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 | Col_8             | Col_9        | Col_10   | Col_11   |   Col_12 | Col_13    |
|--------:|--------:|:--------------------------|--------:|:------------------------------|--------:|--------:|--------:|:------------------|:-------------|:---------|:---------|---------:|:----------|
|     2.2 |  711101 | Cash and cash equivalents |      90 | Non Expenditure/Revenue Items |     413 |     831 |     948 | 711101.831.413.90 | 831.0948.413 | X        | Active   |    41456 |           |
|     2.2 |  711101 | Cash and cash equivalents |      90 | Non Expenditure/Revenue Items |     536 |     881 |     948 | 711101.881.536.90 | 881.0948.536 | X        | Active   |    41456 | 2016_0721 |
|     2.2 |  711101 | Cash and cash equivalents |      90 | Non Expenditure/Revenue Items |     229 |     834 |     576 | 711101.834.229.90 | 834.0576.229 | X        | Active   |    41456 |           |
|     2.2 |  711101 | Cash and cash equivalents |      90 | Non Expenditure/Revenue Items |     442 |     881 |     948 | 711101.881.442.90 | 881.0948.442 | X        | Active   |    41456 | 2016_0721 |
|     2.2 |  711101 | Cash and cash equivalents |      90 | Non Expenditure/Revenue Items |     433 |     833 |     948 | 711101.833.433.90 | 833.0948.433 | X        | Active   |    41456 |           |

Candidate column headers:
Effective Date
State Fund Number
Program Group Code
GAAP Object Code
Csu Fund Code
Net Position
GAAP Edit Key
Program Group Name
Obsolete Flag
GAAP Object Name
Supporting Document Ref.
Matrix No
Edit Type Excel File Format
.



headers: Effective Date
State Fund Number
Program Group Code
GAAP Object Code
Csu Fund Code
Net Position
GAAP Edit Key
Program Group Name
Obsolete Flag
GAAP Object Name
Supporting Document Ref.
Matrix No
Edit Type Excel File Format
.
label: Matrix No
GAAP Object Code
GAAP Object Name
Program Group Code
Program Group Name
Csu Fund Code
Net Position
State Fund Number
.
GAAP Edit Key
Edit Type Excel File Format
Obsolete Flag
Effective Date
Supporting Document Ref.
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   |
|--------:|:---------------------|--------:|--------:|------------:|--------:|------------:|------------:|-----------------:|--------:|-----------------:|---------:|---------:|---------:|---------:|---------:|---------:|:---------|
|     332 | Dudley               |       0 |       1 | 9.27714e+06 |  671851 | 4.77991e+06 | 1.47289e+07 |      1.58576e+06 |   64110 |      1.11889e+06 |   733520 |    19662 |    76085 |     4    |     8.02 |     4    | PerHour  |
|     342 | St Helens            |       0 |       1 | 4.9421e+06  |  203824 | 2.61832e+06 | 7.76424e+06 | 960654           |   39620 | 508955           |   274870 |    11336 |   145626 |     4    |     4    |     4    | PerHour  |
|     331 | Coventry             |       0 |       1 | 8.99422e+06 |  242873 | 6.16294e+06 | 1.54e+07    |      1.64349e+06 |   61241 |      1.42895e+06 |   692668 |     1843 |   171814 |     3.85 |     3.85 |     3.85 | PerHour  |
|     344 | Wirral               |       0 |       1 | 6.11718e+06 |  584905 | 3.44053e+06 | 1.01426e+07 |      1.66228e+06 |  116981 | 934926           |        0 |        0 |        0 |     3.68 |     5    |     3.68 | PerHour  |
|     318 | Richmond upon Thames |       0 |       1 | 1.10015e+07 |  352397 | 2.70244e+06 | 1.40563e+07 |      1.58055e+06 |   43320 | 511860           |   568176 |     4560 |    15960 |     5.12 |     7.36 |     5.12 | PerHour  |

