MMTU-benchmark/MMTU huggingface dataset:
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0   | Col_1   | Col_2                    | Col_3                        | Col_4   |   Col_5 |
|:--------|:--------|:-------------------------|:-----------------------------|:--------|--------:|
| Allegan | Wayland | Wayland Union Schools    | Dorr Elementary              | 2-3     |   42299 |
| Alcona  | Lincoln | Alcona Community Schools | Alcona Community Schools     | 9-12    |   42282 |
| Allegan | OTSEGO  | Otsego Public Schools    | Washington Street Elementary | K-1     |   42422 |
| Allegan | Wayland | Wayland Union Schools    | Wayland Union Middle School  | 6-8     |   42296 |
| Alcona  | Lincoln | Alcona Community Schools | Alcona Community Schools     | 2-3     |   42282 |

Candidate column headers:
City
County
Date
School District Name
School Building
OK2SAY Presentations



headers: City
County
Date
School District Name
School Building
OK2SAY Presentations
label: County
City
School District Name
School Building
OK2SAY Presentations
Date
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 |
|---------:|--------:|--------:|--------:|--------:|--------:|:--------|---------:|
| 118.772  |    4100 |    1453 |   64.64 | 3430400 |   64.64 | 1961/06 | 116.658  |
| 100.174  |    3458 |    1257 |   57.59 | 3606800 |   57.59 | 1959/04 | 103.934  |
|  97.8563 |    3378 |     966 |   58.28 | 3587800 |   58.28 | 1959/11 | 105.18   |
| 116.86   |    4034 |    1205 |   54.75 | 4994200 |   54.75 | 1962/06 |  98.8089 |
| 102.202  |    3528 |    1079 |   59.6  | 2532800 |   59.6  | 1959/08 | 107.562  |

Candidate column headers:
Margin debt
S&P500_Volume
margin debt2
S&P500_Adj Close
Date_bymonth
S&P500_Close2
S&P500_Close
Credit balances in margin accounts



headers: Margin debt
S&P500_Volume
margin debt2
S&P500_Adj Close
Date_bymonth
S&P500_Close2
S&P500_Close
Credit balances in margin accounts
label: margin debt2
Margin debt
Credit balances in margin accounts
S&P500_Close
S&P500_Volume
S&P500_Adj Close
Date_bymonth
S&P500_Close2
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 |
|--------:|--------:|--------:|--------:|--------:|--------:|--------:|:-------------------|:------------|:------------------------|:-------------------------|---------:|
| 0_053_2 |       0 |      31 |      53 |   0_053 | 0_053_1 |       2 | MBA                | Nonresident | 31 - MBA                | 4. Grad/Prof Nonresident |        0 |
| 0_006_2 |       0 |       4 |       6 |   0_006 | 0_006_1 |       2 | Grad School Tier I | Nonresident | 04 - Grad School Tier I | 4. Grad/Prof Nonresident |        0 |
| 0_038_2 |       0 |      25 |      38 |   0_038 | 0_038_1 |       2 | CBE Masters        | Nonresident | 25 - CBE Masters        | 4. Grad/Prof Nonresident |        0 |
| 0_007_2 |       0 |       5 |       7 |   0_007 | 0_007_1 |       2 | Graduate Tier II   | Nonresident | 05 - Graduate Tier II   | 4. Grad/Prof Nonresident |        0 |
| 0_011_1 |       0 |       7 |      11 |   0_011 | 0_011_1 |       1 | Graduate Tier III  | Resident    | 07 - Graduate Tier III  | 3. Grad/Prof Resident    |        0 |

Candidate column headers:
TuitionCategory
TuitionResidency
EnrChg1
CampCat
TuitionCampus
TGsort
TuitionGroup
StdtLvl
StdtGp
ResCampCat
TuitionResidencyName
TuitionGroupName



headers: TuitionCategory
TuitionResidency
EnrChg1
CampCat
TuitionCampus
TGsort
TuitionGroup
StdtLvl
StdtGp
ResCampCat
TuitionResidencyName
TuitionGroupName
label: StdtGp
TuitionCampus
TuitionGroup
TuitionCategory
CampCat
ResCampCat
TuitionResidency
TuitionGroupName
TuitionResidencyName
TGsort
StdtLvl
EnrChg1
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              |
|:--------|--------:|--------:|----------:|:-------------------|
| E       |      15 | 20348.6 |   6091.43 | 14257.210000000008 |
| A       |      30 | 30506.6 |  27496.6  | 3010.08            |
| C       |      17 |  1092   |   1092    |                    |
| E       |       3 | 30563.2 |   4672.02 | 25891.18999999999  |
| E       |      43 | 91529.5 | -26971.5  | 118501.03000000064 |

