[ { "instance_id": "id0", "train_trial_index": 0, "predicted_text": " SELECT district.district_id, COUNT(account.account_id) as total_accounts\nFROM district\nLEFT JOIN account ON district.district_id = account.district_id\nGROUP BY district.district_id;", "base64_images": [], "stats": { "num_prompt_tokens": 1200.0, "num_output_tokens": 56.0, "inference_runtime": 1.2291274070739746, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT district.district_id, COUNT(account.account_id) as total_accounts\nFROM district\nLEFT JOIN account ON district.district_id = account.district_id\nGROUP BY district.district_id;", "result": "[(1, 554), (2, 42), (3, 50), (4, 48), (5, 65), (6, 48), (7, 44), (8, 56), (9, 46), (10, 48), (11, 48), (12, 42), (13, 37), (14, 41), (15, 50), (16, 52), (17, 42), (18, 46), (19, 55), (20, 36), (21, 48), (22, 36), (23, 50), (24, 42), (25, 42), (26, 45), (27, 39), (28, 45), (29, 43), (30, 38), (31, 50), (32, 48), (33, 49), (34, 39), (35, 43), (36, 57), (37, 37), (38, 54), (39, 41), (40, 44), (41, 45), (42, 48), (43, 49), (44, 50), (45, 41), (46, 59), (47, 53), (48, 53), (49, 37), (50, 48), (51, 49), (52, 57), (53, 50), (54, 128), (55, 53), (56, 44), (57, 41), (58, 32), (59, 57), (60, 51), (61, 45), (62, 44), (63, 49), (64, 92), (65, 39), (66, 53), (67, 43), (68, 83), (69, 48), (70, 152), (71, 42), (72, 88), (73, 56), (74, 135), (75, 51), (76, 55), (77, 40)]", "error": null, "gold_result": "[(1, 554), (2, 42), (3, 50), (4, 48), (5, 65), (6, 48), (7, 44), (8, 56), (9, 46), (10, 48), (11, 48), (12, 42), (13, 37), (14, 41), (15, 50), (16, 52), (17, 42), (18, 46), (19, 55), (20, 36), (21, 48), (22, 36), (23, 50), (24, 42), (25, 42), (26, 45), (27, 39), (28, 45), (29, 43), (30, 38), (31, 50), (32, 48), (33, 49), (34, 39), (35, 43), (36, 57), (37, 37), (38, 54), (39, 41), (40, 44), (41, 45), (42, 48), (43, 49), (44, 50), (45, 41), (46, 59), (47, 53), (48, 53), (49, 37), (50, 48), (51, 49), (52, 57), (53, 50), (54, 128), (55, 53), (56, 44), (57, 41), (58, 32), (59, 57), (60, 51), (61, 45), (62, 44), (63, 49), (64, 92), (65, 39), (66, 53), (67, 43), (68, 83), (69, 48), (70, 152), (71, 42), (72, 88), (73, 56), (74, 135), (75, 51), (76, 55), (77, 40)]", "gold_error": null } } }, { "instance_id": "id1", "train_trial_index": 0, "predicted_text": " To find the average age of clients in each district, you would first need to calculate the age of each client by subtracting their birth date from the current date. However, SQLite does not have a built-in function to get the current date, so we'll use a workaround by assuming that the current date is a fixed date for the calculation.", "base64_images": [], "stats": { "num_prompt_tokens": 1199.0, "num_output_tokens": 76.0, "inference_runtime": 1.5453481674194336, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To find the average age of clients in each district, you would first need to calculate the age of each client by subtracting their birth date from the current date. However, SQLite does not have a built-in function to get the current date, so we'll use a workaround by assuming that the current date is a fixed date for the calculation.", "result": null, "error": "near \"To\": syntax error", "gold_result": "[(1, 1969.0), (2, 1969.0), (3, 1969.0), (4, 1969.0), (5, 1969.0), (6, 1969.0), (7, 1969.0), (8, 1969.0), (9, 1969.0), (10, 1969.0), (11, 1969.0), (12, 1969.0), (13, 1969.0), (14, 1969.0), (15, 1969.0), (16, 1969.0), (17, 1969.0), (18, 1969.0), (19, 1969.0), (20, 1969.0), (21, 1969.0), (22, 1969.0), (23, 1969.0), (24, 1969.0), (25, 1969.0), (26, 1969.0), (27, 1969.0), (28, 1969.0), (29, 1969.0), (30, 1969.0), (31, 1969.0), (32, 1969.0), (33, 1969.0), (34, 1969.0), (35, 1969.0), (36, 1969.0), (37, 1969.0), (38, 1969.0), (39, 1969.0), (40, 1969.0), (41, 1969.0), (42, 1969.0), (43, 1969.0), (44, 1969.0), (45, 1969.0), (46, 1969.0), (47, 1969.0), (48, 1969.0), (49, 1969.0), (50, 1969.0), (51, 1969.0), (52, 1969.0), (53, 1969.0), (54, 1969.0), (55, 1969.0), (56, 1969.0), (57, 1969.0), (58, 1969.0), (59, 1969.0), (60, 1969.0), (61, 1969.0), (62, 1969.0), (63, 1969.0), (64, 1969.0), (65, 1969.0), (66, 1969.0), (67, 1969.0), (68, 1969.0), (69, 1969.0), (70, 1969.0), (71, 1969.0), (72, 1969.0), (73, 1969.0), (74, 1969.0), (75, 1969.0), (76, 1969.0), (77, 1969.0)]", "gold_error": null } } }, { "instance_id": "id2", "train_trial_index": 0, "predicted_text": " To get the total number of loans granted to clients in each district, you can use the following SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1202.0, "num_output_tokens": 24.0, "inference_runtime": 0.4000370502471924, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To get the total number of loans granted to clients in each district, you can use the following SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": "[(1, 46), (2, 1), (3, 4), (4, 6), (6, 3), (7, 1), (8, 1), (9, 7), (10, 2), (11, 3), (12, 3), (13, 3), (14, 4), (15, 4), (16, 3), (17, 6), (18, 6), (19, 1), (20, 3), (21, 6), (22, 2), (23, 2), (24, 3), (25, 1), (26, 4), (27, 3), (28, 1), (29, 4), (30, 5), (31, 5), (33, 4), (34, 2), (35, 2), (36, 5), (37, 1), (39, 4), (40, 3), (42, 4), (43, 9), (44, 2), (45, 4), (46, 5), (47, 7), (48, 4), (49, 1), (50, 5), (51, 3), (52, 1), (53, 7), (54, 8), (55, 2), (56, 3), (57, 1), (59, 2), (60, 3), (61, 1), (62, 3), (63, 7), (64, 8), (65, 1), (66, 9), (67, 1), (69, 4), (70, 8), (71, 4), (72, 11), (73, 2), (74, 12), (75, 2), (76, 5), (77, 2)]", "gold_error": null } } }, { "instance_id": "id3", "train_trial_index": 0, "predicted_text": " To find the average balance of accounts in each district, you would need to join the `account` and `district` tables on the `district_id` field and then group the results by `district_id`. However, the balance is not directly available in the `account` table.", "base64_images": [], "stats": { "num_prompt_tokens": 1199.0, "num_output_tokens": 64.0, "inference_runtime": 0.8137540817260742, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To find the average balance of accounts in each district, you would need to join the `account` and `district` tables on the `district_id` field and then group the results by `district_id`. However, the balance is not directly available in the `account` table.", "result": null, "error": "near \"To\": syntax error", "gold_result": "[(1, 37756.85687154847), (2, 34289.917055547194), (3, 37484.546982272775), (4, 38918.05956722587), (5, 33196.19431829162), (6, 38860.79303655299), (7, 38942.02600330678), (8, 41193.24001956947), (9, 35207.96321541897), (10, 34549.176671547095), (11, 36082.82454348104), (12, 34116.86183685111), (13, 37822.68695652174), (14, 42133.15815147625), (15, 36422.29261579347), (16, 37449.10333333333), (17, 41304.19039735099), (18, 36681.21701153324), (19, 35887.16124096898), (20, 34109.59693053312), (21, 36482.730208333334), (22, 35364.80264244426), (23, 38436.96347782131), (24, 35784.99279798343), (25, 41144.34759358289), (26, 34100.39197405756), (27, 41817.75286415712), (28, 34946.39370485036), (29, 42282.25449072725), (30, 40319.41596595038), (31, 33897.25787965616), (32, 37177.70204788001), (33, 37413.70859805167), (34, 38878.4827768246), (35, 37343.09820222384), (36, 36892.40708627703), (37, 33500.9261019879), (38, 35166.32583350378), (39, 39087.53978614081), (40, 39100.51872871737), (41, 35578.642590802374), (42, 35487.75379396536), (43, 44268.37994350283), (44, 35663.292031038305), (45, 36995.017517783206), (46, 36151.67823410061), (47, 38202.136757425746), (48, 38905.4236688484), (49, 34367.251482649845), (50, 35242.872592428605), (51, 35694.21030685341), (52, 39075.51681927711), (53, 35773.01796338673), (54, 40579.290425900275), (55, 38402.936616029336), (56, 34541.52659310071), (57, 40644.15710306407), (58, 33605.30040216821), (59, 33727.714704087855), (60, 34996.51224511814), (61, 33986.233850129196), (62, 34446.62150837989), (63, 41593.10410334347), (64, 39659.690373331614), (65, 40868.41913250148), (66, 37090.089342117135), (67, 36162.49796983759), (68, 39151.42975899406), (69, 37430.64058657437), (70, 38656.44067135051), (71, 35008.359269351095), (72, 39089.14620360485), (73, 36056.52115093387), (74, 38652.555407492655), (75, 37891.48183556405), (76, 41969.155322496066), (77, 31931.398058252427)]", "gold_error": null } } }, { "instance_id": "id4", "train_trial_index": 0, "predicted_text": " To get the total number of credit card transactions in each district, we need to join the `trans` table with the `account` table and then join the result with the `district` table. We will filter the transactions to only include credit card transactions and then group the results by district to get the total count for each district.", "base64_images": [], "stats": { "num_prompt_tokens": 1201.0, "num_output_tokens": 70.0, "inference_runtime": 0.9277405738830566, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To get the total number of credit card transactions in each district, we need to join the `trans` table with the `account` table and then join the result with the `district` table. We will filter the transactions to only include credit card transactions and then group the results by district to get the total count for each district.", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: cd.account_id" } } }, { "instance_id": "id5", "train_trial_index": 0, "predicted_text": " To find the average salary of clients in each district, we need to join the \"client\" and \"district\" tables using the \"district_id\" as the common field. However, the provided schema does not have a salary field for clients.", "base64_images": [], "stats": { "num_prompt_tokens": 1199.0, "num_output_tokens": 54.0, "inference_runtime": 1.2459864616394043, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To find the average salary of clients in each district, we need to join the \"client\" and \"district\" tables using the \"district_id\" as the common field. However, the provided schema does not have a salary field for clients.", "result": null, "error": "near \"To\": syntax error", "gold_result": "[(1, 12541.0), (2, 8507.0), (3, 8980.0), (4, 9753.0), (5, 9307.0), (6, 8546.0), (7, 9920.0), (8, 11277.0), (9, 8899.0), (10, 10124.0), (11, 9622.0), (12, 8754.0), (13, 8598.0), (14, 10045.0), (15, 9045.0), (16, 8427.0), (17, 8114.0), (18, 8968.0), (19, 8402.0), (20, 8547.0), (21, 9104.0), (22, 8620.0), (23, 8624.0), (24, 8991.0), (25, 8554.0), (26, 10787.0), (27, 8561.0), (28, 8594.0), (29, 8843.0), (30, 9650.0), (31, 8930.0), (32, 9272.0), (33, 8705.0), (34, 9675.0), (35, 