Candidate column headers:
UnitValuePVI
UnitValueNurseryClass
AddUnitNumberNursery
UnitNumberNursery
PVIAmount
AddUnitNumberNurseryClass
NurseryAmount
LEA
RowNumber
Number
UnitNumberPVI
NurseryClassAmount
UnitType
TotalBudget
LAName
UnitNumberNurseryClass
AddUnitNumberPVI
UnitValueNursery



headers: UnitValuePVI
UnitValueNurseryClass
AddUnitNumberNursery
UnitNumberNursery
PVIAmount
AddUnitNumberNurseryClass
NurseryAmount
LEA
RowNumber
Number
UnitNumberPVI
NurseryClassAmount
UnitType
TotalBudget
LAName
UnitNumberNurseryClass
AddUnitNumberPVI
UnitValueNursery
label: LEA
LAName
RowNumber
Number
PVIAmount
NurseryAmount
NurseryClassAmount
TotalBudget
UnitNumberPVI
UnitNumberNursery
UnitNumberNurseryClass
AddUnitNumberPVI
AddUnitNumberNursery
AddUnitNumberNurseryClass
UnitValuePVI
UnitValueNursery
UnitValueNurseryClass
UnitType
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 |
|------------:|-----------------:|------------:|-----------------:|-----------------:|------------:|-----------------:|------------:|------------:|------------:|------------:|------------:|
| 3.31409e+07 |      2.26614e+06 | 3.46509e+07 | 169105           | 463242           | 2.51049e+06 |      1.26128e+06 | 2.29505e+06 | 2.421e+06   | 1.70029e+06 | 8.08784e+07 | 3.67643e+07 |
| 1.77023e+07 | 842326           | 8.68186e+07 |      9.47524e+06 |      1.16471e+06 | 4.49292e+06 |      3.17599e+06 | 6.49966e+06 | 5.57107e+06 | 9.32589e+06 | 1.45069e+08 | 5.95955e+07 |
| 7.57493e+07 | 283397           | 1.03591e+08 |      1.73586e+07 |      1.53151e+06 | 3.55365e+06 |      2.66353e+06 | 1.35431e+07 | 3.31954e+06 | 3.458e+06   | 2.25052e+08 | 7.85693e+07 |
| 1.43417e+07 |      1.63545e+06 | 7.15852e+07 |      1.60491e+07 |      2.69545e+06 | 8.59639e+06 |      2.59282e+06 | 9.25816e+06 | 5.54849e+06 | 3.59098e+06 | 1.35894e+08 | 5.95656e+07 |
| 4.0534e+06  | 280424           | 5.4903e+07  |      2.90169e+06 | 745762           | 2.25882e+06 | 734997           | 4.38939e+06 | 5.10972e+06 | 3.45523e+06 | 7.88324e+07 | 5.25827e+07 |

Candidate column headers:
Trade [SIC 6]
General Government [SIC 99]
Agriculture [SIC 1]
Mining [SIC 2]
Finance [SIC 8]
Electricity [SIC 4]
Community services [SIC 93-96; 98]
Construction [SIC 5]
Total Economy (excl Residential)
Residential
Transport [SIC 7]
Manufacturing [SIC 3]



headers: Trade [SIC 6]
General Government [SIC 99]
Agriculture [SIC 1]
Mining [SIC 2]
Finance [SIC 8]
Electricity [SIC 4]
Community services [SIC 93-96; 98]
Construction [SIC 5]
Total Economy (excl Residential)
Residential
Transport [SIC 7]
Manufacturing [SIC 3]
label: Agriculture [SIC 1]
Mining [SIC 2]
Manufacturing [SIC 3]
Electricity [SIC 4]
Construction [SIC 5]
Trade [SIC 6]
Transport [SIC 7]
Finance [SIC 8]
Community services [SIC 93-96; 98]
General Government [SIC 99]
Total Economy (excl Residential)
Residential
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   |
|:--------|--------:|--------:|:--------|
| COE     |     119 |    2006 | Summer  |
| CHPA    |      67 |    2006 | Summer  |
| CNAS    |      41 |    2006 | Summer  |
| CHPA    |      54 |    2006 | Summer  |
| COBA    |      46 |    2006 | Summer  |

