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 |
|:--------------|:--------------------|---------:|-----------:|-----------:|-----------:|
| B - 4         | -0.3632714452717129 | 0.495495 |  0.0855547 | 0.101601   | -0.0212539 |
| A - 2         |                     | 0.433333 |  0.076407  | 0.0816428  | -0.0122505 |
| A - 3         | -0.2745884804888659 | 0.75     |  0.0278075 | 0.0936248  | -0.165108  |
| Total A       | -4.517147279830377  | 0.409357 | -0.0404082 | 0.00951566 | -0.243909  |
| Total general | -1.1739557786951818 | 0.421708 |  0.141892  | 0.162067   |  0.0359043 |

Candidate column headers:
Compltos No Sal.
% mujeres
Salario Base
Salario Base + Complem
Compltos Sal.
Niveles de Responsabilidad // Exigencia



headers: Compltos No Sal.
% mujeres
Salario Base
Salario Base + Complem
Compltos Sal.
Niveles de Responsabilidad // Exigencia
label: Niveles de Responsabilidad // Exigencia
Compltos No Sal.
% mujeres
Salario Base + Complem
Salario Base
Compltos Sal.
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   |
|--------:|:---------------|:--------|--------:|--------:|--------:|--------:|---------:|---------:|--------:|:------------|:---------|
|    2014 | P              | Median  |  618.8  |  531.5  |     942 |    1342 | 0.14108  | 0.587566 |    2014 | Revised     | 16.1a    |
|    2015 | Private sector | Median  |  498.3  |  402.5  |    8592 |    4037 | 0.192254 | 0.319661 |    2015 | Provisional | 13.3a    |
|    2014 | Public sector  | Median  |   16.48 |   14.66 |    1758 |    2418 | 0.110437 | 0.579023 |    2014 | Revised     | 13.6a    |
|    2014 | Public sector  | Median  |  634.9  |  539.9  |    1758 |    2418 | 0.14963  | 0.579023 |    2014 | Revised     | 13.1a    |
|    2015 | Public sector  | Median  |  648.4  |  545.8  |    1780 |    2443 | 0.158236 | 0.578499 |    2015 | Provisional | 13.1a    |

Candidate column headers:
Female earnings
Gender pay gap %
Earnings table
Female jobs (thousands)
Male earnings
Provisional?
Code ref
Year
Year ref
Male jobs (thousands)
Salary at
Gender balance  (% females)



headers: Female earnings
Gender pay gap %
Earnings table
Female jobs (thousands)
Male earnings
Provisional?
Code ref
Year
Year ref
Male jobs (thousands)
Salary at
Gender balance  (% females)
label: Year
Code ref
Salary at
Male earnings
Female earnings
Male jobs (thousands)
Female jobs (thousands)
Gender pay gap %
Gender balance  (% females)
Year ref
Provisional?
Earnings table
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 |
|:--------|:-------------------|--------:|
|         | Graphic Designer   |  -42153 |
|         | Promotions Manager |  -42153 |
|         | Organization Admin |  -42153 |
|         | Finance Director   |  -42153 |
|         | Donation Manager   |  -42153 |

Candidate column headers:
Status
Responsibility
Type



headers: Status
Responsibility
Type
label: Type
Responsibility
Status
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 |
|:------------|:-------------|--------:|
| Confections | Pavlova      |   18    |
| Condiments  | Soy Sauce    |    3    |
| Condiments  | Oyster Sauce |    4.25 |
| Confections | Nougat       |    3.5  |
| Beverages   | Water        |    1.5  |

Candidate column headers:
Product
Unit Price
Category



headers: Product
Unit Price
Category
label: Category
Product
Unit Price
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 |
|--------:|:------------|--------:|
|   40165 | Joe Jab     |   57.65 |
|   40165 | Chris Cram  |    8.07 |
|   40159 | Joe Jab     |   99.5  |
|   40165 | Harold Hold |   22.65 |
|   40162 | Harold Hold |   96.66 |

