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 |
|--------:|:--------|--------:|--------:|
|   42381 | Toto    |      65 |    2933 |
|   42385 | Tata    |      87 |    1253 |
|   42383 | Toto    |      68 |     238 |
|   42387 | Titi    |      23 |    1133 |
|   42389 | Toto    |     868 |     909 |

Candidate column headers:
Qui
ComulParDateEtQui
Date
Produit



headers: Qui
ComulParDateEtQui
Date
Produit
label: Date
Qui
Produit
ComulParDateEtQui
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 |
|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|
|   18752 |       0 |       4 |      11 |       0 |       0 |       0 |       1 |      45 |       0 |       62 |        1 |
|   20852 |       0 |       0 |      12 |       1 |       0 |       0 |       0 |       1 |       0 |       19 |        0 |
|   11186 |       0 |       0 |      12 |       0 |       0 |       0 |       0 |       0 |       0 |       18 |        0 |
|   28440 |       0 |       4 |      12 |       0 |       0 |       0 |       1 |      24 |       1 |       42 |        0 |
|   16796 |       0 |       4 |      12 |       0 |       0 |       0 |       1 |      14 |       1 |       32 |        0 |

Candidate column headers:
Industry
Marr
South
Ex
Fe
Union
Wage
Occupation
Ed
Age
Nonwh
Hisp



headers: Industry
Marr
South
Ex
Fe
Union
Wage
Occupation
Ed
Age
Nonwh
Hisp
label: Wage
Industry
Occupation
Ed
South
Nonwh
Hisp
Fe
Ex
Marr
Age
Union
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 |
|:------------|--------:|--------:|
| Belgium     |    25.4 |    26.1 |
| Portugal    |    27.2 |    29.1 |
| Sweden      |    15.8 |    16.6 |
| Italy       |    33.3 |    38.6 |
| Netherlands |    22.2 |    21.7 |

Candidate column headers:
2010
2011
Column1



headers: 2010
2011
Column1
label: Column1
2010
2011
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   |
|:--------------------|:--------------|:--------|
| Charles S. Billings | Pennsylvania  | Male    |
| Gary Lees           | New York      | Male    |
| Joseph Christensen  | Massachusetts | Male    |
| Janie Little        | Massachusetts | Female  |
| Hosea Pierson       | New York      | Male    |

Candidate column headers:
Employee
Sex
Location



headers: Employee
Sex
Location
label: Employee
Location
Sex
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 |
|:-------------------|:-----------|:-----------------|--------:|--------:|
| KENDELL L. KILBORN | EX12-20000 | DEC11 MLGE KK-JA |   40926 |   27.75 |
| ROBERT K. GOOD     | EX12-08791 | AUG11 MLGE BG-JA |   40805 |    3.33 |
| KENDELL L. KILBORN | EX12-08780 | AUG11 MLGE KK-JA |   40805 |   51.62 |
| ROBERT K. GOOD     | EX12-11825 | SEP11 MLGE BG-JA |   40835 |   42.46 |
| ROBERT K. GOOD     | EX12-14134 | OCT11 MLGE RG-JA |   40863 |    3.89 |

Candidate column headers:
Transaction Description
Effective
JE#
Vendor Name
Actual



headers: Transaction Description
Effective
JE#
Vendor Name
Actual
label: Vendor Name
JE#
Transaction Description
Effective
Actual
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   |
|:--------------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|
| Jackson       | AL      | 5.5     |         |         |         |         |         | 5.5     | No      |
| Fort Wayne    | IN      |         |         | 6.0     |         |         |         |         | No      |
| Abilene       | TX      |         |         | 5.5     |         | 6.0     |         |         | No      |
| Los Angeles   | CA      |         |         | 7.0     |         |         |         |         | No      |
| San Francisco | CA      |         |         | 6.0     |         | 6.0     |         |         | No      |

