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 |
|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|
|   43103 |   23343 |   12192 |   35535 |     420 |     505 |   36460 |   33564 |    2896 |   27227 |     9233 | 0.339112 |
|   43152 |   23056 |   13031 |   36087 |     525 |     721 |   37333 |   34408 |    2925 |   27270 |    10063 | 0.369014 |
|   43138 |   23050 |   12826 |   35876 |     520 |     787 |   37183 |   34270 |    2913 |   27270 |     9913 | 0.363513 |
|   43124 |   23307 |   12340 |   35647 |     412 |     481 |   36540 |   33659 |    2881 |   27227 |     9313 | 0.34205  |
|   43173 |   23218 |   12974 |   36192 |     529 |     586 |   37307 |   34371 |    2936 |   27270 |    10037 | 0.36806  |

Candidate column headers:
 PPL POR DELITOS (c)=a+b
PPL MUJERES
PPL HOMBRES
FECHA DE REPORTE
PLAZAS FALTANTES (h)=f-g
 PPL SENTENCIADOS  (a)
TOTAL PPL (f)=c+d+e
PPL APREMIO (e)
PPL CONTRAVENTORES (d)
 CAPACIDAD INSTALADA EFECTIVA (g)
 PPL PROCESADOS (b)
% HACINAMIENTO* (i)=((f/g)-1)*100



headers:  PPL POR DELITOS (c)=a+b
PPL MUJERES
PPL HOMBRES
FECHA DE REPORTE
PLAZAS FALTANTES (h)=f-g
 PPL SENTENCIADOS  (a)
TOTAL PPL (f)=c+d+e
PPL APREMIO (e)
PPL CONTRAVENTORES (d)
 CAPACIDAD INSTALADA EFECTIVA (g)
 PPL PROCESADOS (b)
% HACINAMIENTO* (i)=((f/g)-1)*100
label: FECHA DE REPORTE
 PPL SENTENCIADOS  (a)
 PPL PROCESADOS (b)
 PPL POR DELITOS (c)=a+b
PPL CONTRAVENTORES (d)
PPL APREMIO (e)
TOTAL PPL (f)=c+d+e
PPL HOMBRES
PPL MUJERES
 CAPACIDAD INSTALADA EFECTIVA (g)
PLAZAS FALTANTES (h)=f-g
% HACINAMIENTO* (i)=((f/g)-1)*100
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 |
|:--------|--------:|--------:|--------:|--------:|--------:|--------:|
| SNP     |   11.63 |   12.45 |    0.32 |    0.91 |    2.63 |    4.56 |
| CVX     |   69.42 |   22.47 |    1.61 |    1.37 |    4.05 |    4.07 |
| SU      |   47.04 |   22.27 |    2.41 |    1.57 |    2.85 |    2.96 |
| STO     |    0    |  129.87 |    1.11 |    1.57 |    5.01 |    5.01 |
| XOM     |   34.42 |   20.16 |    1.45 |    2    |    3.66 |    3.73 |

Candidate column headers:
%  Dividend  Yield TTM
Price/  Sales
Price/  Earnings  Trailing
Price/  Book  TTM
Price/  Earnings  Forward
Ticker
% Forward  Dividend  Yield



headers: %  Dividend  Yield TTM
Price/  Sales
Price/  Earnings  Trailing
Price/  Book  TTM
Price/  Earnings  Forward
Ticker
% Forward  Dividend  Yield
label: Ticker
Price/  Earnings  Trailing
Price/  Earnings  Forward
Price/  Sales
Price/  Book  TTM
%  Dividend  Yield TTM
% Forward  Dividend  Yield
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   |
|--------:|:--------|:--------|:----------------------|:---------------------|:-----------|:--------|:-----------|:--------|
|   12963 | CA      | Active  | 21st Century Plaza    | 3 Beaver Valley Road | Wilmington | DE      | 19803      | USA     |
|   12305 | MI      | Active  | P.O. Box 40790        |                      | Lansing    | MI      | 48901-7990 | USA     |
|   12304 | MI      | Active  | P.O. Box 40790        |                      | Lansing    | MI      | 48901-7990 | USA     |
|   33987 | AZ      | Active  | 76 St. Paul St        | #500                 | Burlington | VT      | 05401      | USA     |
|   36153 | CT      | Active  | 151 Farmington Avenue | RT21                 | Hartford   | CT      | 06156      | USA     |

