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 |
|--------:|--------:|--------:|--------:|--------:|
|       3 |   38279 |       4 |      46 |       6 |
|       3 |   37489 |       7 |      20 |      15 |
|       2 |   37939 |       6 |      35 |      14 |
|       1 |   37221 |       4 |      11 |       7 |
|       4 |   37334 |       9 |      15 |       4 |

Candidate column headers:
ACCDDATE
MonthID
QtySold
ProdID
StoreID



headers: ACCDDATE
MonthID
QtySold
ProdID
StoreID
label: ProdID
ACCDDATE
QtySold
MonthID
StoreID
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 |
|--------:|:----------|--------:|:------------|:--------------|:--------------|--------:|--------:|--------:|--------:|
|   42939 | Sunday    |       5 | S. Lorenz   | Q. Vanderberg | S. Seronick   | 1018.75 |     150 |     129 |      21 |
|   42933 | Monday    |       0 | -           | -             | -             |    0    |       0 |       0 |       0 |
|   42934 | Tuesday   |       0 | -           | -             | -             |    0    |       0 |       0 |       0 |
|   42935 | Wednesday |       5 | A. Miller   | Q. Vanderberg | S. Seronick   | 1045    |     150 |     131 |      19 |
|   42925 | Sunday    |       8 | L. Hefferon | H. Murakami   | Q. Vanderberg | 1528    |     200 |     180 |      20 |

Candidate column headers:
Lead Chef
Assistant
Day of Week
Waste
Items Sold
Revenue
Date Active
Hours Active
Cashier
Items Available



headers: Lead Chef
Assistant
Day of Week
Waste
Items Sold
Revenue
Date Active
Hours Active
Cashier
Items Available
label: Date Active
Day of Week
Hours Active
Lead Chef
Cashier
Assistant
Revenue
Items Available
Items Sold
Waste
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 |
|:--------|:------------|--------:|
| 2013/14 | Bournemouth |   0.611 |
| 2006/7  | South East  |   0.494 |
| 2010/11 | Bournemouth |   0.574 |
| 2007/8  | Bournemouth |   0.489 |
| 2011/12 | Dorset      |   0.541 |

Candidate column headers:
Geography
%
Year



headers: Geography
%
Year
label: Year
Geography
%
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 |
|:--------|--------:|--------:|--------:|
| A       |      18 |      26 |      22 |
| S       |      16 |      23 |      21 |
| M       |      13 |      18 |      17 |
| J       |       5 |       5 |      13 |
| M       |       8 |       9 |      14 |

Candidate column headers:
London
Tokyo
Los Angeles
Month



headers: London
Tokyo
Los Angeles
Month
label: Month
London
Tokyo
Los Angeles
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   |
|:-------------------------------|--------:|--------:|:--------|
| Aurora_ CO . . . . . . . . .   |     7.7 |    13.4 | 13.4    |
| Detroit_ MI. . . . . . . . . . |    56.8 |    81.6 |         |
| Las Vegas_ NV . . . . . .      |    12.7 |    10.4 | 10.4    |
| Corpus Christi_ TX . . . .     |    12.5 |     4.7 | 4.7     |
| Houston_ TX . . . . . . . .    |    25.9 |    25.3 | 25.3    |

Candidate column headers:
2000  Black Population_ 4 Outliers Removed
City
1994 Homicide Rate/100k
2000 Census_ Black Population of Cities_ %



headers: 2000  Black Population_ 4 Outliers Removed
City
1994 Homicide Rate/100k
2000 Census_ Black Population of Cities_ %
label: City
1994 Homicide Rate/100k
2000 Census_ Black Population of Cities_ %
2000  Black Population_ 4 Outliers Removed
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 |
|------------:|--------:|--------:|--------:|--------:|--------:|
| 6.00051e+06 |   15580 |      28 |      70 |    1960 |  254.8  |
| 6.00078e+06 |   10507 |      81 |      36 |    2916 |  758.16 |
| 6.00081e+06 |   11470 |      91 |      84 |    7644 | 1223.04 |
| 6.00014e+06 |   11470 |      58 |      96 |    5568 | 1224.96 |
| 6.00075e+06 |   15506 |      92 |      87 |    8004 | 2241.12 |