Candidate column headers:
Departement
Ca 2012
Ca 2011
Evolution
Item



headers: Departement
Ca 2012
Ca 2011
Evolution
Item
label: Item
Departement
Ca 2012
Evolution
Ca 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 |     Col_3 |     Col_4 |      Col_5 |
|:----------------------------|:--------|---------:|----------:|----------:|-----------:|
| Gas for Electric Generation |         |  13.32   | 0.0126965 | 0.0778154 | 0.00623742 |
| Elec - Lighting             | 6.03    |  20.5744 | 0.0196113 | 0.550539  | 0.0441294  |
| Gas - Motors/Drives         |         |  17.31   | 0.0164997 | 0.101125  | 0.00810584 |
| Gas - Process Cooking/Refr  |         |  10.65   | 0.0101515 | 0.0622173 | 0.00498713 |
| Gas - Process Heating       |         | 264.98   | 0.252576  | 1.54801   | 0.124084   |

Candidate column headers:
$/sqft
% of Energy
kBtu/sqft
% of Cost
kWh/sqft
Application



headers: $/sqft
% of Energy
kBtu/sqft
% of Cost
kWh/sqft
Application
label: Application
kWh/sqft
kBtu/sqft
% of Energy
$/sqft
% of Cost
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 |
|:--------|:---------|--------:|--------:|--------:|--------:|--------:|
| May     | Monthly  |       5 |   42125 |   42155 |   42153 |   42135 |
|         | Biweekly |      24 |   41946 |   41959 |   41969 |   41953 |
| June    | Monthly  |       6 |   42156 |   42185 |   42185 |   42139 |
|         | Biweekly |       7 |   42072 |   42085 |   42095 |   42082 |
|         | Biweekly |       3 |   42016 |   42029 |   42039 |   42027 |

Candidate column headers:
Payroll Cycle
Forms Due in Comp
Period Start Date
Pay Date
PP#
Month
Period End Date



headers: Payroll Cycle
Forms Due in Comp
Period Start Date
Pay Date
PP#
Month
Period End Date
label: Month
Payroll Cycle
PP#
Period Start Date
Period End Date
Pay Date
Forms Due in Comp
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 |
|:--------|:--------|:-----------------------|:--------|:---------|:--------|--------:|:--------|:--------|:--------|:---------|:---------|:---------|---------:|
| ANTH    | E455    | Anthro Of Religion     | 01      | Borbieva | Noor    |       7 | 2.0     | 1.0     |         | 1.0      |          | 7.0      |       18 |
| ARET    | 16700   | Constr Systems & Matls | 01I     | Marshall | Dennis  |      16 |         |         |         |          |          | 7.0      |       23 |
| ANTH    | E105    | Culture And Society    | 06I     | Kline    | Douglas |       2 |         | 1.0     | 2.0     | 5.0      |          | 2.0      |       12 |
| ANTH    | E105    | Culture and Society    | 05I     | Kline    | Douglas |       7 | 6.0     | 8.0     | 2.0     | 3.0      |          | 6.0      |       32 |
| BIOL    | 10001   | Intro Biol World Lab   | 05Y     | Price    | John    |       7 | 5.0     | 3.0     |         |          |          |          |       15 |

Candidate column headers:
Ssbsect Seq Numb
B Students
Shrtckn Crse Title
D Students
A Students
Shrtckn Subj Code
W Students
Spriden First Name
Total Students
Shrtckn Crse Numb
F Students
C Students
Oth Students
Spriden Last Name



headers: Ssbsect Seq Numb
B Students
Shrtckn Crse Title
D Students
A Students
Shrtckn Subj Code
W Students
Spriden First Name
Total Students
Shrtckn Crse Numb
F Students
C Students
Oth Students
Spriden Last Name
label: Shrtckn Subj Code
Shrtckn Crse Numb
Shrtckn Crse Title
Ssbsect Seq Numb
Spriden Last Name
Spriden First Name
A Students
B Students
C Students
D Students
F Students
Oth Students
W Students
Total Students
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 |
|:--------|:--------|--------:|:-----------------------|:--------|--------:|--------:|
| Asia    | Direct  |   40929 | Super Soft             | 250ml   |     187 |  841.5  |
| Asia    | Online  |   41016 | Detafast Stain Remover | 800ml   |     111 |  999    |
| Asia    | Online  |   40998 | Detafast Stain Remover | 200ml   |      84 |  546    |
| Asia    | Online  |   40861 | Pure Soft Detergent    | 100ml   |     170 |  510    |
| Asia    | Direct  |   40629 | Super Soft Bulk        | 1 Litre |     159 | 1588.41 |