Candidate column headers:
MLG_ADDRESS1
DOM. STATE
MAILING_CITY
COMPANY_STATUS
COUNTRY
ZIP
STATE
MLG_ADDRESS2
NAIC



headers: MLG_ADDRESS1
DOM. STATE
MAILING_CITY
COMPANY_STATUS
COUNTRY
ZIP
STATE
MLG_ADDRESS2
NAIC
label: NAIC
DOM. STATE
COMPANY_STATUS
MLG_ADDRESS1
MLG_ADDRESS2
MAILING_CITY
STATE
ZIP
COUNTRY
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 |
|--------:|--------:|:--------|:--------|:--------|--------:|:--------|--------:|--------:|--------:|---------:|
|      50 |       0 | IN0021  | Item 21 | Desc 21 |      19 |         |     112 |    2128 |      75 |       11 |
|      50 |       0 | IN0020  | Item 20 | Desc 20 |      16 |         |     124 |    1984 |     113 |       14 |
|      50 |       0 | IN0004  | Item 4  | Desc 4  |      19 |         |     186 |    3534 |     158 |        6 |
|     150 |       0 | IN0023  | Item 23 | Desc 23 |      29 | Yes     |     106 |    3074 |     142 |        1 |
|     100 |       1 | IN0011  | Item 11 | Desc 11 |      59 |         |     176 |   10384 |     229 |        1 |

Candidate column headers:
Name
Inventory Value
Discontinued?
Column1
Quantity in Stock
Description
Reorder Time in Days
Unit Price
Inventory ID
Quantity in Reorder
Reorder Level



headers: Name
Inventory Value
Discontinued?
Column1
Quantity in Stock
Description
Reorder Time in Days
Unit Price
Inventory ID
Quantity in Reorder
Reorder Level
label: Quantity in Reorder
Column1
Inventory ID
Name
Description
Unit Price
Discontinued?
Quantity in Stock
Inventory Value
Reorder Level
Reorder Time in Days
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 |
|:-----------|--------:|--------:|--------:|---------:|--------:|--------:|---------:|--------:|--------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
| 25/01/2016 |       4 |       1 |    2016 | 1013.4   | 1862.25 | 2875.64 | 1030.01  | 1839.94 | 2869.95 |  966.296 |  1862.25 |  2828.54 |  977.931 |  1854.07 |  2832    |  54.4712 |
| 03/02/2016 |       5 |       2 |    2016 | 1596.09  | 2217.06 | 3813.15 | 1618.51  | 2194.89 | 3813.4  | 1523.25  |  2176.8  |  3700.05 | 1637.71  |  2203.61 |  3841.31 |  56.4739 |
| 14/02/2016 |       7 |       2 |    2016 |  613.832 | 2032.28 | 2646.11 |  704.793 | 1888.31 | 2593.11 |  604.522 |  1937.47 |  2542    |  645.995 |  1888.25 |  2534.24 |  46.4922 |
| 10/02/2016 |       6 |       2 |    2016 |  284.569 | 2280.59 | 2565.15 |  324.63  | 2258.28 | 2582.91 |  287.236 |  2280.59 |  2567.82 |  257.903 |  2277.69 |  2535.59 |  44.7516 |
| 03/01/2016 |       1 |       1 |    2016 | 2632.12  | 1750.25 | 4382.36 | 2642.59  | 1724.33 | 4366.92 | 2804.23  |  1462.01 |  4266.23 | 2789.78  |  1633.84 |  4423.62 |  54.4558 |

Candidate column headers:
Net Market Cost3
Net Market Cost5
Week
Datetime
Year
TGC
Total_Smart
Market Costs
TGC6
Month
Total_Base
Load
Total_timer
TGC4
Net Market Cost
Total_Reg
Net Market Cost2



headers: Net Market Cost3
Net Market Cost5
Week
Datetime
Year
TGC
Total_Smart
Market Costs
TGC6
Month
Total_Base
Load
Total_timer
TGC4
Net Market Cost
Total_Reg
Net Market Cost2
label: Datetime
Week
Month
Year
Net Market Cost
Market Costs
Total_Base
Net Market Cost2
TGC
Total_Reg
Net Market Cost3
TGC4
Total_Smart
Net Market Cost5
TGC6
Total_timer
Load
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 |
|:-----------------|:--------|--------:|
|                  | Yes     |       3 |
| Finance          | No      |       3 |
| Accounting       | No      |       6 |
| Accounting Total |         |      10 |
| Delivery Total   |         |       5 |