Candidate column headers:
Specific Graduate Programs that accept IELTS
Undergraduate Degree Minimum Score Requirement
Restrictions - Graduate
Restrictions - Undergraduate
State
Other Minimum Score Requirements
Graduate Degree Minimum Score Requirement
City
Associate Degree Minimum Score Requirement
Restrictions - Associate



headers: Specific Graduate Programs that accept IELTS
Undergraduate Degree Minimum Score Requirement
Restrictions - Graduate
Restrictions - Undergraduate
State
Other Minimum Score Requirements
Graduate Degree Minimum Score Requirement
City
Associate Degree Minimum Score Requirement
Restrictions - Associate
label: City
State
Associate Degree Minimum Score Requirement
Restrictions - Associate
Undergraduate Degree Minimum Score Requirement
Restrictions - Undergraduate
Graduate Degree Minimum Score Requirement
Restrictions - Graduate
Other Minimum Score Requirements
Specific Graduate Programs that accept IELTS
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 |
|--------:|--------:|--------:|--------:|--------:|
|   42892 |   42902 |   42912 |   42922 |   42934 |
|   42857 |   42867 |   42877 |   42887 |   42906 |
|   43060 |   43070 |   43080 |   43090 |   43102 |
|   42948 |   42958 |   42968 |   42985 |   42997 |
|   43025 |   43035 |   43045 |   43055 |   43074 |

Candidate column headers:
Town Council 
Submittal Date
Revisions Due Date
Planning & Zoning Commission
Staff Comments Due Date



headers: Town Council 
Submittal Date
Revisions Due Date
Planning & Zoning Commission
Staff Comments Due Date
label: Submittal Date
Staff Comments Due Date
Revisions Due Date
Planning & Zoning Commission
Town Council
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 |
|:--------|--------:|--------:|--------:|--------:|
| uid1    | 2013101 |       0 |       0 |       4 |
| uid1    | 2013114 |       1 |      13 |       4 |
| uid3    | 2013104 |       1 |       8 |       2 |
| uid3    | 2013101 |       0 |       0 |       2 |
| uid2    | 2013107 |       0 |       0 |       2 |

Candidate column headers:
UserID
Number of visits
Revenue
Transactions
Date



headers: UserID
Number of visits
Revenue
Transactions
Date
label: UserID
Date
Transactions
Revenue
Number of visits
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 |
|--------:|:----------------------|--------:|
|       7 | 7                     |       6 |
|       5 | 4                     |       5 |
|       9 | 8                     |       8 |
|       8 | Eerste toer 8_ rest 9 |       8 |
|       6 | 5                     |       5 |

Candidate column headers:
Royal
Conform patroon
Julia



headers: Royal
Conform patroon
Julia
label: Conform patroon
Royal
Julia
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 |
|--------:|--------:|--------:|--------:|--------:|--------:|
|   41910 |    1001 |      51 |    2074 |  371.92 |       9 |
|   41740 |    1001 |      53 |    2100 |  503.28 |       9 |
|   41839 |    1001 |      53 |    2004 |  645.79 |      10 |
|   41893 |    1001 |      53 |    2050 |  290.18 |       1 |
|   41942 |    1001 |      51 |    2089 |  629.41 |      12 |

Candidate column headers:
Item Count
Sale
Customer #
Salesperson #
Date
Store #



headers: Item Count
Sale
Customer #
Salesperson #
Date
Store #
label: Date
Store #
Salesperson #
Customer #
Sale
Item Count
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 |   Col_1 |   Col_2 |    Col_3 |       Col_4 |    Col_5 |       Col_6 |    Col_7 |
|--------:|--------:|--------:|---------:|------------:|---------:|------------:|---------:|
|    0.02 |    0.98 |     0.6 | 0.200003 | 4.18879e-06 | 0.999996 | 4.18879e-06 | 0.199999 |
|    0.24 |    0.76 |     0.6 | 0.205791 | 0.00723823  | 0.992762 | 0.00723823  | 0.198552 |
|    0.3  |    0.7  |     0.6 | 0.21131  | 0.0141372   | 0.985863 | 0.0141372   | 0.197173 |
|    0.19 |    0.81 |     0.6 | 0.202873 | 0.00359136  | 0.996409 | 0.00359136  | 0.199282 |
|    0.14 |    0.86 |     0.6 | 0.201149 | 0.00143676  | 0.998563 | 0.00143676  | 0.199713 |