8867.0), (36, 9198.0), (37, 9065.0), (38, 8965.0), (39, 10446.0), (40, 9317.0), (41, 9832.0), (42, 8388.0), (43, 9425.0), (44, 8254.0), (45, 8390.0), (46, 8369.0), (47, 9538.0), (48, 9060.0), (49, 8208.0), (50, 8187.0), (51, 8541.0), (52, 8363.0), (53, 8240.0), (54, 9897.0), (55, 8743.0), (56, 8772.0), (57, 8720.0), (58, 8757.0), (59, 8444.0), (60, 8441.0), (61, 8814.0), (62, 8544.0), (63, 8288.0), (64, 9624.0), (65, 8403.0), (66, 8512.0), (67, 8110.0), (68, 9893.0), (69, 8173.0), (70, 10177.0), (71, 8678.0), (72, 8994.0), (73, 8746.0), (74, 10673.0), (75, 8819.0), (76, 8369.0), (77, 8909.0)]", "gold_error": null } } }, { "instance_id": "id6", "train_trial_index": 0, "predicted_text": " SELECT district.district_id, COUNT(client.client_id) as total_clients\nFROM district\nLEFT JOIN client ON district.district_id = client.district_id\nGROUP BY district.district_id;", "base64_images": [], "stats": { "num_prompt_tokens": 1199.0, "num_output_tokens": 56.0, "inference_runtime": 1.1715409755706787, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT district.district_id, COUNT(client.client_id) as total_clients\nFROM district\nLEFT JOIN client ON district.district_id = client.district_id\nGROUP BY district.district_id;", "result": "[(1, 663), (2, 46), (3, 63), (4, 50), (5, 71), (6, 53), (7, 45), (8, 69), (9, 60), (10, 54), (11, 56), (12, 44), (13, 53), (14, 45), (15, 63), (16, 61), (17, 54), (18, 60), (19, 66), (20, 43), (21, 57), (22, 51), (23, 53), (24, 43), (25, 48), (26, 50), (27, 55), (28, 54), (29, 54), (30, 48), (31, 59), (32, 55), (33, 69), (34, 45), (35, 56), (36, 67), (37, 48), (38, 65), (39, 52), (40, 59), (41, 45), (42, 59), (43, 55), (44, 54), (45, 52), (46, 76), (47, 63), (48, 60), (49, 46), (50, 61), (51, 61), (52, 73), (53, 57), (54, 155), (55, 63), (56, 54), (57, 51), (58, 44), (59, 64), (60, 61), (61, 54), (62, 59), (63, 56), (64, 109), (65, 44), (66, 66), (67, 45), (68, 86), (69, 61), (70, 169), (71, 48), (72, 104), (73, 60), (74, 180), (75, 56), (76, 61), (77, 50)]", "error": null, "gold_result": "[(1, 663), (2, 46), (3, 63), (4, 50), (5, 71), (6, 53), (7, 45), (8, 69), (9, 60), (10, 54), (11, 56), (12, 44), (13, 53), (14, 45), (15, 63), (16, 61), (17, 54), (18, 60), (19, 66), (20, 43), (21, 57), (22, 51), (23, 53), (24, 43), (25, 48), (26, 50), (27, 55), (28, 54), (29, 54), (30, 48), (31, 59), (32, 55), (33, 69), (34, 45), (35, 56), (36, 67), (37, 48), (38, 65), (39, 52), (40, 59), (41, 45), (42, 59), (43, 55), (44, 54), (45, 52), (46, 76), (47, 63), (48, 60), (49, 46), (50, 61), (51, 61), (52, 73), (53, 57), (54, 155), (55, 63), (56, 54), (57, 51), (58, 44), (59, 64), (60, 61), (61, 54), (62, 59), (63, 56), (64, 109), (65, 44), (66, 66), (67, 45), (68, 86), (69, 61), (70, 169), (71, 48), (72, 104), (73, 60), (74, 180), (75, 56), (76, 61), (77, 50)]", "gold_error": null } } }, { "instance_id": "id7", "train_trial_index": 0, "predicted_text": " I'm sorry for the confusion, but based on the provided database schema, there is no table that contains information about crimes committed in each district. Therefore, it is not possible to generate a SQL query to find the average number of crimes committed in each district with the given schema.", "base64_images": [], "stats": { "num_prompt_tokens": 1200.0, "num_output_tokens": 59.0, "inference_runtime": 1.1338660717010498, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " I'm sorry for the confusion, but based on the provided database schema, there is no table that contains information about crimes committed in each district. Therefore, it is not possible to generate a SQL query to find the average number of crimes committed in each district with the given schema.", "result": null, "error": "near \"I\": syntax error", "gold_result": "[(1, 85677.0), (2, 2159.0), (3, 2824.0), (4, 5244.0), (5, 2616.0), (6, 2640.0), (7, 4289.0), (8, 5179.0), (9, 2987.0), (10, 3810.0), (11, 3475.0), (12, 3804.0), (13, 1597.0), (14, 6604.0), (15, 1845.0), (16, 1874.0), (17, 1003.0), (18, 1740.0), (19, 999.0), (20, 1563.0), (21, 2299.0), (22, 1089.0), (23, 2879.0), (24, 5198.0), (25, 1822.0), (26, 6041.0), (27, 1029.0), (28, 1580.0), (29, 818.0), (30, 2985.0), (31, 1328.0), (32, 4340.0), (33, 4650.0), (34, 5323.0), (35, 3384.0), (36, 5796.0), (37, 4147.0), (38, 2653.0), (39, 4947.0), (40, 6949.0), (41, 6445.0), (42, 1658.0), (43, 4085.0), (44, 2166.0), (45, 2080.0), (46, 2854.0), (47, 6079.0), (48, 1655.0), (49, 1660.0), (50, 2123.0), (51, 3496.0), (52, 2564.0), (53, 1850.0), (54, 18721.0), (55, 3659.0), (56, 2854.0), (57, 3729.0), (58, 2212.0), (59, 2595.0), (60, 1879.0), (61, 2112.0), (62, 2719.0), (63, 1562.0), (64, 4484.0), (65, 2157.0), (66, 2247.0), (67, 3244.0), (68, 5623.0), (69, None), (70, 9878.0), (71, 4980.0), (72, 9672.0), (73, 4355.0), (74, 18782.0), (75, 4063.0), (76, 3736.0), (77, 3460.0)]", "gold_error": null } } }, { "instance_id": "id8", "train_trial_index": 0, "predicted_text": " SELECT COUNT(*) FROM loan WHERE duration > 12;", "base64_images": [], "stats": { "num_prompt_tokens": 1205.0, "num_output_tokens": 16.0, "inference_runtime": 0.35344767570495605, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT COUNT(*) FROM loan WHERE duration > 12;", "result": "[(551,)]", "error": null, "gold_result": "[(551,)]", "gold_error": null } } }, { "instance_id": "id9", "train_trial_index": 0, "predicted_text": " To find the average amount of loans granted to clients, we need to look at the `loan` table and calculate the average of the `amount` column. Here's the SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1199.0, "num_output_tokens": 42.0, "inference_runtime": 1.187617301940918, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To find the average amount of loans granted to clients, we need to look at the `loan` table and calculate the average of the `amount` column. Here's the SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": "[(151410.1759530792,)]", "gold_error": null } } }, { "instance_id": "id10", "train_trial_index": 0, "predicted_text": " SELECT COUNT(*) FROM trans WHERE type = 'VYBER KARTOU';", "base64_images": [], "stats": { "num_prompt_tokens": 1210.0, "num_output_tokens": 21.0, "inference_runtime": 0.5842990875244141, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT COUNT(*) FROM trans WHERE type = 'VYBER KARTOU';", "result": "[(0,)]", "error": null, "gold_result": "[(0,)]", "gold_error": null } } }, { "instance_id": "id11", "train_trial_index": 0, "predicted_text": " SELECT AVG(balance)\nFROM trans\nWHERE account_id IN (\n SELECT account_id\n FROM account\n WHERE frequency = 'POPLATEK MESICNE'\n);", "base64_images": [], "stats": { "num_prompt_tokens": 1210.0, "num_output_tokens": 48.0, "inference_runtime": 0.4974699020385742, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT AVG(balance)\nFROM trans\nWHERE account_id IN (\n SELECT account_id\n FROM account\n WHERE frequency = 'POPLATEK MESICNE'\n);", "result": "[(37346.81918755991,)]", "error": null, "gold_result": "[(37346.81918755991,)]", "gold_error": null } } }, { "instance_id": "id12", "train_trial_index": 0, "predicted_text": " SELECT COUNT(*) FROM loan WHERE status = 'A';", "base64_images": [], "stats": { "num_prompt_tokens": 1202.0, "num_output_tokens": 15.0, "inference_runtime": 0.3724558353424072, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT COUNT(*) FROM loan WHERE status = 'A';", "result": "[(203,)]", "error": null, "gold_result": "[(203,)]", "gold_error": null } } }, { "instance_id": "id13", "train_trial_index": 0, "predicted_text": " SELECT AVG(amount) FROM trans WHERE type = 'PRIJEM';", "base64_images": [], "stats": { "num_prompt_tokens": 1204.0, "num_output_tokens": 18.0, "inference_runtime": 0.5989475250244141, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT AVG(amount) FROM trans WHERE type = 'PRIJEM';", "result": "[(7967.460160016589,)]", "error": null, "gold_result": "[(7967.460160016589,)]", "gold_error": null } } }, { "instance_id": "id14", "train_trial_index": 0, "predicted_text": " SELECT COUNT(*) FROM client WHERE strftime('%Y', birth_date) = '1990';", "base64_images": [], "stats": { "num_prompt_tokens": 1208.0, "num_output_tokens": 28.0, "inference_runtime": 0.967139720916748, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT COUNT(*) FROM client WHERE strftime('%Y', birth_date) = '1990';", "result": "[(0,)]", "error": null, "gold_result": "[(0,)]", "gold_error": null } } }, { "instance_id": "id15", "train_trial_index": 0, "predicted_text": " SELECT AVG(balance) FROM trans\nINNER JOIN account ON trans.account_id = account.account_id\nWHERE account.district_id = 1;", "base64_images": [], "stats": { "num_prompt_tokens": 1204.0, "num_output_tokens": 41.0, "inference_runtime": 1.1451151371002197, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT AVG(balance) FROM trans\nINNER JOIN account ON trans.account_id = account.account_id\nWHERE account.district_id = 1;", "result": "[(38883.807138063385,)]", "error": null, "gold_result": "[(38883.807138063385,)]", "gold_error": null } } }, { "instance_id": "id16", "train_trial_index": 0, "predicted_text": " SELECT COUNT(*) FROM loan WHERE duration = 6;", "base64_images": [], "stats": { "num_prompt_tokens": 1203.0, "num_output_tokens": 15.0, "inference_runtime": 0.3723421096801758, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT COUNT(*) FROM loan WHERE duration = 6;", "result": "[(0,)]", "error": null, "gold_result": "[(0,)]", "gold_error": null } } }, { "instance_id": "id17", "train_trial_index": 0, "predicted_text": " SELECT AVG(amount) FROM trans WHERE type = 'VYDAJ';", "base64_images": [], "stats": { "num_prompt_tokens": 1205.0, "num_output_tokens": 19.0, "inference_runtime": 0.8303971290588379, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT AVG(amount) FROM trans WHERE type = 'VYDAJ';", "result": "[(4446.74555723473,)]", "error": null, "gold_result": "[(4446.74555723473,)]", "gold_error": null } } }, { "instance_id": "id18", "train_trial_index": 0, "predicted_text": " SELECT COUNT(*) FROM client WHERE gender = 'M';", "base64_images": [], "stats": { "num_prompt_tokens": 1202.0, "num_output_tokens": 15.0, "inference_runtime": 0.9039227962493896, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT COUNT(*) FROM client WHERE gender = 'M';", "result": "[(2724,)]", "error": null, "gold_result": "[(0,)]", "gold_error": null } } }, { "instance_id": "id19", "train_trial_index": 0, "predicted_text": " SELECT AVG(balance)\nFROM trans\nJOIN account ON trans.account_id = account.account_id\nWHERE