Candidate column headers:
Answer
Count
Department



headers: Answer
Count
Department
label: Department
Answer
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    |
|:---------|:----------|:----------|:-----------|:--------|:--------|:---------|
| Mike     | Wieczorek | Elected   | 2          | 43281   | Yes     | 845-9604 |
| Mitchell | Heather   |           |            | 43646   | Yes     | 845-7173 |
| DeLong   | Rhonda    | Appointed | Indefinite | none    | Yes     | 317-6800 |
| Hatala   | Rick      | Elected   | 2          | 43281   | No      | 317-6516 |
| Chears   | Ashlei    | Elected   | 2          | 43646   | Yes     | 845-6486 |

Candidate column headers:
Phone
First Name
Type
Term Expiration
Term
Last Name
New?



headers: Phone
First Name
Type
Term Expiration
Term
Last Name
New?
label: Last Name
First Name
Type
Term
Term Expiration
New?
Phone
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 |
|--------:|--------:|--------:|--------:|--------:|
|       4 |       0 |       0 |       0 |       0 |
|      13 |       0 |       0 |       0 |       0 |
|       3 |      12 |       1 |       0 |       0 |
|       9 |       6 |       0 |       1 |       0 |
|      14 |       0 |       4 |       0 |       0 |

Candidate column headers:
Dec
Feb
Nov
Number
Jan



headers: Dec
Feb
Nov
Number
Jan
label: Number
Nov
Dec
Jan
Feb
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 |
|:--------|:--------|:--------|--------:|
| AUB010  | COM     |         |   87.51 |
| GAR030  | COM     | COR320  |   27.82 |
| DOR024  | ETA     | INF200  |  238.36 |
| LAN040  | ETA     | RH460   |  544.54 |
| DOR024  | ETA     | ACH120  |  407.47 |

Candidate column headers:
SERVICES
DEPENSES ENGAGES
DEPARTEMENT
OPER



headers: SERVICES
DEPENSES ENGAGES
DEPARTEMENT
OPER
label: DEPARTEMENT
OPER
SERVICES
DEPENSES ENGAGES
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 |
|:--------|--------:|--------:|--------:|------------:|
| I       |    5080 |  4856.7 |   223.3 |  0.0459777  |
| C       |    7810 |  7917.7 |  -107.7 | -0.0136024  |
| M       |   59938 | 59629.7 |   308.3 |  0.00517024 |
| T       |   10202 |  9989.1 |   212.9 |  0.0213132  |
| Q       |   57296 | 58187.1 |  -891.1 | -0.0153144  |

Candidate column headers:
POIDS 2
Ecart en %
Ecart en kg
PRODUIT
POIDS 1



headers: POIDS 2
Ecart en %
Ecart en kg
PRODUIT
POIDS 1
label: PRODUIT
POIDS 1
POIDS 2
Ecart en kg
Ecart en %
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           |
|:-------------|:--------|:-------------|:------------|:---------------|:------------------------|:-----------------------------|:--------------|:-------------------------|:--------|:----------|:---------|:-----------------|
| 17-p145nb    | 499     | 889894336873 | P1E09EA#UUG | 4GB DDR3L 1DM  | 500GB 5400RPM           | AMD Radeon R4 Graphics - UMA | Wifi bgn + BT | LOC W10H6 1.1 EUROA4     | DVD-RW  | AMD SOC   | 2 Years  | HP               |
| Product name | Street  | EAN          | PN          | RAM            | HDD                     | GFX                          | Wifi          | OS                       | ODD     | Chipset   | Wty      | Brand            |
| 17-g186nb    | 899     | 889899055229 | V0X15EA#UUG | 12GB DDR3L 2DM | 256GB M.2               | Intel HD Graphics - UMA      | Wifi bgn + BT | LOC W10H6 1.1 EUROA4     | DVD-RW  | Intel SOC | 2 Years  | HP Pavilion      |
| 15-ae113nb   | 1399    | 889899055199 | V0X06EA#UUG | 12GB DDR3L 2DM | 1TB 5400RPM + 256GB M.2 | Nvidia GeForce GTX950M 4GB   | Wifi bgn + BT | LOC W10H6 HIE 1.1 EUROA4 | DVD-RW  | Intel SOC | 2 Years  | HP ENVY          |
| 13-s162nb    | 879     | 889899054932 | V0W99EA#UUG | 8GB DDR3L 1DM  | 128GB M.2 Value         | Intel HD Graphics - UMA      | Wifi bgn + BT | LOC W10H6 HIE 1.1 EUROA4 | .       | Intel SOC | 2 Years  | HP Pavilion x360 |