Candidate column headers:
Costo B
Costo fissato
Lunghezza A
Lunghezza B
Volume A
Costo totale
Volume B
Costo A



headers: Costo B
Costo fissato
Lunghezza A
Lunghezza B
Volume A
Costo totale
Volume B
Costo A
label: Lunghezza A
Lunghezza B
Costo fissato
Costo totale
Volume A
Volume B
Costo A
Costo B
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   |
|:--------|--------:|:--------|:---------|:--------------------|:--------|:--------|:--------|:--------|:--------|:---------|:---------|:---------|
| Wed     |   41598 | 9:30am  | CPO      | Progression & Final | LSC     | LSC     | TK      | CPO     | KL/JP   | CPO      | Feb'14   | KL/JP    |
| Thu     |   41655 | 2pm     | CSE      | Progression & Final | Cyn     | tbc     | tbc     | KJ      | KJ      | KJ       | July'14  | CW       |
| Wed     |   41675 | 11am    | CSHS/CPO | Progression & Final | Llan    | M101a   | AP      | CPO     | KL      | CPO      | July'14  | KL       |
| Fri     |   41684 | 12pm    | CPO      | Progression & Final | Llan    | O1.56   | tbc     | CPO     | KL      | CPO      | July'14  | KL       |
| Fri     |   41551 | 1pm     | CPO      | Progression & Final | Llan    | M1.01b  | PW      | CPO     | GLC     | CPO      |          | KL/JP    |

Candidate column headers:
Date
Venue
Time
School
Notes Recorder
Site
Chair
A&A
NoRF/Conf Rep
Recorder
Day
Graduation
Board



headers: Date
Venue
Time
School
Notes Recorder
Site
Chair
A&A
NoRF/Conf Rep
Recorder
Day
Graduation
Board
label: Day
Date
Time
School
Board
Site
Venue
Chair
Notes Recorder
NoRF/Conf Rep
Recorder
Graduation
A&A
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   |
|:----------|--------:|:--------|
| 999-2013  |   41428 | junio   |
| 1011-2013 |   41429 | junio   |
| 1040-2013 |   41431 | junio   |
| 1017-2013 |   41429 | junio   |
| 1037-2013 |   41431 | junio   |

Candidate column headers:
fecha
mes
num resolucion



headers: fecha
mes
num resolucion
label: num resolucion
fecha
mes
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 |
|--------:|--------:|--------:|
|   98144 |  124307 |  222451 |
|  100376 |  121588 |  221964 |
|  171759 |  173872 |  345631 |
|   21927 |   28067 |   49994 |
|   98742 |  122857 |  221599 |

Candidate column headers:
Column3
Column1
Column2



headers: Column3
Column1
Column2
label: Column1
Column2
Column3
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 |
|--------:|--------:|--------:|
|   39072 | 33025.8 |   4.206 |
|   39029 | 33128.6 |   4.085 |
|   39016 | 32856.3 |   4.091 |
|   39023 | 33473.2 |   4.129 |
|   39049 | 31362.1 |   3.903 |

Candidate column headers:
Mkt Capitalization
Index
Date



headers: Mkt Capitalization
Index
Date
label: Date
Index
Mkt Capitalization
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   |
|:------------------|--------:|:--------|:--------|:--------|:--------|
| Cristobo_ Ivan    |       4 | No      | No      | No      |         |
| Bailey_ Swneke    |       2 | Yes     | No      | No      |         |
| Xin_ Xiaofeng     |      12 | No      | Yes     | Yes     | No      |
| Marcotte_ Richard |       8 | Yes     | Yes     | Yes     |         |
| Babu_ Mohan       |       1 | Yes     | No      | No      |         |

