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     |
|:-------------|:--------|--------:|--------:|:--------|:----------|
|              |         |      17 |       0 |         | Materials |
|              |         |      15 |       0 |         | Materials |
|              |         |      22 |       0 |         | Materials |
| Asian Paints | 500.0   |       7 |   25000 | 50.0    | Materials |
|              |         |       6 |       0 |         | Materials |

Candidate column headers:
Item
VoucherNo.
Rate
Category
Details(Kgs./Cft./No.of Items
Amount



headers: Item
VoucherNo.
Rate
Category
Details(Kgs./Cft./No.of Items
Amount
label: Item
Rate
VoucherNo.
Amount
Details(Kgs./Cft./No.of Items
Category
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0         |   Col_1 | Col_2   |   Col_3 |   Col_4 | Col_5   | Col_6   |   Col_7 |   Col_8 |   Col_9 |   Col_10 |   Col_11 |   Col_12 |   Col_13 |
|:--------------|--------:|:--------|--------:|--------:|:--------|:--------|--------:|--------:|--------:|---------:|---------:|---------:|---------:|
| UA 40 EH 5000 |      43 | 5000.0  |  215000 |  258000 | 43.0    |         |      41 |       0 |      41 |        0 |        0 |        0 |        0 |
| PS 43 F 4500  |       0 | 4194.9  |       0 |       0 |         |         |       0 |       0 |       0 |        0 |        0 |        0 |        0 |
| UA 46 EH 5000 |       0 |         |       0 |       0 |         |         |       0 |       0 |       0 |        0 |        0 |        0 |        0 |
| PS 43 E490    |       0 | 4886.0  |       0 |       0 |         |         |       0 |       0 |       0 |        0 |        0 |        0 |        0 |
| UA 40 ES 6600 |       0 |         |       0 |       0 |         |         |       0 |       0 |       0 |        0 |        0 |        0 |        0 |

Candidate column headers:
STOCK INITIAL
depot2
PU HT
MONTANT  TOTAL  TTC
depot1
VALEURE S I( HT)
depot5
S.I  depot5
FAMILLES
TOTALSTOCK  
depot4
VALEURE S I(TTC)
depot3
S.I depot1



headers: STOCK INITIAL
depot2
PU HT
MONTANT  TOTAL  TTC
depot1
VALEURE S I( HT)
depot5
S.I  depot5
FAMILLES
TOTALSTOCK  
depot4
VALEURE S I(TTC)
depot3
S.I depot1
label: FAMILLES
STOCK INITIAL
PU HT
VALEURE S I( HT)
VALEURE S I(TTC)
S.I depot1
S.I  depot5
TOTALSTOCK  
MONTANT  TOTAL  TTC
depot1
depot2
depot3
depot4
depot5
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 |
|--------:|:-------------|--------:|--------:|---------:|--------:|
|   56015 | Goshen_ WY   |  542.1  | 33306   |   9.1553 |  0.0003 |
|    8023 | Costilla_ CO |   46.7  | 26927.3 |   1.2832 |  0      |
|   49015 | Emery_ UT    | 2190.2  | 28635.5 |  18.227  |  0.0006 |
|   16029 | Caribou_ ID  | 6254.6  | 31816.7 | 133.635  |  0.0042 |
|   30041 | Hill_ MT     |  341.82 | 39166.7 |   5.2168 |  0.0001 |

Candidate column headers:
Percent of Per Capita Income Explained by Protected Federal Land
FIPS
Per Capita Income Explained by Protected Federal Land
Per Capita Income 2010
Protected Federal Land (Acres)
Name



headers: Percent of Per Capita Income Explained by Protected Federal Land
FIPS
Per Capita Income Explained by Protected Federal Land
Per Capita Income 2010
Protected Federal Land (Acres)
Name
label: FIPS
Name
Protected Federal Land (Acres)
Per Capita Income 2010
Per Capita Income Explained by Protected Federal Land
Percent of Per Capita Income Explained by Protected Federal Land
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                   |
|--------:|:---------------------|:------------------------|
|      17 | Please select a date | Please select a process |
|      10 | Please select a date | Please select a process |
|      27 | Please select a date | Please select a process |
|      29 | Please select a date | Please select a process |
|      24 | Please select a date | Please select a process |