Candidate column headers:
Column13
Column15
Column14
Column7
Column12
Column2
Column11
Column1
Column8
Column3
Column10
Column9
Column4



headers: Column13
Column15
Column14
Column7
Column12
Column2
Column11
Column1
Column8
Column3
Column10
Column9
Column4
label: Column1
Column2
Column3
Column4
Column7
Column8
Column9
Column10
Column11
Column12
Column13
Column14
Column15
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 |
|:--------------|:--------|--------:|--------:|--------:|--------:|--------:|--------:|
| Sprint Nr. 13 |         |       0 |      25 |       4 |      10 |      10 |      11 |
| Sprint Nr. 30 | 8.0     |       7 |      96 |       1 |      47 |      54 |      41 |
| Sprint Nr. 5  | 3.0     |       0 |      23 |       5 |       7 |       7 |      11 |
| Sprint Nr. 28 | 7.0     |       1 |      88 |       0 |      45 |      46 |      42 |
| Sprint Nr. 14 |         |       0 |      25 |       4 |      10 |      10 |      11 |

Candidate column headers:
neue Backlogs
SUMME Backlogs
im Sprint umgesetzte Backlog Items (User Stories)
bisher abgearbeite Backlogs Items (User Stories)
Sprint Phase (Iteration)
insgesamt abgearbeite Backlogs Items (User Stories)
Backlog Items in der Pipeline (User Stories)
offene Backlogs Items (User Stories)



headers: neue Backlogs
SUMME Backlogs
im Sprint umgesetzte Backlog Items (User Stories)
bisher abgearbeite Backlogs Items (User Stories)
Sprint Phase (Iteration)
insgesamt abgearbeite Backlogs Items (User Stories)
Backlog Items in der Pipeline (User Stories)
offene Backlogs Items (User Stories)
label: Sprint Phase (Iteration)
neue Backlogs
im Sprint umgesetzte Backlog Items (User Stories)
SUMME Backlogs
Backlog Items in der Pipeline (User Stories)
bisher abgearbeite Backlogs Items (User Stories)
insgesamt abgearbeite Backlogs Items (User Stories)
offene Backlogs Items (User Stories)
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 |
|:---------|:--------------------|:--------|--------:|---------:|
| France   | 0.55710171356808125 | FR      |      37 | 66415161 |
| Slovenia | -                   | SI      |       0 |  2062874 |
| Portugal | 0.38554878339117532 | PT      |       4 | 10374822 |
| Denmark  | 1.236811394213313   | DK      |       7 |  5659715 |
| Sweden   | 1.1285112730581783  | SE      |      11 |  9747355 |

Candidate column headers:
2015
Abbreviation
StG/Mh
Country
Population



headers: 2015
Abbreviation
StG/Mh
Country
Population
label: Country
StG/Mh
Abbreviation
2015
Population
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 |
|:--------|:--------|-------------:|-------------:|--------:|--------:|
| CAUCA   | Argelia | 219050000551 | 219050000551 |       3 |       3 |
| CAUCA   | Balboa  | 219075000041 | 219075000741 |       1 |       0 |
| CAUCA   | Argelia | 219050001352 | 219050001352 |       1 |       0 |
| CAUCA   | Argelia | 219050000135 | 219050001212 |       2 |       0 |
| CAUCA   | Argelia | 219050000089 | 219050000089 |       3 |       0 |