Candidate column headers:
Name
Wrestling Score
Date



headers: Name
Wrestling Score
Date
label: Date
Name
Wrestling Score
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 |
|:--------|:---------|--------:|
| REH     | Smith    |   41330 |
| REH     | Williams |   41330 |
| GER     | Williams |   41278 |
| FAF     | Smith    |   41278 |
| TIM     | Williams |   41317 |

Candidate column headers:
DDate
Presenter
Class



headers: DDate
Presenter
Class
label: Class
Presenter
DDate
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 |
|:---------|:--------|:--------------|:-------------------|:------------------|--------:|
| srv_bdca | public  | t_totalizador | codigomunicipio    | character varying |       6 |
| srv_bdca | public  | t_totalizador | nombredepartamento | character varying |       2 |
| srv_bdca | public  | t_totalizador | grupo              | character varying |      13 |
| srv_bdca | public  | t_totalizador | provincia          | text              |       3 |
| srv_bdca | public  | t_totalizador | p24_1_tc           | character varying |      22 |

Candidate column headers:
column_name
table_name
table_schema
table_catalog
data_type
ordinal_position



headers: column_name
table_name
table_schema
table_catalog
data_type
ordinal_position
label: table_catalog
table_schema
table_name
column_name
data_type
ordinal_position
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 |
|--------:|--------:|--------------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|
|      11 |      11 |      6.58e+07 |       1 |       1 |    1600 |   22.45 |    0.21 |    0.42 |    0.93 |
|       1 |       1 | 790000        |       3 |       3 |     210 |    3.29 |    0.28 |    0.08 |    0.47 |
|       1 |       1 |      1.49e+08 |       3 |       3 |    2400 |    9.57 |    3.2  |    2.71 |    0.84 |
|       2 |       2 |      5.38e+07 |       3 |       3 |    5400 |   26.53 |    1.99 |    4.68 |    0.94 |
|       1 |       1 |      1.11e+07 |       3 |       3 |     590 |   28.02 |    0.78 |    1.95 |    0.91 |

Candidate column headers:
Traffic Cost (%)
Search Volume
Weighted previous
Position
CPC
Competition
Number of Results
Previous Position
Weighted
Traffic (%)



headers: Traffic Cost (%)
Search Volume
Weighted previous
Position
CPC
Competition
Number of Results
Previous Position
Weighted
Traffic (%)
label: Position
Previous Position
Number of Results
Weighted
Weighted previous
Search Volume
CPC
Traffic (%)
Traffic Cost (%)
Competition
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 |
|:--------|:--------|:-------------|--------:|
| Central | Ghazni  | Jaghuri      |     106 |
| Central | Ghazni  | Abband       |       3 |
| Central | Logar   | Mohammadagha |    6911 |
| Central | Kapisa  | Alasay       |      46 |
| Central | Ghazni  | Nawa         |      22 |

Candidate column headers:
Column2
Column1
Column4
Column3



headers: Column2
Column1
Column4
Column3
label: Column1
Column2
Column3
Column4
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 |
|--------:|:--------|---------:|------------:|
|   25069 | O       |    54    | 7.97369e+07 |
|   25075 | O       | 30930.1  | 7.97369e+07 |
|   25056 | E       | 24391.8  | 7.97369e+07 |
|   25075 | E       |  3811.23 | 7.97369e+07 |
|   25051 | O       |   430    | 7.97369e+07 |

Candidate column headers:
CODE
SOMME
ID
MONTANTS



headers: CODE
SOMME
ID
MONTANTS
label: ID
CODE
MONTANTS
SOMME
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 |
|--------:|--------:|--------:|--------:|--------:|
| 8.9e+06 |     281 |    4532 |     203 |    3422 |
| 6.1e+06 |     188 |    2671 |     141 |    2219 |
| 6.7e+06 |     219 |    3047 |     140 |    2485 |
| 6.9e+06 |     218 |    3172 |     172 |    2547 |
| 5.5e+06 |     172 |    2297 |     125 |    1953 |