account.frequency = 'POPLATEK TYDNE';", "base64_images": [], "stats": { "num_prompt_tokens": 1210.0, "num_output_tokens": 45.0, "inference_runtime": 0.5448920726776123, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT AVG(balance)\nFROM trans\nJOIN account ON trans.account_id = account.account_id\nWHERE account.frequency = 'POPLATEK TYDNE';", "result": "[(52019.53688046414,)]", "error": null, "gold_result": "[(52019.53688046414,)]", "gold_error": null } } }, { "instance_id": "id20", "train_trial_index": 0, "predicted_text": " SELECT COUNT(*) FROM loan WHERE status = 'C';", "base64_images": [], "stats": { "num_prompt_tokens": 1202.0, "num_output_tokens": 15.0, "inference_runtime": 0.5771913528442383, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT COUNT(*) FROM loan WHERE status = 'C';", "result": "[(403,)]", "error": null, "gold_result": "[(403,)]", "gold_error": null } } }, { "instance_id": "id21", "train_trial_index": 0, "predicted_text": " SELECT AVG(amount) FROM trans WHERE type = 'VKLAD';", "base64_images": [], "stats": { "num_prompt_tokens": 1205.0, "num_output_tokens": 19.0, "inference_runtime": 0.3714616298675537, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT AVG(amount) FROM trans WHERE type = 'VKLAD';", "result": "[(None,)]", "error": null, "gold_result": "[(None,)]", "gold_error": null } } }, { "instance_id": "id22", "train_trial_index": 0, "predicted_text": " SELECT COUNT(*) FROM client WHERE strftime('%Y', birth_date) = '1980';", "base64_images": [], "stats": { "num_prompt_tokens": 1208.0, "num_output_tokens": 28.0, "inference_runtime": 0.41878795623779297, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT COUNT(*) FROM client WHERE strftime('%Y', birth_date) = '1980';", "result": "[(94,)]", "error": null, "gold_result": "[(94,)]", "gold_error": null } } }, { "instance_id": "id23", "train_trial_index": 0, "predicted_text": " SELECT AVG(balance) FROM trans\nINNER JOIN account ON trans.account_id = account.account_id\nWHERE account.district_id = 2;", "base64_images": [], "stats": { "num_prompt_tokens": 1204.0, "num_output_tokens": 41.0, "inference_runtime": 0.5483312606811523, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT AVG(balance) FROM trans\nINNER JOIN account ON trans.account_id = account.account_id\nWHERE account.district_id = 2;", "result": "[(38406.21400629711,)]", "error": null, "gold_result": "[(38406.21400629711,)]", "gold_error": null } } }, { "instance_id": "id24", "train_trial_index": 0, "predicted_text": " SELECT COUNT(*) FROM loan WHERE duration = 3;", "base64_images": [], "stats": { "num_prompt_tokens": 1203.0, "num_output_tokens": 15.0, "inference_runtime": 0.86086106300354, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT COUNT(*) FROM loan WHERE duration = 3;", "result": "[(0,)]", "error": null, "gold_result": "[(0,)]", "gold_error": null } } }, { "instance_id": "id25", "train_trial_index": 0, "predicted_text": " SELECT AVG(amount) FROM trans WHERE type = 'PREVOD Z UCTU';", "base64_images": [], "stats": { "num_prompt_tokens": 1208.0, "num_output_tokens": 22.0, "inference_runtime": 0.5796082019805908, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT AVG(amount) FROM trans WHERE type = 'PREVOD Z UCTU';", "result": "[(None,)]", "error": null, "gold_result": "[(None,)]", "gold_error": null } } }, { "instance_id": "id26", "train_trial_index": 0, "predicted_text": " SELECT COUNT(*) FROM client WHERE gender = 'F';", "base64_images": [], "stats": { "num_prompt_tokens": 1202.0, "num_output_tokens": 15.0, "inference_runtime": 0.8471221923828125, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT COUNT(*) FROM client WHERE gender = 'F';", "result": "[(2645,)]", "error": null, "gold_result": "[(0,)]", "gold_error": null } } }, { "instance_id": "id27", "train_trial_index": 0, "predicted_text": " SELECT AVG(balance)\nFROM trans\nJOIN account ON trans.account_id = account.account_id\nWHERE account.frequency = 'POPLATEK PO OBRATU';", "base64_images": [], "stats": { "num_prompt_tokens": 1211.0, "num_output_tokens": 46.0, "inference_runtime": 0.5269351005554199, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT AVG(balance)\nFROM trans\nJOIN account ON trans.account_id = account.account_id\nWHERE account.frequency = 'POPLATEK PO OBRATU';", "result": "[(50388.21816326531,)]", "error": null, "gold_result": "[(50388.21816326531,)]", "gold_error": null } } }, { "instance_id": "id28", "train_trial_index": 0, "predicted_text": " SELECT COUNT(*) FROM loan WHERE status = 'D';", "base64_images": [], "stats": { "num_prompt_tokens": 1202.0, "num_output_tokens": 15.0, "inference_runtime": 0.3272247314453125, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT COUNT(*) FROM loan WHERE status = 'D';", "result": "[(45,)]", "error": null, "gold_result": "[(45,)]", "gold_error": null } } }, { "instance_id": "id29", "train_trial_index": 0, "predicted_text": " SELECT AVG(amount) FROM trans WHERE type = 'PREVOD NA UCET';", "base64_images": [], "stats": { "num_prompt_tokens": 1208.0, "num_output_tokens": 22.0, "inference_runtime": 0.8697118759155273, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT AVG(amount) FROM trans WHERE type = 'PREVOD NA UCET';", "result": "[(None,)]", "error": null, "gold_result": "[(None,)]", "gold_error": null } } }, { "instance_id": "id30", "train_trial_index": 0, "predicted_text": " SELECT COUNT(*) FROM client WHERE strftime('%Y', birth_date) = '1995';", "base64_images": [], "stats": { "num_prompt_tokens": 1208.0, "num_output_tokens": 28.0, "inference_runtime": 1.0093400478363037, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT COUNT(*) FROM client WHERE strftime('%Y', birth_date) = '1995';", "result": "[(0,)]", "error": null, "gold_result": "[(0,)]", "gold_error": null } } }, { "instance_id": "id31", "train_trial_index": 0, "predicted_text": " SELECT AVG(balance) FROM trans\nINNER JOIN account ON trans.account_id = account.account_id\nWHERE account.district_id = 3;", "base64_images": [], "stats": { "num_prompt_tokens": 1204.0, "num_output_tokens": 41.0, "inference_runtime": 0.5872602462768555, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT AVG(balance) FROM trans\nINNER JOIN account ON trans.account_id = account.account_id\nWHERE account.district_id = 3;", "result": "[(36723.97896693406,)]", "error": null, "gold_result": "[(36723.97896693406,)]", "gold_error": null } } }, { "instance_id": "id32", "train_trial_index": 0, "predicted_text": " SELECT COUNT(*) FROM loan WHERE duration = 9;", "base64_images": [], "stats": { "num_prompt_tokens": 1203.0, "num_output_tokens": 15.0, "inference_runtime": 0.3357982635498047, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT COUNT(*) FROM loan WHERE duration = 9;", "result": "[(0,)]", "error": null, "gold_result": "[(0,)]", "gold_error": null } } }, { "instance_id": "id33", "train_trial_index": 0, "predicted_text": " SELECT AVG(amount) FROM trans WHERE type = 'VYBER';", "base64_images": [], "stats": { "num_prompt_tokens": 1204.0, "num_output_tokens": 18.0, "inference_runtime": 0.40674686431884766, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT AVG(amount) FROM trans WHERE type = 'VYBER';", "result": "[(12516.726149045962,)]", "error": null, "gold_result": "[(12516.726149045962,)]", "gold_error": null } } }, { "instance_id": "id34", "train_trial_index": 0, "predicted_text": " SELECT district.district_id, COUNT(account.account_id) as total_accounts\nFROM district\nLEFT JOIN account ON district.district_id = account.district_id\nGROUP BY district.district_id;", "base64_images": [], "stats": { "num_prompt_tokens": 1200.0, "num_output_tokens": 56.0, "inference_runtime": 1.2291274070739746, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT district.district_id, COUNT(account.account_id) as total_accounts\nFROM district\nLEFT JOIN account ON district.district_id = account.district_id\nGROUP BY district.district_id;", "result": "[(1, 554), (2, 42), (3, 50), (4, 48), (5, 65), (6, 48), (7, 44), (8, 56), (9, 46), (10, 48), (11, 48), (12, 42), (13, 37), (14, 41), (15, 50), (16, 52), (17, 42), (18, 46), (19, 55), (20, 36), (21, 48), (22, 36), (23, 50), (24, 42), (25, 42), (26, 45), (27, 39), (28, 45), (29, 43), (30, 38), (31, 50), (32, 48), (33, 49), (34, 39), (35, 43), (36, 57), (37, 37), (38, 54), (39, 41), (40, 44), (41, 45), (42, 48), (43, 49), (44, 50), (45, 41), (46, 59), (47, 53), (48, 53), (49, 37), (50, 48), (51, 49), (52, 57), (53, 50), (54, 128), (55, 53), (56, 44), (57, 41), (58, 32), (59, 57), (60, 51), (61, 45), (62, 44), (63, 49), (64, 92), (65, 39), (66, 53), (67, 43), (68, 83), (69, 48), (70, 152), (71, 42), (72, 88), (73, 56), (74, 135), (75, 51), (76, 55), (77, 40)]", "error": null, "gold_result": "[(1, 554), (2, 42), (3, 50), (4, 48), (5, 65), (6, 48), (7, 44), (8, 56), (9, 46), (10, 48), (11, 48), (12, 42), (13, 37), (14, 41), (15, 50), (16, 52), (17, 42), (18, 46), (19, 55), (20, 36), (21, 48), (22, 36), (23, 50), (24, 42), (25, 42), (26, 45), (27, 39), (28, 45), (29, 43), (30, 38), (31, 50), (32, 48), (33, 49), (34, 39), (35, 43), (36, 57), (37, 37), (38, 54), (39, 41), (40, 44), (41, 45), (42, 48), (43, 49), (44, 50), (45, 41), (46, 59), (47, 53), (48, 53), (49, 37), (50, 48), (51, 49), (52, 57), (53, 50), (54, 128), (55, 53), (56, 44), (57, 41), (58, 32), (59, 57), (60, 51), (61, 45), (62, 44), (63, 49), (64, 92), (65, 39), (66, 53), (67, 43), (68, 83), (69, 48), (70, 152), (71, 42), (72, 88), (73, 56), (74, 135), (75, 51), (76, 55), (77, 40)]", "gold_error": null } } }, { "instance_id": "id35", "train_trial_index": 0, "predicted_text": " To find the average balance of accounts in each district, you would need to join the `account` and `district` tables on the `district_id` field and then group the results by `district_id`. However, the balance is not directly available in the `account` table.", "base64_images": [], "stats": { "num_prompt_tokens": 1199.0, "num_output_tokens": 64.0, "inference_runtime": 0.8137540817260742, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To find the average balance of accounts in each district, you would need to join the `account` and `district` tables on the `district_id` field and then group the results by `district_id`. However, the balance is not directly available in the `account` table.", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: district_id" } } }, { "instance_id": "id36", "train_trial_index": 0, "predicted_text": " SELECT district_id, COUNT(client_id) as client_count\nFROM client\nGROUP BY district_id\nORDER BY client_count DESC\nLIMIT 