Candidate column headers:
Semester
Year
Hits
College



headers: Semester
Year
Hits
College
label: College
Hits
Year
Semester
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               |
|:--------|:-----------|:-----------------------|:--------|--------:|:-----------|:--------|:--------|:---------------|:--------|:------------------|:----------------|:---------|:---------|:-------------|:-------------|:---------------------|
| B28010  | Algoma DSB | Pub Dist Sch Brd (E/F) | English |  442054 | Elementary | English | Public  | Not applicable |         | 3924 Queen St E   | Sault Ste Marie | Ontario  | P6A5K9   | 705-945-7133 | 705-945-7225 | JK-8                 |
| B28010  | Algoma DSB | Pub Dist Sch Brd (E/F) | English |  101214 | Elementary | English | Public  | Not applicable | Box 700 | 24 Pine St        | Chapleau        | Ontario  | P0M1K0   | 705-864-1170 | 705-864-1606 | JK-8                 |
| B28010  | Algoma DSB | Pub Dist Sch Brd (E/F) | English |  926086 | Secondary  | English | Public  | Not applicable | 560     | 86 Magpie Rd      | Wawa            | Ontario  | P0S1K0   | 705-856-4464 | 705-856-1948 | 9-12                 |
| B28010  | Algoma DSB | Pub Dist Sch Brd (E/F) | English |  992569 | Secondary  | English | Public  | Night          |         | 231 Hillside Dr S | Elliot Lake     | Ontario  | P5A1N7   |              |              | No Grades Applicable |
| B28010  | Algoma DSB | Pub Dist Sch Brd (E/F) | English |  900869 | Secondary  | English | Public  | Not applicable | 880     | 20 Teak St        | Chapleau        | Ontario  | P0M1K0   | 705-864-1452 | 705-864-2367 | 9-12                 |

Candidate column headers:
Phone
Postal Code
Board Name
PO Box
School Language
School Special Conditions Code
Fax
Province
Board Number
Grade range
City
School Number
Street
Board Type
School Type
Board Language
School Level



headers: Phone
Postal Code
Board Name
PO Box
School Language
School Special Conditions Code
Fax
Province
Board Number
Grade range
City
School Number
Street
Board Type
School Type
Board Language
School Level
label: Board Number
Board Name
Board Type
Board Language
School Number
School Level
School Language
School Type
School Special Conditions Code
PO Box
Street
City
Province
Postal Code
Phone
Fax
Grade range
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 |
|--------:|--------:|--------:|
|   38169 | 110.6   | 110.121 |
|   37895 | 107.133 | 107.716 |
|   37530 | 104.733 | 103.219 |
|   38899 | 111.467 | 116.266 |
|   40725 | 110.8   | 119.598 |

Candidate column headers:
Date
GDP
TSI Freight



headers: Date
GDP
TSI Freight
label: Date
TSI Freight
GDP
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   |
|:------------------------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|--------:|:--------|---------:|:---------|:---------|:---------|:---------|---------:|:---------|:---------|:---------|:---------|
| Bathurst Panorama       |         | 334.0   | 409.0   | 495.0   | 541.0   | 495.0   | 482.0   |     530 |         |      567 |          | 589.0    |          | 595.0    |      600 |          | 580.0    | 598.0    |          |
| Brindabella             |         |         |         | 319.0   | 412.0   | 437.0   | 461.0   |     477 |         |      483 | 558.0    |          | 573.0    |          |      567 | 577.0    | 497.0    | 575.0    | 595.0    |
| Wollongong Harmony      |         | 356.0   | 363.0   | 462.0   | 574.0   | 596.0   | 561.0   |     575 |         |      505 | 501.0    |          | 591.0    |          |      544 |          | 540.0    |          | 559.0    |
| Out of the Blue Singers |         |         |         |         |         |         |         |     542 |         |      514 |          | 422.0    |          | 488.0    |      435 |          |          | 441.0    |          |
| Southern Sounds         |         |         |         |         | 400.0   | 334.0   | 365.0   |     356 |         |      340 |          |          | 470.0    |          |      463 | 464.0    |          |          | 513.0    |