Candidate column headers:
quadHeap_insert
size
quadHeap_delete
binHeap_insert
binHeap_delete



headers: quadHeap_insert
size
quadHeap_delete
binHeap_insert
binHeap_delete
label: size
binHeap_insert
binHeap_delete
quadHeap_insert
quadHeap_delete
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   |
|--------:|--------:|--------:|--------:|:--------|
|    1987 |  115.72 |  100.13 |   69.18 |         |
|    1991 |  108.96 |  105.5  |   67.48 |         |
|    1985 |  151.16 |  106.11 |  128.51 |         |
|    1958 |  203.63 |  131.18 |   19.28 |         |
|    1961 |  198.69 |  136.67 |   22.22 |         |

Candidate column headers:
Energy
Agriculture
Event
Metals
Year



headers: Energy
Agriculture
Event
Metals
Year
label: Year
Agriculture
Metals
Energy
Event
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 |
|:-------------|:--------------|--------:|
| USA Bay Area | Oakland_ CA   |   42864 |
| Canada       | Saskatoon     |   42029 |
| USA Bay Area | Oakland_ CA   |   42849 |
| USA Mountain | Denver_ CO    |   42275 |
| USA Central  | St. Louis_ MO |   42872 |

Candidate column headers:
Local Commercial Territory
Reporting Region
Last Updated



headers: Local Commercial Territory
Reporting Region
Last Updated
label: Reporting Region
Local Commercial Territory
Last Updated
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 |
|:----------|:---------------------|:--------|--------:|:--------|---------:|
| Lincoln   | Forest Park          |         |       1 | 1.0     | 0.833333 |
| Madison   | Scott                | 4.0     |       2 | 3.0     | 2.83333  |
| Jefferson | Boise-Eliot/Humboldt |         |       1 |         | 0.333333 |
| Cleveland | Woodstock            |         |       1 | 1.0     | 0.833333 |
| Franklin  | Kelly                | 9.0     |       3 | 3.0     | 4        |

Candidate column headers:
3 Year Weighted Avg
2013
Attendance Area
2015
Cluster
2014



headers: 3 Year Weighted Avg
2013
Attendance Area
2015
Cluster
2014
label: Cluster
Attendance Area
2013
2014
2015
3 Year Weighted Avg
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     |
|:-------------------|:---------|:----------|
| Physical Activity  | Adults   | General   |
| Work Environment   | Adults   | General   |
| Abuse              | Adults   | General   |
| Cognitive Problems | Children | General   |
| Attitudes          | Adults   | Arthritis |

Candidate column headers:
Diagnosis (use "General" if no specific diagnosis)
Construct
Population



headers: Diagnosis (use "General" if no specific diagnosis)
Construct
Population
label: Construct
Population
Diagnosis (use "General" if no specific diagnosis)
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   |
|:--------|--------:|--------:|--------:|:---------------|--------:|:-----------------|--------:|--------:|:--------------|---------:|:---------|:----------|----------:|:---------|:---------|
| S       |       1 |       0 |       0 | DELIVERY ORDER |    1700 | DEPT OF THE NAVY |   38876 |    3010 | N4034506D0001 |   561990 | R706     | SINGAPORE | 894682517 | SN       |          |
| S       |       1 |       0 |       0 | DELIVERY ORDER |    1700 | DEPT OF THE NAVY |   40569 |   20000 | N4034511D0009 |   561990 | R706     | SINGAPORE | 894682517 |          | 609266.0 |
| S       |       1 |       1 |       0 | DELIVERY ORDER |    1700 | DEPT OF THE NAVY |   40528 |  -50000 | N4034510D0006 |   561990 | R706     | SINGAPORE | 894682517 |          | 609266.0 |
| S       |       1 |       0 |       0 | DELIVERY ORDER |    1700 | DEPT OF THE NAVY |   40480 |   20000 | N4034511D0002 |   561990 | R706     | SINGAPORE | 894682517 |          | 609266.0 |
| S       |       1 |       1 |       0 | DELIVERY ORDER |    1700 | DEPT OF THE NAVY |   40869 | -100000 | N4034511D0017 |   561990 | R706     | SINGAPORE | 894682517 |          | 609266.0 |

