MMTU-benchmark/MMTU huggingface dataset:
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 |   Col_1 |   Col_2 |   Col_3 | Col_4   |   Col_5 |     Col_6 |    Col_7 |
|--------:|--------:|--------:|--------:|:--------|--------:|----------:|---------:|
|       5 |     244 |    1287 |       4 | YES     |   25000 |   862.753 |  25862.8 |
|       8 |     231 |     102 |     336 | YES     |   50000 | 72471.2   | 122471   |
|       2 |     365 |     801 |      54 | YES     |   50000 | 11647.2   |  61647.2 |
|       6 |      59 |     155 |     261 | YES     |   50000 | 56294.6   | 106295   |
|      25 |     133 |    1389 |     130 | YES     |   50000 | 28039.5   |  78039.5 |

Candidate column headers:
Total 2018-19 CSI Allocation 
Enrollment all Grades K-12 (March 2018)
Additional Allocation per Student
Title I-A Status
Number of Schools
Base Allocation > 20 students-$25_000 <20 students-$50_000
LEA Number
School Number



headers: Total 2018-19 CSI Allocation 
Enrollment all Grades K-12 (March 2018)
Additional Allocation per Student
Title I-A Status
Number of Schools
Base Allocation > 20 students-$25_000 <20 students-$50_000
LEA Number
School Number
label: Number of Schools
LEA Number
School Number
Enrollment all Grades K-12 (March 2018)
Title I-A Status
Base Allocation > 20 students-$25_000 <20 students-$50_000
Additional Allocation per Student
Total 2018-19 CSI Allocation
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0   | Col_1                | Col_2     |   Col_3 |   Col_4 |   Col_5 |   Col_6 |   Col_7 |   Col_8 |   Col_9 |
|:--------|:---------------------|:----------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|
| FY2008  | Southeastern region  | POW       |      10 |      41 |       3 |       3 |       0 |      10 |      67 |
| FY2006  | Southeastern region  | Ketchikan |      50 |     186 |      29 |      47 |       3 |     105 |     420 |
| FY2008  | Southeastern region  | Ketchikan |      49 |     120 |      47 |      50 |       2 |     119 |     387 |
| FY2007  | South central region | Kodiak    |      34 |     113 |      14 |      21 |       1 |      58 |     241 |
| FY2007  | Anchorage region     | Anchorage |     604 |    1634 |     246 |     170 |      69 |     809 |    3532 |

Candidate column headers:
Total
Drug & Alcohol
PV/CV*
Against Persons
Against Public Order
Fiscal Year
Weapon
Region
Office
Against Property



headers: Total
Drug & Alcohol
PV/CV*
Against Persons
Against Public Order
Fiscal Year
Weapon
Region
Office
Against Property
label: Fiscal Year
Region
Office
Against Persons
Against Property
Against Public Order
Drug & Alcohol
Weapon
PV/CV*
Total
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|     Col_0 |      Col_1 |   Col_2 |
|----------:|-----------:|--------:|
| -14.1495  |  0.222629  | 62.3728 |
| -23.4958  |  0.259339  | 60.1323 |
|   4.62704 |  0.0757889 | 84.0344 |
| -78.2223  | -0.776241  | 42.2207 |
| -17.6     |  0.252826  | 58.9582 |

Candidate column headers:
Vitesse en m/s
Fusion Retour - Aller Amplitude
Fusion Retour - Aller MF



headers: Vitesse en m/s
Fusion Retour - Aller Amplitude
Fusion Retour - Aller MF
label: Fusion Retour - Aller Amplitude
Fusion Retour - Aller MF
Vitesse en m/s
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0           | Col_1   | Col_2   |   Col_3 |   Col_4 |   Col_5 |   Col_6 |   Col_7 |   Col_8 |   Col_9 |   Col_10 |   Col_11 |
|:----------------|:--------|:--------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|
| Evgeni Malkin   | PIT     | C       |      60 |      23 |      49 |      72 |      30 |     191 |    12   | 0.835417 |     21.4 |
| Alexander Steen | STL     | L       |      68 |      33 |      29 |      62 |      17 |     211 |    15.6 | 0.844444 |     27.5 |
| Bryan Little    | WPG     | C       |      82 |      23 |      41 |      64 |      15 |     170 |    13.5 | 0.833333 |     26.1 |
| Blake Wheeler   | WPG     | R       |      82 |      28 |      41 |      69 |      19 |     225 |    12.4 | 0.778472 |     24   |
| Ryan Johansen   | CBJ     | C       |      82 |      33 |      30 |      63 |      20 |     237 |    13.9 | 0.734722 |     22.5 |

