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 |
|:------------|--------:|--------:|--------:|--------:|
| Bangladesh  |      29 |      48 |     440 |      55 |
| Bahrain     |      29 |      48 |     440 |      55 |
| El Salvador |      29 |      48 |     440 |      55 |
| Cape Verde  |      29 |      48 |     440 |      55 |
| Botswana    |      29 |      48 |     440 |      55 |

Candidate column headers:
B4.3
Country
B4.1
B4.2
B4.4



headers: B4.3
Country
B4.1
B4.2
B4.4
label: Country
B4.1
B4.2
B4.3
B4.4
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 |
|:---------------------|--------:|--------:|--------:|--------:|:--------|:--------|:--------|--------:|--------:|
| Massachusetts        |    3338 |    6683 |    9787 |   19808 | -       | -       | -       |    9.05 |   38478 |
| District of Columbia |     128 |     270 |     386 |     784 | 137     | 0       | 0       |    2.95 |    1375 |
| Connecticut          |     490 |    1353 |    2883 |    4726 | -       | -       | -       |    4.27 |    9509 |
| Alaska               |     217 |     294 |     360 |     871 | -       | -       | -       |    2.59 |    1793 |
| Mississippi          |     216 |     594 |    1156 |    1966 | -       | -       | -       |    1.72 |    3533 |

Candidate column headers:
Number served birth through 2 years
Number served 3 years to 4 years
Number served 5 years or older
Number served 2 years to 3 years
Percentage of population1_  birth through 2 years (%)
Number served birth to 1 year
Number served 4 years to 5 years
Number served 1 year to 2 years
State
Birth through 2 years (cumulative count) - Race Ethnicity Total



headers: Number served birth through 2 years
Number served 3 years to 4 years
Number served 5 years or older
Number served 2 years to 3 years
Percentage of population1_  birth through 2 years (%)
Number served birth to 1 year
Number served 4 years to 5 years
Number served 1 year to 2 years
State
Birth through 2 years (cumulative count) - Race Ethnicity Total
label: State
Number served birth to 1 year
Number served 1 year to 2 years
Number served 2 years to 3 years
Number served birth through 2 years
Number served 3 years to 4 years
Number served 4 years to 5 years
Number served 5 years or older
Percentage of population1_  birth through 2 years (%)
Birth through 2 years (cumulative count) - Race Ethnicity Total
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 |   Col_1 | Col_2   |   Col_3 | Col_4     | Col_5   | Col_6          | Col_7                        | Col_8   | Col_9   |   Col_10 | Col_11        |   Col_12 |   Col_13 |   Col_14 |   Col_15 |   Col_16 | Col_17   |
|--------:|--------:|:--------|--------:|:----------|:--------|:---------------|:-----------------------------|:--------|:--------|---------:|:--------------|---------:|---------:|---------:|---------:|---------:|:---------|
|    2014 |       1 | C019    |     105 | 01C019105 | Adair   | Peavine        | Peavine Public School        |         | PK      |        8 | Middle School |       50 |       25 |       25 |        8 |       50 | F        |
|    2014 |       1 | I004    |     705 | 01I004705 | Adair   | Watts          | Watts HS                     |         | 9       |       12 | High School   |       50 |       50 |        0 |        7 |       61 | D-       |
|    2014 |       5 | I006    |     115 | 05I006115 | Beckham | Elk City       | Grandview 5th/6th Grade Ctr  |         | 5       |        6 | Elementary    |       50 |       25 |       25 |       10 |       75 | C        |
|    2014 |       1 | C024    |     105 | 01C024105 | Adair   | Rocky Mountain | Rocky Mountain Public School |         | PK      |        8 | Middle School |       50 |       25 |       25 |        8 |       87 | B+       |
|    2014 |       4 | I128    |     105 | 04I128105 | Beaver  | Turpin         | Turpin ES                    |         | PK      |        8 | Middle School |       50 |       25 |       25 |        8 |       68 | D+       |