Candidate column headers:
Units Sold
Quantity
Sales Channel
Region
Date Sold
Sales Amount
Product Name



headers: Units Sold
Quantity
Sales Channel
Region
Date Sold
Sales Amount
Product Name
label: Region
Sales Channel
Date Sold
Product Name
Quantity
Units Sold
Sales Amount
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 |
|:--------|:--------|--------:|--------:|--------:|
| EUR     | LTL     |   41913 |   41943 |  3.4528 |
| EUR     | DKK     |   41852 |   41882 |  7.4568 |
| EUR     | GBP     |   41913 |   41943 |  0.7807 |
| EUR     | CZK     |   41913 |   41943 | 27.534  |
| EUR     | RON     |   41883 |   41912 |  4.3976 |

Candidate column headers:
DAT_START
MON_UNIT_COD
EXCH_RATE
DAT_END
MON_UNIT_COD2



headers: DAT_START
MON_UNIT_COD
EXCH_RATE
DAT_END
MON_UNIT_COD2
label: MON_UNIT_COD
MON_UNIT_COD2
DAT_START
DAT_END
EXCH_RATE
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0    | Col_1   | Col_2      | Col_3   | Col_4      |   Col_5 |
|:---------|:--------|:-----------|:--------|:-----------|--------:|
| KENTONAI | Z1      | 08/01/2016 | S       | 16/10/2013 | 5649119 |
| KENTONAI | Z3      | 28/01/2016 | S       | 16/10/2013 | 5649100 |
| KENTY    | Z1      | 11/12/2015 | S       | 20/11/2015 | 5634904 |
| KENTONAI | AX      | 08/01/2016 | S       | 17/10/2013 | 5650129 |
| KENTONAI | Z2      | 20/10/2015 | S       | 16/10/2013 | 5649099 |

Candidate column headers:
DAT.GAM
PRODUIT
DAT.CREAT
SUIVI
GAMME
PROG.FOUR



headers: DAT.GAM
PRODUIT
DAT.CREAT
SUIVI
GAMME
PROG.FOUR
label: PROG.FOUR
GAMME
DAT.GAM
SUIVI
DAT.CREAT
PRODUIT
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 |
|:-----------|:----------|:--------|:------------|:---------------|:------------|--------:|--------:|--------:|
| Arthropoda | Arachnida | Araneae | Miturgidae  | Cheiracanthium | terrestrial |       9 | 91.6422 | 107.667 |
| Arthropoda | Arachnida | Araneae | Linyphiidae | Erigone        | terrestrial |       1 | 91      | 100     |
| Arthropoda | Arachnida | Araneae | Agelenidae  | Tegenaria      | terrestrial |       2 | 90.38   | 104     |
| Arthropoda | Arachnida | Araneae | Cybaeidae   | Cybaeus        | terrestrial |       1 | 90.29   | 103     |
| Arthropoda | Arachnida | Ixodida | Ixodidae    | Ixodes         | terrestrial |       2 | 93.79   | 104.5   |

Candidate column headers:
Average of % of identical matches
Phylum
Genus
Habitat
Average reference alignment length
Number of Sequences
Family
Class
Order



headers: Average of % of identical matches
Phylum
Genus
Habitat
Average reference alignment length
Number of Sequences
Family
Class
Order
label: Phylum
Class
Order
Family
Genus
Habitat
Number of Sequences
Average of % of identical matches
Average reference alignment length
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 |
|--------:|--------:|--------:|--------:|--------:|
|   42765 | 55060.4 | 2051.49 | 2386.4  |       0 |
|   42763 | 55657.7 | 2084.05 | 2420.55 |       0 |
|   42751 | 53706.4 | 2025.25 | 2345.91 |       0 |
|   42778 | 57385   | 2154.79 | 2505.06 |       0 |
|   42746 | 53709.3 | 2030.64 | 2348.49 |       0 |