Candidate column headers:
TOI/G
A
Pos.
Team
PPP
Sft/G
Games
Player
P
G
Shots
Shot %



headers: TOI/G
A
Pos.
Team
PPP
Sft/G
Games
Player
P
G
Shots
Shot %
label: Player
Team
Pos.
Games
G
A
P
PPP
Shots
Shot %
TOI/G
Sft/G
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0   | Col_1              |   Col_2 |   Col_3 |   Col_4 |   Col_5 |   Col_6 |   Col_7 |   Col_8 |   Col_9 |   Col_10 |   Col_11 |   Col_12 |   Col_13 |
|:--------|:-------------------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|---------:|
| RBS     | Organisation Total |     462 |     328 |     491 |     420 |     324 |     330 |     482 |     524 |      379 |      439 |      392 |      472 |
| RTQ     | Organisation Total |     539 |     346 |     426 |     339 |     338 |     470 |     700 |     513 |      514 |      716 |      113 |      286 |
| RTK     | Low                |     258 |     219 |     208 |     310 |     229 |     191 |     229 |     235 |      184 |      211 |      403 |      201 |
| RCF     | Severe             |       0 |       0 |       0 |       1 |       0 |       2 |       0 |       1 |        1 |        1 |        0 |        1 |
| REM     | Moderate           |       5 |      11 |      14 |       6 |      10 |      12 |      11 |      10 |       11 |        9 |       12 |       16 |

Candidate column headers:
Feb-18
Oct-17
Dec-17
Jun-18
Jan-18
May-18
Organisation code
Apr-18
Sep-17
Nov-17
Mar-18
Degree of harm
Jul-18
Aug-17



headers: Feb-18
Oct-17
Dec-17
Jun-18
Jan-18
May-18
Organisation code
Apr-18
Sep-17
Nov-17
Mar-18
Degree of harm
Jul-18
Aug-17
label: Organisation code
Degree of harm
Aug-17
Sep-17
Oct-17
Nov-17
Dec-17
Jan-18
Feb-18
Mar-18
Apr-18
May-18
Jun-18
Jul-18
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0                |   Col_1 | Col_2               |     Col_3 | Col_4             |    Col_5 |   Col_6 | Col_7                       | Col_8            |   Col_9 |
|:---------------------|--------:|:--------------------|----------:|:------------------|---------:|--------:|:----------------------------|:-----------------|--------:|
| South East           |    3430 | Gold Coast (C)      | 309091263 | Ashmore           | 1        |   30909 | Southport                   | Gold Coast       |     309 |
| Brisbane             |    1000 | Brisbane (C)        | 302021027 | Aspley            | 1        |   30202 | Chermside                   | Brisbane - North |     302 |
| Brisbane             |    1000 | Brisbane (C)        | 301011002 | Belmont - Gumdale | 1        |   30101 | Capalaba                    | Brisbane - East  |     301 |
| South East           |    6250 | Redland (C)         | 301011001 | Alexandra Hills   | 1        |   30101 | Capalaba                    | Brisbane - East  |     301 |
| Far North Queensland |    2260 | Cassowary Coast (R) | 306031158 | Babinda           | 0.176185 |   30603 | Innisfail - Cassowary Coast | Cairns           |     306 |

Candidate column headers:
SA4 Name
LGA11 Code
% of SA2
SA2 Name
SA2 Code
Department of Communities_ Child Safety and Disability Services Administrative Boundary
SA3 Name
LGA Name (2011)
SA4 Code
SA3 Code



headers: SA4 Name
LGA11 Code
% of SA2
SA2 Name
SA2 Code
Department of Communities_ Child Safety and Disability Services Administrative Boundary
SA3 Name
LGA Name (2011)
SA4 Code
SA3 Code
label: Department of Communities_ Child Safety and Disability Services Administrative Boundary
LGA11 Code
LGA Name (2011)
SA2 Code
SA2 Name
% of SA2
SA3 Code
SA3 Name
SA4 Name
SA4 Code
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0    | Col_1   |   Col_2 |   Col_3 |   Col_4 |   Col_5 |   Col_6 |
|:---------|:--------|--------:|--------:|--------:|--------:|--------:|
| Baylor   | Thomas  |      11 |       7 |       3 |      10 |      20 |
| Diaz     | Lorenso |      11 |       9 |       7 |       3 |      19 |
| Fisher   | Jimbo   |      10 |       3 |       6 |       4 |      13 |
| Williams | Shantel |       9 |       8 |      11 |       5 |      24 |
| Peralta  | Angee   |      10 |       2 |       0 |      11 |      13 |