5;", "base64_images": [], "stats": { "num_prompt_tokens": 1202.0, "num_output_tokens": 40.0, "inference_runtime": 0.5492649078369141, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT district_id, COUNT(client_id) as client_count\nFROM client\nGROUP BY district_id\nORDER BY client_count DESC\nLIMIT 5;", "result": "[(1, 663), (74, 180), (70, 169), (54, 155), (64, 109)]", "error": null, "gold_result": "[(1, 663), (74, 180), (70, 169), (54, 155), (64, 109)]", "gold_error": null } } }, { "instance_id": "id37", "train_trial_index": 0, "predicted_text": " To find the average age of clients in each district, you would first need to calculate the age of each client by subtracting their birth date from the current date. However, SQLite does not have a built-in function to get the current date, so we'll use a workaround by assuming that the current date is a fixed date for the calculation.", "base64_images": [], "stats": { "num_prompt_tokens": 1199.0, "num_output_tokens": 76.0, "inference_runtime": 1.5453481674194336, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To find the average age of clients in each district, you would first need to calculate the age of each client by subtracting their birth date from the current date. However, SQLite does not have a built-in function to get the current date, so we'll use a workaround by assuming that the current date is a fixed date for the calculation.", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such function: DATEDIFF" } } }, { "instance_id": "id38", "train_trial_index": 0, "predicted_text": " SELECT district.district_id, COUNT(loan.loan_id) as total_loans\nFROM district\nLEFT JOIN account ON district.district_id = account.district_id\nLEFT JOIN loan ON account.account_id = loan.account_id\nGROUP BY district.district_id;", "base64_images": [], "stats": { "num_prompt_tokens": 1200.0, "num_output_tokens": 75.0, "inference_runtime": 0.7609515190124512, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT district.district_id, COUNT(loan.loan_id) as total_loans\nFROM district\nLEFT JOIN account ON district.district_id = account.district_id\nLEFT JOIN loan ON account.account_id = loan.account_id\nGROUP BY district.district_id;", "result": "[(1, 84), (2, 6), (3, 6), (4, 7), (5, 10), (6, 9), (7, 6), (8, 5), (9, 9), (10, 5), (11, 11), (12, 8), (13, 8), (14, 8), (15, 7), (16, 11), (17, 4), (18, 3), (19, 14), (20, 6), (21, 7), (22, 2), (23, 4), (24, 8), (25, 3), (26, 6), (27, 9), (28, 7), (29, 8), (30, 2), (31, 8), (32, 6), (33, 4), (34, 9), (35, 1), (36, 7), (37, 7), (38, 13), (39, 5), (40, 6), (41, 3), (42, 7), (43, 5), (44, 8), (45, 7), (46, 6), (47, 10), (48, 6), (49, 3), (50, 12), (51, 6), (52, 14), (53, 7), (54, 24), (55, 10), (56, 7), (57, 6), (58, 4), (59, 9), (60, 9), (61, 6), (62, 10), (63, 7), (64, 17), (65, 6), (66, 7), (67, 6), (68, 16), (69, 8), (70, 24), (71, 6), (72, 14), (73, 8), (74, 20), (75, 4), (76, 5), (77, 6)]", "error": null, "gold_result": null, "gold_error": "no such column: district_id" } } }, { "instance_id": "id39", "train_trial_index": 0, "predicted_text": " SELECT d.district_id, AVG(l.amount) as avg_loan_amount\nFROM loan l\nJOIN account a ON l.account_id = a.account_id\nJOIN district d ON a.district_id = d.district_id\nGROUP BY d.district_id;", "base64_images": [], "stats": { "num_prompt_tokens": 1198.0, "num_output_tokens": 75.0, "inference_runtime": 0.7474691867828369, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT d.district_id, AVG(l.amount) as avg_loan_amount\nFROM loan l\nJOIN account a ON l.account_id = a.account_id\nJOIN district d ON a.district_id = d.district_id\nGROUP BY d.district_id;", "result": "[(1, 153957.2857142857), (2, 147992.0), (3, 243466.0), (4, 158074.2857142857), (5, 190116.0), (6, 232886.66666666666), (7, 92966.0), (8, 123504.0), (9, 111676.0), (10, 171892.8), (11, 112099.63636363637), (12, 162399.0), (13, 120093.0), (14, 251365.5), (15, 83475.42857142857), (16, 121779.27272727272), (17, 103923.0), (18, 159164.0), (19, 168335.14285714287), (20, 204082.0), (21, 137849.14285714287), (22, 198504.0), (23, 109704.0), (24, 105649.5), (25, 90292.0), (26, 126134.0), (27, 143798.66666666666), (28, 191283.42857142858), (29, 166150.5), (30, 74262.0), (31, 119973.0), (32, 77114.0), (33, 127488.0), (34, 102657.33333333333), (35, 127200.0), (36, 138756.0), (37, 159461.14285714287), (38, 118465.84615384616), (39, 123012.0), (40, 145458.0), (41, 115828.0), (42, 170458.2857142857), (43, 126134.4), (44, 178960.5), (45, 121623.42857142857), (46, 294730.0), (47, 164836.8), (48, 125674.0), (49, 92060.0), (50, 175007.0), (51, 179430.0), (52, 158082.85714285713), (53, 170146.2857142857), (54, 168725.0), (55, 123223.2), (56, 168113.14285714287), (57, 198584.0), (58, 91524.0), (59, 106674.66666666667), (60, 126534.66666666667), (61, 105000.0), (62, 168204.0), (63, 111027.42857142857), (64, 177221.64705882352), (65, 157396.0), (66, 189505.7142857143), (67, 212966.0), (68, 128359.5), (69, 242304.0), (70, 127492.5), (71, 115728.0), (72, 163399.7142857143), (73, 186634.5), (74, 163011.0), (75, 173907.0), (76, 165928.8), (77, 81830.0)]", "error": null, "gold_result": null, "gold_error": "no such column: district_id" } } }, { "instance_id": "id40", "train_trial_index": 0, "predicted_text": " To get the top 5 districts with the highest number of credit card holders, you can use the following SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1205.0, "num_output_tokens": 27.0, "inference_runtime": 0.9688112735748291, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To get the top 5 districts with the highest number of credit card holders, you can use the following SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: district_id" } } }, { "instance_id": "id41", "train_trial_index": 0, "predicted_text": " To find the average credit card limit in each district, we first need to understand that the credit card limit is not directly available in the given schema. However, we can assume that the \"balance\" column in the \"trans\" table represents the credit card limit for each account.", "base64_images": [], "stats": { "num_prompt_tokens": 1199.0, "num_output_tokens": 58.0, "inference_runtime": 1.0110962390899658, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To find the average credit card limit in each district, we first need to understand that the credit card limit is not directly available in the given schema. However, we can assume that the \"balance\" column in the \"trans\" table represents the credit card limit for each account.", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: district_id" } } }, { "instance_id": "id42", "train_trial_index": 0, "predicted_text": " To get the total number of transactions in each district, you would need to join the `account` and `trans` tables, group the results by `district_id`, and then count the number of transactions for each district. Here's the SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1199.0, "num_output_tokens": 56.0, "inference_runtime": 1.0085103511810303, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To get the total number of transactions in each district, you would need to join the `account` and `trans` tables, group the results by `district_id`, and then count the number of transactions for each district. Here's the SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: district_id" } } }, { "instance_id": "id43", "train_trial_index": 0, "predicted_text": " To find the average transaction amount in each district, you would need to join the `account` and `trans` tables, group the results by `district_id`, and calculate the average `amount` for each group. However, since the `district_id` is not directly present in the `trans` table, you would first need to join the `account` table to get the `district_id`. Here's the SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1198.0, "num_output_tokens": 96.0, "inference_runtime": 1.5815441608428955, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To find the average transaction amount in each district, you would need to join the `account` and `trans` tables, group the results by `district_id`, and calculate the average `amount` for each group. However, since the `district_id` is not directly present in the `trans` table, you would first need to join the `account` table to get the `district_id`. Here's the SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: district_id" } } }, { "instance_id": "id44", "train_trial_index": 0, "predicted_text": " To get the top 5 districts with the highest number of debit transactions, you can use the following SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1204.0, "num_output_tokens": 26.0, "inference_runtime": 0.8597104549407959, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To get the top 5 districts with the highest number of debit transactions, you can use the following SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: district_id" } } }, { "instance_id": "id45", "train_trial_index": 0, "predicted_text": " To find the average debit transaction amount in each district, you can use the following SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1200.0, "num_output_tokens": 22.0, "inference_runtime": 0.39064693450927734, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To find the average debit transaction amount in each district, you can use the following SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: district_id" } } }, { "instance_id": "id46", "train_trial_index": 0, "predicted_text": " To get the top 5 districts with the highest number of credit transactions, you can use the following SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1203.0, "num_output_tokens": 25.0, "inference_runtime": 0.33336877822875977, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To get the top 5 districts with the highest number of credit transactions, you can use the following SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: district_id" } } }, { "instance_id": "id47", "train_trial_index": 0, "predicted_text": " To find the average credit transaction amount in each district, we need to join the `trans` table with the `account` table and then join the result with the `district` table. After that, we can filter the transactions to only include credit transactions and calculate the average amount for each district.", "base64_images": [], "stats": { "num_prompt_tokens": 1199.0, "num_output_tokens": 63.0, "inference_runtime": 1.1336116790771484, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To