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 |
|:--------|:--------|:--------|--------:|--------:|
| Bob     | North   | Dec     |   23179 |     465 |
| Doug    | South   | Mar     |   25041 |      86 |
| Doug    | South   | Aug     |   29506 |     103 |
| Chuck   | South   | Sep     |   21584 |     114 |
| Amy     | North   | Feb     |   24131 |      79 |

Candidate column headers:
Month
Sales
Units Sold
SalesRep
Region



headers: Month
Sales
Units Sold
SalesRep
Region
label: SalesRep
Region
Month
Sales
Units 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   |
|--------:|:--------|:--------|:--------|
|   41061 | 28.48   | 312.64  |         |
|   41153 |         |         |         |
|   41000 | 30.37   | 131.04  |         |
|   41091 | 88.82   | 401.46  |         |
|   40969 | 49.0    | 100.67  |         |

Candidate column headers:
Collected
Bags
Month
Total



headers: Collected
Bags
Month
Total
label: Month
Collected
Total
Bags
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 |
|:--------|:--------|:-------------|:--------|:--------|:--------|--------:|--------:|
| Asia    | ASX     | EnElecBs-AUD | HQ      | AUD     | Future  |   42446 |   42446 |
| Asia    | ASX     | All          | All     | AUD     | Mixed   |   40949 |   40949 |
| Asia    | ASX     | ALL-O        | All     | AUD     | Option  |   40949 |   40949 |
| Asia    | ASX     | Ag-AUD       | VW      | AUD     | Option  |   42445 |   42445 |
| Asia    | ASX     | Ag-AUD       | All     | AUD     | Mixed   |   40949 |   40949 |

Candidate column headers:
Created On
Region
Updated On
Product Type
Product Code
Product Group
ExchangeCode
Currency



headers: Created On
Region
Updated On
Product Type
Product Code
Product Group
ExchangeCode
Currency
label: Region
ExchangeCode
Product Group
Product Code
Currency
Product Type
Created On
Updated On
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 |
|---------:|--------:|--------:|--------:|--------:|---------:|
| 1.61242  | 589.387 |      14 |     196 |  347376 |  8251.41 |
| 1.4809   | 469.19  |      24 |     576 |  220139 | 11260.5  |
| 1.69166  | 581.652 |      13 |     169 |  338319 |  7561.48 |
| 0.639277 | 664.393 |      21 |     441 |  441418 | 13952.2  |
| 1.61242  | 456.197 |      38 |    1444 |  208116 | 17335.5  |

Candidate column headers:
Qty DS
Y^2
Seasonals
X
XY
X^2



headers: Qty DS
Y^2
Seasonals
X
XY
X^2
label: Seasonals
Qty DS
X
X^2
Y^2
XY
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   |
|:--------|:-----------------|--------:|--------:|--------:|-------------:|-----------------:|-----------------:|--------:|:--------|:---------|:---------|
| ABS     | GN 704665        |    5    |   40289 |   50997 | 682830       | 709039           | 757386           | 48347.2 | Aaa     | AA+      | AAA      |
| ABS     | G2 4883          |    4.5  |   40528 |   51490 | 397798       | 406555           | 432058           | 25502.6 | Aaa     | AA+      | AAA      |
| ABS     | GN 737424        |    4    |   40599 |   51394 | 530180       | 530882           | 561112           | 30230.1 | Aaa     | AA+      | AAA      |
| ABS     | COMET 2015-A4 A4 |    2.75 |   42822 |   45792 |      5.6e+06 |      5.69486e+06 |      5.76145e+06 | 66590.5 | NR      | AAA      | AAA      |
| ABS     | GN 731660        |    4.5  |   40602 |   51363 | 530904       | 546921           | 572592           | 25671.4 | Aaa     | AA+      | AAA      |