Candidate column headers:
Data
WIG
WIG30
WIG20
Warunek



headers: Data
WIG
WIG30
WIG20
Warunek
label: Data
WIG
WIG20
WIG30
Warunek
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   |
|:----------|--------:|:--------|
| Saturday  |   42224 | 7 days  |
| Wednesday |   42207 | 4 hours |
| Wednesday |   42095 | 4 hours |
| Saturday  |   42217 | 1 day   |
| Wednesday |   42060 | 4 hours |

Candidate column headers:
Timeline
Date
Day of the week



headers: Timeline
Date
Day of the week
label: Day of the week
Date
Timeline
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   |
|:------------------|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|------------:|:---------|---------:|---------:|:---------|:---------|:---------|
| GRMZM2G134747_P01 | 1.31403  | 1.36506  | 1.39407  | 0.982025 | 1.01798  | 0.94956  | 0.928559 | 0.840613 | 0.939945 | 1.06006  | 0.000516274 | b        | 1.35772  | 0.906244 | a        | a        | a        |
| GRMZM2G397247_P04 | 0.702409 | 0.682043 | 0.674224 | 1.00632  | 0.993676 | 1.0916   | 0.966589 | 1.02538  | 0.988249 | 1.01175  | 4.04624e-05 | a        | 0.686225 | 1.02785  | b        | b        | b        |
| GRMZM2G150893_P01 | 0.552091 | 0.516713 | 0.608307 | 1.07786  | 0.922141 | 0.964074 | 0.973186 | 1.06036  | 1.01901  | 0.980989 | 0.000169522 | a        | 0.559037 | 0.999208 | b        | b        | b        |
| GRMZM2G422750_P01 | 0.301164 | 0.426745 | 0.381    | 1.04538  | 0.954621 | 1.35957  | 1.11543  | 1.47977  | 0.9974   | 1.0026   | 0.000119251 | a        | 0.369637 | 1.31826  | b        | b        | b        |
| GRMZM2G107562_P01 | 1.45374  | 1.4503   | 1.45623  | 0.968713 | 1.03129  | 0.895149 | 0.654744 | 0.720218 | 1.00717  | 0.992833 | 0.000891367 | b        | 1.45342  | 0.756704 | a        | a        | a        |

Candidate column headers:
Lc/averageLc2
Rin/averageRc6
Rc/averageRC1
Lin/averageLC1
Rin/averageRc1
Protein
Rin/Rc
Rc/averageRC2
Lc/averageLc1
Lin/averageLC2
Lin/averageLC3
group Lin/averageLC
Rin/averageRc2
PVAL
group Rin/averageRC
group Rc/averageRC
group Lc/averageLC
Lin/Lc



headers: Lc/averageLc2
Rin/averageRc6
Rc/averageRC1
Lin/averageLC1
Rin/averageRc1
Protein
Rin/Rc
Rc/averageRC2
Lc/averageLc1
Lin/averageLC2
Lin/averageLC3
group Lin/averageLC
Rin/averageRc2
PVAL
group Rin/averageRC
group Rc/averageRC
group Lc/averageLC
Lin/Lc
label: Protein
Lin/averageLC1
Lin/averageLC2
Lin/averageLC3
Lc/averageLc1
Lc/averageLc2
Rin/averageRc1
Rin/averageRc2
Rin/averageRc6
Rc/averageRC1
Rc/averageRC2
PVAL
group Lc/averageLC
Lin/Lc
Rin/Rc
group Lin/averageLC
group Rin/averageRC
group Rc/averageRC
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 |
|--------:|--------:|:--------|--------:|:-----------------|--------:|--------:|--------:|:--------------|:-------------------|:--------------|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
|   42375 |       9 | Week 1  |       0 | Calvin Wood      |     475 | 227.722 |       0 | Inside Backs  | StrengthSpeedPower | Full Training |  2.08587 |       10 |     1425 |        0 |     1425 |     1425 |      455 |
|   42374 |       9 | Week 1  |       0 | Nate Christensen |     450 | 227.722 |       0 | Outside Backs | Sport Training     | Full Training |  1.97609 |        4 |      900 |        0 |      900 |      900 |      660 |
|   42374 |       8 | Week 1  |       0 | Lance Wallace    |     365 | 227.722 |       0 | Front Row     | Sport Training     | Full Training |  1.60283 |       10 |      730 |        0 |      730 |      730 |      330 |
|   42374 |       4 | Week 1  |       0 | Geoff Powrie     |     625 | 227.722 |       0 | Front Row     | Sport Training     | Full Training |  2.74457 |        6 |     1250 |        0 |     1250 |     1250 |      770 |
|   42374 |       7 | Week 1  |       0 | Dave Black       |     485 | 227.722 |       0 | LocksLoose    | Sport Training     | Full Training |  2.12979 |        4 |      970 |        0 |      970 |      970 |      330 |