Candidate column headers:
1998
2000
2014
2013
2012
2005
2007
1994
2002
2006
2004
1996
2015
2008
2010
1990
2009
Chorus
2011
1992



headers: 1998
2000
2014
2013
2012
2005
2007
1994
2002
2006
2004
1996
2015
2008
2010
1990
2009
Chorus
2011
1992
label: Chorus
1990
1992
1994
1996
1998
2000
2002
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
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 |
|:--------|:--------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|
|         |         |       0 |       0 |       0 |       0 |       0 |       0 |       0 |       0 |
|         |         |       0 |       0 |       0 |       0 |       0 |       0 |       0 |       0 |
|         |         |       0 |       0 |       0 |       0 |       0 |       0 |       0 |       0 |
|         |         |       0 |       0 |       0 |       0 |       0 |       0 |       0 |       0 |
|         |         |       0 |       0 |       0 |       0 |       0 |       0 |       0 |       0 |

Candidate column headers:
2015
2014
2012
2010
Zone
Trials
2009
2011
Site
2013



headers: 2015
2014
2012
2010
Zone
Trials
2009
2011
Site
2013
label: Site
Zone
2009
2010
2011
2012
2013
Trials
2015
2014
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 |
|:------------------------|:---------------------------|--------:|
| Pima Community College  | Health Related Professions |       1 |
| Pima Community College  | Health Related Professions |       1 |
| Central Arizona College | Education Careers          |       1 |
| Pima Community College  | Education Careers          |       1 |
| Pima Community College  | Health Related Professions |       1 |

Candidate column headers:
Program Types
Count
AZ Community College



headers: Program Types
Count
AZ Community College
label: AZ Community College
Program Types
Count
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         |
|:------------------------|:-----------------------|:-----------|:--------|--------:|:--------|:--------------|
|                         | 16 Sunderland Street   | MAYFIELD   | NSW     |    2304 |         | Newcastle     |
| Grand Western Lodge     | 27 Victoria Street     | MILLTHORPE | NSW     |    2798 |         | Blayney Shire |
|                         | 40 Campsie Street      | CAMPSIE    | NSW     |    2194 |         | Canterbury    |
| Bellevue Guest House    | 35 Jarvie Road         | CRINGILA   | NSW     |    2502 |         | Wollongong    |
| Rochester Private Hotel | 68-70 Charlotte Street | ASHFIELD   | NSW     |    2131 |         | Ashfield      |

Candidate column headers:
Website
Local Government/Council Area
Address Line
Postcode
Trading Name
Suburb
State



headers: Website
Local Government/Council Area
Address Line
Postcode
Trading Name
Suburb
State
label: Trading Name
Address Line
Suburb
State
Postcode
Website
Local Government/Council Area
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   |
|--------:|--------:|:--------|
|      26 |   42461 | blank   |
|      84 |   42461 | blank   |
|      88 |   42461 | blank   |
|      54 |   42461 | blank   |
|      87 |   42461 | blank   |

Candidate column headers:
Termination Date
MS-DRG List
Effective Date



headers: Termination Date
MS-DRG List
Effective Date
label: MS-DRG List
Effective Date
Termination Date
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 |
|:---------------------|--------:|:--------|:--------|--------:|--------:|:--------|--------:|--------:|
| North Dakota         |    1150 | 119     | 14      |      30 |      22 | 4       |      31 |     930 |
| Connecticut          |    4675 | 12      | 151     |     499 |    1419 | 21      |      96 |    2477 |
| District of Columbia |     635 | x       | x       |     309 |     135 | x       |      28 |     152 |
| Oklahoma             |    2558 | 144     | 52      |     171 |     428 | 14      |     236 |    1513 |
| Utah                 |    3841 | 44      | 60      |      35 |     728 | 38      |      99 |    2837 |

Candidate column headers:
Two or More Races
State
White
Race/ethnicity total2
Native Hawaiian or Pacific Islander
Asian
Hispanic/ Latino
Black or African American
American Indian or Alaska Native



headers: Two or More Races
State
White
Race/ethnicity total2
Native Hawaiian or Pacific Islander
Asian
Hispanic/ Latino
Black or African American
American Indian or Alaska Native
label: State
Race/ethnicity total2
American Indian or Alaska Native
Asian
Black or African American
Hispanic/ Latino
Native Hawaiian or Pacific Islander
Two or More Races
White
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 |
|--------:|--------:|---------:|---------:|
|   11933 |      86 |  34830   | 11493.9  |
|    9270 |     250 | 101250   | 33412.5  |
|   14040 |     383 | 155115   | 51187.9  |
|   11306 |      75 |  16788.6 |  5540.25 |
|    8071 |     214 |  86670   | 28601.1  |