Candidate column headers:
Date Signed
Contract ID
Reference IDV
Contracting Agency ID
Award/IDV Type
NAICS
Transaction Number
Vendor DUNS
PSC Type
PSC
Modification Number
Vendor ZIP Code
Vendor State
Action Obligation ($)
Vendor City
Contracting Agency



headers: Date Signed
Contract ID
Reference IDV
Contracting Agency ID
Award/IDV Type
NAICS
Transaction Number
Vendor DUNS
PSC Type
PSC
Modification Number
Vendor ZIP Code
Vendor State
Action Obligation ($)
Vendor City
Contracting Agency
label: PSC Type
Contract ID
Modification Number
Transaction Number
Award/IDV Type
Contracting Agency ID
Contracting Agency
Date Signed
Action Obligation ($)
Reference IDV
NAICS
PSC
Vendor City
Vendor DUNS
Vendor State
Vendor ZIP 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 |   Col_7 |   Col_8 |   Col_9 |
|:---------|------------:|:-----------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|
| ALLEGANY | 22601136563 | Non Public |       0 |       1 |       1 |       0 |       3 |     120 |       9 |
| ALBANY   | 10623995677 | Non Public |       0 |      24 |       4 |       0 |       5 |      49 |       2 |
| ALBANY   | 10100997616 | Non Public |       0 |       7 |       1 |       0 |      11 |      20 |       7 |
| ALLEGANY | 22001807067 | Non Public |       0 |      19 |      50 |       0 |       1 |      70 |       0 |
| ALBANY   | 10100115684 | Non Public |       0 |      27 |      13 |       6 |      15 |      83 |      15 |

Candidate column headers:
Hawaiian/ Pacific Islander
Multi-Racial
County
Asian
School Type
BEDS Code
American Indian/Alaska Native
Black
White
Hispanic



headers: Hawaiian/ Pacific Islander
Multi-Racial
County
Asian
School Type
BEDS Code
American Indian/Alaska Native
Black
White
Hispanic
label: County
BEDS Code
School Type
American Indian/Alaska Native
Black
Asian
Hawaiian/ Pacific Islander
Hispanic
White
Multi-Racial
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 |
|:--------|:-------------------|:--------|:----------------|:-----------------|:--------|:---------------------|--------:|
| AC/P36  | Exploration Permit | Active  | Bonaparte Basin | Vulcan Sub-basin | 44050.0 | SD51 BRUNSWICK BAY   |     977 |
| AC/P36  | Exploration Permit | Active  | Bonaparte Basin | Vulcan Sub-basin | 44050.0 | SD51 BRUNSWICK BAY   |     757 |
| AC/L5   | Production Licence | Active  | Bonaparte Basin | Laminaria High   | 50806.0 | SC52 MELVILLE ISLAND |    2233 |
| AC/P32  | Exploration Permit | Active  | Bonaparte Basin | Vulcan Sub-basin | 44370.0 | SD51 BRUNSWICK BAY   |     417 |
| AC/L7   | Production Licence | Active  | Bonaparte Basin | Vulcan Sub-basin |         | SD51 BRUNSWICK BAY   |     630 |

Candidate column headers:
SubBasin
Status
Block
MapSheet
TitleNumber
Basin
TitleType
TitleExpiryDate



headers: SubBasin
Status
Block
MapSheet
TitleNumber
Basin
TitleType
TitleExpiryDate
label: TitleNumber
TitleType
Status
Basin
SubBasin
TitleExpiryDate
MapSheet
Block
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 |
|:--------|---------:|---------:|
| Average | 0.188422 | 0.176872 |
| LI      | 0        | 0        |
| LU      | 0        | 0        |
| GR      | 0        | 0        |
| SK      | 0        | 0        |

