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 |
|------------:|--------:|--------:|--------:|
| 1.18295e+06 |       1 |    9.33 |    9.33 |
| 1.22361e+06 |       1 | 1400    | 1400    |
| 1.22297e+06 |       1 |  288    |  288    |
| 1.1829e+06  |       1 | 2194.22 | 2194.22 |
| 1.23416e+06 |       1 |  296.6  |  296.6  |

Candidate column headers:
Columna5
ID
UNIDAD
PRECIO USD



headers: Columna5
ID
UNIDAD
PRECIO USD
label: ID
UNIDAD
PRECIO USD
Columna5
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 |
|:--------|--------:|--------:|--------:|--------:|--------:|
| Mefek   |      47 |      52 |      68 |       5 |      19 |
| Sinub   |      23 |      49 |      56 |      86 |      20 |
| Sinub   |      88 |      88 |      83 |      70 |       2 |
| Mefek   |      58 |      45 |       2 |      13 |      82 |
| Mefek   |      15 |      72 |      64 |      26 |      35 |

Candidate column headers:
Set2
Set1
Set3
Set5
Set4
Sel



headers: Set2
Set1
Set3
Set5
Set4
Sel
label: Sel
Set1
Set2
Set3
Set4
Set5
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 |
|:-----------|--------:|--------:|
| NORTH      |   42359 |   94908 |
| WEST       |   42369 |   57650 |
| SOUTH      |   42369 |   94908 |
| NORTH WEST |   42369 |   90967 |
| WEST       |   42369 |   90967 |

Candidate column headers:
ORDER DATE
REGION
SALES



headers: ORDER DATE
REGION
SALES
label: REGION
ORDER DATE
SALES
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 |   Col_1 | Col_2      | Col_3             |
|--------:|--------:|:-----------|:------------------|
|      11 |   40577 | Ian Kenney | Chairman          |
|      10 |   40577 | Ian Kenney | Chairman          |
|      40 |   40675 | Ian Kenney | General Evaluator |
|      46 |   40675 | Ian Kenney | General Evaluator |
|      34 |   40675 | Ian Kenney | General Evaluator |

Candidate column headers:
Name
Date
Column1
Role



headers: Name
Date
Column1
Role
label: Column1
Date
Name
Role
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 |
|:---------|--------:|--------:|--------:|
| Result10 |       2 |     4   |      20 |
| Result07 |       2 |     3.5 |      60 |
| Result21 |       4 |     7   |     285 |
| Result08 |       1 |     3.5 |      50 |
| Result12 |       2 |     4   |      32 |

Candidate column headers:
Result
Value1
Value2
Value3



headers: Result
Value1
Value2
Value3
label: Result
Value1
Value2
Value3
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              |
|:--------|:--------|:--------|--------:|:-------------------|
|         | OLW-M   | 2.35    | 2.44    |                    |
|         | SPN-M   | 2.05    | 2.21    |                    |
|         | OLW-M   |         | 2.308   | 2.4778333333333333 |
|         | SST-M   |         | 1.9075  | 2.0625             |
|         | SPN-M   |         | 1.84786 | 1.55               |

Candidate column headers:
Column2
2012
2013
Column1
2014



headers: Column2
2012
2013
Column1
2014
label: Column1
Column2
2012
2013
2014
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 |
|:----------|--------:|----------:|
| 1745-039X |   1.887 | 0.141667  |
| 1008-682X |   3.259 | 0.0833333 |
| 0960-7692 |   5.654 | 0.0483871 |
| 0308-521X |   3.004 | 0.0446429 |
| 0032-079X |   3.306 | 0.0747126 |

Candidate column headers:
IMPACT_FACTOR
TOP
ISSN



headers: IMPACT_FACTOR
TOP
ISSN
label: ISSN
IMPACT_FACTOR
TOP
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 |
|--------:|--------:|:--------|--------:|
|   40002 |      58 | SR1     |   37.12 |
|   40002 |      78 | SR2     |   52.26 |
|   40000 |       1 | SR1     |    0.5  |
|   40001 |      72 | SR1     |   50.4  |
|   40001 |      60 | SR2     |   34.2  |