Candidate column headers:
FUNDED ADM TOTAL
 ADMIN COST MAX REIMBURSEMENT
 IRN
 ADMIN COST PAID AMOUNT



headers: FUNDED ADM TOTAL
 ADMIN COST MAX REIMBURSEMENT
 IRN
 ADMIN COST PAID AMOUNT
label:  IRN
FUNDED ADM TOTAL
 ADMIN COST MAX REIMBURSEMENT
 ADMIN COST PAID AMOUNT
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 |
|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|
| 41307.3 |      30 |      85 |      12 |    1020 |       8 |     680 |     340 |     572 |    2013 |        2 |      140 | 33.3333  |
| 41310.8 |      16 |      74 |      15 |    1110 |      14 |    1036 |      74 |       6 |    2013 |        2 |      472 |  6.66667 |
| 41303.5 |      31 |       5 |      21 |     105 |      12 |      60 |      45 |       6 |    2013 |        1 |      315 | 42.8571  |
| 41279.4 |      30 |      27 |      12 |     324 |       8 |     216 |     108 |     233 |    2013 |        1 |      475 | 33.3333  |
| 41288.3 |      16 |      28 |      15 |     420 |      14 |     392 |      28 |     572 |    2013 |        1 |      230 |  6.66667 |

Candidate column headers:
OrderDate
CustomerID
Quantity
Year
Unit Price
Total Sales
Order Profit
ProductID
Month
Unit Cost
Profit
SalesRep_ID
Total Cost



headers: OrderDate
CustomerID
Quantity
Year
Unit Price
Total Sales
Order Profit
ProductID
Month
Unit Cost
Profit
SalesRep_ID
Total Cost
label: OrderDate
ProductID
Quantity
Unit Price
Total Sales
Unit Cost
Total Cost
Profit
CustomerID
Year
Month
SalesRep_ID
Order Profit
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 |
|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|---------:|
|     100 |    3.65 |    0.05 |    3.32 |    8.67 |    0.82 |    4.73 |    5.02 |    0.08 |   19.81 |    50.01 |     3.34 |     0.44 |     0.05 |
|     100 |    3.51 |    0    |    1.02 |    7.79 |    0.54 |    8    |    0.18 |    0.09 |   27.95 |    47.81 |     2.69 |     0.28 |     0.14 |
|     100 |    2.35 |    0    |    1.88 |   12.21 |    0    |    5.16 |   10.8  |    0    |   23    |    38.97 |     5.16 |     0.47 |     0    |
|     100 |    5.04 |    0.01 |    0    |   19.55 |    1.04 |    8.21 |    0.09 |    0.22 |   18.52 |    41.25 |     5.37 |     0.28 |     0.4  |
|     100 |    4.2  |    0.05 |    1.4  |   20.53 |    0.92 |    1.64 |    1.27 |    0.63 |   17.57 |    46.58 |     4.2  |     0.77 |     0.24 |

Candidate column headers:
Emotional disturbance (%)
Orthopedic impairments (%)
Autism (%)
Traumatic brain injury (%)
Other health impairments (%)
All disabilities3 (%)
Multiple disabilities (%)
Visual impairments (%)
Deaf-blindness (%)
Intellectual disabilities (%)
Speech or language impairments (%)
Developmental delay4 (%)
Specific learning disabilities (%)
Hearing impairments (%)