Candidate column headers:
First Name
Last Name
Grade Level
Total
Carnations
Daisies
Roses



headers: First Name
Last Name
Grade Level
Total
Carnations
Daisies
Roses
label: Last Name
First Name
Grade Level
Roses
Carnations
Daisies
Total
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1               | Col_2   | Col_3   | Col_4   | Col_5    | Col_6   | Col_7   | Col_8   | Col_9    |   Col_10 |   Col_11 |   Col_12 |   Col_13 |   Col_14 |   Col_15 |   Col_16 |    Col_17 |   Col_18 |
|--------:|:--------------------|:--------|:--------|:--------|:---------|:--------|:--------|:--------|:---------|---------:|---------:|---------:|---------:|---------:|---------:|---------:|----------:|---------:|
|      41 | mAb PASS rate       | All     | All     | ip      | multiple | All     | All     | fp      | multiple |        6 |      151 |  77.7833 |  72.6397 |  40.3668 |  37.117  | 0.923252 | 0.770452  | 0.138171 |
|      35 | minichip PASS rate  | Domain  | Domain  | fp      | multiple | Domain  | Domain  | All     | multiple |       37 |       45 |  12.7054 |  18.4222 |  11.3956 |  23.1415 | 0.366676 | 0.150004  | 0.304285 |
|       9 | huprot PASS rate    | All     | All     | ip      | All      | All     | All     | fp      | All      |      240 |      512 |  46.8721 |  43.2598 |  35.5589 |  34.0346 | 0.301978 | 0.188736  | 0.104621 |
|      31 | pipeline.efficiency | Domain  | FL      | ip      | All      | Domain  | FL      | All     | All      |       88 |      146 |  14.3763 |  11.2123 |  15.1401 |  13.2452 | 0.284478 | 0.106767  | 0.226232 |
|      37 | huprot PASS rate    | All     | All     | ip      | multiple | All     | All     | fp      | multiple |        6 |      153 |  74.1    |  46.4196 |  29.0579 |  26.8817 | 0.130733 | 0.0668522 | 1.02696  |

Candidate column headers:
G1_immunization_method
# uni1
G1_antigen_type
mean2
mean1
effect size
# uni2
parameter
STD1
P value
G2_recognition_type
STD2
G2_immunization_method
Sr.NO.
G2_antigen_type
FDR
G1_recognition_type
G1_Immunization pool
G2_Immunization pool



headers: G1_immunization_method
# uni1
G1_antigen_type
mean2
mean1
effect size
# uni2
parameter
STD1
P value
G2_recognition_type
STD2
G2_immunization_method
Sr.NO.
G2_antigen_type
FDR
G1_recognition_type
G1_Immunization pool
G2_Immunization pool
label: Sr.NO.
parameter
G1_antigen_type
G1_recognition_type
G1_immunization_method
G1_Immunization pool
G2_antigen_type
G2_recognition_type
G2_immunization_method
G2_Immunization pool
# uni1
# uni2
mean1
mean2
STD1
STD2
FDR
P value
effect size
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0   | Col_1    | Col_2      | Col_3             | Col_4     |
|:--------|:---------|:-----------|:------------------|:----------|
| TPEP    | LeAnn    | Alexander  | TPEP Coordinator  | Eastern   |
| TPEP    | Mark     | Barnstable | TPEP Coordinator  | Southwest |
| ADPEP   | Dee      | Butler     | ADPEP Coordinator | Valley    |
| SPF     | Deangela | Dobson     | SPF Coordinator   | Eastern   |
| TPEP    | Lanette  | Humphrey   | TPEP Coordinator  | Eastern   |

Candidate column headers:
Position
RSN
First Name
Last Name
Program



headers: Position
RSN
First Name
Last Name
Program
label: Program
First Name
Last Name
Position
RSN
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1      | Col_2    | Col_3                   | Col_4   | Col_5   | Col_6   | Col_7    |   Col_8 | Col_9       |   Col_10 | Col_11      |   Col_12 | Col_13      |   Col_14 | Col_15      |   Col_16 | Col_17      |   Col_18 |   Col_19 |
|--------:|:-----------|:---------|:------------------------|:--------|:--------|:--------|:---------|--------:|:------------|---------:|:------------|---------:|:------------|---------:|:------------|---------:|:------------|---------:|---------:|
|     188 | James      | Wakeford | Langland Bay Lifeguards | OL      | MO      | M       | 00:28:53 |      11 | 00:01:11.05 |       15 | 01:10:03.60 |       12 | 00:00:48.00 |       22 | 00:47:00.85 |       29 | 02:27:56.45 |       12 |        6 |
|     208 | Only Fools |          | Pencoed                 | OL      | R       | N       | 00:29:37 |      14 | 00:00:53.25 |        5 | 01:14:06.60 |       31 | 00:00:48.60 |       27 | 00:44:51.10 |       16 | 02:30:16.35 |       16 |        2 |
|      40 | Marc       | Davies   | Team Cranc              | OL      | MV      | M       | 00:28:33 |       8 | 00:00:50.95 |        3 | 01:07:57.25 |        5 | 00:00:34.95 |        4 | 00:34:40.25 |        1 | 02:12:36.30 |        2 |        1 |
|     114 | Helen      | Marshall | Intrtri                 | OL      | FO      | F       | 00:26:43 |       4 | 00:01:11.45 |       16 | 01:12:49.80 |       21 | 00:00:52.20 |       35 | 00:44:14.40 |       13 | 02:25:51.00 |        6 |        1 |
|     160 | Chris      | Sandifer |                         | OL      | MO      | M       | 00:34:45 |      93 | 00:01:59.35 |       66 | 01:16:42.40 |       44 | 00:00:48.00 |       23 | 00:50:36.95 |       46 | 02:44:51.90 |       48 |       28 |