Candidate column headers:
Maturity Date
Cpn/Dvd
Moody's
S&P
Fitch
Reported GAAP Carrying Value
Par/Shares
Purchase Date
Ledger Class
Net Unrealized Gain (Loss)
Description
Reported Book Value



headers: Maturity Date
Cpn/Dvd
Moody's
S&P
Fitch
Reported GAAP Carrying Value
Par/Shares
Purchase Date
Ledger Class
Net Unrealized Gain (Loss)
Description
Reported Book Value
label: Ledger Class
Description
Cpn/Dvd
Purchase Date
Maturity Date
Par/Shares
Reported Book Value
Reported GAAP Carrying Value
Net Unrealized Gain (Loss)
Moody's
S&P
Fitch
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 |
|:---------------|--------:|--------:|
| Germany        | 39094   | 65964.7 |
| United Kingdom | 71371.9 | 94909.2 |
| Spain          | 32707.6 | 51174.8 |
| Hungary        |  7138.5 | 19607.7 |
| Czech Republic | 10260.8 | 20022.4 |

Candidate column headers:
Column1
1998
2008



headers: Column1
1998
2008
label: Column1
1998
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      | Col_4               |
|--------:|:--------|:--------|:-----------|:--------------------|
|      22 | belum   | belum   | 11A07007   | IQBAL SAPUTRA       |
|      14 | belum   | belum   | 1300003142 | WARSAM YOGA PRATAMA |
|       5 | belum   | belum   | 1300023085 | NUR FADHLILAH       |
|      10 | belum   | belum   | 1300029036 | SAUSAN IRIKA RAHINA |
|      16 | belum   | belum   | 1300004231 | VITA RIZKA NURMALA  |

Candidate column headers:
IS SINKRON
NAMA
NIM
NO.
IS VERIFIKASI



headers: IS SINKRON
NAMA
NIM
NO.
IS VERIFIKASI
label: NO.
IS VERIFIKASI
IS SINKRON
NIM
NAMA
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 |
|:------------|:---------------|----------:|--------:|:--------|--------:|
| HI Africa 2 | KEN-CFUND-1505 |  262257   |   42384 | CCM     |    2016 |
| AME-CA      | CAF-CFUND-1505 |   74047.7 |   42426 | CCM     |    2016 |
| AME-SEA     | MUS-CFUND-1603 |   63131   |   42411 | CCM     |    2016 |
| AELAC-EECA  | KAZ-CFUND-1604 |  124548   |   42389 | CCM     |    2016 |
| AELAC-EECA  | KAZ-CFUND-1604 | -124548   |   42393 | CCM     |    2016 |

Candidate column headers:
Grant Agreement Number
Disb Date
Type
Year
Disbursement Amount (USD)
Region



headers: Grant Agreement Number
Disb Date
Type
Year
Disbursement Amount (USD)
Region
label: Region
Grant Agreement Number
Disbursement Amount (USD)
Disb Date
Type
Year
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   |
|:--------|:--------|--------:|--------:|:-----------------|:---------------|:--------|:--------|:--------|
| NM1106  | drama   |    2013 |     7.2 | juliette binoche | nikolaj waldau | 1.02GB  | MP4     |         |
| NM0642  | action  |    2013 |     4.7 | ben kingsley     | ben cross      | 0.76GB  | MP4     |         |
| NM1565  | sci-fi  |    2015 |     4.2 | dane cook        | brandon routh  | 0.82GB  | MP4     |         |
| NM0738  | drama   |    2014 |     6.4 | cam gigandet     | kim basinger   | 0.80GB  | MP4     |         |
| NM1872  | drama   |    2015 |     6.6 | tilda swinton    | ralph fiennes  | 1.04GB  | MP4     |         |

Candidate column headers:
ACTOR
SUPPORTING ACTOR
IGNORE COLUMN
IMDB
FORMAT
SIZE
CODE
GENRE
YEAR



headers: ACTOR
SUPPORTING ACTOR
IGNORE COLUMN
IMDB
FORMAT
SIZE
CODE
GENRE
YEAR
label: CODE
GENRE
YEAR
IMDB
ACTOR
SUPPORTING ACTOR
SIZE
FORMAT
IGNORE COLUMN
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 |
|--------:|:--------|:------------------|:--------|:--------|:--------|:--------|:--------|--------:|--------:|
|   42174 | Frank   | Necessary         | S       | S       | F       |         |         |    5.75 | -8.625  |
|   42134 | Jekyll  | Acceptable        | F       | G       |         |         |         |    3.75 |  1.875  |
|   42124 | Jekyll  | Unacceptable      | G       | G       | S       | F       | G       |    7.25 | 14.5    |
|   42149 | Frank   | Unacceptable      | G       | BS      | BS      | D       |         |    7.75 | 15.5    |
|   42134 | Frank   | Mildly Acceptable | S       | S       | G       | F       | BS      |    9.75 |  2.4375 |