Candidate column headers:
Docentes focalizados
CODIGO_DANE_SEDE
CODIGO_DANE
Municipio
Docentes retirados
ETC



headers: Docentes focalizados
CODIGO_DANE_SEDE
CODIGO_DANE
Municipio
Docentes retirados
ETC
label: ETC
Municipio
CODIGO_DANE
CODIGO_DANE_SEDE
Docentes focalizados
Docentes retirados
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 |
|:--------|--------:|--------:|
| COAMIN  |   43056 |  61.08  |
| C0202Y  |   43056 |   3.62  |
| C4802Y  |   43056 |   0.443 |
| COMBAK  |   43056 | 309.1   |
| CDS5BR  |   43056 | 177.72  |

Candidate column headers:
GPD_KODU
GPD_DEGER
GPD_TARIH



headers: GPD_KODU
GPD_DEGER
GPD_TARIH
label: GPD_KODU
GPD_TARIH
GPD_DEGER
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 |
|:--------------|--------:|--------:|
| Sweden        | 7.75954 | 8.38813 |
| Belgium       | 4.26558 | 4.99446 |
| Spain         | 3.52433 | 3.58711 |
| OECD1         | 5.48104 | 5.76543 |
| United States | 5.77464 | 5.47941 |

Candidate column headers:
Share of ICT in Business sector
1995
2008



headers: Share of ICT in Business sector
1995
2008
label: Share of ICT in Business sector
1995
2008
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              |
|--------:|--------:|--------:|:-------------------|
|      11 | 216.458 | 233.686 |                    |
|       6 | 240     | 197.674 | 1.2141177613287306 |
|       3 | 150     | 176.067 | 0.8519474382508496 |
|       5 | 215     | 190.472 | 1.128774833046327  |
|       1 | 160     | 161.662 | 0.9897168420115005 |

Candidate column headers:
Sales
From trend equation
Period
Ratio of Actual / Trend



headers: Sales
From trend equation
Period
Ratio of Actual / Trend
label: Period
Sales
From trend equation
Ratio of Actual / Trend
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   |
|--------:|:--------|--------:|--------:|:---------|:--------|
|   43216 | 3       |    1000 |    3    | A Taylor |         |
|   43373 | Payment |    1035 |    6.75 | T Cook   | P       |
|   43223 | 4       |    1025 |    4    | C Jones  |         |
|   43271 | 5       |    1000 |    9    | A Taylor |         |
|   43450 | 9       |    1050 |    5    | W Clark  |         |

Candidate column headers:
Number of Days
Payment Indicator
Employee Name
Employee Code
Date of Leave
Form Number



headers: Number of Days
Payment Indicator
Employee Name
Employee Code
Date of Leave
Form Number
label: Date of Leave
Form Number
Employee Code
Number of Days
Employee Name
Payment Indicator
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   |
|--------:|--------:|:--------|
|      25 |       6 | E       |
|      19 |       1 | E       |
|      52 |       2 | E       |
|      67 |       5 | O       |
|      31 |       6 | D       |

Candidate column headers:
LOCATION
TYPE
REGION



headers: LOCATION
TYPE
REGION
label: LOCATION
REGION
TYPE
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 |
|:--------|:-------------------|:--------|:----------------|--------:|:--------|:--------------------|--------:|
| B9      |                    |         |                 |   0.119 | M       | BICINE              |       8 |
| C2      | 0.2979999999999999 | M       | Sodium chloride |   0.119 | M       | Citrate             |       4 |
| A4      |                    |         |                 |   0.119 | M       | Citrate             |       5 |
| A11     |                    |         |                 |   0.119 | M       | Potassium phosphate |       7 |
| D1      | 0.2979999999999999 | M       | Sodium chloride |   0.119 | M       | MOPS                |       7 |

Candidate column headers:
Units
Conc2
Well_No
Units2
Buffer
Conc
pH
Reagent



headers: Units
Conc2
Well_No
Units2
Buffer
Conc
pH
Reagent
label: Well_No
Conc
Units
Reagent
Conc2
Units2
Buffer
pH
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   |
|--------:|:--------|--------:|--------:|:---------|--------:|:--------|:--------|:------------------------|:-------------|:---------------|:----------------------------|:-----------------|:---------|:---------|:---------|
|   42096 | April   |      14 |       2 | Thursday |    2015 | EST     | SAMPLE  | SAMPLE Enterprise Group | County Staff | Environmental  | Code Enforcement            | North            | 100575   | Ja.Au    |          |
|   42096 | April   |      14 |       2 | Thursday |    2015 | EST     | SAMPLE  | SAMPLE Enterprise Group | County Staff | Admin          | Business and Financial Mgmt | Out-of-town Pool | 108327   | Br.Sm    |          |
|   42096 | April   |      14 |       2 | Thursday |    2015 | EST     | SAMPLE  | SAMPLE Enterprise Group | County Staff | Environmental  | Code Enforcement            | South            | 105620   | Jo.Fr    |          |
|   42096 | April   |      14 |       2 | Thursday |    2015 | EST     | SAMPLE  | SAMPLE Enterprise Group | County Staff | Public Library |                             |                  | Outreach | Pa.Mc    |          |
|   42096 | April   |      14 |       2 | Thursday |    2015 | EST     | SAMPLE  | SAMPLE Enterprise Group | County Staff | Environmental  | Code Enforcement            | North            | 110921   | Fr.Ro    |          |