Candidate column headers:
Test Case ID
Process
Date required



headers: Test Case ID
Process
Date required
label: Test Case ID
Date required
Process
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 |
|:---------------|--------:|--------:|--------:|---------:|--------:|----------:|---------:|--------:|------------:|---------:|----------:|
| New Jersey     |    3577 |    3445 |     754 | 0.218868 |     106 | 0.0307692 | 0.140584 |       5 | 0.00145138  |      132 | 0.0369024 |
| North Carolina |    5009 |    4825 |     933 | 0.193368 |     125 | 0.0259067 | 0.133976 |       6 | 0.00124352  |      184 | 0.0367339 |
| Ohio           |   13764 |   13222 |    2559 | 0.193541 |     375 | 0.0283618 | 0.146542 |       5 | 0.000378158 |      542 | 0.0393781 |
| Florida        |    6236 |    5997 |    1328 | 0.221444 |     141 | 0.0235118 | 0.106175 |       5 | 0.00083375  |      239 | 0.0383258 |
| Missouri       |    5365 |    5121 |    1072 | 0.209334 |     328 | 0.06405   | 0.30597  |       2 | 0.000390549 |      244 | 0.04548   |

Candidate column headers:
Sent
Edition
Unsub Rate
Bounced
Opens
Unsubs
Delivered
Bounce Rate
CT/O
Open Rate
Clicks
CTR



headers: Sent
Edition
Unsub Rate
Bounced
Opens
Unsubs
Delivered
Bounce Rate
CT/O
Open Rate
Clicks
CTR
label: Edition
Sent
Delivered
Opens
Open Rate
Clicks
CTR
CT/O
Unsubs
Unsub Rate
Bounced
Bounce Rate
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   |
|:----------|:--------|:--------|
| 1A6046    | AR      | NOZZLE  |
| 1A6145-01 | AR      | TUBE    |
| 14441-1   | AR      | DUCT    |
| 1A7497-01 | AR      | TUBE    |
| 1A7480    | AR      | LEVER   |

Candidate column headers:
Description
Part Number
Condition



headers: Description
Part Number
Condition
label: Part Number
Condition
Description
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 |
|:--------|:--------|:--------|:--------|:---------|:--------|:--------|--------:|--------:|
|         |         |         |         |          |         |         |     0   |     0   |
|         |         |         |         |          |         |         |     0   |     0   |
|         |         |         |         |          |         |         |     0   |     0   |
| 5479.0  | 41130.0 | Blaise  | C02489  | Binche   | G       | 0.8     |  3287.4 |   547.9 |
| 6489.0  | 41129.0 | Arthur  | C02479  | Jemappes | G       | 1.0     |  3893.4 |   648.9 |

Candidate column headers:
Nom
Chef equipe
Date de pose
Tps 
10%
Mag
Ref
60%
Montant



headers: Nom
Chef equipe
Date de pose
Tps 
10%
Mag
Ref
60%
Montant
label: Montant
Date de pose
Nom
Ref
Mag
Chef equipe
Tps 
60%
10%
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 |
|:------------------|--------:|--------:|--------:|
| COOKWARE          |       1 | 1299.99 | 1299.99 |
| BABY              |       1 |  299.99 |  299.99 |
| FURNITURE         |       1 |  299.99 |  299.99 |
| FURNITURE         |       1 |  324.99 |  324.99 |
| WINDOW TREATMENTS |       6 |  103.99 |  623.94 |

Candidate column headers:
Department
Qty
Ext Retail
Retail



headers: Department
Qty
Ext Retail
Retail
label: Department
Qty
Retail
Ext Retail
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 |
|:------------|---------:|--------:|--------:|--------:|---------:|
| Mexico      |  1.91354 | 2.67567 | 3.38932 | 6.23985 |  9.10236 |
| Korea       |  6.95561 | 3.95722 | 5.06741 | 7.23292 |  6.87413 |
| Switzerland | 11.8045  | 4.70084 | 2.20716 | 7.26081 |  7.79894 |
| Netherlands | 22.9745  | 7.9626  | 3.62418 | 6.54448 | 10.0566  |
| Germany     | 25.3573  | 4.84376 | 1.67695 | 8.01018 |  7.82309 |