Candidate column headers:
Offered position
Met Selection Criteria
Name
Interviewed
CDN or PR
Applicant Name or Number



headers: Offered position
Met Selection Criteria
Name
Interviewed
CDN or PR
Applicant Name or Number
label: Name
Applicant Name or Number
CDN or PR
Met Selection Criteria
Interviewed
Offered position
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0     | Col_1       | Col_2   | Col_3   |   Col_4 |   Col_5 |   Col_6 |   Col_7 |   Col_8 |   Col_9 |   Col_10 |   Col_11 |   Col_12 |   Col_13 |   Col_14 |   Col_15 |   Col_16 |
|:----------|:------------|:--------|:--------|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
| NuTech    | 5N-800      | GT3K    | GT_ LL  |   215.3 |      12 | 222.687 | 207.404 |    17.2 |     0.2 |      0.3 |  673.631 |    226.9 |    229.6 |    211.6 |    199.2 |    211.4 |
| Epley     | E9830       | None    | None    |   202.1 |      48 | 213.221 | 191.094 |    16.8 |     0.6 |      0.7 |  637.796 |    223.7 |    213.1 |    202.8 |    192.7 |    177.7 |
| DEKALB    | DKC52-68RIB | VT2P    | RR2     |   215.1 |      13 | 221.548 | 204.887 |    18.2 |     1.7 |      0.7 |  659.194 |    229.8 |    230.9 |    204   |    203.6 |    207   |
| Cornelius | C324SS      | SSX     | LL_RR2  |   208.3 |      28 | 214.615 | 203.51  |    17.3 |     0   |      0.1 |  650.91  |    218.2 |    214.1 |    211.6 |    201.7 |    197.2 |
| Renk      | RK608DGVT2P | DGVT2P  | RR2     |   211   |      23 | 220.421 | 201.906 |    16.3 |     0.1 |      0.4 |  671.774 |    225.2 |    225.1 |    210.9 |    196.2 |    198.6 |

Candidate column headers:
Pocahontas
Herb
Yield Bu/A
Moist %
Root Lodg (%)
AGV $
NW Yield
NE Yield
Stalk Lodg (%)
Brand Name
Hybrid
Sheldon
Dist Yield Rank
Maynard
Package
Nashua
Corwith



headers: Pocahontas
Herb
Yield Bu/A
Moist %
Root Lodg (%)
AGV $
NW Yield
NE Yield
Stalk Lodg (%)
Brand Name
Hybrid
Sheldon
Dist Yield Rank
Maynard
Package
Nashua
Corwith
label: Brand Name
Hybrid
Package
Herb
Yield Bu/A
Dist Yield Rank
NW Yield
NE Yield
Moist %
Root Lodg (%)
Stalk Lodg (%)
AGV $
Sheldon
Pocahontas
Corwith
Nashua
Maynard
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 |
|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|-------------:|------------:|
|   41684 |  1.3693 |  139.46 |  0.8177 |  1.2222 |   8.823 |  1.5159 | 103.355 |  0.000461006 | -0.00767427 |
|   41701 |  1.3736 |  139.3  |  0.8242 |  1.2128 |   8.895 |  1.5391 | 103.647 | -0.00456615  | -0.00487007 |
|   41673 |  1.3523 |  136.85 |  0.8291 |  1.2189 |   8.854 |  1.5457 | 102.291 |  0.00187156  | -0.0178876  |
|   41660 |  1.3558 |  141.3  |  0.823  |  1.2346 |   8.786 |  1.5403 | 102.78  |  0.000639883 | -0.0131958  |
|   41708 |  1.3877 |  143.29 |  0.834  |  1.2181 |   8.843 |  1.5381 | 104.819 |  0.000624134 |  0.00638862 |

Candidate column headers:
Date
Pct. Change
EUR/CHF
EUR/USD
EUR/AUD
EUR/SEK
EUR/JPY
CTS EUR Index
YTD
EUR/GBP



headers: Date
Pct. Change
EUR/CHF
EUR/USD
EUR/AUD
EUR/SEK
EUR/JPY
CTS EUR Index
YTD
EUR/GBP
label: Date
EUR/USD
EUR/JPY
EUR/GBP
EUR/CHF
EUR/SEK
EUR/AUD
CTS EUR Index
Pct. Change
YTD
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   |
|--------:|:-------------|--------:|--------:|:--------|
|   41753 | Milestone 2  |     -10 |       0 | Reese   |
|   41850 | Milestone 9  |      20 |       0 | Jenny   |
|   41862 | Milestone 10 |     -15 |       0 | Reese   |
|   41820 | Milestone 7  |      15 |       0 | Paulo   |
|   41774 | Milestone 5  |      15 |       0 | Steve   |