Candidate column headers:
Belopp
Kundnr
Pris
Antal
Fakturanr
TB



headers: Belopp
Kundnr
Pris
Antal
Fakturanr
TB
label: Fakturanr
Kundnr
Antal
Pris
Belopp
TB
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   |
|:--------|:-----------------|:---------|--------:|--------:|--------:|:--------|:--------|
| mars    | Annecy (74000)   | samedi   |   43533 |   43560 |   43511 | B2      | 43573.0 |
| mai     | Annecy (74000)   | mercredi |   43600 |   43265 |   43560 | Pre A1  |         |
| dec     | Gex (01170)      | samedi   |   43449 |   43486 |   43427 | C1      | 43497.0 |
| mai     | Belley (01300)   | samedi   |   43610 |   43640 |   43560 | B2      | 43651.0 |
| dec     | Combloux (74920) | lundi    |   43437 |   43473 |   43399 | A2      | 43487.0 |

Candidate column headers:
1ER DATE DES RESULTATS
DATE LIMITE D'INSCRIPTION
DERNIERE DATE DES RESULTATS
Niveau d'examen
Jour
Mois
LIEU
DATE D'EXAMEN 



headers: 1ER DATE DES RESULTATS
DATE LIMITE D'INSCRIPTION
DERNIERE DATE DES RESULTATS
Niveau d'examen
Jour
Mois
LIEU
DATE D'EXAMEN 
label: Mois
LIEU
Jour
DATE D'EXAMEN 
1ER DATE DES RESULTATS
DATE LIMITE D'INSCRIPTION
Niveau d'examen
DERNIERE DATE DES RESULTATS
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   |
|:-------------|:-----------|--------:|:--------|
| Company One  | Question 3 |       2 | No      |
| Company Five | Question 4 |       4 | No      |
| Company Two  | Question 1 |       1 | Yes     |
| Company Two  | Question 5 |       2 | No      |
| Company Five | Question 5 |       1 | No      |

Candidate column headers:
Essential 
Company
Score
Question



headers: Essential 
Company
Score
Question
label: Company
Question
Score
Essential
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 |
|:--------|--------:|--------:|--------:|----------:|--------:|--------:|--------:|
| 13-2021 |    1391 |    1423 |      32 | 0.023005  |      32 |      50 |      82 |
| 11-9111 |    5586 |    5978 |     392 | 0.0701754 |     392 |     263 |     655 |
| 11-3011 |    7141 |    7526 |     385 | 0.053914  |     385 |     230 |     615 |
| 11-3121 |    2806 |    2944 |     138 | 0.0491803 |     138 |     157 |     295 |
| 13-2031 |     945 |     981 |      36 | 0.0380952 |      36 |      47 |      83 |

Candidate column headers:
Total2
Percent
2016 Estimate
Due to Separations
Due to Growth
2018 Projection
Numeric
SOC Code



headers: Total2
Percent
2016 Estimate
Due to Separations
Due to Growth
2018 Projection
Numeric
SOC Code
label: SOC Code
2016 Estimate
2018 Projection
Numeric
Percent
Due to Growth
Due to Separations
Total2
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    |
|:---------|:---------|:--------|:---------|:--------|:---------|
| CGGBP1   | CCT3     | BEX1    | ARSB     | GOSR2   | EIF2AK2  |
| CCDC109B | C19orf25 | ATP5G2  | APP      | CTH     | CREBL2   |
| CASC4    | C12orf44 | ATP5B   | ANXA4    | CSPG4   | CNPY3    |
| CDK19    | CCDC51   | BCCIP   | ARHGEF12 | FITM2   | DTX3L    |
| ATM      | ATG4B    | ALDOC   | AHNAK2   | CAP1    | C20orf72 |

Candidate column headers:
Upregulated_C
Upregulated_B
Downregulated_A
Downregulated_C
Upregulated_A
Downregulated_B



headers: Upregulated_C
Upregulated_B
Downregulated_A
Downregulated_C
Upregulated_A
Downregulated_B
label: Upregulated_A
Downregulated_A
Upregulated_B
Downregulated_B
Upregulated_C
Downregulated_C
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   |
|:--------|:---------------|:--------|
| Ankers  | Adam Ankers    |         |
| deRussy | Andrew deRussy |         |
| Craig   | Ed Craig       |         |
| Berry   | Wilbur Berry   |         |
| Davis   | Janna Davis    |         |