Candidate column headers:
LowGrade
SiteCode
BottomGrowthMultiplier
PerformanceMultiplier
CountyCode
SchoolYear
DistrictCode
FeederFullCode
OverallSchoolGrade
CountyName
HighGrade
TotalBonusPoints
SchoolName
SchoolLetterGrade
ReportCardType
FullCode
GrowthMultiplier
DistrictName



headers: LowGrade
SiteCode
BottomGrowthMultiplier
PerformanceMultiplier
CountyCode
SchoolYear
DistrictCode
FeederFullCode
OverallSchoolGrade
CountyName
HighGrade
TotalBonusPoints
SchoolName
SchoolLetterGrade
ReportCardType
FullCode
GrowthMultiplier
DistrictName
label: SchoolYear
CountyCode
DistrictCode
SiteCode
FullCode
CountyName
DistrictName
SchoolName
FeederFullCode
LowGrade
HighGrade
ReportCardType
PerformanceMultiplier
GrowthMultiplier
BottomGrowthMultiplier
TotalBonusPoints
OverallSchoolGrade
SchoolLetterGrade
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 |
|--------:|:---------------------|:--------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
|   80850 | MCKINLAY Jb          | ALPIN   |       3 |     255 |      44 |     159 |     999 |       0 |     999 |        0 |      999 |        0 |       38 |       22 |       23 |       30 |       30 |       13 |      100 |
|   75257 | MACDONALD Liam       | GEORG   |       2 |     364 |     100 |      76 |      24 |      41 |      17 |       70 |      999 |        0 |       24 |       41 |       13 |       35 |       25 |      999 |        0 |
|   75130 | KAPPELE Reid         | CRAIG   |       2 |     513 |     120 |     196 |      17 |      70 |     999 |        0 |      999 |        0 |       22 |       47 |       11 |       24 |       41 |       20 |       55 |
|   89372 | DUHAMEL Leandre      | FORTU   |       2 |     850 |      80 |     375 |     999 |       0 |       9 |      145 |      999 |        0 |      999 |        0 |       15 |       18 |       65 |        7 |      180 |
|   80930 | SILLS SCHVARCZ Jamie | MSFLD   |       3 |     342 |       0 |     189 |     999 |       0 |      32 |       28 |       20 |       55 |       25 |       38 |      999 |        7 |      180 |       51 |        9 |

Candidate column headers:
pos0132
Total
pos0193
pos0135
Card
pts0193
Provincial
YOB
pos0134
pos0136
pts0133
pos0192
pts0135
pts0136
pts0132
pos0133
pts0192
pts0134
Name
Club



headers: pos0132
Total
pos0193
pos0135
Card
pts0193
Provincial
YOB
pos0134
pos0136
pts0133
pos0192
pts0135
pts0136
pts0132
pos0133
pts0192
pts0134
Name
Club
label: Card
Name
YOB
Club
Total
pts0136
Provincial
pos0132
pts0132
pos0133
pts0133
pos0134
pts0134
pos0135
pts0135
pos0136
pos0192
pts0192
pos0193
pts0193
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 |
|--------:|--------:|--------:|--------:|--------:|
|       0 |       0 |       0 |       0 |       0 |
|       0 |       0 |       0 |       0 |       0 |
|       0 |       0 |       0 |       0 |       0 |
|       0 |       0 |       0 |       0 |       0 |
|       0 |       0 |       0 |       0 |       0 |

Candidate column headers:
Hours Day 3
Name2
Miles Calculation
Hours Day 2
Hours Day 1



headers: Hours Day 3
Name2
Miles Calculation
Hours Day 2
Hours Day 1
label: Hours Day 1
Hours Day 2
Hours Day 3
Miles Calculation
Name2
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       |
|--------:|--------:|:------------|
|    6228 |  100300 | 148728470.0 |
|    5725 |  800475 |             |
|    6514 |  801137 | 827521337.0 |
|    4734 |  803945 | 604792791.0 |
|    6270 |  802999 | 189049187.0 |

Candidate column headers:
DART GID
Passkey Entity Prop
Entity Common Code



headers: DART GID
Passkey Entity Prop
Entity Common Code
label: DART GID
Passkey Entity Prop
Entity Common Code
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 | Col_1      |   Col_2 |
|--------:|:-----------|--------:|
|   40359 | Fort Worth |       2 |
|   40299 | Fort Worth |       8 |
|   40213 | Detroit    |       9 |
|   40379 | Newark     |       3 |
|   40344 | Lexington  |       5 |