Candidate column headers:
Assigned To
Baseline
Date
Position
Milestone



headers: Assigned To
Baseline
Date
Position
Milestone
label: Date
Milestone
Position
Baseline
Assigned To
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   |
|:-------------------|:--------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|:---------|
| Michael Pennington | Rifle   |   48.03 |   48.45 |   30.07 |      40 |   62.36 |  228.91 |      50 |       1 | 1.0      |
| Glen Sauln         | Rifle   |   55.02 |   45.69 |   38.11 |      40 |   55.27 |  234.09 |      15 |       2 |          |
| Matt Ferrell       | Rifle   |   43.31 |   25.77 |   16.76 |      37 |   46.32 |  169.16 |      25 |       1 |          |
| Dakota Watson      | Rifle   |   55.41 |   36.48 |   23.71 |      40 |   30.78 |  186.38 |      25 |       1 | 1.0      |
| Michael Watson     | Rifle   |   70.86 |   49.96 |   44.32 |      40 |   47.02 |  252.16 |      75 |       2 |          |

Candidate column headers:
Points Down
Hit Non Threat
Stage 2
Stage 4
Procedurals
Stage 1
Total Score
Stage 3
Class
Stage 5
Name 



headers: Points Down
Hit Non Threat
Stage 2
Stage 4
Procedurals
Stage 1
Total Score
Stage 3
Class
Stage 5
Name 
label: Name 
Class
Stage 1
Stage 2
Stage 3
Stage 4
Stage 5
Total Score
Points Down
Hit Non Threat
Procedurals
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   |
|:-------------|--------:|--------:|:--------|
| Bank Deposit |   41519 |   19055 | Sep     |
| Equity       |   41434 |    7292 | Jun     |
| Equity       |   41392 |   15000 | Apr     |
| Bank Deposit |   41355 |   15000 | Mar     |
| Equity       |   41415 |    5606 | May     |

Candidate column headers:
Investment
Amount
Date
Month



headers: Investment
Amount
Date
Month
label: Investment
Date
Amount
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                 |
|:-----------------------------|:--------|:----------------------|
| Developmental Delay          | 2560    | 0.29561200923787528   |
| Specific Learning Disability | NR      | NR                    |
| Multiple Disabilities        | 75      | 8.6605080831408769E-3 |
| Hearing Impairment           | 64      | 7.3903002309468821E-3 |
| Autism                       | 371     | 4.2840646651270207E-2 |

Candidate column headers:
Disability Category 
Percentage
Student Count



headers: Disability Category 
Percentage
Student Count
label: Disability Category 
Student Count
Percentage
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 |
|:-------------------------|--------:|--------:|
| Onchidoris bilamellata   |   41558 |       1 |
| Hermissenda crassicornis |   41562 |      35 |
| Diaulula sandiegensis    |   41551 |       0 |
| Aeolidia papillosa       |   41544 |       7 |
| Flabellina Japonica      |   41551 |       0 |

Candidate column headers:
Date
Species
Value



headers: Date
Species
Value
label: Species
Date
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 |
|:--------|:------------|:------------------|:------------|:--------|:--------|--------:|--------:|
|         |             |                   |             |         |         |       0 |    0    |
| 42864.0 | Store 2134  | Home Office       | BA Training | 36103.0 | 36108.0 |       5 |    2.65 |
|         |             |                   |             |         |         |       0 |    0    |
| 42864.0 | Home Office | Northwind Traders | Store Visit | 36098.0 | 36103.0 |       5 |    2.65 |
|         |             |                   |             |         |         |       0 |    0    |