headers: Emotional disturbance (%)
Orthopedic impairments (%)
Autism (%)
Traumatic brain injury (%)
Other health impairments (%)
All disabilities3 (%)
Multiple disabilities (%)
Visual impairments (%)
Deaf-blindness (%)
Intellectual disabilities (%)
Speech or language impairments (%)
Developmental delay4 (%)
Specific learning disabilities (%)
Hearing impairments (%)
label: All disabilities3 (%)
Autism (%)
Deaf-blindness (%)
Developmental delay4 (%)
Emotional disturbance (%)
Hearing impairments (%)
Intellectual disabilities (%)
Multiple disabilities (%)
Orthopedic impairments (%)
Other health impairments (%)
Specific learning disabilities (%)
Speech or language impairments (%)
Traumatic brain injury (%)
Visual impairments (%)
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   |
|--------:|:--------|--------:|:--------|
|   40558 | Young   |  755.89 | Woods   |
|   40551 | Flynn   | 1608.53 | Woods   |
|   40557 | Ford    |  350.92 | Shoes   |
|   40552 | Flynn   | 1050.38 | Irons   |
|   40552 | Flynn   | 1050.38 | Irons   |

Candidate column headers:
Order Amount
Category
Salesperson
Order Date



headers: Order Amount
Category
Salesperson
Order Date
label: Order Date
Salesperson
Order Amount
Category
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 |
|--------:|--------:|--------:|
|   36599 |  102.75 |   36599 |
|   36565 |  107.7  |   36565 |
|   36544 |  116.7  |   36544 |
|   36572 |  104.95 |   36572 |
|   36529 |  116.25 |   36529 |

Candidate column headers:
USD BRL Copy
Settle
Date



headers: USD BRL Copy
Settle
Date
label: Date
Settle
USD BRL Copy
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   |
|--------:|--------:|:--------|:--------|
|      90 |      88 | WA      | BID     |
|     100 |      72 | CA      | POK     |
|     120 |     104 | AZ      | TUG     |
|     100 |      96 | CA      | SUF     |
|     120 |      87 | AZ      | PIG     |

Candidate column headers:
Res
State
Targ
Name



headers: Res
State
Targ
Name
label: Targ
Res
State
Name
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 |
|:-----------|--------:|--------:|
| La Chouffe |   42341 |      35 |
| La Chouffe |   42343 |      45 |
| Maes       |   42343 |      36 |
| Jupiler    |   42339 |      10 |
| La Chouffe |   42341 |      21 |

Candidate column headers:
Drank
Datum
Consumptie



headers: Drank
Datum
Consumptie
label: Drank
Datum
Consumptie
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 |
|--------:|:---------|--------:|--------:|--------:|---------:|---------:|
|      10 | MUKONO   |      89 |      55 |      59 | 0.617978 | 0.662921 |
|      22 | WAKISO   |     278 |     133 |     165 | 0.478417 | 0.593525 |
|      28 | BUSHENYI |     118 |      52 |      65 | 0.440678 | 0.550847 |
|      29 | MUKONO   |      85 |      37 |      58 | 0.435294 | 0.682353 |
|      21 | MUKONO   |     231 |     113 |     144 | 0.489177 | 0.623377 |

Candidate column headers:
% Bs
Bs
District
Students
As
Position
% As



headers: % Bs
Bs
District
Students
As
Position
% As
label: Position
District
Students
As
Bs
% As
% Bs
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 |
|:------------|:------------|:---------------------|--------:|:---------------------|--------:|:------------------|:------------------------------|:-------------|:-----------|---------:|---------:|---------:|---------:|
| Jan 9 1977  | XI (11)     | Oakland Raiders      |      32 | Minnesota Vikings    |      14 | Fred Biletnikoff+ | Rose Bowl                     | Pasadena     | California |       35 |       18 |    12.98 |    0.714 |
| Jan 30 1994 | XXVIII (28) | Dallas Cowboys       |      30 | Buffalo Bills        |      13 | Emmitt Smith+     | Georgia Dome                  | Atlanta      | Georgia    |       15 |       17 |     6.89 |    0.306 |
| Feb 6 2005  | XXXIX (39)  | New England Patriots |      24 | Philadelphia Eagles  |      21 | Deion Branch      | Alltel Stadium                | Jacksonville | Florida    |        8 |        3 |     4.53 |    0.163 |
| Jan 30 1983 | XVII (17)   | Washington Redskins  |      27 | Miami Dolphins       |      17 | John Riggins+     | Rose Bowl                     | Pasadena     | California |       12 |       10 |     5.71 |    0.244 |
| Feb 3 2008  | XLII (42)   | New York Giants      |      17 | New England Patriots |      14 | Eli Manning       | University of Phoenix Stadium | Glendale     | Arizona    |        6 |        3 |     3.75 |    0.122 |