Candidate column headers:
Full Name
Frequent
Sort By



headers: Full Name
Frequent
Sort By
label: Sort By
Full Name
Frequent
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   |
|:-------------|:----------|:------------|:-------------|:------------|:----------|:--------|:--------|:--------|:--------|:---------|:---------|:---------|:---------|:---------|:---------|
| Azambuja     | Megan     | Christian   | Azambuja     | COACH PITCH | MUD HENS  |         |         |         |         |          |          |          |          |          |          |
| Cartagena    | Christina | Evyn        | Cartagena    | COACH PITCH | MUD HENS  |         |         |         |         |          |          |          |          |          |          |
| Carrasquillo | Juan      | Jasmine     | Carrasquillo | SOFTBALL    | LSU       |         |         |         |         |          |          |          |          |          |          |
| Censoplano   | Michael   | Christopher | Censoplano   | MAJOR       | White Sox |         | 2.0     | 1.0     | 1.0     | 1.0      | 1.0      |          | 3.0      | 1.0      | 1.0      |
| Bertelin     | Al        | Cameron     | Bertelin     | MINOR       | Cardinals |         |         |         | 1.0     | 2.0      | 2.0      | 2.0      |          |          |          |

Candidate column headers:
First Name*
5/6/2017
3/11/2017
4/22/2017
5/20/2017
MC Last Name*
Division*
3/25/2017
MC First Name*
6/10/2017
4/29/2017
4/15/2017
Team*
4/8/2017
Last Name*
6/3/2017



headers: First Name*
5/6/2017
3/11/2017
4/22/2017
5/20/2017
MC Last Name*
Division*
3/25/2017
MC First Name*
6/10/2017
4/29/2017
4/15/2017
Team*
4/8/2017
Last Name*
6/3/2017
label: MC Last Name*
MC First Name*
First Name*
Last Name*
Division*
Team*
3/11/2017
3/25/2017
4/8/2017
4/15/2017
4/22/2017
4/29/2017
5/6/2017
5/20/2017
6/3/2017
6/10/2017
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    |
|:---------|:----------------|:--------|:---------|:---------|
| pBL137-C | Spectinomycin   | p15a    |          |          |
| pBL182-Y | Spectinomycin   | p15a    |          |          |
| pBL137-T | Spectinomycin   | p15a    |          |          |
| pBL119-2 | Chloramphenicol | colEI   | MG323871 | 104336.0 |
| pBL182-Q | Spectinomycin   | p15a    |          |          |

Candidate column headers:
Origin
Genbank Accession Number
Name
Addgene #
Antibiotic



headers: Origin
Genbank Accession Number
Name
Addgene #
Antibiotic
label: Name
Antibiotic
Origin
Genbank Accession Number
Addgene #
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   |
|--------:|--------:|--------:|--------:|:--------|:--------|
|     110 |       0 |       0 |    30   |         |         |
|      38 |       0 |       1 |     9   |         |         |
|      26 |       0 |       2 |     5   |         |         |
|      60 |       0 |       3 |    12   |         |         |
|     100 |       8 |       7 |     0.3 | 27.0    | 176.0   |

Candidate column headers:
Cholesterol (mg)
Carbs (g)
Fat (g)
Calories (kCal)
Sodium (mg)
Protein (g)



headers: Cholesterol (mg)
Carbs (g)
Fat (g)
Calories (kCal)
Sodium (mg)
Protein (g)
label: Calories (kCal)
Fat (g)
Protein (g)
Carbs (g)
Cholesterol (mg)
Sodium (mg)
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 |
|:--------|--------:|--------:|--------:|:--------|--------:|--------:|--------:|--------:|
| PP5287  |       1 |       1 |       1 | Yes     |       3 |   0.313 |   0.346 |   0.306 |
| HM5583  |       1 |       1 |       1 | Yes     |       3 |   0.312 |   0.322 |   0.26  |
| BA5343  |       0 |       0 |       0 | No      |       0 |   0.04  |   0.063 |   0.091 |
| RC5436  |       0 |       1 |       0 | Yes     |       1 |   0.25  |   0.271 |   0.252 |
| PP5324  |       1 |       1 |       1 | Yes     |       3 |   0.35  |   0.31  |   0.271 |