Candidate column headers:
Description/Notes
Mileage
Destination
Date
Starting Location
Odometer Start
Odometer End
Reimbursement



headers: Description/Notes
Mileage
Destination
Date
Starting Location
Odometer Start
Odometer End
Reimbursement
label: Date
Starting Location
Destination
Description/Notes
Odometer Start
Odometer End
Mileage
Reimbursement
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 |   Col_1 |   Col_2 |   Col_3 |   Col_4 |
|--------:|--------:|--------:|--------:|--------:|
|       4 |       5 |      20 |      40 |  25.6   |
|      18 |       5 |      20 |      24 | 839.808 |
|      16 |       5 |      20 |       8 |  65.536 |
|      18 |       5 |      20 |      16 | 373.248 |
|       4 |       5 |      20 |       8 |   1.024 |

Candidate column headers:
h
e
F
d
g



headers: h
e
F
d
g
label: d
e
g
h
F
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               |
|--------:|:---------------------|:--------------------|
|      21 | Elli Howard          | Versions last Dance |
|      95 | Garret Thompson      | KC country cash     |
|     332 | Carly Van Slembrouck | Pine Skipper        |
|      57 | Jenny Clark          | Unforgetable Dream  |
|      22 | Emily Pardue         | Top Hats & Roses    |

Candidate column headers:
RiderName
BackNo
HorseName



headers: RiderName
BackNo
HorseName
label: BackNo
RiderName
HorseName
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   |
|:---------|:--------|:--------|:--------|:-------------------|:------------------------|---------:|:--------|
| Oct. 25  | KIL     | Pirates | VAFC    | Tottenham Hotspurs | Bobolink South East     | 0.458333 | Yassine |
| Oct. 4   | KIL     | Pirates | VAFC    | Real Madrid        | Bobolink South East     | 0.458333 | Anthony |
| Nov. 15  | KIL     | Pirates | KIL     | Sharks             | Bobolink South East     | 0.5      | Marcus  |
| Sept. 27 | KLM     | Dynamos | KIL     | Pirates            | KENSINGTON NORTH (EAST) | 0.375    | Rylan   |
| Nov. 8   | ICSF    | 05Roma  | KIL     | Pirates            | Beaconsfield SE         | 0.5      | Jaskarn |

Candidate column headers:
Home Team Name
Home League
Field
Away Team Name
Away League
Time
Orange Duty
Date



headers: Home Team Name
Home League
Field
Away Team Name
Away League
Time
Orange Duty
Date
label: Date
Home League
Home Team Name
Away League
Away Team Name
Field
Time
Orange Duty
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           |
|:--------|:---------|:--------|--------:|:--------|--------:|---------:|:----------|--------:|---------:|---------:|---------:|---------:|:---------|:----------|:-----------------|
| us      | 2012gnam | ?       |     196 | W       | 6235.03 | 0.645509 | Wednesday | 41192.6 |  -6.482  | 153.72   |      4.8 |    36.3  | ?        | Panguna   | Papua New Guinea |
| pr      | 12284000 | ?       |      15 | SSW     | 3712.82 | 0.967245 | Wednesday | 41193   |  17.8565 | -66.7549 |      2.5 |     6    | ?        | Tallaboa  | Puerto Rico      |
| us      | c000d4st | ?       |     119 | SSE     | 7317.66 | 0.77419  | Tuesday   | 41191.8 |  -4.4749 | 129.245  |      4.7 |    51.98 | ?        | Saparua   | Indonesia        |
| us      | b000d3w8 | ?       |      27 | WNW     | 7267.28 | 0.705961 | Wednesday | 41192.7 |  29.329  |  52.511  |      5.2 |    21.2  | ?        | Akbarabad | Iran             |
| us      | b000d4cj | ?       |     116 | SE      | 6075.64 | 0.128495 | Thursday  | 41193.1 | -10.0159 | 160.837  |      4.8 |    27.21 | ?        | Honiara   | Solomon Islands  |