Candidate column headers:
COGS
Sales
Date
SR



headers: COGS
Sales
Date
SR
label: Date
Sales
SR
COGS
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   |
|:-----------------------------|:---------|--------:|--------:|--------:|:--------|
| Sea Trout                    | Sr-89/90 |   41311 | 0.00146 | 0.00116 | No      |
| Sea Trout                    | Cs-137   |   41311 | 0.0101  | 0.00922 | No      |
| Sea Trout                    | H-3      |   41325 | 0.112   | 0.02    | Yes     |
| Red Fish (drum) - non-edible | Sr-89/90 |   41415 | 0.103   | 0.101   | No      |
| Marine Mullet                | Co-60    |   41297 | 0.0185  | 0.00924 | No      |

Candidate column headers:
SampleStd
Fish Species
Nuclide
Sig
SampleCon
CollectDate



headers: SampleStd
Fish Species
Nuclide
Sig
SampleCon
CollectDate
label: Fish Species
Nuclide
CollectDate
SampleCon
SampleStd
Sig
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 |
|:-----------------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
| Northwest Region | 5.09091 | 4.54545 | 4.90909 | 4.90909 | 5.09091 | 5.09091 | 5.63636 |     6.8 |     6.6 |      7.4 |      7.8 |      7.8 |      7.8 |      7.8 |      8.6 |      7.8 |        7 |        7 |        8 |
| Circuit 17       | 6       | 8       | 6       | 7       | 7       | 5       | 7       |     5   |     5   |      8   |      2   |      4   |      6   |      4   |      2   |      4   |        2 |        2 |        4 |
| Circuit 07       | 2       | 2       | 2       | 4       | 4       | 3       | 3       |     3   |     3   |      3   |      3   |      5   |      5   |      5   |      5   |      5   |        5 |        5 |        5 |
| Circuit 09       | 6       | 4       | 4       | 2       | 2       | 2       | 4       |     6   |     6   |      8   |      4   |      6   |      6   |      8   |      4   |      2   |        2 |        2 |        4 |
| Circuit 01       | 5       | 5       | 5       | 5       | 7       | 8       | 6       |     8   |     6   |      8   |      8   |      8   |      6   |      8   |      8   |      8   |        6 |        4 |        8 |

Candidate column headers:
Dec-14
May-14
Jul-14
Oct-13
Jun-14
Mar-14
Nov-13
Sep-13
Aug-14
Sep-14
Jan-14
Jan-15
Jul-13
Aug-13
Dec-13
Apr-14
Circuit
Feb-14
Nov-14
Oct-14



headers: Dec-14
May-14
Jul-14
Oct-13
Jun-14
Mar-14
Nov-13
Sep-13
Aug-14
Sep-14
Jan-14
Jan-15
Jul-13
Aug-13
Dec-13
Apr-14
Circuit
Feb-14
Nov-14
Oct-14
label: Circuit
Jul-13
Aug-13
Sep-13
Oct-13
Nov-13
Dec-13
Jan-14
Feb-14
Mar-14
Apr-14
May-14
Jun-14
Jul-14
Aug-14
Sep-14
Oct-14
Nov-14
Dec-14
Jan-15
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   |
|:--------|:--------|:--------|--------:|:--------|--------:|:--------|
| Q1_1    | 67.0    |         |      46 | M       | 67      | MALE    |
| Q1_15   | 76.0    |         |      48 | F       | 76      | FEMALE  |
| Q1_8    |         | 166.0   |      26 | F       | 65.3543 | FEMALE  |
| Q1_4    | 67.0    |         |      27 | f       | 67      | FEMALE  |
| Q1_1    | 63.0    |         |      27 | f       | 63      | FEMALE  |