Candidate column headers:
Column1
Number of policies per inhabitant 2016
Number of policies per inhabitan 2015



headers: Column1
Number of policies per inhabitant 2016
Number of policies per inhabitan 2015
label: Column1
Number of policies per inhabitan 2015
Number of policies per inhabitant 2016
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 |
|:--------------|:--------------|:--------|:-----------|:--------|--------:|:-----------|:-----------|--------:|--------:|
| Caltanissetta | Caltanissetta | 8ENRPG  | ZFA1C67D04 | SI      | 2590    | 30/12/2016 | 05/02/2017 | 2590    | 3500    |
| Caltanissetta | Caltanissetta | 8ENRPG  | Z3B1BB0DD2 | NO      |  500    | 21/10/2016 | 20/12/2016 |  500    |  500    |
| Caltanissetta | Caltanissetta | 8ENRPG  | Z5E1C363EE | NO      |  409    | 22/11/2016 | 22/12/2016 |  409    |  409    |
| Caltanissetta | Caltanissetta | 8ENRPG  | Z84IC184EB | NO      |  224    | 27/12/2016 | 31/12/2017 |  224    |  224    |
| Caltanissetta | Caltanissetta | 8ENRPG  | Z8E1BE1240 | NO      | 3700.82 | 07/11/2016 | 21/11/2016 | 3700.82 | 3700.82 |

Candidate column headers:
Distretto
Codice IPA
Somme Liquidate
comune
Importo Aggiudicazione
Data Scadenza
Mepa SI/NO
Spesa Prevista
CIG
Data Decorrenza



headers: Distretto
Codice IPA
Somme Liquidate
comune
Importo Aggiudicazione
Data Scadenza
Mepa SI/NO
Spesa Prevista
CIG
Data Decorrenza
label: Distretto
comune
Codice IPA
CIG
Mepa SI/NO
Importo Aggiudicazione
Data Decorrenza
Data Scadenza
Somme Liquidate
Spesa Prevista
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   |
|:---------------|:-------------------------------|--------:|:-----------------------|:--------|:-----------|:--------|
| B3007PDD       | Persistent Drunk Driver        |   12301 | B3007PDD - 12301       |         | GR00000256 |         |
| B3006PDD       | Persistent Drunk Driver        |   12301 | B3006PDD - 12301       |         | GR00000020 |         |
| E3036EQ17AIR   | Air Pollution Activities FY17- |   14301 | E3036EQ17AIR - 14301   |         | GR00000516 |         |
| E3032_CHCCDP15 | Cancer_ Cardiovascular Disease |   14301 | E3032_CHCCDP15 - 14301 |         | GR00000030 |         |
| E2071EECBGBC   | EECBG Banking Fees             |   11217 | E2071EECBGBC - 11217   |         | GR00000488 |         |

Candidate column headers:
ID - Fund
PeopleSoft Project ID
WD Grant ID
PeopleSoft Project Name
WD Capital Program
Fund
Workday Project ID



headers: ID - Fund
PeopleSoft Project ID
WD Grant ID
PeopleSoft Project Name
WD Capital Program
Fund
Workday Project ID
label: PeopleSoft Project ID
PeopleSoft Project Name
Fund
ID - Fund
Workday Project ID
WD Grant ID
WD Capital Program
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   |
|--------:|--------:|--------:|:--------|:--------|
|       3 |       3 |       3 | B4      |         |
|       3 |       6 |       6 | B1+B3   | F09     |
|       3 |       6 |       3 | C2      | F11     |
|       3 |       6 |       3 | C2      |         |
|       3 |       3 |       0 |         |         |