Candidate column headers:
Run
T1 Pos
Category
T2 Pos
Bike Pos
Finish
Bib No
Swim
Category Pos
Bike
Race
Overall Pos
First Name
T1
Club
Gender
Swim Pos
T2
Last Name
Run Pos



headers: Run
T1 Pos
Category
T2 Pos
Bike Pos
Finish
Bib No
Swim
Category Pos
Bike
Race
Overall Pos
First Name
T1
Club
Gender
Swim Pos
T2
Last Name
Run Pos
label: Bib No
First Name
Last Name
Club
Race
Category
Gender
Swim
Swim Pos
T1
T1 Pos
Bike
Bike Pos
T2
T2 Pos
Run
Run Pos
Finish
Overall Pos
Category Pos
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0   | Col_1   | Col_2   | Col_3   | Col_4   | Col_5   | Col_6   | Col_7   |   Col_8 |
|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|--------:|
| 4.0     | 4.0     | 6.0     | 1.0     | 2.0     | 2.0     |         | 2.0     |      21 |
| 1.0     |         | 4.0     |         |         |         |         |         |       5 |
|         |         | 1.0     | 17.0    | 10.0    |         | 6.0     | 1.0     |      35 |
| 2.0     | 3.0     |         |         | 2.0     | 4.0     |         | 4.0     |      15 |
|         | 1.0     |         |         | 1.0     | 1.0     |         |         |       3 |

Candidate column headers:
2004
2008
2009
2003
2010
2007
TOTAL POR TIPO
2006
2005



headers: 2004
2008
2009
2003
2010
2007
TOTAL POR TIPO
2006
2005
label: 2003
2004
2005
2006
2007
2008
2009
2010
TOTAL POR TIPO
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1      | Col_2           | Col_3   | Col_4   |   Col_5 | Col_6   |   Col_7 |
|--------:|:-----------|:----------------|:--------|:--------|--------:|:--------|--------:|
|       6 | IRINA      | KABANOVA        | RUS     |         |    1914 | F       |    1945 |
|      17 | RESZONE    | KATUSAK-HUZSVAR | HUN     |         |    1372 | F       |    1942 |
|       9 | JULIE ANNE | O NEILL         | USA     |         |    1774 | F       |    1948 |
|       1 | NONA       | GAPRINDASHVILI  | GEO     | GM      |    2311 | F       |    1941 |
|      14 | NINA       | KLIMINA         | RUS     |         |    1559 | F       |    1951 |

Candidate column headers:
SURNAME
NO.
TITLE
BORN
TYPE
FIRST NAME
RTG
FED



headers: SURNAME
NO.
TITLE
BORN
TYPE
FIRST NAME
RTG
FED
label: NO.
FIRST NAME
SURNAME
FED
TITLE
RTG
TYPE
BORN
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0   | Col_1   | Col_2   | Col_3   | Col_4   |
|:--------|:--------|:--------|:--------|:--------|
| X       | X       | X       | X       | X       |
| X       |         | X       |         | X       |
|         | X       |         | X       |         |
|         | X       |         | X       |         |
| X       |         | X       |         |         |