Candidate column headers:
Age
Q
Height(in)
Height
Gender
Height(cm
Gender2



headers: Age
Q
Height(in)
Height
Gender
Height(cm
Gender2
label: Q
Height(in)
Height(cm
Age
Gender
Height
Gender2
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 |
|:---------------------|---------:|---------:|-----------:|
| CAMERAS & CAMCORDERS | 302103   | 342376   |  0.13331   |
| CAMERAS & CAMCORDERS |  53837.1 |  51549.3 | -0.0424947 |
| CAMERAS & CAMCORDERS |  74391.2 |  84305.8 |  0.133275  |
| CAMERAS & CAMCORDERS | 145630   | 160526   |  0.102281  |
| CAMERAS & CAMCORDERS | 332346   | 312086   | -0.0609603 |

Candidate column headers:
Last Month
This Month
ProductCategoryName
% Change



headers: Last Month
This Month
ProductCategoryName
% Change
label: ProductCategoryName
Last Month
This Month
% Change
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 |
|--------:|--------:|:-----------|--------:|
|       9 |   41354 | Widget #9  |      90 |
|       1 |   41297 | Widget #1  |      10 |
|      15 |   41332 | Widget #15 |     150 |
|       4 |   41286 | Widget #4  |      40 |
|      17 |   41331 | Widget #17 |     170 |

Candidate column headers:
Date
Invoice #
Amount
Description



headers: Date
Invoice #
Amount
Description
label: Invoice #
Date
Description
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   |
|:--------|--------:|:--------|--------:|:--------|
| Non-IP  |    2020 | No      |  0.0209 | Exit    |
| Non-IP  |    2020 | Yes     |  0.0398 | Entry   |
| IP      |    2019 | Yes     |  0.0295 | Exit    |
| IP      |    2019 | No      |  0.0258 | Entry   |
| Non-IP  |    2020 | No      |  0.0328 | Entry   |

Candidate column headers:
Entry or Exit
Revenue Recovery Rate
Year
Optional Charge Inclusion
Type of Point



headers: Entry or Exit
Revenue Recovery Rate
Year
Optional Charge Inclusion
Type of Point
label: Type of Point
Year
Optional Charge Inclusion
Revenue Recovery Rate
Entry or Exit
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 |
|--------:|:--------|:--------|:--------|:--------|--------:|
|       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     |       0 |
|       0 | 0.0     | 0.0     | 0.0     | 0.0     |       0 |

Candidate column headers:
Anxiety
ADHD problemen
Depressieve problemen
Ontwijkende pers.h. problemen
Antisoc. Pers.h. problemen
Somatische problemen



headers: Anxiety
ADHD problemen
Depressieve problemen
Ontwijkende pers.h. problemen
Antisoc. Pers.h. problemen
Somatische problemen
label: Depressieve problemen
Anxiety
Somatische problemen
Ontwijkende pers.h. problemen
ADHD problemen
Antisoc. Pers.h. problemen
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 |
|:-------------------|:----------|--------:|
| DNDMOM2            | Green Bay |       3 |
| DONT STOP BELIEVIN | Green Bay |       3 |
| SHANSKY3           | Carolina  |       3 |
| CAUFFDROP 2        | Green Bay |       3 |
| EVERYGIVENSUNDAY   | Green Bay |       3 |

Candidate column headers:
Team
NFL_Team
Week_Number



headers: Team
NFL_Team
Week_Number
label: Team
NFL_Team
Week_Number
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 | Col_1   | Col_2   |   Col_3 |   Col_4 |   Col_5 |   Col_6 |   Col_7 |   Col_8 |   Col_9 |
|--------:|:--------|:--------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|
|       3 | HRS     | $4.3B   |  0.0304 |  0.2055 |     3.9 |     8.5 |  0.2189 |    1.93 |    0.96 |
|      31 | EEI     | $67.9M  |  0.03   |  0.0696 |     3.1 |    10.6 |  0.2057 |    1.33 |    0.76 |
|      30 | MGA     | $7.9B   |  0.0298 |  0.0564 |     3.8 |     0   |  0.0722 |    0.98 |    1.82 |
|      13 | AFL     | $20.1B  |  0.0306 |  0.1558 |     3   |    10.9 |  0.1113 |    1.58 |    1.44 |
|      26 | CAH     | $14.2B  |  0.021  |  0.1903 |     3   |    15.9 | -0.0113 |    2.47 |    1.13 |