Candidate column headers:
Athlete
Monotony
Position
Date
Rolling 3 Day Sum of  Load
7Day AvgLoad
Soreness
Training Type
Fatigue
Total Load Week-1
Special Chronic Load Calculation
Rolling 28 Day Sum of  Load
Load
Week
Attendance
Avg Total Weekly Load 1-4
7DayStDev
Total Load Week-0



headers: Athlete
Monotony
Position
Date
Rolling 3 Day Sum of  Load
7Day AvgLoad
Soreness
Training Type
Fatigue
Total Load Week-1
Special Chronic Load Calculation
Rolling 28 Day Sum of  Load
Load
Week
Attendance
Avg Total Weekly Load 1-4
7DayStDev
Total Load Week-0
label: Date
Soreness
Week
Avg Total Weekly Load 1-4
Athlete
7Day AvgLoad
7DayStDev
Special Chronic Load Calculation
Position
Training Type
Attendance
Monotony
Fatigue
Rolling 3 Day Sum of  Load
Total Load Week-1
Total Load Week-0
Rolling 28 Day Sum of  Load
Load
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 |   Col_1 |   Col_2 |   Col_3 |    Col_4 |   Col_5 |    Col_6 | Col_7   | Col_8          | Col_9        | Col_10               | Col_11                  |
|--------:|--------:|--------:|--------:|---------:|--------:|---------:|:--------|:---------------|:-------------|:---------------------|:------------------------|
|      28 |   40911 |       1 |    2012 | 6021.83  |      55 | -572.902 | South   | Tennessee      | Murfreesboro | Fashion Jewellery    | Rings                   |
|      20 |   40910 |       1 |    2012 | 6974.8   |      99 | 1160.66  | West    | California     | Fresno       | Electronics          | Batteries & Chargers    |
|      34 |   40911 |       1 |    2012 |  100.999 |      72 |    0.763 | South   | North Carolina | Garner       | Office Supplies      | Pens & Art Supplies     |
|      19 |   40910 |       1 |    2012 | 1113.35  |      98 | -238.022 | Central | Wisconsin      | Oak Creek    | Toys & Baby Products | Die-Cast & Toy Vehicles |
|      37 |   40911 |       1 |    2012 | 1535.69  |      45 |  141.086 | South   | Florida        | Jacksonville | Electronics          | Memory Cards            |

Candidate column headers:
Oder Date
Order Year
Sub-Category
Profit
Quantity
State
Order ID
Sales
City
Category
Region
Order Month



headers: Oder Date
Order Year
Sub-Category
Profit
Quantity
State
Order ID
Sales
City
Category
Region
Order Month
label: Order ID
Oder Date
Order Month
Order Year
Sales
Quantity
Profit
Region
State
City
Category
Sub-Category
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0        | Col_1                   | Col_2   |   Col_3 | Col_4     | Col_5     | Col_6     |   Col_7 |   Col_8 |
|:-------------|:------------------------|:--------|--------:|:----------|:----------|:----------|--------:|--------:|
| Marantz 2270 | Muting Amp              | P550    |       2 | 10uF/16V  | 10uF/25V  | P11212-ND |    0.3  |    0.6  |
| Marantz 2270 | Power Amp Right Channel | P750B   |       2 |           |           | 4651K-ND  |    0.19 |    0.38 |
| Marantz 2270 | Power Amp Left Channel  | P750A   |       2 |           |           | 4651K-ND  |    0.19 |    0.38 |
| Marantz 2270 | FM IF Pre Amp           | P200    |       2 | 4.7uF/25V | 4.7uF/50V | P10315-ND |    0.33 |    0.66 |
| Marantz 2270 | Tone Amp                | P400    |       4 | 3.3uF/25V | 3.3uF/50V | P10314-ND |    0.33 |    1.32 |