Candidate column headers:
Tix
Date
Dest



headers: Tix
Date
Dest
label: Date
Dest
Tix
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 |
|--------:|--------:|--------:|--------:|----------:|--------:|
|    1765 |     105 |     180 |    2055 | 0.139434  |     149 |
|     380 |     180 |       0 |     560 | 0.319842  |     116 |
|    1035 |     390 |     945 |    2370 | 0.56389   |      41 |
|     680 |      60 |       0 |     740 | 0.0801853 |     155 |
|     585 |     415 |     685 |    1685 | 0.65302   |      19 |

Candidate column headers:
Total staff
Insecurity ranking
Open-ended/Permanent
% of staff insecure (fixed-term + atypical)
Atypical 
Fixed-term



headers: Total staff
Insecurity ranking
Open-ended/Permanent
% of staff insecure (fixed-term + atypical)
Atypical 
Fixed-term
label: Open-ended/Permanent
Fixed-term
Atypical 
Total staff
% of staff insecure (fixed-term + atypical)
Insecurity ranking
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    |
|--------:|:------------|:---------------|:---------|
|   20618 | Bushwood    | Saint Marys    | View Map |
|   20606 | Abell       | Saint Marys    | View Map |
|   20625 | Cobb Island | Charles        | View Map |
|   20660 | Morganza    | Saint Marys    | View Map |
|   20608 | Aquasco     | Prince Georges | View Map |

Candidate column headers:
City
County
Zip Code Map
Zip Code



headers: City
County
Zip Code Map
Zip Code
label: Zip Code
City
County
Zip Code Map
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 |
|--------:|:--------|:--------|:--------|:--------------------|:---------------|:------------------|:------------------------|:--------|--------:|---------:|
|  231161 | RP      | N       | Ruth    | Ruth Owen           | Owen           | 978-1-910549-75-9 | Ruby Tuesday Books      | LY      |     0.5 |      4.1 |
|  230964 | RP      | N       | Angela  | Angela Royston      | Royston        | 978-1-4062-8739-4 | Raintree                | LY      |     0.5 |      4.3 |
|  230926 | RP      | F       | Lauren  | Lauren St John      | St John        | 978-1-4440-0806-7 | Orion Books Ltd         | LY      |     0.5 |      3.4 |
|  229552 | RP      | F       | Paul    | Paul Shipton        | Shipton        | 978-0-19-839051-0 | Oxford University Press | LY      |     0.5 |      2.3 |
|  230923 | RP      | F       | Adam    | Adam Reis-Frankfort | Reis-Frankfort | 978-1-78369-281-1 | Phonic Books            | LY      |     0.5 |      3.6 |

Candidate column headers:
AuthorLN
Quiz #
ISBN
Interest Level
Pts
Author
Quiz Type
Book Level
Publisher
AuthorFN
Fiction



headers: AuthorLN
Quiz #
ISBN
Interest Level
Pts
Author
Quiz Type
Book Level
Publisher
AuthorFN
Fiction
label: Quiz #
Quiz Type
Fiction
AuthorFN
Author
AuthorLN
ISBN
Publisher
Interest Level
Pts
Book Level
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 |   Col_1 | Col_2       | 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 |
|--------:|--------:|:------------|:-----------|--------:|:-----------|:-----------|--------:|--------:|:----------|---------:|---------:|---------:|:----------|---------:|---------:|---------:|
|    2013 |       1 | Pre Monsoon | Magway     |       1 | Dry Zone   | Magway     |       1 |    36   | Salin     |       16 |        1 |        3 | MMR009010 |        1 |       -1 |       -1 |
|    2013 |       1 | Pre Monsoon | Chin       |       1 | S Chin     | Chin       |       3 |    69   | Kanpetlet |       62 |        3 |        0 | MMR004008 |        1 |        2 |       -1 |
|    2013 |       1 | Pre Monsoon | Magway     |      -1 | Dry Zone   | Magway     |       1 |    38   | Pwintbyu  |       12 |        1 |        4 | MMR009008 |        1 |       -1 |       -1 |
|    2013 |       1 | Pre Monsoon | Kayah      |       3 | Kayh       | Kayah      |       4 |   131.5 | Hpruso    |       50 |        3 |        0 | MMR002003 |        1 |        2 |        1 |
|    2013 |       1 | Pre Monsoon | Ayeyarwady |      -1 | Ayeyarwady | Ayeyarwady |       1 |    18   | Ngapudaw  |        3 |        1 |        0 | MMR017004 |        1 |       -1 |       -1 |