Candidate column headers:
Phi_2012
phi2014
Section_Field
Phi_2014
Recommended to be Key Indicators
How many times (out of 3) in meeting the cut-off (phi>=.255)
phi2012
Phi_2013
phi2013



headers: Phi_2012
phi2014
Section_Field
Phi_2014
Recommended to be Key Indicators
How many times (out of 3) in meeting the cut-off (phi>=.255)
phi2012
Phi_2013
phi2013
label: Section_Field
phi2014
phi2013
phi2012
Recommended to be Key Indicators
How many times (out of 3) in meeting the cut-off (phi>=.255)
Phi_2014
Phi_2013
Phi_2012
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 |
|--------:|:---------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|---------:|---------:|
|   39990 | Martinez |   39989 |       0 |       0 |       0 |       2 |       0 |       0 |       0 |        0 |        0 |        0 |        0 |        0 |
|   39909 | Thomas   |   39908 |       0 |       2 |       0 |       0 |       0 |       0 |       0 |        0 |        0 |        0 |        0 |        0 |
|   39984 | Martinez |   39979 |       0 |       0 |       0 |       6 |       0 |       0 |       0 |        0 |        0 |        0 |        0 |        0 |
|   40007 | Moore    |   40005 |       0 |       0 |       0 |       0 |       3 |       0 |       0 |        0 |        0 |        0 |        0 |        0 |
|   39913 | Hall     |   39911 |       0 |       3 |       0 |       0 |       0 |       0 |       0 |        0 |        0 |        0 |        0 |        0 |

Candidate column headers:
11/30/09
12/31/09
01/31/10
07/31/09
08/31/09
04/30/09
09/30/09
10/31/09
NName
End
02/28/10
05/31/09
06/30/09
03/31/09
Start



headers: 11/30/09
12/31/09
01/31/10
07/31/09
08/31/09
04/30/09
09/30/09
10/31/09
NName
End
02/28/10
05/31/09
06/30/09
03/31/09
Start
label: End
NName
Start
03/31/09
04/30/09
05/31/09
06/30/09
07/31/09
08/31/09
09/30/09
10/31/09
11/30/09
12/31/09
01/31/10
02/28/10
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       |
|:--------------|:-----------|:----------|:------------|
| Bedford_ TX   | 76021-5708 | Tuesday   | 07:00 PM_ E |
| Burleson_ TX  | 76028-4257 | Wednesday | 12:00 PM_ E |
| Arlington_ TX | 76013-2206 | Tuesday   | 11:00 AM_ E |
| Arlington_ TX | 76013-2206 | Monday    | 11:00 AM_ E |
| Arlington_ TX | 76016      | Friday    | 06:30 PM_ E |

Candidate column headers:
Time and Codes
City_ State
Zip
MeetingDay



headers: Time and Codes
City_ State
Zip
MeetingDay
label: City_ State
Zip
MeetingDay
Time and Codes
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   |
|:-------------------------|--------:|--------:|--------:|:--------|
| Staple length (mm)       |   100.7 |    93.3 |     7.4 | 6th     |
| Post Weaning (kg)        |    33.7 |    31.5 |     2.2 | 4th     |
| Birth Weight (kg)        |     5.3 |     4.9 |     0.4 |         |
| % footscore 4 or greater |    62.1 |    86.5 |   -24.5 | 2nd     |
| Wrinkle Score (1-5)      |     1.9 |     2.4 |    -0.5 | 5th     |

Candidate column headers:
Overall CPT Average
Sire Deviation from CPT Average
Trigger Vale Sire Average
Ranking
Column1



headers: Overall CPT Average
Sire Deviation from CPT Average
Trigger Vale Sire Average
Ranking
Column1
label: Column1
Trigger Vale Sire Average
Overall CPT Average
Sire Deviation from CPT Average
Ranking
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 |   Col_1 |   Col_2 |   Col_3 |   Col_4 |
|--------:|--------:|--------:|--------:|--------:|
|      18 |    3740 |    3480 |    1870 |    3660 |
|       7 |    3040 |    1700 |    2390 |    1660 |
|       8 |    2160 |    3330 |    4040 |    3300 |
|       2 |    1480 |    1490 |    3190 |    2950 |
|      13 |    1920 |    4210 |    1580 |    3910 |