Candidate column headers:
 Intermediate consumption
 Social benefits and social transfers in kind
 Other
 Compensation of employees
 Gross fixed capital formation
Column1



headers:  Intermediate consumption
 Social benefits and social transfers in kind
 Other
 Compensation of employees
 Gross fixed capital formation
Column1
label: Column1
 Social benefits and social transfers in kind
 Intermediate consumption
 Gross fixed capital formation
 Other
 Compensation of employees
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 |
|:--------|--------:|--------:|--------:|--------:|
| Picin   |     377 |     995 |     178 |     958 |
| Wutif   |     122 |     568 |     666 |     376 |
| Wamub   |     509 |     745 |     728 |     232 |
| Bahud   |     549 |     350 |     658 |     222 |
| Tokam   |     902 |     654 |     139 |     337 |

Candidate column headers:
2008
2007
2009
Product
2006



headers: 2008
2007
2009
Product
2006
label: Product
2006
2007
2008
2009
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 |
|--------:|:----------|:------------|---------:|
|    2004 | Northwest | Towels      | 58221.6  |
|    2003 | Northeast | Area rugs   | 47924.5  |
|    2004 | Northwest | Slipcovers  | 41433.6  |
|    2006 | Northwest | Tablecloths | 12167.8  |
|    2005 | Northeast | Towels      |  2544.17 |

Candidate column headers:
Region
Year
Sales
Product



headers: Region
Year
Sales
Product
label: Year
Region
Product
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 |
|:---------|--------:|--------:|
| Standard |    2.99 |    2915 |
| Value    |    1.5  |    5129 |
| Value    |    1.5  |    5371 |
| Standard |    2.99 |    2712 |
| Lux      |    3.99 |     910 |

Candidate column headers:
Price per pack
Product
Packs Sold



headers: Price per pack
Product
Packs Sold
label: Product
Price per pack
Packs Sold
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   |
|--------:|:----------|:-------------|:-------------------|:--------|
|   42722 | 42722     | 9 AM TO 3 PM | MOCK HERDING TRIAL |         |
|   42805 | 42805     | 9 AM TO 3 PM | CLASS WO DOGS      | CINDY   |
|   42783 | 42785     | ALL DAY      | HERDING TRIAL      |         |
|   42786 | 42786     | 9 AM TO 3 PM | MOCK HERDING TRIAL |         |
|   42797 | 03/05/147 | ALL DAY      | HERDING TRIAL      | CINDY   |

Candidate column headers:
TYPE
TIME
DATES
TO
Column2



headers: TYPE
TIME
DATES
TO
Column2
label: DATES
TO
TIME
TYPE
Column2
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      |
|--------:|:--------|:---------|:-------------------------------|:--------|:--------|:------------|--------:|--------:|--------:|:------------|
|     660 | Iestyn  | Rhodes   | Pontypool And District Runners | MS      | M       | 00:48:29.45 |      29 |      21 |      28 | 00:48:27.35 |
|     227 | Clara   | Evans    | Pontypridd Roadents            | FS      | F       | 00:45:49.75 |      13 |       1 |       1 | 00:45:49.90 |
|     337 | Tom     | Henry    | Lliswerry Runners              | MS      | M       | 00:48:09.30 |      27 |      19 |      26 | 00:48:08.85 |
|     664 | Samuel  | Richards | Pontypridd Roadents            | MS      | M       | 00:43:06.20 |       4 |       3 |       4 | 00:43:04.65 |
|     684 | David   | Rowland  | Ebbw Vale Running Club         | M40     | M       | 00:47:34.15 |      23 |       5 |      22 | 00:47:32.65 |

Candidate column headers:
Overall Pos
Finish
Gender
Last Name
Category
Gender Pos
Chip Time
First Name
Club
Bib No
Category Pos



headers: Overall Pos
Finish
Gender
Last Name
Category
Gender Pos
Chip Time
First Name
Club
Bib No
Category Pos
label: Bib No
First Name
Last Name
Club
Category
Gender
Finish
Overall Pos
Category Pos
Gender Pos
Chip 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   |
|--------:|:--------|:--------|:----------------|:--------|
| 43390.2 | Bartels | Kenneth | Russell         |         |
| 43329.8 | Biggs   | Rikki   | Ann             |         |
| 43063.5 | Cavasos | Ravi    | Kaimana         |         |
| 43285.8 | Chuitt  | Jessie  | Alberta         |         |
| 43187.7 | Cain    | Cody    | Randall Stephan |         |