Candidate column headers:
#
Dividend Yield
EPS Growth (Past 5 Yrs)
P/E (TTM)
Dividend Coverage
Symbol
PEG
Market Cap
Price/Book
Dividend Growth Rate (5 Yr Avg)



headers: #
Dividend Yield
EPS Growth (Past 5 Yrs)
P/E (TTM)
Dividend Coverage
Symbol
PEG
Market Cap
Price/Book
Dividend Growth Rate (5 Yr Avg)
label: #
Symbol
Market Cap
Dividend Yield
Dividend Growth Rate (5 Yr Avg)
Dividend Coverage
P/E (TTM)
EPS Growth (Past 5 Yrs)
Price/Book
PEG
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 |
|:-----------------------------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
| Alimentos_ Domesticos        |      14 |       0 |       0 |       0 |       0 |       0 |      20 |       5 |     160 |        0 |        0 |        0 |        0 |        4 |        1 |       15 |        0 |        0 |        0 |
| Restaurantes y vida nocturna |       4 |       0 |       0 |       0 |       0 |       0 |      14 |       1 |     175 |        0 |        0 |        0 |        0 |        0 |        0 |       16 |        0 |        0 |        0 |
| Vehiculos                    |      15 |       0 |       0 |       0 |       0 |       0 |      16 |      19 |     141 |        0 |        0 |        0 |        0 |        7 |        0 |        2 |        0 |        0 |        0 |
| Internet y Comunicaciones    |       0 |       0 |       0 |       0 |       1 |       0 |       0 |       0 |     242 |        0 |        0 |        0 |        0 |        0 |        0 |       15 |        0 |        0 |        0 |
| Ley y Gobierno               |       6 |       0 |       0 |       0 |       7 |       0 |       8 |      45 |     125 |        0 |        0 |        0 |        1 |        2 |        0 |       26 |        0 |        0 |        8 |

Candidate column headers:
ar
eu
gov
es
cu
Vertical
biz
co
cc
de
com/ org/ net/ tv
coop
bo
cat
cr
edu
ec
fm
cl
ca



headers: ar
eu
gov
es
cu
Vertical
biz
co
cc
de
com/ org/ net/ tv
coop
bo
cat
cr
edu
ec
fm
cl
ca
label: Vertical
ar
biz
bo
ca
cat
cc
cl
co
com/ org/ net/ tv
coop
cr
cu
de
ec
edu
es
eu
fm
gov
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 |
|--------:|--------:|--------:|
|     132 |       2 |       3 |
|     131 |       1 |      12 |
|     135 |       1 |       2 |
|     134 |       3 |       2 |
|     131 |       1 |       2 |

Candidate column headers:
iddet3
iddet2
idnumleilao



headers: iddet3
iddet2
idnumleilao
label: iddet2
iddet3
idnumleilao
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 |
|--------:|--------:|--------:|--------:|
|       5 |    0.41 |     438 |     438 |
|       5 |    0.22 |     421 |     421 |
|       5 |    0.19 |     418 |     418 |
|       5 |    0.12 |     407 |     408 |
|       5 |    0    |     305 |     347 |

Candidate column headers:
MAXIMUM SCORE
MATH PERCENTILE
GRADE
MINIMUM SCORE



headers: MAXIMUM SCORE
MATH PERCENTILE
GRADE
MINIMUM SCORE
label: GRADE
MATH PERCENTILE
MINIMUM SCORE
MAXIMUM SCORE
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   |
|:----------------|---------:|:--------|
| dHsaEXD16235334 | 12003908 | No      |
| dHsaEXD93314426 | 12003899 | No      |
| dHsaEXD46675715 | 12003908 | No      |
| dHsaEXD74403961 | 12003908 | No      |
| dHsaEXD87213280 | 12003908 | No      |