Candidate column headers:
Spring 8W2
Spring 8W1
Summer
Fall 8W1
Fall 8W2



headers: Spring 8W2
Spring 8W1
Summer
Fall 8W1
Fall 8W2
label: Fall 8W1
Fall 8W2
Spring 8W1
Spring 8W2
Summer
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0     | Col_1   | Col_2   | Col_3   | Col_4   | Col_5   | Col_6   | Col_7             | Col_8   | Col_9             | Col_10   | Col_11   | Col_12   | Col_13   |   Col_14 |   Col_15 | Col_16   | Col_17   | Col_18   | Col_19   |
|:----------|:--------|:--------|:--------|:--------|:--------|:--------|:------------------|:--------|:------------------|:---------|:---------|:---------|:---------|---------:|---------:|:---------|:---------|:---------|:---------|
| AUGUST    | 71.6    | 20.7    | 68.2    | -4.9    | 70.3    | 3.1     | 75.0              | 6.7     | 79.0              | 5.3      | 84.4     | 6.7      | 89.9     |  6.5     |     91.4 | 1.7      | 98.0     | 7.2      | 103.0    |
| NOVEMBER  | 69.6    | 16.5    | 68.2    | -1.9    | 70.7    | 3.6     | 76.09999999999998 | 7.7     | 79.59999999999998 | 4.6      | 85.2     | 6.8      | 90.4     |  6.2     |     91.9 | 1.7      | 99.1     | 7.8      | 105.5    |
|           |         |         |         |         |         |         |                   |         |                   |          |          |          |          |  5.61329 |     10   |          |          |          |          |
|           |         |         |         |         |         |         |                   |         |                   |          |          |          |          |  5.61371 |     12   |          |          |          |          |
| SEPTEMBER | 71.2    | 20.1    | 68.5    | -3.8    | 70.3    | 2.7     | 75.3              | 7.1     | 79.2              | 5.1      | 84.9     | 7.0      | 90.1     |  6.1     |     91.3 | 1.3      | 98.1     | 7.4      | 104.0    |

Candidate column headers:
( 2016%)
( 2016)
( 2011%)
( 2015)
( 2011)
( 2009)
( 2009%)
( 2010%)
( 2012)
( 2010)
( 2008%)
( 2013)
( 2015%)
( 2014)
( 2012%)
( 2017)
( 2013%)
#
( 2008)
( 2014%)



headers: ( 2016%)
( 2016)
( 2011%)
( 2015)
( 2011)
( 2009)
( 2009%)
( 2010%)
( 2012)
( 2010)
( 2008%)
( 2013)
( 2015%)
( 2014)
( 2012%)
( 2017)
( 2013%)
#
( 2008)
( 2014%)
label: #
( 2008)
( 2008%)
( 2009)
( 2009%)
( 2010)
( 2010%)
( 2011)
( 2011%)
( 2012)
( 2012%)
( 2013)
( 2013%)
( 2014)
( 2014%)
( 2015)
( 2015%)
( 2016)
( 2016%)
( 2017)
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 |   Col_1 |   Col_2 |
|--------:|--------:|--------:|
|      22 |   43252 |   43252 |
|      25 |   43270 |   43270 |
|      25 |   43273 |   43273 |
|      24 |   43263 |   43263 |
|      23 |   43257 |   43257 |

Candidate column headers:
KW
Datum
Wochentag



headers: KW
Datum
Wochentag
label: KW
Wochentag
Datum
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|    Col_0 |   Col_1 |    Col_2 |    Col_3 |      Col_4 |      Col_5 |     Col_6 |   Col_7 |      Col_8 |      Col_9 |     Col_10 |
|---------:|--------:|---------:|---------:|-----------:|-----------:|----------:|--------:|-----------:|-----------:|-----------:|
|  560.924 | 409.789 | 345.675  | 415.293  | -0.38374   | -0.156456  | -0.167635 |       1 | 0.730561   |  0.61626   |  0.740372  |
| 2280     |  20     |  41      | 190      | -0.982018  |  1.05      | -0.784211 |       1 | 0.00877193 |  0.0179825 |  0.0833333 |
|    0     |   0     |   0      |   0      |  0         |  0         |  0        |       0 | 0          |  0         |  0         |
|  -26.512 | -13.202 |  32.3695 |  25.3471 | -2.22094   | -3.45187   |  0.277052 |       1 | 0.497963   | -1.22094   | -0.956061  |
|    5.06  |   5.138 |   4.75   |  11.6293 | -0.0612648 | -0.0755158 | -0.59155  |       1 | 1.01542    |  0.938735  |  2.29829   |

Candidate column headers:
Revised Target
Current Month
Revised Current Month
Target
Base
Variance with Target
Revised Base
Variance with Base
Variance with Corresponding Month
Corresponding Month
Revised Corresponding Month