Candidate column headers:
(Do Not Modify) Modified On
First Name
Middle Name
Suffix
Last Name



headers: (Do Not Modify) Modified On
First Name
Middle Name
Suffix
Last Name
label: (Do Not Modify) Modified On
Last Name
First Name
Middle Name
Suffix
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 |
|--------:|--------:|:--------|:--------|:-------------|:--------|:--------|:--------|--------:|--------:|
|    2029 |       5 | COMBES  |         | Aitchison    |         | F714    |         |       5 |       0 |
|    1982 |       5 | COMBES  |         | broyeur      |         | F714    |         |       5 |       1 |
|    2144 |       2 | COMBES  |         | round baller |         | F714    | 57.0    |       2 |       1 |
|    2172 |       4 | COMBES  | carbo   | remorque     |         | F714    |         |       4 |       0 |
|    2035 |       2 | COMBES  |         | Aitchison    |         | F714    |         |       2 |       0 |

Candidate column headers:
chauffeur
heure
proprietaire
compteur
heure outil
Tracteur Cuma
OUTIL
balles
mouvement
mat avant



headers: chauffeur
heure
proprietaire
compteur
heure outil
Tracteur Cuma
OUTIL
balles
mouvement
mat avant
label: compteur
heure
proprietaire
chauffeur
OUTIL
mat avant
Tracteur Cuma
balles
heure outil
mouvement
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 |
|-----------:|:--------|--------:|--------:|
| 0.0141815  | A       |    60   |  220.51 |
| 0.00669493 | A       |    65.1 |  104.1  |
| 0.0414821  | B       |    56.1 |  692.19 |
| 0.00959284 | A       |    52.6 |  149.16 |
| 0.0308012  | A       |    58.7 |  478.93 |

Candidate column headers:
Pct
Bin
Amt
LTV



headers: Pct
Bin
Amt
LTV
label: Pct
Bin
LTV
Amt
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 |   Col_1 |   Col_2 | Col_3   | Col_4   | Col_5              | Col_6   | Col_7   | Col_8   | Col_9               | Col_10   | Col_11      | Col_12   | Col_13   | Col_14   | Col_15   | Col_16   | Col_17   | Col_18   |
|--------:|--------:|--------:|:--------|:--------|:-------------------|:--------|:--------|:--------|:--------------------|:---------|:------------|:---------|:---------|:---------|:---------|:---------|:---------|:---------|
|       1 |       2 |       2 |         |         |                    |         |         |         |                     |          | Jimmy Quinn |          | 8.0      | 120.0    | 2.0      | 8.0      |          | 1.0      |
|       1 |       1 |       1 | 1.0     | 3.0     | 0.3333333333333333 | 34.0    |         |         | 77.08333333333333   |          | Jimmy Quinn |          | 1.0      | 18.0     |          | 4.0      |          |          |
|       1 |       3 |       3 | 15.0    | 25.0    | 0.6                | 254.0   | 7.0     |         | 187.75              |          | Jimmy Quinn |          | 2.0      | 19.0     | 1.0      | 3.0      |          |          |
|       1 |       1 |       1 | 0.0     | 1.0     | 0.0                |         |         | 1.0     | -414.58333333333326 |          | Jimmy Quinn |          |          |          |          |          |          |          |
|       1 |       3 |       3 | 3.0     | 4.0     | 0.75               | 21.0    | 1.0     |         | 169.79166666666666  |          | Jimmy Quinn |          | 9.0      | 168.0    | 5.0      | 11.0     |          | 1.0      |