Candidate column headers:
CW3
Weighted Total
Raw Total
Date
CW1
CW5
Child
Context
CW4
CW2



headers: CW3
Weighted Total
Raw Total
Date
CW1
CW5
Child
Context
CW4
CW2
label: Date
Child
Context
CW1
CW2
CW3
CW4
CW5
Raw Total
Weighted 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   |
|--------:|:------------|:--------|
|   41098 | SDCA50P     | 52.26   |
|   41101 | SDCA        | 56.99   |
|   41105 | SDCA50P/T20 |         |
|   41105 | SDCA50P/T20 | 52.21   |
|   41100 | PERML       |         |

Candidate column headers:
ES
DATE
PRODUIT



headers: ES
DATE
PRODUIT
label: DATE
PRODUIT
ES
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 |
|:--------|:--------|:--------|:---------------|:--------|--------:|
| Paul    | BRA     | Simmons | Cleveland      | AL      |      38 |
| Paul    | POR     | Stevens | Montgomery     | OH      |      31 |
| Dale    | BRA     | Garcia  | Virginia Beach | NV      |      60 |
| Tony    | POR     | Simmons | Montgomery     | AL      |      12 |
| Bobby   | BRA     | Ross    | Virginia Beach | OH      |      26 |

Candidate column headers:
FName
State
LName
Number
City
Country



headers: FName
State
LName
Number
City
Country
label: FName
Country
LName
City
State
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   | Col_3               | Col_4          |
|:--------|:--------------|:--------|:--------------------|:---------------|
| H170001 | JM-USDA HNRCA | Boston  | Jenna Sills         | Meghan Faherty |
| H000001 | JM-USDA HNRCA | Boston  | Xiaoqing (Kathy) Li | Meghan Faherty |
| H190001 | JM-USDA HNRCA | Boston  | Meghan Faherty      | Meghan Faherty |
| H121001 | JM-USDA HNRCA | Boston  | Christopher Pereira | Meghan Faherty |
| H111001 | JM-USDA HNRCA | Boston  | Jenna Sills         | Meghan Faherty |

Candidate column headers:
Back-Up
Primary RA
School
Campus
DeptID



headers: Back-Up
Primary RA
School
Campus
DeptID
label: DeptID
School
Campus
Primary RA
Back-Up
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 |
|--------:|:------------------|--------:|--------:|-----------:|-----------:|---------:|
|   20910 | SILVER SPRING_ MD |      10 |    1795 | 0.00557103 | 0.0112613  | 0.408784 |
|   21236 | NOTTINGHAM_ MD    |      10 |    1112 | 0.00899281 | 0.0112613  | 0.442568 |
|   21401 | ANNAPOLIS_ MD     |      17 |    2765 | 0.00614828 | 0.0191441  | 0.168919 |
|   21237 | ROSEDALE_ MD      |       7 |     833 | 0.00840336 | 0.00788288 | 0.582207 |
|   20879 | GAITHERSBURG_ MD  |      23 |    1006 | 0.0228628  | 0.0259009  | 0.102477 |

Candidate column headers:
Percent of Total Bio-Tech Firms
Number of Bio-Tech Firms
Zip Code
ZIP Code Name
Percentage of Bio-Tech Firms in Zip Code
Total Firms in Zip Code
Cumulative Percent of Total Bio-Tech Firms