headers: Revised Target
Current Month
Revised Current Month
Target
Base
Variance with Target
Revised Base
Variance with Base
Variance with Corresponding Month
Corresponding Month
Revised Corresponding Month
label: Base
Corresponding Month
Current Month
Target
Variance with Base
Variance with Corresponding Month
Variance with Target
Revised Base
Revised Corresponding Month
Revised Current Month
Revised Target
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0   | Col_1                 | Col_2                     | Col_3                | Col_4              | Col_5              |   Col_6 |   Col_7 |   Col_8 |   Col_9 | Col_10   |   Col_11 |
|:--------|:----------------------|:--------------------------|:---------------------|:-------------------|:-------------------|--------:|--------:|--------:|--------:|:---------|---------:|
| EMPC358 | Ganesh Chandra Santra | Deputy Manager            | Fabrication Products | Jaipur             | Manager            |   21961 |      54 |   38397 |       9 | MALE     |  1934000 |
| EMPC54  | Dinesh Kumar Nayak    | Regional Manager          | Refurbishment        | Thiruvananthapuram | Department Manager |   21805 |      55 |   32601 |      25 | MALE     |  4895000 |
| EMPC36  | V Chandrasekaran      | Assistant General Manager | Commercial Sales     | Mumbai             | General Manager    |   21288 |      56 |   31989 |      27 | MALE     |  7863000 |
| EMPC47  | Dilip Kr Pandey       | Accounts Executive        | Rural Business       | Indore             | Officer            |   21948 |      54 |   32143 |      26 | MALE     |  1090000 |
| EMPC164 | Sadasivan Paramasivam | CLERK-C-TYPIST GRADE II   | Medical Products     | Bhopal             | Staff              |   21510 |      56 |   35156 |      18 | MALE     |   280000 |

Candidate column headers:
DOB
Tenure
DOJ
Band
Department
Gender
CTC
Name
E.Code
Age
Designation
Location



headers: DOB
Tenure
DOJ
Band
Department
Gender
CTC
Name
E.Code
Age
Designation
Location
label: E.Code
Name
Designation
Department
Location
Band
DOB
Age
DOJ
Tenure
Gender
CTC
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 |   Col_1 |   Col_2 |
|--------:|--------:|--------:|
|   35277 |       0 |       0 |
|   36191 |       0 |       0 |
|   35703 |       0 |       0 |
|   36372 |       0 |       0 |
|   35033 |       0 |       0 |

Candidate column headers:
Benchmark Index 3 cumulative return
Date
Benchmark Index 2 cumulative return



headers: Benchmark Index 3 cumulative return
Date
Benchmark Index 2 cumulative return
label: Date
Benchmark Index 2 cumulative return
Benchmark Index 3 cumulative return
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0   | Col_1   | Col_2                   | Col_3     | Col_4   | Col_5   | Col_6   | Col_7   | Col_8   | Col_9   | Col_10   | Col_11   | Col_12   | Col_13   | Col_14   | Col_15   | Col_16   | Col_17   | Col_18   | Col_19   |
|:--------|:--------|:------------------------|:----------|:--------|:--------|:--------|:--------|:--------|:--------|:---------|:---------|:---------|:---------|:---------|:---------|:---------|:---------|:---------|:---------|
| Alaska  | AK      | Medical Condition (All) | Medicaid  |         |         |         |         |         |         |          |          |          |          |          |          |          |          |          |          |
| Alaska  | AK      | Medical Cond-CHF        | Uninsured |         |         |         |         |         |         |          |          |          |          |          |          |          |          |          |          |
| Arizona | AZ      | Injury                  | Medicaid  | 1100.0  | 1250.0  | 1350.0  | 1100.0  | 1050.0  | 1300.0  | 1650.0   | 1450.0   | 1400.0   | 1750.0   | 1750.0   | 1650.0   | 1550.0   | 1750.0   | 1600.0   | 1500.0   |
| Arizona | AZ      | Maternal                | Medicaid  | 8350.0  | 8250.0  | 8800.0  | 8450.0  | 7950.0  | 7600.0  | 11100.0  | 11800.0  | 11050.0  | 11150.0  | 12950.0  | 12400.0  | 11600.0  | 11550.0  | 13200.0  | 12750.0  |
| Alaska  | AK      | Mental Health           | Uninsured |         |         |         |         |         |         |          |          |          |          |          |          |          |          |          |          |

Candidate column headers:
Expected_Payer
Y2004_Q3
Y2003_Q2
Y2006_Q2
Y2005_Q4
Y2004_Q4
Y2006_Q3
Y2004_Q1
State_Code
Y2006_Q1
Y2005_Q2
State
Y2005_Q1
Y2004_Q2
Y2003_Q3
Y2003_Q1
Y2003_Q4
Y2006_Q4
Y2005_Q3
Hospitalization_Type