Candidate column headers:
Version
Direction
Location
Lat
Distance in Miles to POI
Lon
Weekday
Distance
Date
Magnitude
NST
Src
Time
EqId
Area
Depth



headers: Version
Direction
Location
Lat
Distance in Miles to POI
Lon
Weekday
Distance
Date
Magnitude
NST
Src
Time
EqId
Area
Depth
label: Src
EqId
Version
Distance
Direction
Distance in Miles to POI
Time
Weekday
Date
Lat
Lon
Magnitude
Depth
NST
Location
Area
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0   | Col_1   |   Col_2 |   Col_3 |   Col_4 |
|:--------|:--------|--------:|--------:|--------:|
|         |         |       1 |       1 |       1 |
|         |         |       1 |       1 |       1 |
| 1.0     | 1.0     |       1 |       1 |       1 |
| 1.0     | 1.0     |       1 |       1 |       1 |
|         |         |       1 |       1 |       1 |

Candidate column headers:
Thurs.
Fri.
Mon.
Wed.
Tues.



headers: Thurs.
Fri.
Mon.
Wed.
Tues.
label: Mon.
Tues.
Wed.
Thurs.
Fri.
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 |
|:--------|--------:|--------:|--------:|--------:|-----------:|
| Greta   |    1978 |       4 |     947 |       5 |   26000000 |
| Fabian  |    2003 |       4 |     939 |       4 |  300000000 |
| Flora   |    1963 |       4 |     940 |    7193 |  529000000 |
| Cleo    |    1964 |       4 |     950 |     217 |  198000000 |
| Hugo    |    1989 |       5 |     918 |      56 | 8500000000 |

Candidate column headers:
Damage (USD)
Name
Year
Peak Classification
Pressure (hPa)
Deaths



headers: Damage (USD)
Name
Year
Peak Classification
Pressure (hPa)
Deaths
label: Name
Year
Peak Classification
Pressure (hPa)
Deaths
Damage (USD)
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     |
|:---------------|------------:|:----------------|:--------|--------:|:--------|:----------|
| Urasawa_ Naoki | 9.78159e+12 | VIZ Media LLC   | Manga   |       1 | 16+     | Oversized |
| Mukai_ Natsumi | 9.7816e+12  | TokyoPop        | Manga   |       2 | 13+     |           |
| Kawahara_ Reki | 9.78032e+12 | Yen On          | Novel   |       1 | 13+     | Oversized |
| Mukai_ Natsumi | 9.7816e+12  | TokyoPop        | Manga   |      10 | 13+     |           |
| Kuroda_ Kenji  | 9.78194e+12 | Kodansha Comics | Manga   |       1 | 13+     |           |

Candidate column headers:
CATEGORY1
VOLUME
ISBN
TYPE
AUTHOR
PUBLISHER
AGE



headers: CATEGORY1
VOLUME
ISBN
TYPE
AUTHOR
PUBLISHER
AGE
label: AUTHOR
ISBN
PUBLISHER
CATEGORY1
VOLUME
AGE
TYPE
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0    | Col_1     | Col_2   | Col_3              | Col_4   | Col_5   |   Col_6 |   Col_7 | Col_8    |   Col_9 |
|:---------|:----------|:--------|:-------------------|:--------|:--------|--------:|--------:|:---------|--------:|
| Corradi  | Chiara    |         | DOCENZA            |         |         |   42675 |   42916 |          |    1500 |
| Catanese | Antonino  |         | DOCENZE            |         |         |   42436 |   42436 |          |     100 |
| Paoli    | Donatella | F       | DOCENZE            | NO      |         |   42370 |   42735 | Presunto |    5000 |
| Vinante  | Lorenzo   | M       | DOCENTE            | NO      |         |   42385 |   42389 | Previsto |     700 |
| Plattner | Brigitte  |         | CONSULENZA TECNICA |         |         |   42517 |   42517 |          |     165 |