Candidate column headers:
Old Value
DigiKey Price - Single Order
PC Board Name
DigiKey Part No.
PC Board Number
Number Required
New Value
Total
Equipment Name



headers: Old Value
DigiKey Price - Single Order
PC Board Name
DigiKey Part No.
PC Board Number
Number Required
New Value
Total
Equipment Name
label: Equipment Name
PC Board Name
PC Board Number
Number Required
Old Value
New Value
DigiKey Part No.
DigiKey Price - Single Order
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 |
|:--------|:--------|:--------|:--------|:--------------|:----------------------|:--------|--------:|--------:|--------:|---------:|---------:|---------:|---------:|
|         | CF09482 | F       | BFTM    | 1000-000-0001 | Servicios Financieros | N       |   40132 |       1 |   43.49 |    43.5  |        0 |        1 |    43.49 |
| ADHESON | CF09550 | F       | BFTM    | ADHE-ADH-0004 | ADHEGRIS              | N       |   40137 |       2 |   45.29 |    90.58 |        0 |        2 |    90.58 |
| ADHESON | CF09355 | F       | BFTM    | ADHE-ADH-0004 | ADHEGRIS              | N       |   40120 |       3 |   46.95 |   140.85 |        0 |        3 |   140.85 |
| ADHESON | CF09384 | F       | BFTM    | ADHE-ADH-0004 | ADHEGRIS              | N       |   40122 |      10 |   47.43 |   474.3  |        0 |       10 |   474.3  |
| ADHESON | CF09533 | F       | BFTM    | ADHE-ADH-0001 | ADHEMARMOL            | N       |   40136 |      10 |   45.22 |   452.2  |        0 |       10 |   452.2  |

Candidate column headers:
CANTIDAD_REAL
PRECIO_UNITARIO
CLASIFICACION_1
ANULADA
FECHA_FACTURA
PRECIO_TOTAL_REAL
BODEGA
PRECIO_TOTAL
CANTIDAD_DEVUELT
ARTICULO
CANTIDAD
TIPO_DOCUMENTO
FACTURA
DESCRIPCION



headers: CANTIDAD_REAL
PRECIO_UNITARIO
CLASIFICACION_1
ANULADA
FECHA_FACTURA
PRECIO_TOTAL_REAL
BODEGA
PRECIO_TOTAL
CANTIDAD_DEVUELT
ARTICULO
CANTIDAD
TIPO_DOCUMENTO
FACTURA
DESCRIPCION
label: CLASIFICACION_1
FACTURA
TIPO_DOCUMENTO
BODEGA
ARTICULO
DESCRIPCION
ANULADA
FECHA_FACTURA
CANTIDAD
PRECIO_UNITARIO
PRECIO_TOTAL
CANTIDAD_DEVUELT
CANTIDAD_REAL
PRECIO_TOTAL_REAL
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   |
|:--------------|--------:|--------:|--------:|--------:|--------:|:--------|:----------------------|:--------|
| as003a5       |    2054 |       1 |       1 |       1 |       0 |         | Survey  - Categorical |         |
| as003b6       |    2050 |       1 |       1 |       1 |       0 | 2011.0  | Survey  - Categorical |         |
| age_4554      |    2065 |       1 |       1 |       1 |       1 |         | Created               |         |
| as003e1       |    2046 |       1 |       1 |       1 |       0 |         | Survey  - Categorical |         |
| abp_other_t_m |    2030 |       1 |       1 |       0 |       0 |         | Created               |         |

Candidate column headers:
2009
N
2012
2010
Variable Name
2011
Filter Condition
Variable Type
Last Change



headers: 2009
N
2012
2010
Variable Name
2011
Filter Condition
Variable Type
Last Change
label: Variable Name
N
2012
2011
2010
2009
Last Change
Variable Type
Filter Condition
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 |
|:-------------|:-----------------------|:--------|--------:|--------:|
| Sulcorebutia | kamiensis              | HS189   |      25 |   40621 |
| Sulcorebutia | breviflora v. lauii    | L314    |      20 |   40621 |
| Sulcorebutia | mentosa                | HS48    |      25 |   40621 |
| Sulcorebutia | alba                   | WR472   |      20 |   40621 |
| Sulcorebutia | steinbachii v. horrida |         |      20 |   40621 |