find the average credit transaction amount in each district, we need to join the `trans` table with the `account` table and then join the result with the `district` table. After that, we can filter the transactions to only include credit transactions and calculate the average amount for each district.", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: district_id" } } }, { "instance_id": "id48", "train_trial_index": 0, "predicted_text": " SELECT COUNT(*) FROM loan WHERE duration > 12;", "base64_images": [], "stats": { "num_prompt_tokens": 1205.0, "num_output_tokens": 16.0, "inference_runtime": 0.35344767570495605, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT COUNT(*) FROM loan WHERE duration > 12;", "result": "[(551,)]", "error": null, "gold_result": "[(551,)]", "gold_error": null } } }, { "instance_id": "id49", "train_trial_index": 0, "predicted_text": " SELECT AVG(amount)\nFROM loan\nWHERE duration > 365;", "base64_images": [], "stats": { "num_prompt_tokens": 1206.0, "num_output_tokens": 21.0, "inference_runtime": 0.6362526416778564, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT AVG(amount)\nFROM loan\nWHERE duration > 365;", "result": "[(None,)]", "error": null, "gold_result": "[(174656.05807622505,)]", "gold_error": null } } }, { "instance_id": "id50", "train_trial_index": 0, "predicted_text": " To get the top 5 districts with the highest number of clients with a loan, you can use the following SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1205.0, "num_output_tokens": 27.0, "inference_runtime": 0.38184642791748047, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To get the top 5 districts with the highest number of clients with a loan, you can use the following SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": "[(1, 46), (74, 12), (72, 11), (43, 9), (66, 9)]", "gold_error": null } } }, { "instance_id": "id51", "train_trial_index": 0, "predicted_text": " To find the average age of clients with a loan in each district, you'll need to join the relevant tables and calculate the average age grouped by district. Here's the SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1202.0, "num_output_tokens": 42.0, "inference_runtime": 0.5865211486816406, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To find the average age of clients with a loan in each district, you'll need to join the relevant tables and calculate the average age grouped by district. Here's the SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such function: DATEDIFF" } } }, { "instance_id": "id52", "train_trial_index": 0, "predicted_text": " SELECT d.district_id, COUNT(c.card_id) as total_credit_cards\nFROM card c\nJOIN disp d_c ON c.disp_id = d_c.disp_id\nJOIN account a ON d_c.account_id = a.account_id\nJOIN district d ON a.district_id = d.district_id\nWHERE c.type = 'credit'\nGROUP BY d.district_id;", "base64_images": [], "stats": { "num_prompt_tokens": 1201.0, "num_output_tokens": 110.0, "inference_runtime": 1.530656099319458, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT d.district_id, COUNT(c.card_id) as total_credit_cards\nFROM card c\nJOIN disp d_c ON c.disp_id = d_c.disp_id\nJOIN account a ON d_c.account_id = a.account_id\nJOIN district d ON a.district_id = d.district_id\nWHERE c.type = 'credit'\nGROUP BY d.district_id;", "result": "[]", "error": null, "gold_result": null, "gold_error": "no such column: district_id" } } }, { "instance_id": "id53", "train_trial_index": 0, "predicted_text": " To find the average credit limit of credit cards issued in each district, we need to join the `card`, `disp`, and `district` tables. However, the schema provided does not include a credit limit column in any of the tables.", "base64_images": [], "stats": { "num_prompt_tokens": 1202.0, "num_output_tokens": 52.0, "inference_runtime": 0.9200530052185059, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To find the average credit limit of credit cards issued in each district, we need to join the `card`, `disp`, and `district` tables. However, the schema provided does not include a credit limit column in any of the tables.", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: district_id" } } }, { "instance_id": "id54", "train_trial_index": 0, "predicted_text": " To get the top 5 districts with the highest number of debit transactions per account, you can use the following SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1206.0, "num_output_tokens": 28.0, "inference_runtime": 0.853062629699707, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To get the top 5 districts with the highest number of debit transactions per account, you can use the following SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: district_id" } } }, { "instance_id": "id55", "train_trial_index": 0, "predicted_text": " To find the average debit transaction amount per account in each district, you can use the following SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1202.0, "num_output_tokens": 24.0, "inference_runtime": 0.8649003505706787, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To find the average debit transaction amount per account in each district, you can use the following SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: district_id" } } }, { "instance_id": "id56", "train_trial_index": 0, "predicted_text": " To get the top 5 districts with the highest number of credit transactions per account, you can use the following SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1205.0, "num_output_tokens": 27.0, "inference_runtime": 0.62306809425354, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To get the top 5 districts with the highest number of credit transactions per account, you can use the following SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: district_id" } } }, { "instance_id": "id57", "train_trial_index": 0, "predicted_text": " To find the average credit transaction amount per account in each district, we need to join the `account`, `trans`, and `district` tables. We'll filter for credit transactions and then group by district and account to calculate the average amount. Here's the SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1201.0, "num_output_tokens": 60.0, "inference_runtime": 0.818183183670044, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To find the average credit transaction amount per account in each district, we need to join the `account`, `trans`, and `district` tables. We'll filter for credit transactions and then group by district and account to calculate the average amount. Here's the SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: district_id" } } }, { "instance_id": "id58", "train_trial_index": 0, "predicted_text": " To get the total number of accounts with a balance greater than 100,000, you would need to join the \"account\" table with the \"trans\" table, since the balance information is stored in the \"trans\" table. However, SQLite does not support subqueries in the FROM clause, so we cannot directly join these tables.", "base64_images": [], "stats": { "num_prompt_tokens": 1209.0, "num_output_tokens": 76.0, "inference_runtime": 0.8491463661193848, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To get the total number of accounts with a balance greater than 100,000, you would need to join the \"account\" table with the \"trans\" table, since the balance information is stored in the \"trans\" table. However, SQLite does not support subqueries in the FROM clause, so we cannot directly join these tables.", "result": null, "error": "near \"To\": syntax error", "gold_result": "[(19108,)]", "gold_error": null } } }, { "instance_id": "id59", "train_trial_index": 0, "predicted_text": " SELECT AVG(balance)\nFROM trans\nWHERE balance > 100000;", "base64_images": [], "stats": { "num_prompt_tokens": 1209.0, "num_output_tokens": 24.0, "inference_runtime": 1.0201356410980225, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT AVG(balance)\nFROM trans\nWHERE balance > 100000;", "result": "[(113874.11455934687,)]", "error": null, "gold_result": "[(113874.11455934687,)]", "gold_error": null } } }, { "instance_id": "id60", "train_trial_index": 0, "predicted_text": " To get the top 5 districts with the highest number of accounts with a balance greater than 100,000, you can use the following SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1215.0, "num_output_tokens": 37.0, "inference_runtime": 0.578047513961792, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To get the top 5 districts with the highest number of accounts with a balance greater than 100,000, you can use the following SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: district_id" } } }, { "instance_id": "id61", "train_trial_index": 0, "predicted_text": " To find the average age of clients with a balance greater than 100,000 in each district, you'll need to join the `client`, `disp`, `account`, and `trans` tables. Since the `trans` table contains the balance information, we'll use it to filter clients with a balance greater than 100,000.", "base64_images": [], "stats": { "num_prompt_tokens": 1212.0, "num_output_tokens": 81.0, "inference_runtime": 1.7172980308532715, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To find the average age of clients with a balance greater than 100,000 in each district, you'll need to join the `client`, `disp`, `account`, and `trans` tables. Since the `trans` table contains the balance information, we'll use it to filter clients with a balance greater than 100,000.", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such function: DATEDIFF" } } }, { "instance_id": "id62", "train_trial_index": 0, "predicted_text": " SELECT COUNT(DISTINCT account_id)\nFROM trans\nWHERE type = 'debit' AND amount > 10000;", "base64_images": [], "stats": { "num_prompt_tokens": 1211.0, "num_output_tokens": 36.0, "inference_runtime": 0.5354809761047363, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT COUNT(DISTINCT account_id)\nFROM trans\nWHERE type = 'debit' AND amount > 10000;", "result": "[(0,)]", "error": null, "gold_result": "[(74686,)]", "gold_error": null } } }, { "instance_id": "id63", "train_trial_index": 0, "predicted_text": " SELECT AVG(amount)\nFROM trans\nWHERE type = 'debit' AND amount > 10000;", "base64_images": [], "stats": { "num_prompt_tokens": 1214.0, "num_output_tokens": 30.0, "inference_runtime": 0.5547263622283936, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT AVG(amount)\nFROM trans\nWHERE type = 