Candidate column headers:
Crop Prod Classif
Year
State_region
Hunger mean  (%of HH)
PP_ classif
Diet_ Inadequacy classif
PP_ median  (% of income)
TSPName
PA Name
Market  stock  classif
RD Code
Round  Name
Diet_Inadequacy   (%of hh) mean
Acute malnutrition classif
TSPCode
Hunger classif
PA Name2



headers: Crop Prod Classif
Year
State_region
Hunger mean  (%of HH)
PP_ classif
Diet_ Inadequacy classif
PP_ median  (% of income)
TSPName
PA Name
Market  stock  classif
RD Code
Round  Name
Diet_Inadequacy   (%of hh) mean
Acute malnutrition classif
TSPCode
Hunger classif
PA Name2
label: Year
RD Code
Round  Name
PA Name
Market  stock  classif
PA Name2
State_region
PP_ classif
PP_ median  (% of income)
TSPName
Diet_Inadequacy   (%of hh) mean
Diet_ Inadequacy classif
Hunger mean  (%of HH)
TSPCode
Hunger classif
Crop Prod Classif
Acute malnutrition classif
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 |
|:---------------|:----------|:---------|:----------|:--------|--------:|:--------|--------:|--------:|--------:|---------:|:---------|---------:|
| John Spriggs   | Gentlemen | Recurve  | Frostbite | Outdoor |   42771 | Club    |     234 |      36 |       0 |        4 | -        |       57 |
| Chris Hutchins | Gentlemen | Recurve  | Frostbite | Outdoor |   42743 | Club    |     298 |      36 |       0 |        5 | -        |       41 |
| Andrew Mushens | Gentlemen | Compound | Frostbite | Outdoor |   42771 | Club    |     327 |      36 |       0 |        7 | -        |       31 |
| David Button   | Gentlemen | Recurve  | Frostbite | Outdoor |   42771 | Club    |     228 |      36 |       0 |        2 | -        |       57 |
| David Short    | Gentlemen | Recurve  | Frostbite | Outdoor |   42743 | Club    |     288 |      36 |       0 |        4 | -        |       45 |

Candidate column headers:
Classification
Score
Golds
Type
Class
Category
Tens
Handicap
Shot At
Hits
Date Shot
Round
Member



headers: Classification
Score
Golds
Type
Class
Category
Tens
Handicap
Shot At
Hits
Date Shot
Round
Member
label: Member
Category
Class
Round
Type
Date Shot
Shot At
Score
Hits
Golds
Tens
Classification
Handicap
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 |
|:-----------------|--------:|:------------------|:----------------------|---------:|
| Carl Crawford    |      32 | Left field        | Los Angeles Dodgers   | 20250000 |
| Justin Verlander |      31 | Starting pitcher  | Detroit Tigers        | 20000000 |
| Adam Dunn        |      34 | Designated hitter | Chicago White Sox     | 15000000 |
| Andre Ethier     |      32 | Right field       | Los Angeles Dodgers   | 15500000 |
| Cliff Lee        |      35 | Starting pitcher  | Philadelphia Phillies | 25000000 |

Candidate column headers:
Teams
Salary
Positions 
 Age
Players



headers: Teams
Salary
Positions 
 Age
Players
label: Players
 Age
Positions 
Teams
Salary
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 |
|:------------------|:--------|--------:|--------:|--------:|--------:|--------:|--------:|
| Republic of Congo | COG     |      20 |      23 |      -3 |     159 |     146 |     -13 |
| Greece            | GRC     |      44 |      46 |      -2 |      69 |      58 |     -11 |
| Qatar             | QAT     |      61 |      71 |     -10 |      31 |      22 |      -9 |
| Comoros           | COM     |      24 |      26 |      -2 |     153 |     136 |     -17 |
| Saudi Arabia      | SAU     |      46 |      52 |      -6 |      62 |      48 |     -14 |