Candidate column headers:
SKU
GeneStrat
Unique Assay ID



headers: SKU
GeneStrat
Unique Assay ID
label: Unique Assay ID
SKU
GeneStrat
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   |
|:--------|--------:|:--------|:--------|:--------|:--------|--------:|:--------|:--------|:-----------|:-----------|:---------|:---------|:---------|:---------|:---------|:---------|
| 2019SP  |      16 | DAY     | A       | ARTS    | DRAM    |    1342 | 001SP   | LEC     | 01/14/2019 | 05/08/2019 | 09:00    | 10:50    |          | Y        |          | Y        |
| 2019SP  |      16 | EVENING | A       | ARTS    | ARTS    |    1311 | 500SP   | LEC     | 01/14/2019 | 05/08/2019 | 17:30    | 20:20    |          | Y        |          | Y        |
| 2019SP  |      16 | DAY     | A       | ARTS    | ARTS    |    1317 | 150SP   | LEC     | 01/14/2019 | 05/08/2019 | 13:00    | 15:50    | Y        |          | Y        |          |
| 2019SP  |      16 | DAY     | A       | ARTS    | ARTS    |    1303 | 001SP   | LEC     | 01/14/2019 | 05/08/2019 | 11:00    | 12:20    | Y        |          | Y        |          |
| 2019SP  |      16 | TBA     | A       | ARTS    | ARTS    |    1301 | 709SP   | LECI    | 01/14/2019 | 05/08/2019 |          |          |          |          |          |          |

Candidate column headers:
Subject
M
Instruction
DtStart
T
Term 
W
R
Meeting
Start
Status
Course
Department
Weeks
End
DtEnd
Section



headers: Subject
M
Instruction
DtStart
T
Term 
W
R
Meeting
Start
Status
Course
Department
Weeks
End
DtEnd
Section
label: Term 
Weeks
Meeting
Status
Department
Subject
Course
Section
Instruction
DtStart
DtEnd
Start
End
M
T
W
R
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   |
|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|:--------|:--------|
|      26 |       6 |      42 |       9 |      58 |      12 |      74 |      11 | 92.0    | 1.0     |
|      25 |       3 |      41 |      10 |      57 |      14 |      73 |      16 | 89.0    | 1.0     |
|      21 |       4 |      37 |       5 |      53 |      13 |      69 |      24 | 85.0    | 7.0     |
|      24 |       4 |      40 |      13 |      56 |      16 |      72 |      16 | 88.0    | 3.0     |
|      28 |       8 |      44 |      11 |      60 |       7 |      76 |      24 |         |         |

Candidate column headers:
Edad4
Votos2
Votos 
Edad
votos
Edad5
Votos4
Edad2
Votos3
Edad3



headers: Edad4
Votos2
Votos 
Edad
votos
Edad5
Votos4
Edad2
Votos3
Edad3
label: Edad
votos
Edad2
Votos2
Edad3
Votos3
Edad4
Votos4
Edad5
Votos
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   |
|--------:|:--------|--------:|--------:|:--------|
|   39968 | Gahag   |   27.01 |   39968 |         |
|   39961 | Datab   |    6.95 |   39961 |         |
|   39969 | Domas   |   96.16 |   39969 |         |
|   39959 | Domas   |   23.72 |   39959 |         |
|   39965 | Gahag   |   76.1  |   39965 |         |

Candidate column headers:
Seller
Comm
Mo_Fri
DDate
Seller.1



headers: Seller
Comm
Mo_Fri
DDate
Seller.1
label: DDate
Seller
Comm
Mo_Fri
Seller.1
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 |
|--------:|:--------|------------:|--------:|:--------|------------:|
|   42855 | -       | 4.40975e+08 |   42855 | -       | 1e+08       |
|   43008 | -       | 4.06464e+08 |   43008 | -       | 1e+08       |
|   43465 | -       | 9.75572e+07 |   43465 | -       | 2.43893e+07 |
|   42551 | -       | 4.626e+08   |   42551 | -       | 1e+08       |
|   43738 | -       | 0           |   43738 | -       | 0           |