'debit' AND amount > 10000;", "result": "[(None,)]", "error": null, "gold_result": "[(20466.919114693515,)]", "gold_error": null } } }, { "instance_id": "id64", "train_trial_index": 0, "predicted_text": " To get the top 5 districts with the highest number of accounts with a debit transaction amount greater than 10,000, you can use the following SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1217.0, "num_output_tokens": 39.0, "inference_runtime": 0.40564775466918945, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To get the top 5 districts with the highest number of accounts with a debit transaction amount greater than 10,000, you can use the following SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: district_id" } } }, { "instance_id": "id65", "train_trial_index": 0, "predicted_text": " To find the average age of clients with a debit transaction amount greater than 10,000 in each district, you can use the following SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1214.0, "num_output_tokens": 36.0, "inference_runtime": 0.6845006942749023, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To find the average age of clients with a debit transaction amount greater than 10,000 in each district, you can use the following SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such function: DATEDIFF" } } }, { "instance_id": "id66", "train_trial_index": 0, "predicted_text": " SELECT COUNT(*)\nFROM account\nWHERE account_id IN (\n SELECT account_id\n FROM trans\n WHERE type = 'credit' AND amount > 10000\n);", "base64_images": [], "stats": { "num_prompt_tokens": 1210.0, "num_output_tokens": 49.0, "inference_runtime": 0.6688783168792725, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT COUNT(*)\nFROM account\nWHERE account_id IN (\n SELECT account_id\n FROM trans\n WHERE type = 'credit' AND amount > 10000\n);", "result": "[(0,)]", "error": null, "gold_result": "[(112238,)]", "gold_error": null } } }, { "instance_id": "id67", "train_trial_index": 0, "predicted_text": " To find the average credit transaction amount of accounts with a credit transaction amount greater than 10,000, you can use the following SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1212.0, "num_output_tokens": 34.0, "inference_runtime": 0.9005148410797119, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To find the average credit transaction amount of accounts with a credit transaction amount greater than 10,000, you can use the following SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": "[(24164.004677560184,)]", "gold_error": null } } }, { "instance_id": "id68", "train_trial_index": 0, "predicted_text": " To find the total number of clients who have taken a loan and have a credit card, we need to join the `client`, `disp`, `card`, and `loan` tables. Here's the SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1206.0, "num_output_tokens": 48.0, "inference_runtime": 0.7545666694641113, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To find the total number of clients who have taken a loan and have a credit card, we need to join the `client`, `disp`, `card`, and `loan` tables. Here's the SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: c.client_id" } } }, { "instance_id": "id69", "train_trial_index": 0, "predicted_text": " To calculate the average balance of accounts that have a credit card, we need to join the `account`, `disp`, and `card` tables based on their relationships. Then, we can filter the results to only include accounts with a credit card and calculate the average balance. Here's the SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1202.0, "num_output_tokens": 64.0, "inference_runtime": 1.2166748046875, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To calculate the average balance of accounts that have a credit card, we need to join the `account`, `disp`, and `card` tables based on their relationships. Then, we can filter the results to only include accounts with a credit card and calculate the average balance. Here's the SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": "[(36724.79971198933,)]", "gold_error": null } } }, { "instance_id": "id70", "train_trial_index": 0, "predicted_text": " SELECT district_id, COUNT(account_id) as account_count\nFROM account\nGROUP BY district_id\nORDER BY account_count DESC\nLIMIT 5;", "base64_images": [], "stats": { "num_prompt_tokens": 1202.0, "num_output_tokens": 40.0, "inference_runtime": 0.6056563854217529, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT district_id, COUNT(account_id) as account_count\nFROM account\nGROUP BY district_id\nORDER BY account_count DESC\nLIMIT 5;", "result": "[(1, 554), (70, 152), (74, 135), (54, 128), (64, 92)]", "error": null, "gold_result": "[(1, 554), (70, 152), (74, 135), (54, 128), (64, 92)]", "gold_error": null } } }, { "instance_id": "id71", "train_trial_index": 0, "predicted_text": " To calculate the total amount of loans granted to clients in each district, you can use the following SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1203.0, "num_output_tokens": 24.0, "inference_runtime": 0.35507798194885254, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To calculate the total amount of loans granted to clients in each district, you can use the following SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": "[(1, 12932412), (2, 887952), (3, 1460796), (4, 1106520), (5, 1901160), (6, 2095980), (7, 557796), (8, 617520), (9, 1005084), (10, 859464), (11, 1233096), (12, 1299192), (13, 960744), (14, 2010924), (15, 584328), (16, 1339572), (17, 415692), (18, 477492), (19, 2356692), (20, 1224492), (21, 964944), (22, 397008), (23, 438816), (24, 845196), (25, 270876), (26, 756804), (27, 1294188), (28, 1338984), (29, 1329204), (30, 148524), (31, 959784), (32, 462684), (33, 509952), (34, 923916), (35, 127200), (36, 971292), (37, 1116228), (38, 1540056), (39, 615060), (40, 872748), (41, 347484), (42, 1193208), (43, 630672), (44, 1431684), (45, 851364), (46, 1768380), (47, 1648368), (48, 754044), (49, 276180), (50, 2100084), (51, 1076580), (52, 2213160), (53, 1191024), (54, 4049400), (55, 1232232), (56, 1176792), (57, 1191504), (58, 366096), (59, 960072), (60, 1138812), (61, 630000), (62, 1682040), (63, 777192), (64, 3012768), (65, 944376), (66, 1326540), (67, 1277796), (68, 2053752), (69, 1938432), (70, 3059820), (71, 694368), (72, 2287596), (73, 1493076), (74, 3260220), (75, 695628), (76, 829644), (77, 490980)]", "gold_error": null } } }, { "instance_id": "id72", "train_trial_index": 0, "predicted_text": " To find the clients who have taken a loan and have a balance of less than 0, we need to join the `client`, `disp`, `account`, and `loan` tables. We'll use a subquery to find the clients with a balance of less than 0 by checking the `trans` table. Here's the SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1207.0, "num_output_tokens": 76.0, "inference_runtime": 0.8624069690704346, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To find the clients who have taken a loan and have a balance of less than 0, we need to join the `client`, `disp`, `account`, and `loan` tables. We'll use a subquery to find the clients with a balance of less than 0 by checking the `trans` table. Here's the SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: c.client_id" } } }, { "instance_id": "id73", "train_trial_index": 0, "predicted_text": " To calculate the average salary of clients who have a credit card, we need to join the relevant tables and filter the results. However, there is no salary column in the provided schema. Assuming that the \"amount\" column in the \"trans\" table represents the salary, and the \"type\" column in the \"card\" table indicates whether a card is a credit card, the SQL query would look like this:", "base64_images": [], "stats": { "num_prompt_tokens": 1202.0, "num_output_tokens": 86.0, "inference_runtime": 1.4201152324676514, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To calculate the average salary of clients who have a credit card, we need to join the relevant tables and filter the results. However, there is no salary column in the provided schema. Assuming that the \"amount\" column in the \"trans\" table represents the salary, and the \"type\" column in the \"card\" table indicates whether a card is a credit card, the SQL query would look like this:", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: d.A11" } } }, { "instance_id": "id74", "train_trial_index": 0, "predicted_text": " To find the top 3 types of transactions with the highest average amount, you can use the following SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1203.0, "num_output_tokens": 25.0, "inference_runtime": 0.8630995750427246, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To find the top 3 types of transactions with the highest average amount, you can use the following SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": "[('VYBER', 12516.726149045962), ('PRIJEM', 7967.460160016589), ('VYDAJ', 4446.74555723473)]", "gold_error": null } } }, { "instance_id": "id75", "train_trial_index": 0, "predicted_text": " To calculate the total number of transactions for each type of card, we need to join the `card`, `disp`, and `trans` tables, group the results by the card type, and count the number of transactions for each group. Here's the SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1202.0, "num_output_tokens": 57.0, "inference_runtime": 0.738569974899292, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To calculate the total number of transactions for each type of card, we need to join the `card`, `disp`, and `trans` tables, group the results by the card type, and count the number of transactions for each group. Here's the SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": "[('classic', 104617), ('gold', 15499), ('junior', 20851)]", "gold_error": null } } }, { "instance_id": "id76", "train_trial_index": 0, "predicted_text": " To find the clients who have a balance of more than 10000 and have a credit card, we need to join the `client`, `disp`, `card`, and `trans` tables. The `client` table contains information about the clients, the `disp` table links clients to accounts, the `card` table contains information about the cards, and the `trans` table contains information about transactions and balances.", "base64_images": [], "stats": { "num_prompt_tokens": 1211.0, "num_output_tokens": 91.0, "inference_runtime": 1.02829909324646, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To