headers: Percent of Total Bio-Tech Firms
Number of Bio-Tech Firms
Zip Code
ZIP Code Name
Percentage of Bio-Tech Firms in Zip Code
Total Firms in Zip Code
Cumulative Percent of Total Bio-Tech Firms
label: Zip Code
ZIP Code Name
Number of Bio-Tech Firms
Total Firms in Zip Code
Percentage of Bio-Tech Firms in Zip Code
Percent of Total Bio-Tech Firms
Cumulative Percent of Total Bio-Tech Firms
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 |
|:-----------|:-----------------|:-----------|--------:|:--------|--------:|
| AP0016FRAR | AP00AGREIMBUR000 | ALLOBJECTS |    4610 | SALT    |       0 |
| AP0016FRAR | AP00AGREIMBUR000 | ALLOBJECTS |    4610 | SALT    |       0 |
| AP0016FRAR | AP00AGREIMBUR000 | ALLOBJECTS |    4610 | SALT    |       0 |
| AP0016FRAR | AP00AGREIMBUR000 | ALLOBJECTS |    4610 | SALT    |       0 |
| AP0016FRAR | AP00AGREIMBUR000 | ALLOBJECTS |    4610 | SALT    |       0 |

Candidate column headers:
Difference
Commitment Item
GLBL
Functional Area
Fund 
Doc Type



headers: Difference
Commitment Item
GLBL
Functional Area
Fund 
Doc Type
label: Fund 
Functional Area
Commitment Item
GLBL
Doc Type
Difference
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   |
|:------------------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|
| French (Canadian) |         |         | X       |         |         |         |         |         |
| German            |         | X       | X       | X       | X       |         | X       | X       |
| Georgian          |         |         |         |         |         | X       |         |         |
| Arabic Dialects   |         |         | X       |         |         |         |         |         |
| Czech             |         |         | X       |         |         |         |         |         |

Candidate column headers:
Avant- WorldSpeak
AAPPL
WAFLT  Custom Test
WPT & OPI
Avant- STAMP
WPT & OPIc
Language
ALTA
College Board AP Exam



headers: Avant- WorldSpeak
AAPPL
WAFLT  Custom Test
WPT & OPI
Avant- STAMP
WPT & OPIc
Language
ALTA
College Board AP Exam
label: Language
Avant- WorldSpeak
AAPPL
ALTA
WPT & OPIc
WPT & OPI
WAFLT  Custom Test
College Board AP Exam
Avant- STAMP
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    |
|--------:|--------:|:---------|:----------|:-------------|:---------|
|   38962 |   11779 | CD       | Teller    | Central      | New      |
|   38962 |     500 | Checking | New Accts | Central      | New      |
|   38961 |    8721 | Savings  | New Accts | Westside     | New      |
|   38961 |   15276 | Savings  | New Accts | North County | Existing |
|   38961 |    4000 | Savings  | New Accts | Central      | Existing |

Candidate column headers:
Branch
Date
Customer
OpenedBy
AcctType
Amount



headers: Branch
Date
Customer
OpenedBy
AcctType
Amount
label: Date
Amount
AcctType
OpenedBy
Branch
Customer
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         |
|:--------------|--------:|:--------------|
| 0201205093 80 |   42999 | 0201205099 80 |
| 0202209020 80 |   42999 | 0202209090 80 |
| 0202100020 80 |   42999 | 0202100090 80 |
| 0201100093 80 |   42999 | 0201100099 80 |
| 0304839011 10 |   42999 | 0304839010 80 |

Candidate column headers:
Entry into force
New codes
Derived from



headers: Entry into force
New codes
Derived from
label: New codes
Entry into force
Derived from
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 |
|--------:|:--------|:--------|--------:|--------:|----------:|----------:|
|   22005 | BIB     | BX      |   39.94 |   48    | 169.679   | 169.679   |
|   21001 | ADL     | EACH    |    5.11 |    4.67 |  16.5084  |  16.5084  |
|   21003 | ADL     | EA      |    1.12 |    1.03 |   3.64103 |   3.64103 |
|   10092 | CFR     | LB      |   17.39 |    0    |  62.4083  |  62.4083  |
|   22006 | BIB     | BX      |   22.92 |   33    | 116.654   | 116.654   |