Candidate column headers:
S
Ratio
Rush Att
G#
Ints2
QBR
Sacks
Atts
TDs
Tacks
Rec Tds
RecYards
Rush Yards
G
Ints
Yards
C2
Comps
Recs



headers: S
Ratio
Rush Att
G#
Ints2
QBR
Sacks
Atts
TDs
Tacks
Rec Tds
RecYards
Rush Yards
G
Ints
Yards
C2
Comps
Recs
label: S
G
G#
Comps
Atts
Ratio
Yards
TDs
Ints
QBR
Rush Att
C2
Rush Yards
Recs
RecYards
Rec Tds
Tacks
Sacks
Ints2
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   |
|--------:|:---------|--------:|--------:|--------:|--------:|:--------|:--------|:--------|:--------|:---------|:---------|
|    8501 | HEULE    |       7 |      12 |       6 |      14 | X       | X       | X       | X       | X        | X        |
|    8500 | KORTRIJK |      27 |      17 |      22 |      21 | X       | X       | X       | X       | X        | X        |
|    8500 | KORTRIJK |       0 |       0 |       0 |       0 | 6       | 1       | 4       | 6       | 8        | 14       |
|    8500 | KORTRIJK |       2 |       4 |       7 |       1 | 9       | 14      | X       | X       | X        |          |
|    8511 | AALBEKE  |      19 |      10 |      10 |      14 | X       | X       | X       | X       | X        |          |

Candidate column headers:
leerjaar 5
leerjaar 1
2013
leerjaar 6
2010
leerjaar 2
leerjaar 4
Gemeente vpl
leerjaar 3
2011
Postnummer vpl
2012



headers: leerjaar 5
leerjaar 1
2013
leerjaar 6
2010
leerjaar 2
leerjaar 4
Gemeente vpl
leerjaar 3
2011
Postnummer vpl
2012
label: Postnummer vpl
Gemeente vpl
2013
2012
2011
2010
leerjaar 1
leerjaar 2
leerjaar 3
leerjaar 4
leerjaar 5
leerjaar 6
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 |
|--------:|--------:|--------:|--------:|
|     -50 |       0 |       0 |       0 |
|     -50 |       0 |       0 |       0 |
|     -50 |       0 |       0 |       0 |
|     -50 |       0 |       0 |       0 |
|     -50 |       0 |       0 |       0 |

Candidate column headers:
Colonne4
Colonne92
Colonne5
Colonne82



headers: Colonne4
Colonne92
Colonne5
Colonne82
label: Colonne5
Colonne4
Colonne82
Colonne92
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    |
|--------:|:------------|--------:|:---------|:--------|:--------|:--------|:--------|:--------|:---------|
|      17 | InpatEpiNo  |      80 | VarChar  |         | O       |         |         |         | Warning  |
|       2 | NhcdcID     |       4 | VarChar  | M       | M       | M       | M       | M       | Critical |
|      14 | ICCUHours   |       5 | SmallInt | C       |         |         | C       | C       | Critical |
|      15 | PsyICUHours |       5 | SmallInt | C       |         |         | C       | C       | Critical |
|       3 | EpiNo       |      80 | VarChar  | M       | M       | M       | M       | M       | Critical |

Candidate column headers:
ED
Len
AC
Field
Item No
MM
Error level
SB
Data Type
OP



headers: ED
Len
AC
Field
Item No
MM
Error level
SB
Data Type
OP
label: Item No
Field
Len
Data Type
AC
ED
OP
SB
MM
Error level
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           |
|:-----------------------|:----------------|:----------------|
| Bellin Oconto Hospital | Stacy Hansen    | 835-1100 X 1963 |
| Surgery                | Maggie Hansen   | 433-3608        |
| Maternity              | Jody Lidsley    | 433-7532        |
| Nursing Services       | Kris Holiday    | 433-3654        |
| Cath Lab               | Beth Watermolen | 433-3683        |

Candidate column headers:
Clinical Area
Name
Office Number



headers: Clinical Area
Name
Office Number
label: Clinical Area
Name
Office Number
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   |
|:--------|:--------|:--------|
| FT4     | CT      | HR      |
| CP      | CN      | HR      |
| FT      | CT      | HR      |
| FY      | CT      | HR      |
| ET      | CN      | HR      |