Candidate column headers:
MONTH
PROJECTED MONTHLY COST
ACTUAL CUMULATIVE COST
PROJECTED CUMULATIVE COST
ACTUAL MONTHLY COST



headers: MONTH
PROJECTED MONTHLY COST
ACTUAL CUMULATIVE COST
PROJECTED CUMULATIVE COST
ACTUAL MONTHLY COST
label: MONTH
PROJECTED MONTHLY COST
PROJECTED CUMULATIVE COST
ACTUAL MONTHLY COST
ACTUAL CUMULATIVE 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 |    Col_7 |   Col_8 |       Col_9 |     Col_10 |
|--------:|---------:|--------:|--------:|--------:|-------------:|-----------:|---------:|--------:|------------:|-----------:|
|     379 | 346.141  |       0 |  0.0038 |  2.0916 | -4.7619e-06  | 0.00381429 | 1.34447  |       0 | 0.000619008 | 0.00125313 |
|     463 | 472.772  |       0 |  0.0034 |  2.4419 | -4.7619e-06  | 0.00425238 | 1.48306  |       0 | 0.000323529 | 0.00140056 |
|     421 | 410.483  |       0 |  0.0036 |  2.2695 | -4.7619e-06  | 0.00404286 | 1.51257  |       0 | 0.000617929 | 0.00132275 |
|     148 |  98.7412 |       0 |  0.0081 |  1.3924 | -6.66667e-05 | 0.00225238 | 0.850301 |       0 | 0.00332883  | 0.00823045 |
|     442 | 441.628  |       0 |  0.0035 |  2.3526 | -4.7619e-06  | 0.00395714 | 1.48306  |       0 | 0.000524344 | 0.00136054 |

Candidate column headers:
Bg
Rssat
DBo/DPo
Tcoil
DRv/DPo
Rvsat
DBg/DPo
Tcgas
DRs/DPo
Press
Bo



headers: Bg
Rssat
DBo/DPo
Tcoil
DRv/DPo
Rvsat
DBg/DPo
Tcgas
DRs/DPo
Press
Bo
label: Press
Rssat
Rvsat
Bg
Bo
DBg/DPo
DBo/DPo
DRs/DPo
DRv/DPo
Tcoil
Tcgas
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 |
|--------:|--------:|:--------|:----------|--------:|--------:|--------:|--------:|--------:|
|      49 |   40227 | South   | Whitesell |       1 |       4 |       4 |       9 |     748 |
|      46 |   40224 | North   | Potok     |       1 |       3 |       0 |       4 |     265 |
|      30 |   40208 | East    | Helstrom  |       1 |       2 |       4 |       7 |     598 |
|      11 |   40189 | West    | Helstrom  |       4 |       4 |       4 |      12 |     868 |
|      39 |   40217 | North   | Minnis    |       0 |       0 |       5 |       5 |     510 |

Candidate column headers:
Small
Sales
Invoice No
Sold
Rep
Date
Area
Medium
Large



headers: Small
Sales
Invoice No
Sold
Rep
Date
Area
Medium
Large
label: Invoice No
Date
Area
Rep
Small
Medium
Large
Sold
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 |
|--------:|--------:|--------:|--------:|
|    2010 |    49.5 |    50.5 |    50   |
|    1996 |    44.7 |    48.3 |    46.4 |
|    1981 |    43.8 |    47.8 |    45.8 |
|    1997 |    45.2 |    48.5 |    46.8 |
|    2000 |    46.2 |    48.8 |    47.5 |

Candidate column headers:
Year
Family medicine
Specialists
Total physicians



headers: Year
Family medicine
Specialists
Total physicians
label: Year
Family medicine
Specialists
Total physicians
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       |
|:--------|:-----------------|:-----------------|:----------------|:-----------|:------------|
| V       |                  |                  | Paste           |            |             |
| S       |                  |                  | Save            |            |             |
| F8      | Extend Selection | Add To Selection | Resize Window   | Macro List |             |
| F11     | New Chart        | New Worksheet    | New Macro Sheet | VB Editor  |             |
| 3 (#)   |                  |                  | Toggle Italics  |            | Date Format |

Candidate column headers:
Alone
Shift
Ctrl
Shift + Ctrl
Alt
Key



headers: Alone
Shift
Ctrl
Shift + Ctrl
Alt
Key
label: Key
Alone
Shift
Ctrl
Alt
Shift + Ctrl
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      |
|:--------|:--------|:--------|:-----------------|--------:|--------:|--------:|--------:|:-----------|
| Italy   | Italy   | Edison  | Other renewables |     7   |     7   |    2002 |       1 | Full       |
| France  | France  | EDF     | Hydropower       |   406   |   406   |    1933 |       1 | Parent Co. |
| France  | France  | EDF     | Hydropower       |   910   |   910   |    1982 |       1 | Parent Co. |
| Italy   | Italy   | Edison  | Other renewables |    34   |    34   |    2008 |       1 | Full       |
| Italy   | Italy   | Edison  | Other renewables |     3.5 |     3.5 |    1998 |       1 | Full       |