headers: Expected_Payer
Y2004_Q3
Y2003_Q2
Y2006_Q2
Y2005_Q4
Y2004_Q4
Y2006_Q3
Y2004_Q1
State_Code
Y2006_Q1
Y2005_Q2
State
Y2005_Q1
Y2004_Q2
Y2003_Q3
Y2003_Q1
Y2003_Q4
Y2006_Q4
Y2005_Q3
Hospitalization_Type
label: State
State_Code
Hospitalization_Type
Expected_Payer
Y2003_Q1
Y2003_Q2
Y2003_Q3
Y2003_Q4
Y2004_Q1
Y2004_Q2
Y2004_Q3
Y2004_Q4
Y2005_Q1
Y2005_Q2
Y2005_Q3
Y2005_Q4
Y2006_Q1
Y2006_Q2
Y2006_Q3
Y2006_Q4
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0   |      Col_1 |      Col_2 |    Col_3 |       Col_4 |
|:--------|-----------:|-----------:|---------:|------------:|
| R1-017  |   221.928  |   306.964  | 0.783071 | 9.63112e+06 |
| R1-023  |    47.7465 |    58.3733 | 0.845989 | 1.42705e+06 |
| R1-057  |   104.967  |   128.483  | 0.845286 | 4.94625e+06 |
| R1-002  | 35317      | 37667.5    | 0.941264 | 1.26193e+09 |
| R1-042  |    99.7147 |   212.857  | 0.652937 | 6.79956e+06 |

Candidate column headers:
KATGRUPO
IBGRUPO
UFOPTIMO
Codigo Empresa
UFEFICIENCIA



headers: KATGRUPO
IBGRUPO
UFOPTIMO
Codigo Empresa
UFEFICIENCIA
label: Codigo Empresa
UFOPTIMO
UFEFICIENCIA
KATGRUPO
IBGRUPO
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1                 | Col_2               | Col_3               | Col_4               | Col_5                    |   Col_6 |
|--------:|:----------------------|:--------------------|:--------------------|:--------------------|:-------------------------|--------:|
|   40838 | Hughes                | Capital Hill        | Not provided        | 12:37               | Aerial Capital Group Ltd |   31.48 |
|   40776 | Buderim               | Brisbane Airport    | Not provided        | Not provided        | Oakcorp Limousines       |  236.36 |
|   40752 | Hughes                | Qantas Airport Desk | 0.22916666666666666 | 0.24236111111111111 | COMCAR                   |   68    |
|   40727 | Buderim               | Brisbane Airport    | Not provided        | Not provided        | Oakcorp Limousines       |  236.36 |
|   40760 | Brisbane Intl Airport | Brisbane City       | Not provided        | Not provided        | Oakcorp Limousines       |   77.27 |

Candidate column headers:
Supplier
From
Departure Date
Arrival Time
Amount             (GST Excl)
Time From
Arrival Location



headers: Supplier
From
Departure Date
Arrival Time
Amount             (GST Excl)
Time From
Arrival Location
label: Departure Date
From
Arrival Location
Time From
Arrival Time
Supplier
Amount             (GST Excl)
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 |   Col_1 | Col_2            | Col_3                | Col_4   |   Col_5 | Col_6     | Col_7   |
|--------:|--------:|:-----------------|:---------------------|:--------|--------:|:----------|:--------|
|      37 |   42064 | Payment_Incoming | Toddler Camp         | TC      |    5000 | ENRIQUEZK |         |
|      42 |   42064 | Payment_Incoming | Toddler Camp         | TC      |    5000 | PALISOCM  |         |
|      28 |   42108 | Payment_Incoming | Theater              | TH      |    8400 | CANTARAA  |         |
|       9 |   42107 | Payment_Incoming | Ready_ Get Set_ Read | RGSR    |    4500 | AGUSTINM  |         |
|       6 |   42109 | Payment_Incoming | Art Attack for Kids  | AAK     |    4000 | ABNERK    |         |

Candidate column headers:
Class
TransactionDate
TransactionCode
ClassCode
TypeOfPayment
StudentID
AmountPaid
TypeOfTransaction



headers: Class
TransactionDate
TransactionCode
ClassCode
TypeOfPayment
StudentID
AmountPaid
TypeOfTransaction
label: TransactionCode
TransactionDate
TypeOfTransaction
Class
ClassCode
AmountPaid
StudentID
TypeOfPayment
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0               |   Col_1 | Col_2   |   Col_3 |   Col_4 |   Col_5 |   Col_6 |   Col_7 | Col_8    |   Col_9 | Col_10   | Col_11            | Col_12   |   Col_13 | Col_14   |
|:--------------------|--------:|:--------|--------:|--------:|--------:|--------:|--------:|:---------|--------:|:---------|:------------------|:---------|---------:|:---------|
| EWG-K-6-02047-00007 |       1 | VY/VA   |    0.31 |    0.24 |    0.35 |      55 |       2 | 0.037(2) |   0.5   | SS-D     | Fill 1: AIR (100) | G        |     0.75 | BZ       |
| EWG-K-6-02047-00013 |       1 | VY/VA   |    0.31 |    0.22 |    0.28 |      55 |       2 | 0.037(2) |   0.5   | SS-D     | Fill 1: AIR (100) | G        |     1.5  | GY       |
| EWG-K-6-02047-00009 |       1 | VY/VA   |    0.31 |    0.24 |    0.35 |      55 |       2 | 0.037(2) |   0.5   | SS-D     | Fill 1: AIR (100) | S        |     0.75 | BZ       |
| EWG-K-6-02050-00001 |       1 | VY/VA   |    0.34 |    0.19 |    0.42 |      52 |       2 | 0.022(2) |   0.375 | SS-D     | Fill 1: AIR (100) | G        |     0.75 | CL       |
| EWG-K-6-02047-00014 |       1 | VY/VA   |    0.31 |    0.24 |    0.31 |      55 |       2 | 0.037(2) |   0.5   | SS-D     | Fill 1: AIR (100) | S        |     0.75 | GY       |