Candidate column headers:
Unit of Measure
Pay Item Code
Pay Item Code Type



headers: Unit of Measure
Pay Item Code
Pay Item Code Type
label: Pay Item Code
Pay Item Code Type
Unit of Measure
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 |
|:--------|:--------|--------:|
| HUF     | JED     |   51.73 |
| HIK     | JED     |  -30.54 |
| HUF     | JED     |   11.88 |
| JED     | NUG     |  -60.6  |
| NUG     | MOF     |   13.27 |

Candidate column headers:
Bal
ProA
ProB



headers: Bal
ProA
ProB
label: ProA
ProB
Bal
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   |
|--------:|:--------|:--------|:---------|:---------------|--------:|:--------|--------:|:-----------|:--------|
|      40 | W9JP    | 3A      | L601547  | ARRL-FIELD-DAY |     291 | USA     |       4 | 2015/06/27 |         |
|      40 | W2FAM   | 2E      | CW4EVER  | ARRL-FIELD-DAY |     291 | USA     |       5 | 2015/06/27 | 7.04197 |
|      40 | N2ETJ   | 1D      | L601547  | ARRL-FIELD-DAY |     291 | USA     |       5 | 2015/06/27 |         |
|      20 | N2LRB   | 1E      | N8HKU-PC | ARRL-FIELD-DAY |     291 | USA     |       5 | 2015/06/27 |         |
|      40 | N8MTM   | 1D      | L601547  | ARRL-FIELD-DAY |     291 | USA     |       4 | 2015/06/27 |         |

Candidate column headers:
fldFrequency
fldCQZone
fldComputerName
fldClass
fldContestID
fldCountryDXCC
fldDateStr
fldCall
fldBand
fldCountryWorked



headers: fldFrequency
fldCQZone
fldComputerName
fldClass
fldContestID
fldCountryDXCC
fldDateStr
fldCall
fldBand
fldCountryWorked
label: fldBand
fldCall
fldClass
fldComputerName
fldContestID
fldCountryDXCC
fldCountryWorked
fldCQZone
fldDateStr
fldFrequency
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 |
|:--------|:--------|--------:|:-------------|:--------------|:--------|:--------|:--------|:--------|--------:|
|         |         |       1 |              |               |         |         |         |         |       1 |
| 821.0   | 300.0   |       1 | Cevi         | 7303 00 10 00 | 1500.0  | 1095.0  | Da      | 259.0   |       1 |
|         |         |       1 |              |               |         |         |         |         |       1 |
| 722.0   | 250.0   |       1 | Cevi         | 7303 00 10 00 | 4000.0  | 4000.0  | Da      | 100.0   |       2 |
| 964.0   | 270.0   |       1 | Led sijalica | 8544 70 90 90 | 135.0   | 158.0   | Ne      | 178.0   |       1 |

Candidate column headers:
Poreklo
Prethodni postupak C5
Naim.
Tarifni broj
Naimen.
Izvozna C3
Vrednost
Faktura
Repromaterijal
Kolicina



headers: Poreklo
Prethodni postupak C5
Naim.
Tarifni broj
Naimen.
Izvozna C3
Vrednost
Faktura
Repromaterijal
Kolicina
label: Faktura
Izvozna C3
Naimen.
Repromaterijal
Tarifni broj
Kolicina
Vrednost
Poreklo
Prethodni postupak C5
Naim.
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 |
|:------------------|:---------|:-------------|--------:|--------:|
| Danelle Mellor    | Flanders | Roeselare    |      12 |    30   |
| Aja Shutts        | Brussels | Saint-Gilles |     897 |  2242.5 |
| Laquanda Giberson | Brussels | Etterbeeck   |     459 |  1147.5 |
| Kasha Veasley     | Wallonia | Bastogne     |      54 |   135   |
| Laura Denn        | Flanders | Kortrijk     |      21 |    52.5 |

Candidate column headers:
Store manager
Region
Location
Units sold
Profit



headers: Store manager
Region
Location
Units sold
Profit
label: Store manager
Location
Region
Units sold
Profit
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0               |   Col_1 |   Col_2 |    Col_3 |   Col_4 |   Col_5 |   Col_6 |
|:--------------------|--------:|--------:|---------:|--------:|--------:|--------:|
| Muffins             |    5.99 |      12 | 0.499167 |     0.5 |      12 |    0.01 |
| Pepsi Products      |    7.29 |      24 | 0.30375  |     0.5 |      24 |    4.71 |
| Dole Fruit Cups     |    4.69 |      12 | 0.390833 |     0.5 |      12 |    1.31 |
| PopTarts            |    7.99 |      24 | 0.332917 |     0.5 |      24 |    4.01 |
| Frito Lay Chip Bags |   11.59 |      30 | 0.386333 |     0.5 |      30 |    3.41 |