Candidate column headers:
Net capacity consolidated by EDF (MW)
Consolidation method
Year of industrial commissioning
Company
Segment
Capacity owned by the entity (MW)
EDF Stake (%)
Country
Technology



headers: Net capacity consolidated by EDF (MW)
Consolidation method
Year of industrial commissioning
Company
Segment
Capacity owned by the entity (MW)
EDF Stake (%)
Country
Technology
label: Segment
Country
Company
Technology
Capacity owned by the entity (MW)
Net capacity consolidated by EDF (MW)
Year of industrial commissioning
EDF Stake (%)
Consolidation method
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 |
|:-----------|:-----------------|:----------|--------:|--------:|-----------:|------------:|------------:|------------:|
| Hunter     | Base-Flat        | Coal      |  12     |   12    | 86692.4    | 0.189917    | 0.102283    | 0.11257     |
| VEYO       | Base             | Renewable |   1.7   |    1.7  | 11689.7    | 0.0256085   | 0.0144902   | 0.0159475   |
| Solar Farm | Base             | Renewable |   0.06  |    0.06 |    89.7178 | 0.000196544 | 0.000511417 | 0.000562852 |
| PPM Wind   | Base             | Renewable |   5     |    5    |  9949.1    | 0.0217954   | 0.0426181   | 0.0469043   |
| CRSP       | Base -Adjustable | Hydro     |  14.771 |    9.64 | 72383.7    | 0.15857     | 0.125902    | 0.0904315   |

Candidate column headers:
Summer Capacity%
Winter Capacity %
Fuel Type
Energy %
RESCOURCE FY 2017
Winter Capacity MW
Base/Peak Load
FY Energy MWH
Summer Capacity MW



headers: Summer Capacity%
Winter Capacity %
Fuel Type
Energy %
RESCOURCE FY 2017
Winter Capacity MW
Base/Peak Load
FY Energy MWH
Summer Capacity MW
label: RESCOURCE FY 2017
Base/Peak Load
Fuel Type
Winter Capacity MW
Summer Capacity MW
FY Energy MWH
Energy %
Winter Capacity %
Summer Capacity%
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   |
|:-------------|:--------------------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:---------|:---------|:---------|:---------|:---------|:---------|:---------|:---------|:---------|:---------|
| AZERBAIJAN   | Diplomatic passport | yes(2)  | yes(2)  | yes(2)  |         | yes(2)  | yes(2)  | yes(2)  | yes(2)  | yes(2)   | yes(2)   | yes(2)   | yes(2)   | yes(2)   | yes(2)   | yes(2)   | yes(2)   | yes(2)   | yes(2)   |
| BANGLADESH   | Special passport    |         |         |         |         |         |         |         |         |          |          |          |          |          |          |          |          |          |          |
| ALGERIA      | Diplomatic passport |         |         |         |         | yes     |         | yes     | yes     | yes      |          | yes      | yes      |          |          | yes      | yes      |          | yes      |
| BURKINA FASO | Diplomatic passport |         |         |         |         |         |         |         |         |          |          |          | yes      |          |          |          |          |          |          |
| BELIZE       | Special passport    |         |         |         |         |         |         |         |         |          |          |          |          |          |          |          |          |          |          |

Candidate column headers:
CY
EE
LV
EL
HU
BNL (1)
FR
CZ
HR
Countries
IT
BG
PL
DK
LT
DE
MT
ES
AT
Type of Passport



headers: CY
EE
LV
EL
HU
BNL (1)
FR
CZ
HR
Countries
IT
BG
PL
DK
LT
DE
MT
ES
AT
Type of Passport
label: Countries
Type of Passport
BNL (1)
BG
CZ
DK
DE
EE
EL
ES
FR
LT
HR
IT
CY
LV
HU
MT
AT
PL
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 |
|:---------|--------:|--------:|--------:|--------:|
| Produs14 |   41930 |      93 |     578 |   53754 |
| Produs9  |   41880 |     207 |     728 |  150696 |
| Produs12 |   41910 |      27 |     841 |   22707 |
| Produs11 |   41900 |     123 |     106 |   13038 |
| Produs4  |   41830 |     157 |     124 |   19468 |