Candidate column headers:
Minimum Passing Score
Removal Date for  GE Breadth*** 
Semester Credits Toward GE Breadth Certification
American Institutions and/or GE Breadth Area**
Minimum Semester Credits Earned towards admission*



headers: Minimum Passing Score
Removal Date for  GE Breadth*** 
Semester Credits Toward GE Breadth Certification
American Institutions and/or GE Breadth Area**
Minimum Semester Credits Earned towards admission*
label: Minimum Passing Score
Minimum Semester Credits Earned towards admission*
Semester Credits Toward GE Breadth Certification
American Institutions and/or GE Breadth Area**
Removal Date for  GE Breadth***
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      |
|-------------:|:--------|:-------------|:---------|:---------------|:-----------|:----------|--------:|:-----------|
| 100000033724 | A019147 | ACCESS POINT | ARUBA    | 93H            | BN0047724  | 6/23/2014 |  213.42 | 2000007101 |
| 100000033310 | 132586  | 3D PRINTER   | MAKERBOT | 5TH GENERATION | R500-16316 | 4/25/2014 | 3183.46 | -          |
| 100000033752 | A019175 | ACCESS POINT | ARUBA    | 93H            | BN0047830  | 6/23/2014 |  213.42 | 2000007101 |
| 100000033750 | A019173 | ACCESS POINT | ARUBA    | 93H            | BN0047526  | 6/23/2014 |  213.42 | 2000007101 |
| 100000033738 | A019161 | ACCESS POINT | ARUBA    | 93H            | BN0046795  | 6/23/2014 |  213.42 | 2000007101 |

Candidate column headers:
Purchase Order ID
Asset Description
Model
Tag Number
Cost Amt
Asset ID
Serial ID
Acquisition Date
Manufacturer



headers: Purchase Order ID
Asset Description
Model
Tag Number
Cost Amt
Asset ID
Serial ID
Acquisition Date
Manufacturer
label: Asset ID
Tag Number
Asset Description
Manufacturer
Model
Serial ID
Acquisition Date
Cost Amt
Purchase Order ID
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 |
|--------:|--------:|--------:|--------:|--------:|--------:|--------:|:--------|--------:|
|     333 |       1 |       0 |     203 |      34 |      68 |      12 | 0       |      15 |
|     533 |       1 |       0 |     333 |      64 |     115 |       1 | 1       |      18 |
|       0 |       0 |       0 |       0 |       0 |       0 |       0 | -       |       0 |
|     376 |       2 |       1 |     186 |      40 |     138 |       0 | 3       |       6 |
|    2389 |       1 |       3 |     976 |     327 |    1062 |       9 | 7       |       4 |

Candidate column headers:
Environments total2
Separate school
Inside regular class 40% through 79% of the day
Inside regular class less than 40% of the day
Residential facility
Inside regular class 80% or more of the day
Homebound / hospital
Parentally placed in private schools
Correctional facility



headers: Environments total2
Separate school
Inside regular class 40% through 79% of the day
Inside regular class less than 40% of the day
Residential facility
Inside regular class 80% or more of the day
Homebound / hospital
Parentally placed in private schools
Correctional facility
label: Environments total2
Correctional facility
Homebound / hospital
Inside regular class 80% or more of the day
Inside regular class less than 40% of the day
Inside regular class 40% through 79% of the day
Parentally placed in private schools
Residential facility
Separate school
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 |
|:--------------|:--------|:--------|:--------|--------:|
| Kentucky      | Central | Feb     | Qtr-1   |    1078 |
| Oregon        | West    | May     | Qtr-2   |    1764 |
| Massachusetts | East    | Jun     | Qtr-2   |    1006 |
| New Jersey    | East    | Jun     | Qtr-2   |    1290 |
| Florida       | East    | Mar     | Qtr-1   |    1225 |

Candidate column headers:
Sales
State
Month
Region
Qtr



headers: Sales
State
Month
Region
Qtr
label: State
Region
Month
Qtr
Sales
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 |
|--------:|:--------|--------:|--------:|--------:|
|   42188 | DUPONT  |     500 |      67 |       2 |
|   42195 | PIERRE  |     416 |      60 |       2 |
|   42220 | MARTIN  |    1875 |      35 |       1 |
|   42198 | JEAN    |    2500 |      57 |       1 |
|   42247 | JEAN    |     500 |       8 |       0 |