Candidate column headers:
State
L_Pts
MVP
Loser
Rank
Date
W_Pts
Winner
Stadium
%Rank
Final Score Point Diff
City
Close Score
SB



headers: State
L_Pts
MVP
Loser
Rank
Date
W_Pts
Winner
Stadium
%Rank
Final Score Point Diff
City
Close Score
SB
label: Date
SB
Winner
W_Pts
Loser
L_Pts
MVP
Stadium
City
State
Rank
Final Score Point Diff
Close Score
%Rank
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 |
|--------:|--------:|---------:|---------:|--------:|--------:|
|   42657 |   42658 | 0.291667 | 0.354167 |   37184 |      78 |
|   42655 |   42656 | 0.416667 | 0.916667 |   45127 |      47 |
|   42656 |   42657 | 0.416667 | 0.916667 |   45127 |      48 |
|   42653 |   42654 | 0.291667 | 0.354167 |   37184 |      74 |
|   42655 |   42656 | 0.291667 | 0.354167 |   37184 |      76 |

Candidate column headers:
debut d
debut h
fin h
Code GroupeB
Code GroupeA
fin d



headers: debut d
debut h
fin h
Code GroupeB
Code GroupeA
fin d
label: debut d
fin d
debut h
fin h
Code GroupeA
Code GroupeB
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 |
|--------:|--------:|--------:|:-----------|------------:|------------:|------------:|---------:|
|   42033 |   42033 |    2014 | 6014948C1A | 12313480159 |   426150488 |   426150488 | 97600_50 |
|   42033 |   42033 |    2014 | 566813227F | 12313480159 |  1466740501 |  1466740501 |  2663_64 |
|   42033 |   42033 |    2014 | 6017726098 | 12313480159 |   747170157 |   747170157 | 67647_10 |
|   42033 |   42033 |    2014 | 565442966B | 12313480159 |  5994810488 |  5994810488 | 42701_40 |
|   42033 |   42033 |    2014 | 5638309FC3 | 12313480159 | 11654150157 | 11654150157 |   343_98 |

Candidate column headers:
codiceFiscale
dataPubblicazioneDataset
importoAggiudicazione
annoRiferimento
codiceFiscaleProp
cig
codiceFiscale2
dataUltimoAggiornamentoDataset



headers: codiceFiscale
dataPubblicazioneDataset
importoAggiudicazione
annoRiferimento
codiceFiscaleProp
cig
codiceFiscale2
dataUltimoAggiornamentoDataset
label: dataPubblicazioneDataset
dataUltimoAggiornamentoDataset
annoRiferimento
cig
codiceFiscaleProp
codiceFiscale
codiceFiscale2
importoAggiudicazione
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 |
|:--------------------------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
| Geintegreerd wonen        |       0 |       0 |       0 |       0 |       0 |       0 |       0 |       0 |       0 |        0 |        0 |        0 |        0 |        0 |       29 |       91 |       91 |      152 |
| Zelfstandig wonen         |      72 |      72 |      72 |     109 |     172 |     187 |     192 |     204 |     211 |      211 |      223 |      236 |      239 |      275 |      288 |      311 |      311 |      325 |
| Thuisbegeleiding plaatsen |    1668 |    1668 |    1668 |    1683 |    1683 |    2030 |    2347 |    2694 |    3138 |     3255 |     3845 |     4190 |     4421 |     4663 |     5502 |     5857 |     5857 |     6283 |
| Begeleid wonen            |     605 |     800 |     800 |     926 |    1000 |    1100 |    1240 |    1440 |    1540 |     1660 |     1955 |     2129 |     2273 |     2408 |     2578 |     2752 |     2752 |     2843 |
| Semi- internaten          |    3536 |    3560 |    3545 |    3561 |    3508 |    3578 |    3554 |    3572 |    3572 |     3588 |     3674 |     3727 |     3744 |     3763 |     3753 |     3751 |     3742 |     3746 |