Candidate column headers:
Actual note balance (Class B)
Payment date
Forecasted note balance (Class B)
Actual note balance (Class A)
Spalte1
Forecasted note balance (Class A)



headers: Actual note balance (Class B)
Payment date
Forecasted note balance (Class B)
Actual note balance (Class A)
Spalte1
Forecasted note balance (Class A)
label: Payment date
Actual note balance (Class A)
Forecasted note balance (Class A)
Spalte1
Actual note balance (Class B)
Forecasted note balance (Class B)
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0    | Col_1   | Col_2     | Col_3   | Col_4         | Col_5    | Col_6   | Col_7   | Col_8   | Col_9   | Col_10   | Col_11   | Col_12   |   Col_13 |   Col_14 |
|:---------|:--------|:----------|:--------|:--------------|:---------|:--------|:--------|:--------|:--------|:---------|:---------|:---------|---------:|---------:|
| AUR21316 | $3_680  | 1/07/2018 |         |               |          | $1_010  | $1_210  | $160    | $552    | $368     | $368     | $736     |      8.4 |    43409 |
| MST20616 | $6_240  | 1/07/2018 |         |               |          | $1_000  | $1_000  | $160    | $936    | $624     | $624     | $1_248   |      8.4 |    43409 |
| ACM20110 | $3_740  | 1/07/2018 | 43431.0 | In Transition | ACM20117 | $1_000  | $1_000  | $160    | $561    | $374     | $374     | $748     |      8.4 |    43409 |
| 22075VIC | $3_880  | 1/07/2018 | 43465.0 |               | PSP20218 | $0      | $0      | $0      | $582    | $388     | $388     | $776     |      8.4 |    43409 |
| AHC20516 | $4_390  | 1/07/2018 |         |               |          | $1_000  | $1_000  | $160    | $659    | $439     | $439     | $878     |      8.4 |    43409 |

Candidate column headers:
First Qualification
TGA Qualification Status
Effective From
Regional
Replaced By
Long Term Unemployment
Published Version Number
Effective To
Concession
Date of Version
Code
Qualification Price
Second or Further Qualification
Remote
ATSI or Dissability



headers: First Qualification
TGA Qualification Status
Effective From
Regional
Replaced By
Long Term Unemployment
Published Version Number
Effective To
Concession
Date of Version
Code
Qualification Price
Second or Further Qualification
Remote
ATSI or Dissability
label: Code
Qualification Price
Effective From
Effective To
TGA Qualification Status
Replaced By
First Qualification
Second or Further Qualification
Concession
ATSI or Dissability
Long Term Unemployment
Regional
Remote
Published Version Number
Date of Version
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 |
|--------:|:---------------|--------:|--------:|
|  125474 | Eagle          |      74 |   11.97 |
|  125463 | Quad           |       6 |   43.65 |
|  125464 | Quad           |      18 |   36.76 |
|  125452 | Yanaki         |      60 |   17.37 |
|  125459 | Majestic Beaut |      26 |   18.82 |

Candidate column headers:
Unit Price
Product
Quanity
Invoice Number



headers: Unit Price
Product
Quanity
Invoice Number
label: Invoice Number
Product
Quanity
Unit Price
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   |
|--------:|:---------------|--------:|:--------|
|   41107 | Parking Ticket |    7.2  | Parking |
|   41086 | Train Ticket   |  140.2  | Travel  |
|   41115 | Car 2 Purchase | 2950    | Car     |
|   41092 | Parking Ticket |    3.5  | Parking |
|   41074 | Spam Blocking  |    3.22 | Website |

Candidate column headers:
Subject
Amount
Category
Date



headers: Subject
Amount
Category
Date
label: Date
Subject
Amount
Category
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0   | Col_1   | Col_2   | Col_3   | Col_4   | Col_5              | Col_6   | Col_7   |   Col_8 | Col_9   | Col_10   | Col_11   |
|:--------|:--------|:--------|:--------|:--------|:-------------------|:--------|:--------|--------:|:--------|:---------|:---------|
|         | OK      | B123    | ITEM B  | Cole    | Item B description | 20.0    | 100.0   |    2000 | 50.0    | 30.0     | 20.0     |
|         | OK      |         |         |         |                    |         |         |       0 |         |          |          |
|         | REORDER | D123    | ITEM D  | Cole    | Item D description | 10.0    | 20.0    |     200 | 50.0    | 14.0     | 10.0     |
|         | OK      |         |         |         |                    |         |         |       0 |         |          |          |
|         | OK      |         |         |         |                    |         |         |       0 |         |          |          |