Candidate column headers:
Country
Rank difference (2015-2016)
CPI Score difference (2016-2015)
CPI2016
WB Code
CPI 2015 Rank
CPI 2016 Rank
CPI 2015



headers: Country
Rank difference (2015-2016)
CPI Score difference (2016-2015)
CPI2016
WB Code
CPI 2015 Rank
CPI 2016 Rank
CPI 2015
label: Country
WB Code
CPI2016
CPI 2015
CPI Score difference (2016-2015)
CPI 2016 Rank
CPI 2015 Rank
Rank difference (2015-2016)
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 |   Col_1 | Col_2             | Col_3   | Col_4        | Col_5      | Col_6       | Col_7   |   Col_8 |
|--------:|--------:|:------------------|:--------|:-------------|:-----------|:------------|:--------|--------:|
|       0 |       0 | 8.006488983742246 | 2.0     | CMAMBlk_01.d | Amp        | Blank       |         | 2.1005  |
|       4 |       3 |                   |         | CMAMCal_L4.d | Cocaine-d3 | Calibration | L4      | 2.44792 |
|       2 |       6 | 5.665168549358823 | 2.0     | CMAMCal_L2.d | Meth       | Calibration | L2      | 2.23721 |
|       6 |       0 | 8.006488983742246 | 2.0     | CMAMQC_L2.d  | Amp        | CC          | L2      | 2.1005  |
|       1 |       3 |                   |         | CMAMCal_L1.d | Cocaine-d3 | Calibration | L1      | 2.44792 |

Candidate column headers:
Max CC %Dev
Level
Sample Type
CompoundID
Data File
Avg RF
SampleID
RetentionTime
Cmpd Name



headers: Max CC %Dev
Level
Sample Type
CompoundID
Data File
Avg RF
SampleID
RetentionTime
Cmpd Name
label: SampleID
CompoundID
Avg RF
Max CC %Dev
Data File
Cmpd Name
Sample Type
Level
RetentionTime
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 |
|:--------|--------:|--------:|--------:|--------:|
| CHN     |      26 |      18 |      26 |      70 |
| CRO     |       5 |       3 |       2 |      10 |
| CUB     |       5 |       2 |       4 |      11 |
| RSA     |       2 |       6 |       2 |      10 |
| JAM     |       6 |       3 |       2 |      11 |

Candidate column headers:
BRONCE
TOTAL
PLATA
PAIS
ORO



headers: BRONCE
TOTAL
PLATA
PAIS
ORO
label: PAIS
ORO
PLATA
BRONCE
TOTAL
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 | Col_1   | Col_2   |   Col_3 |   Col_4 |   Col_5 |   Col_6 |
|--------:|:--------|:--------|--------:|--------:|--------:|--------:|
|   10079 | CFR     | EA      |    6.35 |       0 | 22.2764 | 22.2764 |
|   10040 | CFR     | CS      |   14.22 |       0 | 49.8852 | 49.8852 |
|   10081 | CFR     | EA      |    5.23 |       0 | 18.3474 | 18.3474 |
|   10008 | CFR     | LB      |    5    |       0 | 17.5405 | 17.5405 |
|   10036 | CFR     | CS      |   13.77 |       0 | 48.3066 | 48.3066 |

Candidate column headers:
Std Cost
Most Recent Cost
EC $ PRICE 30/12/2016
Number
Unit
Category
EC$ COST



headers: Std Cost
Most Recent Cost
EC $ PRICE 30/12/2016
Number
Unit
Category
EC$ COST
label: Number
Category
Unit
Most Recent Cost
Std Cost
EC$ COST
EC $ PRICE 30/12/2016
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 | Col_1         | Col_2     | Col_3               |   Col_4 |   Col_5 | Col_6        |
|--------:|:--------------|:----------|:--------------------|--------:|--------:|:-------------|
|   50071 | Middensegment | profiel 7 | Herstel Licht start |     450 |     315 | Traject deel |
|   50031 | Middensegment | profiel 3 | Herstel Licht start |     850 |     595 | Traject deel |
|   50017 | Middensegment | profiel 1 | Duurzaam Licht      |     504 |      42 | Maand        |
|   50027 | Middensegment | profiel 2 | Duurzaam Licht      |     504 |      42 | Maand        |
|   50037 | Middensegment | profiel 3 | Duurzaam Licht      |     504 |      42 | Maand        |