Candidate column headers:
Number
EC$ COST
Category
Std Cost
Unit
Most Recent Cost
EC $ PRICE 17/08/2018



headers: Number
EC$ COST
Category
Std Cost
Unit
Most Recent Cost
EC $ PRICE 17/08/2018
label: Number
Category
Unit
Most Recent Cost
Std Cost
EC$ COST
EC $ PRICE 17/08/2018
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                    |
|:-----------|:--------|:--------|--------:|--------:|--------:|:-------------------------|
| cocoB001   | coco    | B00     |       1 |       1 |       1 | chene                    |
| bananeB001 | banane  | B00     |       1 |       1 |       2 | les carottes sont cuites |
| fraiseA002 | fraise  | A00     |       2 |       2 |       2 | chene                    |
| fraiseA002 | fraise  | A00     |       2 |       2 |       1 | Sapin                    |
| fraiseA001 | fraise  | A00     |       1 |       2 |       2 | castor                   |

Candidate column headers:
Controle
Mois
Version
Loop
Data
Semaine
Code



headers: Controle
Mois
Version
Loop
Data
Semaine
Code
label: Code
Controle
Version
Loop
Semaine
Mois
Data
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 |
|:------------------|--------:|--------:|--------:|--------:|--------:|
| JK Lakshmi Cement |       0 |       0 |       0 |       0 |       0 |
| Ambuja Cement     |       0 |       0 |       0 |       0 |       0 |
| Ambuja Cement     |       0 |       0 |       0 |       0 |       0 |
| Ambuja Cement     |      25 |      25 |      27 |      27 |      30 |
| ACC Ltd           |       0 |       0 |       0 |       0 |       0 |

Candidate column headers:
2011
2015
2014
Company
2013
2012



headers: 2011
2015
2014
Company
2013
2012
label: Company
2011
2012
2013
2014
2015
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0      |   Col_1 | Col_2   |   Col_3 |   Col_4 |   Col_5 |   Col_6 | Col_7   | Col_8                 |
|:-----------|--------:|:--------|--------:|--------:|--------:|--------:|:--------|:----------------------|
| 1993-03-20 |    1993 | L5      |     137 |      41 |   43.96 |       0 | L1T     | LT51370411993079ISP00 |
| 1992-04-18 |    1992 | L5      |     137 |      41 |   35.32 |       0 | L1T     | LT51370411992109BKT00 |
| 1992-08-24 |    1992 | L5      |     137 |      41 |   98.41 |       0 | L1G     | LT51370411992237BKT02 |
| 1992-07-23 |    1992 | L5      |     137 |      41 |   80.49 |       0 | L1G     | LT51370411992205ISP00 |
| 1991-03-31 |    1991 | L5      |     137 |      41 |   93.63 |       0 | L1G     | LT51370411991090ISP00 |

Candidate column headers:
Data Processed Level
Scene ID
Path
< 20% Clouds
Year
Row
Date Acquired
Scene Cloud Cover
Mission



headers: Data Processed Level
Scene ID
Path
< 20% Clouds
Year
Row
Date Acquired
Scene Cloud Cover
Mission
label: Date Acquired
Year
Mission
Path
Row
Scene Cloud Cover
< 20% Clouds
Data Processed Level
Scene ID
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 |   Col_1 |     Col_2 |   Col_3 |
|--------:|--------:|----------:|--------:|
|       1 |   41568 | 0.625     |       5 |
|       1 |   41568 | 0.583333  |       1 |
|       1 |   41568 | 0.125     |       4 |
|       1 |   41568 | 0.0833333 |       5 |
|       1 |   41568 | 0.916667  |      17 |

Candidate column headers:
date
heure
valeur
formule



headers: date
heure
valeur
formule
label: formule
date
heure
valeur
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   |
|:-------------------------|--------:|:-------------------|:--------|:----------|--------:|:-------------|:--------|--------:|:--------|:---------|:---------|:---------|
| Confidence Analytics     |       3 | 14797 NE 95th St   |         | Redmond   |   98052 | 206-743-8843 | Active  |   41809 | Yes     | No       | Yes      | Yes      |
| Medicine Creek Analytics |      18 | 3700 Pacific Hwy E | Ste 400 | Fife      |   98424 | 253-382-6900 | Active  |   42516 | Yes     | No       | Yes      | Yes      |
| Trace Analytics          |      14 | 908 N Howard St    | Ste 101 | Spokane   |   99201 | 509-284-7522 | Active  |   42068 | No      | No       | Yes      | Yes      |
| G.O.A.T. Labs            |       8 | 5501 NE 109th Ct   | Ste N   | Vancouver |   98662 | 360-513-9377 | Active  |   41844 | No      | No       | Yes      | Yes      |
| Analytical 360_ LLC.     |       4 | 31 N 1st Avenue    |         | Yakima    |   98902 | 509-571-1102 | Active  |   41787 | No      | No       | No       | No       |