Candidate column headers:
Number of Items per case
Item
Items Sold
Cost per Item
Profit
Cost
Selling Price Per Item



headers: Number of Items per case
Item
Items Sold
Cost per Item
Profit
Cost
Selling Price Per Item
label: Item
Cost
Number of Items per case
Cost per Item
Selling Price Per Item
Items Sold
Profit
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0      |   Col_1 |       Col_2 |   Col_3 |
|:-----------|--------:|------------:|--------:|
| METHYLPHEN |  150660 | 1.6397e+07  | 108.834 |
| HUMALOG    |   38487 | 1.57757e+07 | 409.896 |
| DULOXETINE |   42042 | 6.44027e+06 | 153.186 |
| VYVANSE    |   80568 | 1.67557e+07 | 207.97  |
| TAMIFLU    |   50433 | 7.93052e+06 | 157.249 |

Candidate column headers:
Drug Name
Number of Rx
Average Reimbursement per Rx
Total Reimbursement



headers: Drug Name
Number of Rx
Average Reimbursement per Rx
Total Reimbursement
label: Drug Name
Number of Rx
Total Reimbursement
Average Reimbursement per Rx
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 |
|--------:|-------------:|:--------------------------|--------:|
|      46 | 546060658378 | 70 gr (Small Rectangular) |     6.7 |
|      22 | 546060658360 | 70 gr (Small Rectangular) |     6.7 |
|      28 | 546060658366 | 70 gr (Small Rectangular) |     6.7 |
|       4 | 546060658342 | 248 gr                    |    13.6 |
|      48 | 546060658380 | 106 gr (Square)           |     7   |

Candidate column headers:
Packaging (Net Weight)
Product Code (SKU)
Unit Price (Euro)
Product Number



headers: Packaging (Net Weight)
Product Code (SKU)
Unit Price (Euro)
Product Number
label: Product Number
Product Code (SKU)
Packaging (Net Weight)
Unit Price (Euro)
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 |
|--------:|--------:|:--------|--------:|--------:|:--------|--------:|--------:|--------:|--------:|
|   19081 |    5072 | J1      |   19081 |    5072 | J1      |    5073 | 29.5628 | 3429.28 | 2571.96 |
|   37609 |    5072 | J1      |   37609 |    5072 | J1      |    5073 | 29.5628 | 3429.28 | 2571.96 |
|   11406 |    5072 | J1      |   11404 |    5072 | J1      |    5073 | 29.5628 | 3429.28 | 2571.96 |
|   19081 |    5072 | J1      |   19083 |    5072 | J1      |    5073 | 29.5628 | 3429.28 | 2571.96 |
|   20225 |    5072 | J1      |   20206 |    5072 | J1      |    5073 | 29.5628 | 3429.28 | 2571.96 |

Candidate column headers:
Primary SI
Secondary J1 or Add-on HCPCS Code
Relative Weight
Primary HCPCS Code
Complexity Adjusted APC Assignment
Hospital
Secondary SI
Secondary APC Assignment
ASC
Primary APC Assignment