Candidate column headers:
Technnicien
nombre de mois du dossier
Mont Prevu
Date ouverture dossier
nombre de jour du dossier



headers: Technnicien
nombre de mois du dossier
Mont Prevu
Date ouverture dossier
nombre de jour du dossier
label: Date ouverture dossier
Technnicien
Mont Prevu
nombre de jour du dossier
nombre de mois du dossier
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   |
|:-----------|:--------|:--------|:--------|:--------|:--------|:--------|
| NeregNereg | TON     | Kocof   | D12     | K6137   | L3      | NOR     |
| NeregNereg | LEK     | Fitoc   | D12     | R1555   | L3      | MEX     |
| WetobWetob | TON     | Fitoc   | D12     | R1555   | S8      | AUT     |
| KesufKesuf | TON     | Kuhom   | D92     | G6049   | G2      | BEL     |
| NeregNereg | HIF     | Kocof   | D92     | B5330   | W9      | AUT     |

Candidate column headers:
Account
Project
Dept
Desc
SubAcct
Company
Product



headers: Account
Project
Dept
Desc
SubAcct
Company
Product
label: Desc
Company
Product
Dept
Account
SubAcct
Project
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 |
|:--------|--------:|--------:|
| J5906   |   40112 |   40125 |
| N2506   |   40099 |       0 |
| P7098   |   40102 |       0 |
| P7098   |   40111 |       0 |
| V3045   |   40109 |       0 |

Candidate column headers:
Ship
Report
ID



headers: Ship
Report
ID
label: ID
Ship
Report
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 |
|--------:|:---------|:---------|:--------|:--------|--------:|
|   78653 | Freshman | Science  | F       | Paul    |   244   |
|   64313 | Freshman | Nursing  | F       | Jackson |   157   |
|   60225 | Junior   | Business | M       | Jackson |   142   |
|   40296 | Freshman | Nursing  | M       | Wills   |   225   |
|   70080 | Freshman | Science  | F       | Paul    |   247.9 |

Candidate column headers:
Instructor
Score
Standing
Gender
Id#
Major



headers: Instructor
Score
Standing
Gender
Id#
Major
label: Id#
Standing
Major
Gender
Instructor
Score
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   |
|:--------|:--------|--------:|--------:|:--------|
| FR      | Prod    |      85 |       1 | Q1      |
| BE      | Prod    |     120 |       3 | Q3      |
| FR      | Sales   |     100 |       4 | Q4      |
| FR      | Prod    |     100 |       2 | Q2      |
| FR      | Prod    |     120 |       1 | Q1      |

Candidate column headers:
Quarter
Q
Value
Cost
Country



headers: Quarter
Q
Value
Cost
Country
label: Country
Cost
Value
Quarter
Q
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 |
|:-------------|--------:|--------:|
| CNY00085USA7 |   93269 | 18875   |
| CNY00083MEX1 |   28058 |     0   |
| CNY00083USA7 |  939000 | 82162.5 |
| CNY00083CN07 |   10000 |     0   |
| CNY00087USA1 |   86200 | 17240   |

Candidate column headers:
Invoice
Commission
Payment



headers: Invoice
Commission
Payment
label: Invoice
Payment
Commission
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 |
|--------:|--------:|:----------|:--------|--------:|--------:|--------:|----------------:|-----------------:|
|    2013 |     201 | 201-02260 | EACH    |  100    | 2000    | 1185.04 |    96           | 113764           |
|    2013 |     202 | 202-04890 | EACH    | 2040    | 2040    | 2040    |     1           |   2040           |
|    2013 |     202 | 202-03729 | LFT     |    8.5  |  100    |   13.53 |  3950           |  53431           |
|    2013 |     202 | 202-04345 | GAL     |    0.25 |    0.76 |    0.33 | 20280           |   6600           |
|    2013 |     202 | 202-02240 | SYS     |    0.01 |   75    |    4.64 |     1.11316e+06 |      5.16778e+06 |