Candidate column headers:
Week
Group Level1
Date
Group Level4
Month
Group Level5
Driver ID
Quarter
Group Level3
EnterpriseID
Weekday
Group Level2
Driver Schedule Profile
Time Zone
Year
Driver Last Name



headers: Week
Group Level1
Date
Group Level4
Month
Group Level5
Driver ID
Quarter
Group Level3
EnterpriseID
Weekday
Group Level2
Driver Schedule Profile
Time Zone
Year
Driver Last Name
label: Date
Month
Week
Quarter
Weekday
Year
Time Zone
EnterpriseID
Group Level1
Group Level2
Group Level3
Group Level4
Group Level5
Driver ID
Driver Last Name
Driver Schedule Profile
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 |
|--------:|:--------|--------:|--------:|
|   42887 | Jun     |    2017 |   35435 |
|   41760 | May     |    2014 |   33936 |
|   41640 | Jan     |    2014 |   32029 |
|   42125 | May     |    2015 |   30244 |
|   42248 | Sep     |    2015 |   30285 |

Candidate column headers:
Year
Month
Date
Budget



headers: Year
Month
Date
Budget
label: Date
Month
Year
Budget
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   |
|:--------|--------:|:--------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|:---------|
| Augusta |    2035 | GT3000  |      85 |       6 |    82.8 |     240 |   0.171 |    56   | 147.464 | 31(2)    |
| Augusta |    2847 | GT3000  |      97 |       6 |   350   |    1305 |   0.181 |    57.9 | 187.404 | 29       |
| Augusta |    5566 | GTCBLL  |     116 |       6 |   350   |    1790 |   0.317 |    55.5 | 214.367 | 5        |
| Pioneer |    1184 | AMX     |     111 |       6 |   350   |    1570 |   0.218 |    57.5 | 215.273 | 21       |
| Augusta |    2852 | GT3000  |     102 |       6 |   350   |    1305 |   0.193 |    55.5 | 184.658 | 28       |

Candidate column headers:
Plot #
Brand
Moisture
RDM
# of Rows
Row Length
Hybrid
Traits
Test Weight
Harvest Weight
Bushels/ Acre



headers: Plot #
Brand
Moisture
RDM
# of Rows
Row Length
Hybrid
Traits
Test Weight
Harvest Weight
Bushels/ Acre
label: Brand
Hybrid
Traits
RDM
# of Rows
Row Length
Harvest Weight
Moisture
Test Weight
Bushels/ Acre
Plot #
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               |
|:--------------|:--------------------|:----------------------|:----------------------|:-----------------------|:---------------------|:--------------------|:-----------------------|:---------------------|:--------------------|
| entreprise 11 | ---                 | ---                   | ---                   | ---                    | ---                  | ---                 | ---                    | ---                  | ---                 |
| entreprise 8  | 0.15                | -5.434782608695652E-2 | 0.12643678160919541   | -2.0408163265306121E-2 | 0.296875             | 0.2289156626506024  | -1.9607843137254902E-2 | -0.03                | 0.14432989690721648 |
| entreprise 15 | 0.33333333333333331 | -1                    | ---                   | ---                    | ---                  | ---                 | ---                    | ---                  | ---                 |
| entreprise 14 | ---                 | ---                   | 2.7932960893854747E-2 | -0.13043478260869565   | -0.28749999999999998 | 0.14912280701754385 | -6.8702290076335881E-2 | -0.28688524590163933 | 0.64367816091954022 |
| entreprise 2  | ---                 | ---                   | ---                   | ---                    | ---                  | ---                 | ---                    | ---                  | 0.15151515151515152 |