find the clients who have a balance of more than 10000 and have a credit card, we need to join the `client`, `disp`, `card`, and `trans` tables. The `client` table contains information about the clients, the `disp` table links clients to accounts, the `card` table contains information about the cards, and the `trans` table contains information about transactions and balances.", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: c.client_id" } } }, { "instance_id": "id77", "train_trial_index": 0, "predicted_text": " To calculate the average age of clients who have taken a loan, you'll need to join the `client`, `disp`, `account`, and `loan` tables, and then calculate the average age based on the `birth_date` column. Here's the SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1202.0, "num_output_tokens": 62.0, "inference_runtime": 1.208662748336792, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To calculate the average age of clients who have taken a loan, you'll need to join the `client`, `disp`, `account`, and `loan` tables, and then calculate the average age based on the `birth_date` column. Here's the SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "near \"FROM\": syntax error" } } }, { "instance_id": "id78", "train_trial_index": 0, "predicted_text": " SELECT district_id, COUNT(client_id) as client_count\nFROM client\nGROUP BY district_id\nORDER BY client_count DESC\nLIMIT 5;", "base64_images": [], "stats": { "num_prompt_tokens": 1202.0, "num_output_tokens": 40.0, "inference_runtime": 0.536104679107666, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT district_id, COUNT(client_id) as client_count\nFROM client\nGROUP BY district_id\nORDER BY client_count DESC\nLIMIT 5;", "result": "[(1, 663), (74, 180), (70, 169), (54, 155), (64, 109)]", "error": null, "gold_result": "[(1, 663), (74, 180), (70, 169), (54, 155), (64, 109)]", "gold_error": null } } }, { "instance_id": "id79", "train_trial_index": 0, "predicted_text": " To calculate the total amount of transactions for each type of loan, we need to join the `loan` and `trans` tables on the `account_id` field. Since the `trans` table contains the transaction amounts, we can group by the `type` field from the `loan` table and calculate the sum of the `amount` field from the `trans` table.", "base64_images": [], "stats": { "num_prompt_tokens": 1202.0, "num_output_tokens": 82.0, "inference_runtime": 1.5397231578826904, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To calculate the total amount of transactions for each type of loan, we need to join the `loan` and `trans` tables on the `account_id` field. Since the `trans` table contains the transaction amounts, we can group by the `type` field from the `loan` table and calculate the sum of the `amount` field from the `trans` table.", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: l.type" } } }, { "instance_id": "id80", "train_trial_index": 0, "predicted_text": " SELECT DISTINCT client.client_id\nFROM client\nJOIN disp ON client.client_id = disp.client_id\nJOIN card ON disp.disp_id = card.disp_id\nJOIN account ON disp.account_id = account.account_id\nJOIN loan ON account.account_id = loan.account_id\nWHERE card.type = 'credit';", "base64_images": [], "stats": { "num_prompt_tokens": 1203.0, "num_output_tokens": 90.0, "inference_runtime": 1.351792573928833, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 0.0 }, "annotations": { "czech_bank_qa": { "query": " SELECT DISTINCT client.client_id\nFROM client\nJOIN disp ON client.client_id = disp.client_id\nJOIN card ON disp.disp_id = card.disp_id\nJOIN account ON disp.account_id = account.account_id\nJOIN loan ON account.account_id = loan.account_id\nWHERE card.type = 'credit';", "result": "[]", "error": null, "gold_result": null, "gold_error": "no such column: c.client_id" } } }, { "instance_id": "id81", "train_trial_index": 0, "predicted_text": " To calculate the average balance of accounts that have a loan, you can use a subquery to find the account IDs that have loans and then calculate the average balance of those accounts. Here's the SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1201.0, "num_output_tokens": 46.0, "inference_runtime": 1.1436123847961426, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To calculate the average balance of accounts that have a loan, you can use a subquery to find the account IDs that have loans and then calculate the average balance of those accounts. Here's the SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": "[(45467.190273340435,)]", "gold_error": null } } }, { "instance_id": "id82", "train_trial_index": 0, "predicted_text": " To find the top 3 types of transactions with the highest number of transactions, you can use the following SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1204.0, "num_output_tokens": 26.0, "inference_runtime": 0.8563327789306641, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To find the top 3 types of transactions with the highest number of transactions, you can use the following SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": "[('VYDAJ', 634571), ('PRIJEM', 405083), ('VYBER', 16666)]", "gold_error": null } } }, { "instance_id": "id83", "train_trial_index": 0, "predicted_text": " To calculate the total number of clients who have a credit card and have taken a loan, you can use a subquery to find the clients who have a credit card and then join that with the loan table to find the clients who have taken a loan. Here's the SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1207.0, "num_output_tokens": 60.0, "inference_runtime": 1.3218986988067627, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To calculate the total number of clients who have a credit card and have taken a loan, you can use a subquery to find the clients who have a credit card and then join that with the loan table to find the clients who have taken a loan. Here's the SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: c.client_id" } } }, { "instance_id": "id84", "train_trial_index": 0, "predicted_text": " To find the clients who have a balance of less than 0 and have a credit card, we need to join the `client`, `disp`, `card`, and `trans` tables. The `client` table contains information about the clients, the `disp` table links clients to accounts, the `card` table contains information about the cards, and the `trans` table contains information about transactions and balances.", "base64_images": [], "stats": { "num_prompt_tokens": 1207.0, "num_output_tokens": 87.0, "inference_runtime": 1.377826452255249, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To find the clients who have a balance of less than 0 and have a credit card, we need to join the `client`, `disp`, `card`, and `trans` tables. The `client` table contains information about the clients, the `disp` table links clients to accounts, the `card` table contains information about the cards, and the `trans` table contains information about transactions and balances.", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: c.client_id" } } }, { "instance_id": "id85", "train_trial_index": 0, "predicted_text": " To calculate the average salary of clients who have taken a loan, we need to join the `client`, `disp`, `account`, and `loan` tables. However, the salary information is not directly available in the provided schema. Assuming that the salary is stored in the `client` table as a column named `salary`, the SQL query would look like this:", "base64_images": [], "stats": { "num_prompt_tokens": 1202.0, "num_output_tokens": 79.0, "inference_runtime": 1.263418436050415, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To calculate the average salary of clients who have taken a loan, we need to join the `client`, `disp`, `account`, and `loan` tables. However, the salary information is not directly available in the provided schema. Assuming that the salary is stored in the `client` table as a column named `salary`, the SQL query would look like this:", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: d.A11" } } }, { "instance_id": "id86", "train_trial_index": 0, "predicted_text": " To find the top 5 districts with the highest average salary, we need to calculate the average salary for each district and then order the results in descending order to get the top 5. However, the provided schema does not have a salary column.", "base64_images": [], "stats": { "num_prompt_tokens": 1201.0, "num_output_tokens": 53.0, "inference_runtime": 0.7911438941955566, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To find the top 5 districts with the highest average salary, we need to calculate the average salary for each district and then order the results in descending order to get the top 5. However, the provided schema does not have a salary column.", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: d.district_id" } } }, { "instance_id": "id87", "train_trial_index": 0, "predicted_text": " To calculate the total amount of transactions for each type of card, we need to join the `card`, `disp`, `account`, and `trans` tables. Since the `trans` table contains the transaction amounts, we'll group by the card type and sum the transaction amounts. Here's the SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1202.0, "num_output_tokens": 67.0, "inference_runtime": 2.386378049850464, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To calculate the total amount of transactions for each type of card, we need to join the `card`, `disp`, `account`, and `trans` tables. Since the `trans` table contains the transaction amounts, we'll group by the card type and sum the transaction amounts. Here's the SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": "[('classic', 566104903), ('gold', 84648012), ('junior', 109573963)]", "gold_error": null } } }, { "instance_id": "id88", "train_trial_index": 0, "predicted_text": " To find the clients who have a credit card and have a balance of more than 10000, we need to join the `client`, `disp`, `card`, and `trans` tables. The `client` table contains information about the clients, the `disp` table links clients to accounts, the `card` table contains information about the cards, and the `trans` table contains information about the transactions and balances.", "base64_images": [], "stats": { "num_prompt_tokens": 1211.0, "num_output_tokens": 92.0, "inference_runtime": 1.454291820526123, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To find the clients who have a credit card and have a balance