Candidate column headers:
Lab #
Heavy Metals
Water Activity
Zip
Address 1
Certification Date
Mycotoxins
Address 2
Phone
City
Status
Lab Name
Pesticides



headers: Lab #
Heavy Metals
Water Activity
Zip
Address 1
Certification Date
Mycotoxins
Address 2
Phone
City
Status
Lab Name
Pesticides
label: Lab Name
Lab #
Address 1
Address 2
City
Zip
Phone
Status
Certification Date
Pesticides
Heavy Metals
Mycotoxins
Water Activity
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 |
|--------:|--------:|--------:|--------:|--------:|
|       1 |      75 |      29 |      26 |      20 |
|       1 |      93 |      38 |      33 |      22 |
|       1 |      66 |      30 |      22 |      14 |
|       1 |      87 |      33 |      32 |      22 |
|       1 |      57 |      25 |      21 |      11 |

Candidate column headers:
isolation
gender
powerl
norml
alienation



headers: isolation
gender
powerl
norml
alienation
label: gender
alienation
isolation
powerl
norml
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 |
|--------:|:-------------------|:-------------------------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:---------|:---------|---------:|---------:|
|       8 | HALL David         | Thanet Road Runners AC   | M40     |         |         |         |         | 15.0    | 15.0    |          | 12.0     |        3 |       42 |
|      25 | BATELY Theo        | Deal Tri                 | M40     |         |         |         | 19.0    |         |         |          |          |        1 |       19 |
|      20 | HOWEY Andrew       | Tunbridge Wells Harriers | M40     |         |         |         |         |         | 20.0    |          |          |        1 |       20 |
|      36 | LOIZOU Christopher | Dulwich Runners AC       | M40     |         |         |         |         |         |         |          | 16.0     |        1 |       16 |
|      50 | HEWETT Mark        | Maidstone Harriers       | M40     |         |         |         |         |         | 4.0     |          | 8.0      |        2 |       12 |

Candidate column headers:
TOTAL
Cat
Dulwich Pk 10K
Canterbury 1/2M
Thanet  Mar
Mt Ephraim 10K
Races
Thanet  20M
Name
PaddWood 1/2M
Team
Bluewater 10K
Dartford 10M
Pos



headers: TOTAL
Cat
Dulwich Pk 10K
Canterbury 1/2M
Thanet  Mar
Mt Ephraim 10K
Races
Thanet  20M
Name
PaddWood 1/2M
Team
Bluewater 10K
Dartford 10M
Pos
label: Pos
Name
Team
Cat
Dartford 10M
Thanet  20M
PaddWood 1/2M
Canterbury 1/2M
Bluewater 10K
Mt Ephraim 10K
Thanet  Mar
Dulwich Pk 10K
Races
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 |
|:----------|--------:|--------:|
| Version 2 |    1793 |   42767 |
| Version 3 |    2870 |   42795 |
| Version 1 |    2501 |   42736 |
| Version 2 |    2546 |   42767 |
| Version 3 |    2878 |   42795 |

Candidate column headers:
Sample # Pages From Cartridge
Date of Sample
Version



headers: Sample # Pages From Cartridge
Date of Sample
Version
label: Version
Sample # Pages From Cartridge
Date of Sample
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 |
|--------:|:--------|----------:|
|   42369 | Target  | 2280      |
|   42247 | Target  |   25.3471 |
|   41882 | Actual  | 5482.24   |
|   42247 | Target  | 5387.46   |
|   42247 | Actual  | 4160.84   |