Candidate column headers:
2006 Revenue($)
Company Name
2005 Revenue($)
2007 Revenue($)
2010 Revenue($)
2003 Revenue($)2
2009 Revenue($)
2002 Revenue($)
2008 Revenue($)
2004 Revenue($)



headers: 2006 Revenue($)
Company Name
2005 Revenue($)
2007 Revenue($)
2010 Revenue($)
2003 Revenue($)2
2009 Revenue($)
2002 Revenue($)
2008 Revenue($)
2004 Revenue($)
label: Company Name
2002 Revenue($)
2003 Revenue($)2
2004 Revenue($)
2005 Revenue($)
2006 Revenue($)
2007 Revenue($)
2008 Revenue($)
2009 Revenue($)
2010 Revenue($)
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 |
|:-----------------|:----------|--------:|---------:|---------:|
| Walk-In Practice | WEDNESDAY |   43264 | 0.4375   | 0.604167 |
| Walk-In Practice | MONDAY    |   43262 | 0.395833 | 0.520833 |
| Walk-In Practice | THURSDAY  |   43209 | 0.645833 | 0.8125   |
| Walk-In Practice | TUESDAY   |   43263 | 0.395833 | 0.520833 |
| Walk-In Practice | MONDAY    |   43269 | 0.395833 | 0.520833 |

Candidate column headers:
Date
Class
End
Day
Start



headers: Date
Class
End
Day
Start
label: Class
Day
Date
Start
End
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 |
|---------:|---------:|-----------:|
| 0.416667 | 0.430556 | 0.0138889  |
| 0.354167 | 0.375    | 0.0208333  |
| 0.399306 | 0.40625  | 0.00694444 |
| 0.4375   | 0.444444 | 0.00694444 |
| 0.486111 | 0.5      | 0.0138889  |

Candidate column headers:
Start
End
Time



headers: Start
End
Time
label: Start
End
Time
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 |
|:--------|:------------|:--------|:--------|--------:|:--------|:--------|--------:|:--------|:--------|---------:|---------:|:---------|---------:|---------:|
| 17I333  | BIG PASTURE | *       | 0       |       0 | *       | *       |       0 | *       | 10      |       16 |        4 | 0        |        0 |       37 |
| 57C035  | AVANT       | *       | 0       |       0 | *       | 0       |       0 | 0       | *       |       19 |        4 | 0        |        0 |       27 |
| 47I029  | BLANCHARD   | 17      | 0       |      13 | *       | 12      |       6 | 0       | 36      |      121 |       58 | *        |        4 |      272 |
| 36I045  | BLACKWELL   | 7       | *       |      10 | *       | 19      |       7 | *       | 22      |      130 |       40 | *        |        0 |      243 |
| 40I026  | BOKOSHE     | 0       | 0       |       0 | *       | 6       |       0 | 0       | *       |       29 |        7 | 0        |        0 |       45 |

Candidate column headers:
Deaf-Blindness
Multiple Disabilities
Traumatic Brain Injury
Intellectual Disabilities
Orthopedic Impairments
Autism
Specific Learning Disability
Hearing Impairment_ including Deafness
Speech or Language Impairment
Grand Total
District Name
CODIST
Emotional Disturbance
Visual Impairment_ including Blindness
Other Health Impairments



headers: Deaf-Blindness
Multiple Disabilities
Traumatic Brain Injury
Intellectual Disabilities
Orthopedic Impairments
Autism
Specific Learning Disability
Hearing Impairment_ including Deafness
Speech or Language Impairment
Grand Total
District Name
CODIST
Emotional Disturbance
Visual Impairment_ including Blindness
Other Health Impairments
label: CODIST
District Name
Autism
Deaf-Blindness
Emotional Disturbance
Hearing Impairment_ including Deafness
Intellectual Disabilities
Multiple Disabilities
Orthopedic Impairments
Other Health Impairments
Specific Learning Disability
Speech or Language Impairment
Traumatic Brain Injury
Visual Impairment_ including Blindness
Grand 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   |
|--------:|:--------------|:-----------------|:--------|:--------|
| 8302142 | East Midlands | Derbyshire       | DE7 4DB | Silver  |
| 9263424 | East          | Norfolk          | NR3 2QU | Gold    |
| 3563528 | North West    | Stockport        | SK8 2LE | Silver  |
| 9192098 | East          | Hertfordshire    | AL3 5NL | Silver  |
| 9287020 | East Midlands | Northamptonshire | NN3 8EZ | Gold    |