Candidate column headers:
REORDER   (auto-fill)
ITEM DISCONTINUED?
STOCK QUANTITY
ITEM REORDER QUANTITY
INVENTORY VALUE
DAYS PER REORDER
NAME
ITEM NO.
DESCRIPTION
MANUFACTURER
REORDER LEVEL
COST PER ITEM



headers: REORDER   (auto-fill)
ITEM DISCONTINUED?
STOCK QUANTITY
ITEM REORDER QUANTITY
INVENTORY VALUE
DAYS PER REORDER
NAME
ITEM NO.
DESCRIPTION
MANUFACTURER
REORDER LEVEL
COST PER ITEM
label: ITEM DISCONTINUED?
REORDER   (auto-fill)
ITEM NO.
NAME
MANUFACTURER
DESCRIPTION
COST PER ITEM
STOCK QUANTITY
INVENTORY VALUE
REORDER LEVEL
DAYS PER REORDER
ITEM REORDER QUANTITY
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   |
|:--------|:-------------------|:--------|--------:|--------:|--------:|:--------|:---------------------|:--------|
| Thu     | 3                  |         |       4 |       5 |       3 |         | NW Network 12-2      | Thu     |
| Thu     | 17                 | 14.0    |      18 |      19 |      17 | 14.0    | 19    School T4 Ends | Thu     |
| Fri     | 11                 | 8.0     |      12 |      13 |      11 | 8.0     | 13                   | Fri     |
| Sun     | 13                 | 10.0    |      14 |      15 |      13 | 10.0    | 15                   | Sun     |
| Tue     | 1    New Years Day |         |       2 |       3 |       1 |         | 3                    | Tue     |

Candidate column headers:
September
December
Day
February
October
 January
July
November
Day2



headers: September
December
Day
February
October
 January
July
November
Day2
label: Day
 January
February
July
September
October
November
December
Day2
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 |
|--------:|:-------------------|:--------|:--------|--------:|
|    2009 | Queensland         | All     | All     |      65 |
|    2010 | Queensland         | All     | All     |      66 |
|    2010 | Northern Territory | Male    | All     |      26 |
|    2010 | Tasmania           | Female  | All     |      46 |
|    2009 | Western Australia  | Male    | All     |      60 |

Candidate column headers:
Sex
Calendar Year
Demographic
Value
State



headers: Sex
Calendar Year
Demographic
Value
State
label: Calendar Year
State
Sex
Demographic
Value
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0         |      Col_1 |      Col_2 |      Col_3 |      Col_4 |      Col_5 |      Col_6 |       Col_7 |      Col_8 |
|:--------------|-----------:|-----------:|-----------:|-----------:|-----------:|-----------:|------------:|-----------:|
| 0610005C13Rik | 147.762    | 136.857    | 146.678    | 154.259    | 125.726    | 124.691    |  88.6386    | 124.264    |
| 1110008L16Rik |   6.95873  |   5.97075  |   3.17299  |   3.95793  |   5.09263  |   7.00385  |   3.38921   |   4.11525  |
| 0610010F05Rik |   4.45037  |   7.54976  |   3.57613  |   4.49232  |   4.76336  |   5.9391   |   4.14357   |   5.01765  |
| 1110028F11Rik |   0.159591 |   0.475117 |   0.264937 |   0.269672 |   0.178286 |   0.238472 |   0.0232289 |   0.133785 |
| 1110008F13Rik | 133.388    | 102.252    | 109.218    | 111.887    | 121.062    | 108.689    | 109.041     |  96.1781   |