Candidate column headers:
Gap Fill
Frame/Sash Type
Tint
Low-E
Condensation Resistance
VT
SHGC
Gap Widths
U-factor
Grid
Divider
Group ID
Spacer
Glazing Layers
CPD #



headers: Gap Fill
Frame/Sash Type
Tint
Low-E
Condensation Resistance
VT
SHGC
Gap Widths
U-factor
Grid
Divider
Group ID
Spacer
Glazing Layers
CPD #
label: CPD #
Group ID
Frame/Sash Type
U-factor
SHGC
VT
Condensation Resistance
Glazing Layers
Low-E
Gap Widths
Spacer
Gap Fill
Grid
Divider
Tint
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 |   Col_1 |   Col_2 |   Col_3 |   Col_4 |   Col_5 |
|--------:|--------:|--------:|--------:|--------:|--------:|
|      14 |      53 |    1991 | 72.4444 |       3 |      18 |
|      14 |      54 |    1992 | 47.75   |       7 |       8 |
|      14 |      54 |    1991 | 67.2632 |      16 |      19 |
|      15 |      53 |    1993 | 49.1667 |       6 |       6 |
|      15 |      54 |    1991 | 70.7143 |       6 |      14 |

Candidate column headers:
Num Acquisition Attempts
Avg Cloud Cover
Path
Year
Num Tier 1
Row



headers: Num Acquisition Attempts
Avg Cloud Cover
Path
Year
Num Tier 1
Row
label: Path
Row
Year
Avg Cloud Cover
Num Tier 1
Num Acquisition Attempts
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1   |   Col_2 |
|--------:|:--------|--------:|
|   42618 | DB-1    |     736 |
|   42617 | DB-1    |     698 |
|   42617 | DB-3    |     618 |
|   42616 | DB-3    |     718 |
|   42619 | DB-4    |    1214 |

Candidate column headers:
Date
KB
DB



headers: Date
KB
DB
label: Date
DB
KB
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0   | Col_1   |   Col_2 |      Col_3 |
|:--------|:--------|--------:|-----------:|
| ME      | Jones   |   40577 | 0.0657108  |
| KY      | Jones   |   40593 | 0.00733869 |
| MA      | Johnson |   40577 | 0.0116995  |
| ME      | Jones   |   40583 | 0.076412   |
| LA      | Johnson |   40575 | 0.0950586  |

Candidate column headers:
Hrs
Worker
Task
DDate



headers: Hrs
Worker
Task
DDate
label: Task
Worker
DDate
Hrs
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0    |   Col_1 |   Col_2 |   Col_3 |
|:---------|--------:|--------:|--------:|
| Finland  | 49.6629 | 13.1794 | 37.1577 |
| Estonia  | 57.9597 | 20.2843 | 21.756  |
| France   | 53.1229 | 16.0344 | 30.8427 |
| Portugal | 58.4938 | 18.2428 | 23.2634 |
| Denmark  | 59.5244 | 12.454  | 28.0216 |

Candidate column headers:
   Corporations
   General Government
  Households
2010



headers:    Corporations
   General Government
  Households
2010
label: 2010
   Corporations
   General Government
  Households
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0        | Col_1             |   Col_2 | Col_3        | Col_4                       |
|:-------------|:------------------|--------:|:-------------|:----------------------------|
| Daunorubicin | Hydrochloric Acid |  563.99 | DAUNORUBICIN | Anthracycline               |
| Belinostat   |                   |  318.35 | BELINOSTAT   | HDACi                       |
| Anagrelide   |                   |  256.09 | ANAGRELIDE   | phosphodiesterase inhibitor |
| Carmustine   |                   |  214.05 | CARMUSTINE   | Alkylating agent            |
| Busulfan     |                   |  246.31 | BUSULFAN     | Alkylating agent            |

Candidate column headers:
DRUG NAME2
DRUG NAME
SIMPLIFIED DRUG CLASS
SALT_DATA
MW



headers: DRUG NAME2
DRUG NAME
SIMPLIFIED DRUG CLASS
SALT_DATA
MW
label: DRUG NAME
SALT_DATA
MW
DRUG NAME2
SIMPLIFIED DRUG CLASS