Candidate column headers:
profiel/product
Eenheid
Tarief oorspronkelijk
Segment/soort
Intensiteit
Tarief 2018
code



headers: profiel/product
Eenheid
Tarief oorspronkelijk
Segment/soort
Intensiteit
Tarief 2018
code
label: code
Segment/soort
profiel/product
Intensiteit
Tarief oorspronkelijk
Tarief 2018
Eenheid
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 |
|:-------------------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|:--------|--------:|
| Eastern Washington |      10 |    2014 |       1 |       3 |     475 |       2 |       2 | 20.0    |       4 |
| Eastern Washington |      10 |    2014 |       1 |       2 |      31 |     163 |      81 | 31.0    |      56 |
| Eastern Washington |      10 |    2014 |       1 |       3 |     551 |       0 |       0 |         |       1 |
| Eastern Washington |      10 |    2014 |       1 |       3 |     254 |       0 |       0 | 1.0     |       2 |
| Eastern Washington |      10 |    2014 |       1 |       2 |      25 |      41 |      18 | 39.0    |      26 |

Candidate column headers:
SumOfvolumet
Year
Digit
JobArea
WDANAME
SumOfvolumenewt
Month
SumOfWorkSource
ONET
SumOfClaimants



headers: SumOfvolumet
Year
Digit
JobArea
WDANAME
SumOfvolumenewt
Month
SumOfWorkSource
ONET
SumOfClaimants
label: WDANAME
JobArea
Year
Month
Digit
ONET
SumOfvolumet
SumOfvolumenewt
SumOfClaimants
SumOfWorkSource
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 |
|:--------|:-----------|:----------|:--------|--------:|--------:|
| Tarea3  | Desarrollo | Pendiente | PM      |      20 |       1 |
| Tarea7  | Desarrollo | Completa  | MB      |      20 |       1 |
| Tarea8  | Desarrollo | Pendiente | MB      |      20 |       1 |
| Tarea12 | Pruebas    | Pendiente | PM      |      28 |       1 |
| Tarea14 | Desarrollo | Pendiente | JR      |      10 |       1 |

Candidate column headers:
Presup
Tarea
Owner
Consumo
Estado
Tipo



headers: Presup
Tarea
Owner
Consumo
Estado
Tipo
label: Tarea
Tipo
Estado
Owner
Presup
Consumo
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   |
|--------:|:-------------------|--------:|:----------------|:--------|
|    1861 | BRISTOL PALACE     |       4 | Karlovy Vary    | GO TO   |
|    1147 | AURA PALACE        |       4 | Karlovy Vary    | GO TO   |
|      42 | KRALOVSKA VILA     |       4 | Karlovy Vary    | GO TO   |
|     398 | GRANDHOTEL PACIFIK |       4 | Marianske Lazne | GO TO   |
|     859 | INTERHOTEL CENTRAL |       4 | Karlovy Vary    | GO TO   |

Candidate column headers:
ID
Number of Stars
Name
GO TO
Location



headers: ID
Number of Stars
Name
GO TO
Location
label: ID
Name
Number of Stars
Location
GO 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 |
|:-----------------------|--------:|--------:|--------:|--------:|--------:|--------:|
| N02 - North 2          |   62463 |   23962 |    2142 |    5590 |    8684 |    3448 |
| N09 - SWAC             |   37501 |   10660 |    1159 |    3000 |    6428 |    1841 |
| N12 - Hillsborough     |   36019 |   11103 |    1185 |    2940 |    6299 |    1755 |
| N01 - GP Association 1 |   40199 |   15542 |    1737 |    4235 |    6857 |    2498 |
| N07 - Peak Edge        |   32976 |   10161 |    1025 |    2621 |    5604 |    1596 |

Candidate column headers:
Non-ACS spells
Neighbourhood
Reg popn
Elective spells
ACS spells
A&E
Non-elective spells



headers: Non-ACS spells
Neighbourhood
Reg popn
Elective spells
ACS spells
A&E
Non-elective spells
label: Neighbourhood
Reg popn
A&E
ACS spells
Non-elective spells
Elective spells
Non-ACS spells
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 |
|:-----------|:--------|--------:|--------:|--------:|--------:|--------:|---------:|
| Panasonic  | H       |     500 |  25     |   30    | 12500   |   15000 |   2500   |
| Miniscribe | H       |    2000 |  15.25  |    9.75 | 30500   |   19500 | -11000   |
| Logitech   | H       |     500 |  12     |    8.5  |  6000   |    4250 |  -1750   |
| Borland    | S       |     100 |  27.75  |   30.75 |  2775   |    3075 |    300   |
| Star       | S       |     700 |  20.125 |   15    | 14087.5 |   10500 |  -3587.5 |