Candidate column headers:
1998
2010
1994
1996
2006
1999
2005
2003
2009
2000
2002
1993
2008
Voorzieningen voor mensen met een handicap 
2001
1997
2004
2007
1995



headers: 1998
2010
1994
1996
2006
1999
2005
2003
2009
2000
2002
1993
2008
Voorzieningen voor mensen met een handicap 
2001
1997
2004
2007
1995
label: Voorzieningen voor mensen met een handicap 
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
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 |
|:--------|--------:|--------:|--------:|--------:|--------:|:--------|--------:|
| 2012/13 |    1683 |    0.81 |    1436 |    0.85 |    0.11 | -       |     186 |
| 2004/05 |    2503 |    0.51 |    2072 |    0.83 |    0.16 | 404     |     238 |
| 2010/11 |    1936 |    0.63 |    1648 |    0.85 |    0.11 | -       |     241 |
| 2009/10 |    2127 |    0.51 |    1824 |    0.86 |    0.1  | -       |     187 |
| 2014/15 |    1512 |    0.74 |    1317 |    0.87 |    0.1  | -       |     143 |

Candidate column headers:
Financial year
Number of review requests
Applications determined within 8 weeks
Number of appeal requests
Number of applications approved 
Number of applications approved
Number of applications determined
Number of applications refused



headers: Financial year
Number of review requests
Applications determined within 8 weeks
Number of appeal requests
Number of applications approved 
Number of applications approved
Number of applications determined
Number of applications refused
label: Financial year
Number of applications determined
Applications determined within 8 weeks
Number of applications approved 
Number of applications approved
Number of applications refused
Number of review requests
Number of appeal requests
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 |
|--------:|:-------------------|:--------|--------:|
|      19 | Shape sorting box  | set     |       1 |
|      18 | Tamboreen          | pcs     |       1 |
|       3 | Animal Set         | Set     |       1 |
|      17 | Hand bell          | pcs     |       1 |
|      11 | Dolls (Family set) | Set     |       1 |

Candidate column headers:
S.No
Unit 
Qty  Per Kit
Item Description



headers: S.No
Unit 
Qty  Per Kit
Item Description
label: S.No
Item Description
Unit 
Qty  Per Kit
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 |
|--------:|--------:|:-------------|--------:|--------:|:--------|:--------------|--------:|--------:|:--------|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
|      46 | 42450.4 | FD-2000-2001 |     130 |      23 | First   | 40minAg_10mCu |       8 |    2016 | Jan     |       21 |        2 |        1 | 0.421655 |   309771 |     2356 |     9531 | 10.8333  |
|      15 | 42450.1 | SND-714-2002 |      68 |      19 | Third   | 22minSn       |       8 |    2016 | Jan     |       21 |        2 |        1 | 0.142199 |   309732 |     5424 |     3037 | 25.2167  |
|       1 | 42451   | FD-3378-2011 |     112 |      93 | Third   | 22minAg_Ni    |       8 |    2016 | Jan     |       21 |        2 |        1 | 0.982639 |   309714 |     9899 |     3037 | 14.2333  |
|      14 | 42450.1 | SD-5022-2001 |     136 |      56 | Third   | 40minAg_10mCu |       8 |    2016 | Jan     |       21 |        2 |        1 | 0.12912  |   309730 |     2484 |     3037 |  5.19445 |
|      12 | 42450.1 | FD-3384-2001 |     800 |      64 | Third   | 46minAg       |       1 |    2016 | Jan     |       21 |        2 |        1 | 0.108877 |   309726 |     9900 |     3037 | 29.7222  |

Candidate column headers:
FBnumber
Quarter
RecordID
Weekday
Time
Quantity
PartName
Day
JobNumber
Area
OperatorID
Recipe
Type
Momth
Shift
Year
Date
RunID



headers: FBnumber
Quarter
RecordID
Weekday
Time
Quantity
PartName
Day
JobNumber
Area
OperatorID
Recipe
Type
Momth
Shift
Year
Date
RunID
label: RecordID
Date
PartName
Quantity
FBnumber
Shift
Recipe
Type
Year
Momth
Day
Weekday
Quarter
Time
RunID
JobNumber
OperatorID
Area