of more than 10000, we need to join the `client`, `disp`, `card`, and `trans` tables. The `client` table contains information about the clients, the `disp` table links clients to accounts, the `card` table contains information about the cards, and the `trans` table contains information about the transactions and balances.", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: c.client_id" } } }, { "instance_id": "id89", "train_trial_index": 0, "predicted_text": " To calculate the average age of clients who have taken a loan and have a credit card, we need to join the relevant tables and filter the results accordingly. Here's the SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1207.0, "num_output_tokens": 40.0, "inference_runtime": 0.6947190761566162, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To calculate the average age of clients who have taken a loan and have a credit card, we need to join the relevant tables and filter the results accordingly. Here's the SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "near \"FROM\": syntax error" } } }, { "instance_id": "id90", "train_trial_index": 0, "predicted_text": " To find the top 3 types of transactions with the highest average amount for clients who have taken a loan, you can use the following SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1210.0, "num_output_tokens": 32.0, "inference_runtime": 0.5899949073791504, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To find the top 3 types of transactions with the highest average amount for clients who have taken a loan, you can use the following SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": "[('VYBER', 12420.655357424648), ('PRIJEM', 11833.18655855066), ('VYDAJ', 5967.301445527258)]", "gold_error": null } } }, { "instance_id": "id91", "train_trial_index": 0, "predicted_text": " To calculate the total number of clients who have taken a loan, have a credit card, and have a balance of less than 0, you can use the following SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1216.0, "num_output_tokens": 38.0, "inference_runtime": 0.6359333992004395, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To calculate the total number of clients who have taken a loan, have a credit card, and have a balance of less than 0, you can use the following SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: c.client_id" } } }, { "instance_id": "id92", "train_trial_index": 0, "predicted_text": " To find the clients who meet the given criteria, we need to join the relevant tables and apply the appropriate conditions. Here's the SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1216.0, "num_output_tokens": 32.0, "inference_runtime": 0.7352595329284668, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To find the clients who meet the given criteria, we need to join the relevant tables and apply the appropriate conditions. Here's the SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: c.client_id" } } }, { "instance_id": "id93", "train_trial_index": 0, "predicted_text": " To calculate the average salary of clients who meet the given conditions, we first need to understand that the salary is not directly available in the provided schema. However, we can assume that the balance in the \"trans\" table represents the salary for this task.", "base64_images": [], "stats": { "num_prompt_tokens": 1216.0, "num_output_tokens": 53.0, "inference_runtime": 1.415393590927124, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To calculate the average salary of clients who meet the given conditions, we first need to understand that the salary is not directly available in the provided schema. However, we can assume that the balance in the \"trans\" table represents the salary for this task.", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: d.A11" } } }, { "instance_id": "id94", "train_trial_index": 0, "predicted_text": " To find the top 5 districts with the highest average salary for clients who have taken a loan, we need to join the `client`, `disp`, `account`, and `loan` tables, group by district, calculate the average salary, and then limit the results to the top 5. However, the salary information is not directly available in the provided schema.", "base64_images": [], "stats": { "num_prompt_tokens": 1208.0, "num_output_tokens": 77.0, "inference_runtime": 1.2210044860839844, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To find the top 5 districts with the highest average salary for clients who have taken a loan, we need to join the `client`, `disp`, `account`, and `loan` tables, group by district, calculate the average salary, and then limit the results to the top 5. However, the salary information is not directly available in the provided schema.", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: d.district_id" } } }, { "instance_id": "id95", "train_trial_index": 0, "predicted_text": " To calculate the total amount of transactions for each type of card for clients who have taken a loan, you'll need to join the `client`, `disp`, `card`, `account`, and `trans` tables. Here's the SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1209.0, "num_output_tokens": 54.0, "inference_runtime": 0.9413418769836426, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To calculate the total amount of transactions for each type of card for clients who have taken a loan, you'll need to join the `client`, `disp`, `card`, `account`, and `trans` tables. Here's the SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": "[('classic', 91303342), ('gold', 9209455), ('junior', 15180935)]", "gold_error": null } } }, { "instance_id": "id96", "train_trial_index": 0, "predicted_text": " To find the clients who meet all the given conditions, we need to join the relevant tables and apply the appropriate filters. Here's the SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1217.0, "num_output_tokens": 33.0, "inference_runtime": 0.635627031326294, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To find the clients who meet all the given conditions, we need to join the relevant tables and apply the appropriate filters. Here's the SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: c.client_id" } } }, { "instance_id": "id97", "train_trial_index": 0, "predicted_text": " To calculate the average age of clients who meet the given criteria, we need to join the relevant tables and apply the conditions in the WHERE clause. However, the balance information is not directly available in the provided schema. I'll assume that the balance is stored in the \"trans\" table, and we'll consider the most recent balance for each client.", "base64_images": [], "stats": { "num_prompt_tokens": 1220.0, "num_output_tokens": 74.0, "inference_runtime": 1.3461580276489258, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To calculate the average age of clients who meet the given criteria, we need to join the relevant tables and apply the conditions in the WHERE clause. However, the balance information is not directly available in the provided schema. I'll assume that the balance is stored in the \"trans\" table, and we'll consider the most recent balance for each client.", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "near \"FROM\": syntax error" } } }, { "instance_id": "id98", "train_trial_index": 0, "predicted_text": " To find the top 3 types of transactions with the highest average amount for clients who have taken a loan, have a credit card, and have a balance of less than 0, you can use the following SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1224.0, "num_output_tokens": 47.0, "inference_runtime": 0.49143528938293457, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To find the top 3 types of transactions with the highest average amount for clients who have taken a loan, have a credit card, and have a balance of less than 0, you can use the following SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": "[('VYBER', 16272.0), ('VYDAJ', 3342.108695652174), ('PRIJEM', 643.829268292683)]", "gold_error": null } } }, { "instance_id": "id99", "train_trial_index": 0, "predicted_text": " To calculate the total number of clients who meet the given conditions, we need to join the relevant tables and apply the appropriate filters. Here's the SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1221.0, "num_output_tokens": 35.0, "inference_runtime": 1.0574829578399658, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To calculate the total number of clients who meet the given conditions, we need to join the relevant tables and apply the appropriate filters. Here's the SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: c.client_id" } } }, { "instance_id": "id100", "train_trial_index": 0, "predicted_text": " To find the clients who meet all the given conditions, we need to join the relevant tables and apply the necessary filters. Here's the SQL query:", "base64_images": [], "stats": { "num_prompt_tokens": 1221.0, "num_output_tokens": 33.0, "inference_runtime": 0.5515782833099365, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To find the clients who meet all the given conditions, we need to join the relevant tables and apply the necessary filters. Here's the SQL query:", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: c.client_id" } } }, { "instance_id": "id101", "train_trial_index": 0, "predicted_text": " To calculate the average salary of clients who meet the given conditions, we first need to identify the clients who have taken a loan, have a credit card, have a balance of less than 0, and have a credit card. Since the salary information is not directly available in the provided schema, I'm assuming that the \"balance\" field in the \"trans\" table represents the salary.", "base64_images": [], "stats": { "num_prompt_tokens": 1221.0, "num_output_tokens": 81.0, "inference_runtime": 1.0551941394805908, "num_train_instances": 0.0, "prompt_truncated": 0.0, "error_rate": 1.0 }, "annotations": { "czech_bank_qa": { "query": " To calculate the average salary of clients who meet the given conditions, we first need to identify the clients who have taken a loan, have a credit card, have a balance of less than 0, and have a credit card. Since the salary information is not directly available in the provided schema, I'm assuming that the \"balance\" field in the \"trans\" table represents the salary.", "result": null, "error": "near \"To\": syntax error", "gold_result": null, "gold_error": "no such column: d.A11" } } } ]