Candidate column headers:
Samenanzahl
Feldnummer
Datum
Gattung
Genauer Name



headers: Samenanzahl
Feldnummer
Datum
Gattung
Genauer Name
label: Gattung
Genauer Name
Feldnummer
Samenanzahl
Datum
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 |
|--------:|:-------------------------------|:---------------|--------:|--------:|--------:|--------:|--------:|
|      91 | Alma Center Sch Dist           | Pupil Services |    3.2  |    3.2  |    3.22 |    1.21 |    2.15 |
|       7 | Abbotsford Sch Dist            | Aides          |    5.79 |    6.4  |    7.09 |    6.72 |    7.71 |
|      84 | Alma Sch Dist                  | Teachers       |   28.45 |   27.05 |   23.67 |   22.44 |   22.73 |
|      14 | Adams-Friendship Area Sch Dist | Pupil Services |    8    |    8    |    8.2  |    8.09 |    8.09 |
|      70 | Algoma Sch Dist                | Other          |   13.45 |   14.25 |   14.25 |   14    |   13.8  |

Candidate column headers:
2011 FTE
2010 FTE
Work District
Current Work District Name
Classification
2013 FTE
2009 FTE
2012 FTE



headers: 2011 FTE
2010 FTE
Work District
Current Work District Name
Classification
2013 FTE
2009 FTE
2012 FTE
label: Work District
Current Work District Name
Classification
2009 FTE
2010 FTE
2011 FTE
2012 FTE
2013 FTE
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   |
|:-----------|:-----------------|:-------------------------|:----------------|:--------|:--------|
| 6:00-7:00  | CIS              | CIS                      | CIS             | CIS     |         |
| 12:00-1:00 | Break            |                          |                 |         | CIS     |
| 2:00-3:00  |                  |                          |                 |         | CIS     |
| 4:00-5:00  | CIS              | CIS                      | CIS             | CIS     | CIS     |
|            | Cornerstone Prep | Grandview Heights Middle | Wooddale Middle |         |         |

Candidate column headers:
Nov. 16
Nov. 17
Nov. 15
Nov. 13
Nov. 14
TIME



headers: Nov. 16
Nov. 17
Nov. 15
Nov. 13
Nov. 14
TIME
label: TIME
Nov. 13
Nov. 14
Nov. 15
Nov. 16
Nov. 17
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   |
|--------:|--------:|:--------|:-------------|:------------|:--------|:---------|:--------|
|     163 |       1 | PKT     |              | 90000000.0  | P-06    | Bidang 2 | UHLTP   |
|     171 |       1 | PKT     | 450000000.0  |             | P-06    | Bidang 2 | UHLTP   |
|     508 |       6 | unit    |              | 122820000.0 | P-14    | Bidang 2 | UHLTP   |
|     169 |       1 | PKT     |              | 128000000.0 | P-06    | Bidang 2 | UHLTP   |
|     160 |  129364 | M2      | 1293640000.0 | 29460000.0  | P-06    | Bidang 2 | UHLTP   |

Candidate column headers:
FAK/UNIT Pengusul
Narasumber Pembahas
Volume
RM (Rp.)
Program Renstra
NO
Satuan
PNBP (Rp.)



headers: FAK/UNIT Pengusul
Narasumber Pembahas
Volume
RM (Rp.)
Program Renstra
NO
Satuan
PNBP (Rp.)
label: NO
Volume
Satuan
RM (Rp.)
PNBP (Rp.)
Program Renstra
Narasumber Pembahas
FAK/UNIT Pengusul
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   |
|:--------|:--------|:--------|
| Ghana   | Ghana   | Africa  |
| Germany | Germany | Europa  |
| Brazil  | Brazil  | America |
| Canad   | Canada  | America |
| DK      | Denmark | Europa  |

Candidate column headers:
Continente
Actual
Mapping



headers: Continente
Actual
Mapping
label: Actual
Mapping
Continente
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 |
|--------:|--------:|--------:|:---------|:---------|:--------|:--------|:-----------|:----------------|:-----------|---------:|:---------|---------:|---------:|:---------|---------:|
|      15 |    2801 |    2801 | Shahan   | Shahan   | YE2801  | YE28    | Al Maharah | Southern Region | Al Maharah |     2801 | Shahan   |     4732 |      118 | Shahan   |    0     |
|       6 |    2802 |    2802 | Hat      | Hat      | YE2802  | YE28    | Al Maharah | Southern Region | Al Maharah |     2802 | Hat      |     4182 |      117 | Hat      |    0     |
|      10 |    2802 |    2802 | Hat      | Hat      | YE2802  | YE28    | Al Maharah | Southern Region | Al Maharah |     2802 | Hat      |     4182 |      117 | Hat      |    0     |
|      16 |    2801 |    2801 | Shahan   | Shahan   | YE2801  | YE28    | Al Maharah | Southern Region | Al Maharah |     2801 | Shahan   |     4732 |      118 | Shahan   |    0     |
|      22 |    2212 |    2212 | As Safra | As Safra | YE2212  | YE22    |            |                 | Sa'ada     |     2212 | As Safra |    71399 |      329 | As Safra |  720.624 |