Candidate column headers:
SECTION
UNIT
ITEM
LOW PRICE
NO ITEMS
TOTAL
YEAR
WGT AVG
HIGH PRICE



headers: SECTION
UNIT
ITEM
LOW PRICE
NO ITEMS
TOTAL
YEAR
WGT AVG
HIGH PRICE
label: YEAR
SECTION
ITEM
UNIT
LOW PRICE
HIGH PRICE
WGT AVG
NO ITEMS
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 |
|:-------------------------|:--------|:--------|:-------------------------------|:-----------------------------|:----------------------|--------:|
| Central African Republic | CAF     | SSA     | Sub-Saharan Africa             | Index1 Social Progress Index | Social Progress Index |   31.42 |
| Croatia                  | HRV     | NAEO    | North America_ Europe_ Oceania | Index1 Social Progress Index | Social Progress Index |   73.3  |
| China                    | CHN     | EAP     | East Asia_ Pacific             | Index1 Social Progress Index | Social Progress Index |   59.07 |
| Guinea                   | GIN     | SSA     | Sub-Saharan Africa             | Index1 Social Progress Index | Social Progress Index |   39.6  |
| Egypt                    | EGY     | MENA    | Middle East and North Africa   | Index1 Social Progress Index | Social Progress Index |   59.91 |

Candidate column headers:
Value Name
Value Type
Country
Country Code
Region Name
Value
Region Abbr



headers: Value Name
Value Type
Country
Country Code
Region Name
Value
Region Abbr
label: Country
Country Code
Region Abbr
Region Name
Value Type
Value Name
Value
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 |
|--------:|:-------------------|--------:|--------:|
|       5 | Vandenberghe Dries |       2 |      14 |
|      12 | Pieters Niek       |       2 |      10 |
|      18 | Vanthournout Luc   |       1 |       8 |
|      13 | Coulier Luc        |       2 |      10 |
|      24 | Heindryckx Ruben   |       1 |       5 |

Candidate column headers:
Punten
Naam
Plaats
Deelnames



headers: Punten
Naam
Plaats
Deelnames
label: Plaats
Naam
Deelnames
Punten
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 |
|--------:|:----------|:--------------------|--------:|--------:|--------:|--------:|--------:|--------:|
|     242 | AAE Solar | ES-250E-20-S20S     |   40693 |   47848 |      14 |      16 |      15 |      13 |
|     246 | AAE Solar | ES-250E-30-S20S     |   40693 |   47848 |      17 |      18 |      19 |      17 |
|     272 | AAE Solar | ES-315H-40-BOT-CONL |   40879 |   47848 |      37 |      41 |      37 |      33 |
|     243 | AAE Solar | ES-250E-30-OP2M     |   40693 |   47848 |      28 |      30 |      28 |      26 |
|     249 | AAE Solar | ES-250E-40-OP2S     |   40693 |   47848 |      19 |      20 |      22 |      20 |

Candidate column headers:
Model
Brand
Eligible to:
Number of certificates for  an installation in Zone1
Number of certificates for  an installation in Zone4
Number of certificates for  an installation in Zone2
Eligible from:
Number of certificates for  an installation in Zone3
Item



headers: Model
Brand
Eligible to:
Number of certificates for  an installation in Zone1
Number of certificates for  an installation in Zone4
Number of certificates for  an installation in Zone2
Eligible from:
Number of certificates for  an installation in Zone3
Item
label: Item
Brand
Model
Eligible from:
Eligible to:
Number of certificates for  an installation in Zone1
Number of certificates for  an installation in Zone2
Number of certificates for  an installation in Zone3
Number of certificates for  an installation in Zone4