headers: Primary SI
Secondary J1 or Add-on HCPCS Code
Relative Weight
Primary HCPCS Code
Complexity Adjusted APC Assignment
Hospital
Secondary SI
Secondary APC Assignment
ASC
Primary APC Assignment
label: Primary HCPCS Code
Primary APC Assignment
Primary SI
Secondary J1 or Add-on HCPCS Code
Secondary APC Assignment
Secondary SI
Complexity Adjusted APC Assignment
Relative Weight
Hospital
ASC
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                  |
|--------:|--------:|------------:|--------:|------------:|------------:|------------:|------------:|:-----------------------|
|      15 |   15.9  | 3.14465e-05 |   13.21 | 3.78501e-05 | 4.99669e-07 | 4.62987e-16 | 1.50872e-19 |                        |
|      18 |   60.27 | 8.296e-06   |    3.96 | 0.000126263 | 2.36914e-07 | 4.93508e-17 | 1.18369e-19 |                        |
|       7 |   12.8  | 3.90625e-05 |    3.2  | 0.00015625  | 5.61777e-07 | 6.57982e-16 | 4.86501e-19 | 4.9996624403041465e-19 |
|       1 |   72.78 | 6.87002e-06 |    3.64 | 0.000137363 | 2.12139e-07 | 3.54309e-17 | 1.09999e-19 | 1.0999874635814221e-19 |
|       9 |   36    | 1.38889e-05 |    3.21 | 0.000155763 | 3.18178e-07 | 1.19545e-16 | 2.15936e-19 |                        |

Candidate column headers:
Oil Drop
Average Rise Speed (m/s)
Average Fall Speed (m/s)
Average Charge For Drop (Coulombs)
Estimated Radius of Drop (in meters)
Estimated Mass of Drop (kg)
Rise Time (sec)
Fall Time (sec)
Estimated Net Charge of Droplet (Coulombs)



headers: Oil Drop
Average Rise Speed (m/s)
Average Fall Speed (m/s)
Average Charge For Drop (Coulombs)
Estimated Radius of Drop (in meters)
Estimated Mass of Drop (kg)
Rise Time (sec)
Fall Time (sec)
Estimated Net Charge of Droplet (Coulombs)
label: Oil Drop
Fall Time (sec)
Average Fall Speed (m/s)
Rise Time (sec)
Average Rise Speed (m/s)
Estimated Radius of Drop (in meters)
Estimated Mass of Drop (kg)
Estimated Net Charge of Droplet (Coulombs)
Average Charge For Drop (Coulombs)
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 | Col_1       |   Col_2 | Col_3   | Col_4   | Col_5   | Col_6   | Col_7   | Col_8   | Col_9   | Col_10   | Col_11   | Col_12   | Col_13   | Col_14   | Col_15   | Col_16   | Col_17   | Col_18   | Col_19   |
|--------:|:------------|--------:|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:---------|:---------|:---------|:---------|:---------|:---------|:---------|:---------|:---------|:---------|
|      38 |             |       0 | PO1     |         |         |         | B*      |         |         |          |          |          |          |          |          |          |          |          |          |
|      21 |             |       0 | CSE     | CSE     | CSE     | CSE     | CSE     | CSE     | CSE     | CSE      | CSE      | CSE      | CSE      | CSE      | CSE      | CSE      | CSE      | CSE      | CSE      |
|      17 | Meivakantie |       0 |         |         |         |         |         |         |         |          |          |          |          |          |          |          |          |          |          |
|      22 | Hemelvaart  |       0 |         |         |         |         |         |         |         |          |          |          |          |          |          |          |          |          |          |
|      40 | Bufferweek  |       0 |         |         |         |         |         |         |         |          |          |          |          |          |          |          |          |          |          |

Candidate column headers:
Activiteit
M&O
Biol
NL&T
O&O
Ak
Dutl
Aantal toetsen
Wis A
Ges
LOB
Week
Nederlands
Maw
Schk
Fatl 
Econ
Nat
Wis B
Engels



headers: Activiteit
M&O
Biol
NL&T
O&O
Ak
Dutl
Aantal toetsen
Wis A
Ges
LOB
Week
Nederlands
Maw
Schk
Fatl 
Econ
Nat
Wis B
Engels
label: Week
Activiteit
Aantal toetsen
Nederlands
Engels
Wis A
Wis B
Fatl 
Dutl
Ak
Ges
Econ
M&O
Biol
Schk
Nat
NL&T
LOB
O&O
Maw
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 |
|--------:|--------:|----------:|
| 43392.8 | 43392.8 | 0.0229167 |
| 43397.3 | 43397.4 | 0.01875   |
| 43399.4 | 43399.4 | 0.0194444 |
| 43393.3 | 43393.3 | 0.0222222 |
| 43391.8 | 43391.8 | 0.0243056 |

Candidate column headers:
2A
Stop
Duration



headers: 2A
Stop
Duration
label: 2A
Stop
Duration