Candidate column headers:
Data Type
Value
Month End



headers: Data Type
Value
Month End
label: Month End
Data Type
Value
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0    | Col_1     | Col_2      | Col_3    |   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 |
|:---------|:----------|:-----------|:---------|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
| Region 1 | Product 2 | Customer 9 | Person 9 |     605 |     749 |     605 |     729 |     600 |     795 |      695 |      680 |      606 |      782 |      765 |      774 |      767 |      705 |      740 |      627 |
| Region 2 | Product 4 | Customer 2 | Person 2 |     733 |     607 |     647 |     786 |     719 |     775 |      567 |      620 |      577 |      671 |      689 |      762 |      609 |      780 |      753 |      590 |
| Region 1 | Product 2 | Customer 7 | Person 7 |     642 |     778 |     843 |     881 |     726 |     850 |      827 |      759 |      721 |      618 |      757 |      899 |      632 |      853 |      824 |      833 |
| Region 2 | Product 5 | Customer 1 | Person 1 |     828 |     752 |     854 |     899 |     822 |     701 |      826 |      833 |      724 |      612 |      872 |      768 |      797 |      699 |      645 |      835 |
| Region 1 | Product 1 | Customer 7 | Person 7 |    1064 |     863 |    1316 |    1141 |     933 |     872 |     1185 |     1358 |     1173 |     1343 |     1164 |     1275 |     1364 |      828 |     1299 |      863 |

Candidate column headers:
Jan-11
Mar-11
Oct-11
Feb-12
Dec-11
May-11
Sales Person - Name
Apr-12
Mar-12
Product
Jun-11
Nov-11
Region
Apr-11
Feb-11
Jan-12
Aug-11
Customer Type
Sep-11
Jul-11



headers: Jan-11
Mar-11
Oct-11
Feb-12
Dec-11
May-11
Sales Person - Name
Apr-12
Mar-12
Product
Jun-11
Nov-11
Region
Apr-11
Feb-11
Jan-12
Aug-11
Customer Type
Sep-11
Jul-11
label: Region
Product
Customer Type
Sales Person - Name
Jan-11
Feb-11
Mar-11
Apr-11
May-11
Jun-11
Jul-11
Aug-11
Sep-11
Oct-11
Nov-11
Dec-11
Jan-12
Feb-12
Mar-12
Apr-12
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 |
|:-------------|:-----------|--------:|--------:|--------:|--------:|--------:|
| t45y6        | Honda      |      11 |       6 |       9 |       9 |      15 |
| Aweoob       | Mazda      |       9 |       8 |      14 |       4 |      12 |
| jarno63100   | Alfa Romeo |      14 |       3 |      12 |       6 |       9 |
| alainv-du-69 | Abarth     |      15 |       2 |      15 |       3 |       5 |
| shenron262   | Nissan     |       7 |      10 |       7 |      11 |      21 |

Candidate column headers:
Pts. Sprint
Pts. Enduro
Pts.
Pos. Sprint
Pos. Enduro
Pilote
Voiture



headers: Pts. Sprint
Pts. Enduro
Pts.
Pos. Sprint
Pos. Enduro
Pilote
Voiture
label: Pilote
Voiture
Pos. Sprint
Pts. Sprint
Pos. Enduro
Pts. Enduro
Pts.
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               |
|:----------------------------|:--------|:--------|:--------------------|:------------------|:--------------------|
| Enrichment                  | OUS     | No      | Portland Metro Area | 1 term (10 weeks) |                     |
| Enrichment                  | OUS     | No      | Portland Metro Area | 1 term (10 weeks  |                     |
| Enrichment                  | OUS     | No      | Portland Metro Area | 2 weeks           | 24/7 for 2 weeks    |
| Enrichment                  | OUS     | No      | Portland Metro Area | 1 term (10 weeks) |                     |
| Dropout Prevention/Recovery | CCWD    | Yes     | Statewide           | 6-8 weeks         | Up to 40 hours/week |

Candidate column headers:
Geographic Service Area
Program Category
Typical Time Intensity (e.g._ hours or days per week or month)
Agency
Typical Duration of Participation
Does this program serve tribal members? If it serves specific tribes_ please list.