Candidate column headers:
gov
dist_name
Sec_Region
population
OBJECTID
OBJECTID_1
distID
GovNameEn
HRPcode
DistNameEn
HRParent
PoP Exposure Flood25Yr
HRname
Dist_Id
DistName_1
DistID_Old



headers: gov
dist_name
Sec_Region
population
OBJECTID
OBJECTID_1
distID
GovNameEn
HRPcode
DistNameEn
HRParent
PoP Exposure Flood25Yr
HRname
Dist_Id
DistName_1
DistID_Old
label: OBJECTID
Dist_Id
DistID_Old
DistNameEn
HRname
HRPcode
HRParent
GovNameEn
Sec_Region
gov
distID
dist_name
population
OBJECTID_1
DistName_1
PoP Exposure Flood25Yr
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                    |
|:------------------|:--------------|:-------------------------|
| Katherine Orlando | 3/Willimantic | katherine.orlando@ct.gov |
| Eduardo Rivera    | 4/Hartford    | eduardo.rivera@ct.gov    |
| Kendra Labrie     | 6/New Britain | kendra.labrie@ct.gov     |
| Kimberly Zarillo  | 5/Waterbury   | Kimberly.Zarillo@ct.gov  |
| Kimberly Lisitano | 6/New Britain | kimberly.lisitano@ct.gov |

Candidate column headers:
Name 
E-mail
Region/AO



headers: Name 
E-mail
Region/AO
label: Name 
Region/AO
E-mail
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   |
|:----------|:--------|:----------------|:------------------|:--------|
| ECED& 107 | English | 6:30-8:50 PM    | In Person_ I-BEST | Campus  |
| ECED& 107 | English | Arranged        | In Person_ I-BEST | Campus  |
| CFS 215   | English | 8:30 AM-4:50 PM | In Person         | Campus  |
| ECED& 120 | English | Arranged        |                   |         |
| ECED& 170 | English | 6:00-8:50 PM    | In Person         | Campus  |

Candidate column headers:
Course Number
Mode
Time
Language
Location



headers: Course Number
Mode
Time
Language
Location
label: Course Number
Language
Time
Mode
Location
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   |
|:---------------|:--------|--------:|:--------|--------:|----------:|:--------|
| Unincorporated | Butte   |    4998 | C03     |      69 |  52885152 |         |
| Unincorporated | Butte   |    4998 | C09     |     865 |  27835935 |         |
| Unincorporated | Butte   |    4998 | TTL     |    2076 | 333899553 |         |
| Unincorporated | Butte   |    4998 | C06     |     116 |    533290 |         |
| Unincorporated | Alameda |    1998 | C05     |      34 | 155282636 |         |

Candidate column headers:
Taxable Transactions
Type code
Jurisdiction
TAC
County
Number of Outlets
IfD



headers: Taxable Transactions
Type code
Jurisdiction
TAC
County
Number of Outlets
IfD
label: Jurisdiction
County
TAC
Type code
Number of Outlets
Taxable Transactions
IfD
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 |
|--------:|:--------|:--------|:--------|--------:|:--------|:-----------|:--------|:--------|:------------|---------:|
|       0 | ESC     | ESC309  | M       |       1 |         |            |         |         | M. Poulette |       10 |
|       2 | AVS     | AVS273  | C       |       1 |         |            |         |         | M. Prinkki  |        9 |
|       6 | MAT     | MAT210  | J       |       3 | MK125   | M T W TH   | 9:00    | 12:00   | U. Hoensch  |       20 |
|       9 | CHM     | CHM101  | M       |       4 | TSB107  | M T W TH F | 1:00    | 3:00    | C. Lowery   |       20 |
|       4 | EDC     | EDC327  | M       |       3 |         |            |         |         | A. Meiers   |       10 |

Candidate column headers:
Instructor
Section
RegStudents
Prefix
StartTime
Credits
Days
Room
MaxStudents
Course
EndTime



headers: Instructor
Section
RegStudents
Prefix
StartTime
Credits
Days
Room
MaxStudents
Course
EndTime
label: RegStudents
Prefix
Course
Section
Credits
Room
Days
StartTime
EndTime
Instructor
MaxStudents