Candidate column headers:
Current Price Per Share
Name
Shares
Total Purchase Price
Purchase Price Per Share
Total Current Price
Category
Profit or Loss



headers: Current Price Per Share
Name
Shares
Total Purchase Price
Purchase Price Per Share
Total Current Price
Category
Profit or Loss
label: Name
Category
Shares
Purchase Price Per Share
Current Price Per Share
Total Purchase Price
Total Current Price
Profit or Loss
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   |
|:----------|:---------|:---------------|--------:|--------:|:--------|:--------|:--------|--------:|--------:|:---------|:---------|
| Bankowski | Brooklyn | Foundations 4B |      20 |      40 |         |         |         |      50 |      15 |          |          |
| Briggs    | Emmy     | Foundations 4A |      15 |      40 |         |         |         |      50 |      15 |          |          |
| Anderson  | Daphne   | Corps A        |      20 |      95 |         |         |         |      80 |      20 |          |          |
| Clement   | Kate     | Apprentice B   |      20 |      95 |         |         |         |      80 |      20 |          |          |
| Brown     | Sophia   | Basic 1A       |      15 |      70 |         |         |         |      65 |      15 |          |          |

Candidate column headers:
Jazz OR Jazz-Tap Combo Recital Participation Fee Due November 30
Jazz OR Jazz-Tap Combo Costume $ Due November 30
Ballet Recital Participation Fee Due November 30
Ballet Level
Jazz Level
Registration Fee Due September 15
Ballet Costume $ Due November 30
First Name
Jazz-Tap Level
Monthly Tuition 
Last Name
Jazz OR Jazz-Tap Combo Time



headers: Jazz OR Jazz-Tap Combo Recital Participation Fee Due November 30
Jazz OR Jazz-Tap Combo Costume $ Due November 30
Ballet Recital Participation Fee Due November 30
Ballet Level
Jazz Level
Registration Fee Due September 15
Ballet Costume $ Due November 30
First Name
Jazz-Tap Level
Monthly Tuition 
Last Name
Jazz OR Jazz-Tap Combo Time
label: Last Name
First Name
Ballet Level
Registration Fee Due September 15
Monthly Tuition 
Jazz Level
Jazz OR Jazz-Tap Combo Time
Jazz-Tap Level
Ballet Costume $ Due November 30
Ballet Recital Participation Fee Due November 30
Jazz OR Jazz-Tap Combo Costume $ Due November 30
Jazz OR Jazz-Tap Combo Recital Participation Fee Due November 30
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 |
|--------:|:--------|:--------|--------:|--------:|
|   39850 | Aspen   | Mexico  | 2750.89 | 4685.81 |
|   40139 | Aspen   | Mexico  | 1908.95 | 3724.14 |
|   39953 | Quad    | Mexico  | 3174.82 | 5417.79 |
|   41656 | Fun Fly | Canada  | 1072.03 | 1957.15 |
|   40548 | Aspen   | Mexico  | 4112.91 | 9823.54 |

Candidate column headers:
Product
COGS
Region
Revenue
Date



headers: Product
COGS
Region
Revenue
Date
label: Date
Product
Region
COGS
Revenue
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 |   Col_1 |   Col_2 |   Col_3 |   Col_4 |   Col_5 |   Col_6 |
|--------:|--------:|--------:|--------:|--------:|--------:|--------:|
|       0 |      15 |    1852 |       0 |       0 |       0 |       0 |
|       0 |      29 |    3420 |       0 |       0 |       0 |       0 |
|       0 |      16 |    1971 |       0 |       0 |       0 |       0 |
|       0 |       8 |     334 |       0 |       0 |       0 |       0 |
|       0 |      11 |     854 |       0 |       0 |       0 |       0 |

Candidate column headers:
Round 3
Place
Round 4
Highest
Team Number
Round 1
Round 2



headers: Round 3
Place
Round 4
Highest
Team Number
Round 1
Round 2
label: Round 4
Place
Team Number
Highest
Round 1
Round 2
Round 3
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   |
|:-----------|:----------|:--------------------|--------:|:--------|:----------|:--------|:-------------------|:--------|
| Sheets     | Christine | Bluffton Branch     |   40476 |         |           |         |                    |         |
| Leistner   | Cheryl    | Berne Branch        |   34899 |         |           |         |                    |         |
| Elzey      | Sharon    | Decatur East Branch |   38670 |         |           |         |                    |         |
| LaFontaine | Sean      | Main Office         |   41043 | Yes     | In Person | 41470.0 | 0.6041666666666666 | 0.625   |
| Schmidt    | Peter     | Main Office         |   40301 | Yes     | In Person | 41470.0 | 0.4375             | 0.46875 |