headers: Geographic Service Area
Program Category
Typical Time Intensity (e.g._ hours or days per week or month)
Agency
Typical Duration of Participation
Does this program serve tribal members? If it serves specific tribes_ please list.
label: Program Category
Agency
Does this program serve tribal members? If it serves specific tribes_ please list.
Geographic Service Area
Typical Duration of Participation
Typical Time Intensity (e.g._ hours or days per week or month)
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             |
|:--------|--------:|:-----------|--------:|:--------|--------:|--------:|:-----------|:---------|:--------|:---------|:-------------------|
| ID0923  | 41056.9 | Rs. 144000 |  144000 | INR     |       4 | 2564.34 | Controller | India    | Asia    | India    | 1) Btw 0 - 5 Yrs   |
| ID0661  | 41055.5 | INR 165000 |  165000 | INR     |      11 | 2938.31 | Manager    | India    | Asia    | India    | 3) Btw 11 - 15 Yrs |
| ID1050  | 41057.6 | 194        |    2400 | USD     |      15 | 2400    | Accountant | Pakistan | Asia    | Pakistan | 3) Btw 11 - 15 Yrs |
| ID0659  | 41055.5 | 200000     |  200000 | PKR     |       2 | 2122.82 | Accountant | Pakistan | Asia    | Pakistan | 1) Btw 0 - 5 Yrs   |
| ID1154  | 41057.9 | 168000     |  168000 | PKR     |      10 | 1783.17 | Accountant | Pakistan | Asia    | Pakistan | 2) Btw 6 - 10 Yrs  |

Candidate column headers:
Unique ID
Timestamp
Years of Experience
Continent
clean Salary (in local currency)
Salary in USD
clean Country
Currency
Your Salary
Where do you work
Yrs of Exp. Range
Job Type



headers: Unique ID
Timestamp
Years of Experience
Continent
clean Salary (in local currency)
Salary in USD
clean Country
Currency
Your Salary
Where do you work
Yrs of Exp. Range
Job Type
label: Unique ID
Timestamp
Your Salary
clean Salary (in local currency)
Currency
Years of Experience
Salary in USD
Job Type
clean Country
Continent
Where do you work
Yrs of Exp. Range
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 |   Col_1 |   Col_2 |   Col_3 |   Col_4 |   Col_5 |   Col_6 | Col_7         |
|--------:|--------:|--------:|--------:|--------:|--------:|--------:|:--------------|
|     267 |    50   |    0    |   50    |       0 |   50    |   39889 | Truck 1 Ton   |
|      58 |    37.5 |    0    |   37.5  |       0 |   37.5  |   39898 | Truck/Tractor |
|     277 |   550   |  348.84 |  898.84 |       0 |  898.84 |   39891 | Truck/Tractor |
|      78 |    25   |    0    |   25    |       0 |   25    |   39890 | Truck 1 Ton   |
|     330 |  3637.5 |    0    | 3637.5  |       0 | 3637.5  |   39934 | Uniloader     |

Candidate column headers:
Sort by Equipment Type
Misc
Date Completed
Parts & Labor Total
All Charges Total
Equipment Num
Parts Total
Labor Total



headers: Sort by Equipment Type
Misc
Date Completed
Parts & Labor Total
All Charges Total
Equipment Num
Parts Total
Labor Total
label: Equipment Num
Labor Total
Parts Total
Parts & Labor Total
Misc
All Charges Total
Date Completed
Sort by Equipment 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 |
|------------:|------------:|--------:|----------:|
| 4.01176e+09 | 6.07615e+09 | 6076.15 | 0.0472074 |
| 2.30507e+09 | 3.57422e+09 | 3574.22 | 0.0499447 |
| 1.9212e+09  | 2.20018e+09 | 2200.18 | 0.0151796 |
| 1.90586e+09 | 3.27088e+09 | 3270.88 | 0.0618516 |
| 5.49591e+09 | 9.20114e+09 | 9201.14 | 0.0589292 |

Candidate column headers:
ASSET SIZE (M) 2013 (Constant $)
FY 2013
10 yr Annual Growth Rate 2004 to 2013
FY 2004



headers: ASSET SIZE (M) 2013 (Constant $)
FY 2013
10 yr Annual Growth Rate 2004 to 2013
FY 2004
label: FY 2004
FY 2013
ASSET SIZE (M) 2013 (Constant $)
10 yr Annual Growth Rate 2004 to 2013