2 |
| mimo | 7,780 | 7,780 | 0 |
| nemotron_science | 150,644 | 150,644 | 0 |
| nemotron_structured | 62,696 | 62,696 | 0 |
| nemotron_terminal | 254,702 | 5 | 254,697 |
| numinamath | 896,215 | 761,733 | 134,482 |
| prometheus_feedback | 99,952 | 99,952 | 0 |
| prometheus_preference | 199,760 | 199,760 | 0 |
| researchrubrics | 101 | 101 | 0 |
| rubrichub | 364,260 | 364,260 | 0 |
| swe_rebench | 32,079 | 32,033 | 46 |
| swe_smith | 50,908 | 39,471 | 11,437 |
| arxivmath | 10,420 | 10,420 | 0 |
Limits and attribution
Task-level taxonomy, rubric grounding, alternative-solution acceptance and difficulty remain unverified unless specifically documented by a source audit. Terminal/repository tasks retain environment pointers and verifier text; complete executable environments are not bundled. Original source benchmarks, licenses, author attribution and repository dependencies remain applicable. DeepResearch Bench II is pinned to its original GitHub release; all its tasks retain evaluation-source protection and per-record licenses, including two noncommercial records.
See LICENSES.md, source audits, manifests, and the reproducible curation scripts. The small interactive Rubric Diversity Atlas is a separate illustrative gallery.
Difficulty for Qwen3.6-27B
Each curated task has difficulty_tier, difficulty_evidence, difficulty_tier_confidence, difficulty_target_model, and difficulty_validation. Original source difficulty metadata is preserved. Source configurations retain their original difficulty columns; the new screen is applied to unique curated task groups.
GSM8K and MBPP supply foundational benchmark controls. Explicit basic/easy source labels and conservative task-structure checks provide additional foundational candidates. Competition/source labels alone do not establish target-model difficulty. Missing labels stay uncalibrated; prompt length, rubric count and schema size are not treated as difficulty measurements. See the difficulty audit and the exact rules.
A calibrated revision would record a pinned Qwen checkpoint, multiple independent attempts, outcome-verified success criteria, and per-task pass rates. No such model run is claimed by this release.
MathArena outputs and source-model evidence
arxivmath preserves all 10,420 captured Qwen3.6-35B attempts from the pinned outputs release, grouped into 1,649 distinct questions. An upstream indexing bug repeated one question under several problem indices; 956 questions in the parent training set have no outputs. The outputs source is complete as published, but it does not cover every parent question.
Gold answers are separate from sampled responses and conversations. source_metadata_json.source_reported_baseline and empirical_difficulty retain per-question source-reported attempt counts, correctness counts and pass rates. These describe Qwen3.6-35B, with no claim of independent samples or measured Qwen3.6-27B performance. Each question has 4 or 12 captured attempts; 725 questions have zero source-reported successes and 378 have all attempts reported correct.
Output annotations carry CC-BY-4.0. Matched article URLs, article-license terms and restriction flags remain in source_metadata_json.licensing, source_metadata_json.source_article, and quality_flags. See the MathArena audit.
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