Candidate column headers:
Region
Postcode
Award
DfE number
Local Authority



headers: Region
Postcode
Award
DfE number
Local Authority
label: DfE number
Region
Local Authority
Postcode
Award
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 | VADACCHINO | GIOELE       |
|      25 | TOCCI      | ALESSIA      |
|      24 | SPIZZIRRI  | MARIANNA     |
|      16 | MILIA      | MARIAROSARIA |
|      13 | GUIDO      | ELISA        |

Candidate column headers:
Colonna1
Colonna3
Colonna2



headers: Colonna1
Colonna3
Colonna2
label: Colonna1
Colonna2
Colonna3
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 |
|--------:|:--------|:---------|:----------|--------:|--------:|
| 4475220 | Fazal   | Haque    | Pakistan  |    2014 |   41828 |
| 2558127 | Badr    | Rashid   | Pakistan  |    2014 |   41646 |
| 2371609 | Lam     | Yeoh     | Singapore |    2015 |   42132 |
| 2640871 | Shah    | Sulaiman | Pakistan  |    2014 |   41950 |
| 4151539 | Har     | Singh    | India     |    2013 |   41567 |

Candidate column headers:
Date of erasure
Year of erasure
First Name
UID
Last Name
Primary Medical Qualification (PMQ) Country



headers: Date of erasure
Year of erasure
First Name
UID
Last Name
Primary Medical Qualification (PMQ) Country
label: UID
First Name
Last Name
Primary Medical Qualification (PMQ) Country
Year of erasure
Date of erasure
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      |
|:-----------|:--------|:--------|:---------|------------:|:------------------|:-----------|:-------------------------------|:--------|:--------------|:------------|:------------|
| Mereb Leke | Tigray  | ET01    | ET010201 | 9.00369e+06 | Medhin Adiferes   |            | ABE Programs                   | EAC     | imagine 1 day | imagine1day | imagine1day |
| Ahferom    | Tigray  | ET01    | ET010202 | 9.01988e+06 | baeker            |            | ABE Programs                   | EAC     | imagine1day   | imagine1day | imagine1day |
| Ahferom    | Tigray  | ET01    | ET010202 | 9.00305e+06 | A/A/mered         | 10204011.0 | Integrated Functional Adult Li | EAC     | imagine1day   | imagine1day | imagine1day |
| Ahferom    | Tigray  | ET01    | ET010202 | 9.00304e+06 | A/tsalqa          | 10204044.0 | Integrated Functional Adult Li | EAC     | imagine1day   | imagine1day | imagine1day |
| Mereb Leke | Tigray  | ET01    | ET010201 | 1.60391e+06 | terawr/Endatedros |            | ABE Programs                   | EAC     | imagine 1 day | imagine1day | imagine1day |

Candidate column headers:
Scool Code2
Implementing Partner
Reporting Agency
R_Pcode
W_Pcode
Project Owner
Donor
School Code
Woreda
Region
School Type
School name



headers: Scool Code2
Implementing Partner
Reporting Agency
R_Pcode
W_Pcode
Project Owner
Donor
School Code
Woreda
Region
School Type
School name
label: Woreda
Region
R_Pcode
W_Pcode
School Code
School name
Scool Code2
School Type
Donor
Project Owner
Reporting Agency
Implementing Partner
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                 |
|--------:|--------:|:-----------|:--------|:----------------------|
|   43223 |   43224 | 95_890:00  | 2 Days  | Lagos & Port Harcourt |
|   43145 |   43147 | 139_820:00 | 3 Days  | Lagos & Port Harcourt |
|   43314 |   43316 | 139_820:00 | 3 Days  | Lagos & Port Harcourt |
|   43285 |   43286 | 95_820:00  | 2 Days  | Lagos & Port Harcourt |
|   43165 |   43167 | 139_820:00 | 3 Days  | Lagos & Port Harcourt |

Candidate column headers:
# Days
Amount
End Date
Location
Start Date



headers: # Days
Amount
End Date
Location
Start Date
label: Start Date
End Date
Amount
# Days
Location