Candidate column headers:
Date of hire
Type
Interview
Begin Time
Last Name
End Time
Date
Location
First Name



headers: Date of hire
Type
Interview
Begin Time
Last Name
End Time
Date
Location
First Name
label: Last Name
First Name
Location
Date of hire
Interview
Type
Date
Begin Time
End Time
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 | Col_1   |   Col_2 |    Col_3 |   Col_4 |   Col_5 |
|--------:|:--------|--------:|---------:|--------:|--------:|
|   40178 | WA      |     245 | -122.345 |      98 | 47.4994 |
|   40178 | WA      |      91 | -122.345 |      86 | 47.4994 |
|   39903 | WA      |      88 | -122.345 |     101 | 47.4994 |
|   40178 | WA      |      77 | -122.345 |     101 | 47.4994 |
|   40908 | WA      |     260 | -122.345 |      98 | 47.4994 |

Candidate column headers:
Latitude
QtrDate
CustomerCount
StateCode
Longitude
BranchKey



headers: Latitude
QtrDate
CustomerCount
StateCode
Longitude
BranchKey
label: QtrDate
StateCode
CustomerCount
Longitude
BranchKey
Latitude
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 |
|:-----------------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|
| Dillingham       |  443521 |  465511 |  449058 |  422247 |  505359 |  500978 |  512136 |  562838 |
| Iditarod         |   38756 |   34705 |   35100 |   29095 |   34977 |   37678 |   36901 |   41082 |
| Unalaska         |  214501 |  216638 |  210234 |  211324 |  246238 |  257728 |  263574 |  268013 |
| Southeast Island |  251040 |  246504 |  226872 |  183805 |  170768 |  147805 |  173793 |  189189 |
| Bristol Bay      |  223308 |  219633 |  230799 |  223621 |  311976 |  354754 |  370516 |  400105 |

Candidate column headers:
Actual FY2006 Grant
Actual FY2012 Grant
Actual FY2008 Grant
Actual FY2005 Grant
Actual FY2009 Grant
Actual FY2010 Grant
Actual FY2011 Grant
School District
Actual FY2007 Grant



headers: Actual FY2006 Grant
Actual FY2012 Grant
Actual FY2008 Grant
Actual FY2005 Grant
Actual FY2009 Grant
Actual FY2010 Grant
Actual FY2011 Grant
School District
Actual FY2007 Grant
label: School District
Actual FY2005 Grant
Actual FY2006 Grant
Actual FY2007 Grant
Actual FY2008 Grant
Actual FY2009 Grant
Actual FY2010 Grant
Actual FY2011 Grant
Actual FY2012 Grant
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   |
|:-------------|:--------|--------:|--------:|--------:|--------:|:--------|
| ALBROOK CINE | TIENDA  |    2016 |       2 |      16 |      13 | TIENDA  |
| AGUADULCE    | TIENDA  |    2016 |       2 |       2 |       1 | TIENDA  |
| AGUADULCE    | TIENDA  |    2017 |       1 |      26 |       2 | TIENDA  |
| ALBROOK CINE | TIENDA  |    2016 |       2 |      11 |       6 | TIENDA  |
| AGUADULCE    | TIENDA  |    2016 |       2 |      16 |       3 | TIENDA  |

Candidate column headers:
CANTIDAD
VW_PRODUCTIVIDAD_PORTA.CANAL
VW_PRODUCTIVIDAD_PORTA.CANAL2
SUCURSAL
MES
ANIO
DIA



headers: CANTIDAD
VW_PRODUCTIVIDAD_PORTA.CANAL
VW_PRODUCTIVIDAD_PORTA.CANAL2
SUCURSAL
MES
ANIO
DIA
label: SUCURSAL
VW_PRODUCTIVIDAD_PORTA.CANAL
ANIO
MES
DIA
CANTIDAD
VW_PRODUCTIVIDAD_PORTA.CANAL2