Candidate column headers:
Wild Type_ Untreated_ Rep 1
Gene Name
Wild Type_ Chronic LPS_ rep 1
Wild Type_ Chronic LPS_ rep 2
Wild Type_ Untreated_ Rep 2
Cyp7a1 Transgene_ Chronic LPS_ rep 1
Cyp7a1 Transgene_ Untreated_ Rep 1
Cyp7a1 Transgene_ Untreated_ Rep 2
Cyp7a1 Transgene_ Chronic LPS_ rep 2



headers: Wild Type_ Untreated_ Rep 1
Gene Name
Wild Type_ Chronic LPS_ rep 1
Wild Type_ Chronic LPS_ rep 2
Wild Type_ Untreated_ Rep 2
Cyp7a1 Transgene_ Chronic LPS_ rep 1
Cyp7a1 Transgene_ Untreated_ Rep 1
Cyp7a1 Transgene_ Untreated_ Rep 2
Cyp7a1 Transgene_ Chronic LPS_ rep 2
label: Gene Name
Wild Type_ Untreated_ Rep 1
Wild Type_ Untreated_ Rep 2
Wild Type_ Chronic LPS_ rep 1
Wild Type_ Chronic LPS_ rep 2
Cyp7a1 Transgene_ Untreated_ Rep 1
Cyp7a1 Transgene_ Untreated_ Rep 2
Cyp7a1 Transgene_ Chronic LPS_ rep 1
Cyp7a1 Transgene_ Chronic LPS_ rep 2
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 |
|--------:|:--------|--------:|--------:|---------:|--------:|---------:|--------:|--------:|
|    2003 | SIME    |   37802 | 2384200 |  3556300 |  865600 |  7969200 | 1243200 | 1163100 |
|    2008 | SIME    |   39629 |  100600 | 10715600 | 7847700 | 21668600 |  536300 | 3004700 |
|    2006 | SIME    |   38898 | 3053300 |  3558000 |  961000 |  8805100 |  990500 | 1232800 |
|    2010 | SIME    |   40359 |  100600 | 10060300 | 7284500 | 20450100 |  680800 | 3004700 |
|    2004 | SIME    |   38168 | 2515400 |  3825600 |  905800 |  8424700 | 1209900 | 1177900 |

Candidate column headers:
Financial Year
Other Reserves
Share Premium
Non-controlling interests
Stock Code
Accumulated Earnings
Year End Date
Paid Up Capital
TOTAL SHAREHOLDERS' FUND



headers: Financial Year
Other Reserves
Share Premium
Non-controlling interests
Stock Code
Accumulated Earnings
Year End Date
Paid Up Capital
TOTAL SHAREHOLDERS' FUND
label: Financial Year
Stock Code
Year End Date
Share Premium
Accumulated Earnings
Other Reserves
TOTAL SHAREHOLDERS' FUND
Non-controlling interests
Paid Up Capital
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0    | Col_1   | Col_2            |   Col_3 |   Col_4 |   Col_5 |    Col_6 |   Col_7 |
|:---------|:--------|:-----------------|--------:|--------:|--------:|---------:|--------:|
| Asia     | ASIA    | Nepal            |   24954 |     874 |       0 |  15471.5 |   62    |
| Asia     | ASIA    | Papua New Guinea |       0 |       0 |       0 |      0   |    0    |
| Asia     | ASIA    | Pakistan         |       0 |       0 |       0 |      0   |    9.12 |
| Africa   | ECARMU  | Burundi          |  553268 |   23799 |      62 | 406265   |   50.15 |
| Americas | LAC     | Guatemala        |       0 |       0 |       0 |      0   |   48.98 |

Candidate column headers:
# of Groups
# of Members
# of Linked Groups
Continent
Column2
# of female members
Total savings by members
Region2



headers: # of Groups
# of Members
# of Linked Groups
Continent
Column2
# of female members
Total savings by members
Region2
label: Continent
Region2
Column2
# of Members
# of Groups
# of Linked Groups
# of female members
Total savings by members
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     |
|--------:|:------------------|:----------|
| 42536.6 | Betty McGillivray | Westview  |
| 42536.7 | Jacquie Williams  | WCS       |
| 42536.6 | Melanie Young     | Hillcrest |
| 42536.6 | Ryan Jutras       | Midale    |
| 42536.7 | Mark Schott       | ECS       |

Candidate column headers:
Timestamp
Your name (first and last please)
Your school



headers: Timestamp
Your name (first and last please)
Your school
label: Timestamp
Your name (first and last please)
Your school