Candidate column headers:
Data fine
Cognome
Oggetto dell'incarico
Data fine2
Importo (euro) previsto presunto
Nome
Data inizio
Importo (euro) previsto/presunto
Doveri d'ufficio
Genere



headers: Data fine
Cognome
Oggetto dell'incarico
Data fine2
Importo (euro) previsto presunto
Nome
Data inizio
Importo (euro) previsto/presunto
Doveri d'ufficio
Genere
label: Cognome
Nome
Genere
Oggetto dell'incarico
Doveri d'ufficio
Importo (euro) previsto presunto
Data inizio
Data fine
Data fine2
Importo (euro) previsto/presunto
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 |
|--------:|:----------------------|:----------|--------:|:--------|:--------|--------:|:--------------|:--------------|--------:|---------:|:--------------|---------:|:-------------------------------|---------:|---------:|---------:|---------:|
|     768 | Jobs to housing ratio | 2007-2011 |       9 | Total   | CO      |    6077 | San Joaquin   | San Joaquin   |    6077 |        1 | Type of ratio |        1 | LowWageJobstoAffordableHousing |    52474 |    35601 |  1.47395 |  1.40663 |
|     768 | Jobs to housing ratio | 2007-2011 |       9 | Total   | CO      |    6039 | Madera        | Madera        |    6039 |        1 | Type of ratio |        1 | LowWageJobstoAffordableHousing |    12171 |     9346 |  1.30227 |  1.17672 |
|     768 | Jobs to housing ratio | 2007-2011 |       9 | Total   | CO      |    6069 | San Benito    | San Benito    |    6069 |        1 | Type of ratio |        1 | LowWageJobstoAffordableHousing |     3241 |     1518 |  2.13505 |  1.38724 |
|     768 | Jobs to housing ratio | 2007-2011 |       9 | Total   | CO      |    6083 | Santa Barbara | Santa Barbara |    6083 |        1 | Type of ratio |        1 | LowWageJobstoAffordableHousing |    42718 |    13899 |  3.07346 |  2.84198 |
|     768 | Jobs to housing ratio | 2007-2011 |       9 | Total   | CO      |    6025 | Imperial      | Imperial      |    6025 |        1 | Type of ratio |        1 | LowWageJobstoAffordableHousing |    24104 |    17462 |  1.38037 |  1.28602 |

Candidate column headers:
race_eth_name
strata_name
housing
ind_id
reportyear
geotype
strata_level_name
ind_definition
ll_95ci
geoname
county_fips
county_name
ratio
geotypevalue
strata_name_code
race_eth_code
jobs
strata_level_name_code



headers: race_eth_name
strata_name
housing
ind_id
reportyear
geotype
strata_level_name
ind_definition
ll_95ci
geoname
county_fips
county_name
ratio
geotypevalue
strata_name_code
race_eth_code
jobs
strata_level_name_code
label: ind_id
ind_definition
reportyear
race_eth_code
race_eth_name
geotype
geotypevalue
geoname
county_name
county_fips
strata_name_code
strata_name
strata_level_name_code
strata_level_name
jobs
housing
ratio
ll_95ci
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 |
|--------:|:----------|:--------|:--------|--------:|--------:|----------:|
|      19 | Bergeyova | Lucie   | F       |      44 |     218 | 0.0148275 |
|      10 | Smith     | Mikaela | F       |      21 |     479 | 0.0143287 |
|      20 | Bessette  | Chris   | M       |      36 |     592 | 0.0148356 |
|      12 | Schoenly  | Keith   | M       |      44 |     466 | 0.0144734 |
|      43 | Saul      | Jason   | M       |      47 |     461 | 0.0165637 |

Candidate column headers:
Time
Last Name
Bib # 
Age
Sex
First Name
Place



headers: Time
Last Name
Bib # 
Age
Sex
First Name
Place
label: Place
Last Name
First Name
Sex
Age
Bib # 
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 |
|----------:|--------:|--------:|
| 0.048206  | 38.9186 | 46.5687 |
| 0.046169  | 31.157  | 46.0891 |
| 0.0460764 | 42.9771 | 48.3487 |
| 0.04625   | 38.7968 | 51.9945 |
| 0.0462963 | 42.3304 | 50.7337 |

Candidate column headers:
ProcessorTime
TotalRunTime
Timestamp



headers: ProcessorTime
TotalRunTime
Timestamp
label: Timestamp
ProcessorTime
TotalRunTime