Candidate column headers:
Nr. Bucati
Total
Data intrare
Pret
Nume produs



headers: Nr. Bucati
Total
Data intrare
Pret
Nume produs
label: Nume produs
Data intrare
Nr. Bucati
Pret
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   |
|:------------------|:------------------|:-------------------|:-------------------|--------:|--------:|--------:|--------:|:--------|
| RESOLYS           | RESOLYS           | ANNULATION VR      | AFFAIRES NOUVELLES |    2356 |   12344 |   88888 |     456 | FEVRIER |
| forfait autonomie | forfait autonomie | VERSEMENT REGULIER | AFFAIRES NOUVELLES |   10000 |   34000 |    2300 |    6788 | JANVIER |
| forfait autonomie | forfait autonomie | SOUSCRIPTION       | STOCK              |   10000 |   34000 |    2300 |     234 | JANVIER |
| forfait autonomie | forfait autonomie | SOUSCRIPTION       | STOCK              |   10000 |   34000 |    2300 |     234 | JANVIER |
| forfait autonomie | forfait autonomie | SOUSCRIPTION       | AFFAIRES NOUVELLES |   10000 |   34000 |    2300 |     123 | JANVIER |

Candidate column headers:
Libelle evenement
 lignes
Produit
Mois
Support
Bases commissionables
Type
Versements
Commissions



headers: Libelle evenement
 lignes
Produit
Mois
Support
Bases commissionables
Type
Versements
Commissions
label: Produit
Support
Libelle evenement
Type
Versements
Bases commissionables
Commissions
 lignes
Mois
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 |
|--------:|--------:|--------:|--------:|
|    1988 |    22.5 |      27 |      27 |
|    1995 |    34   |      44 |     124 |
|    2008 |    63   |      86 |     176 |
|    2012 |    82   |     108 |     220 |
|    2004 |    55   |      75 |     155 |

Candidate column headers:
Out-of-District
Year
Out-of-State
In-District



headers: Out-of-District
Year
Out-of-State
In-District
label: Year
In-District
Out-of-District
Out-of-State
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   |
|:---------------------|:--------|:--------|:--------|:--------|
| Free & Reduced Lunch | #REF!   | #REF!   | #REF!   | #REF!   |
| Male                 | #REF!   | #REF!   | #REF!   | #REF!   |
| Am. Indian/Alaskan   | #REF!   | #REF!   | #REF!   | #REF!   |
| White                | #REF!   | #REF!   | #REF!   | #REF!   |
| Female               | #REF!   | #REF!   | #REF!   | #REF!   |

Candidate column headers:
Program#
Program%
Group
District%
District#



headers: Program#
Program%
Group
District%
District#
label: Group
District#
Program#
District%
Program%
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 |
|--------:|--------:|--------:|--------:|--------:|--------:|--------:|
|      47 |      40 |       0 |       0 |       0 |       0 |      10 |
|      25 |     270 |     180 |     100 |      20 |       0 |      20 |
|      13 |     510 |     340 |     220 |     140 |      80 |      20 |
|      37 |     140 |      60 |       0 |       0 |       0 |      10 |
|      49 |      20 |       0 |       0 |       0 |       0 |      10 |

Candidate column headers:
E
D
B
delta
A
C
poradie



headers: E
D
B
delta
A
C
poradie
label: poradie
A
B
C
D
E
delta
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 |
|--------:|--------:|--------:|
|   39822 |      79 |       3 |
|   39838 |     498 |      26 |
|   39831 |      88 |      19 |
|   39832 |     348 |       2 |
|   39830 |     420 |      16 |

Candidate column headers:
Date
OrderAmount
CustomerID



headers: Date
OrderAmount
CustomerID
label: Date
CustomerID
OrderAmount
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   |
|:---------------------|:-------------|:--------|
| Fixed assets (Asset) | Asset->Asset | Display |
| Fixed assets (Asset) | Asset->Asset | Display |
| Fixed assets (Asset) | Asset->Asset | Display |
| Fixed assets (Asset) | Asset->Asset | Display |
| Fixed assets (Asset) | Asset->Asset | Display |

Candidate column headers:
LicenseCodeLabel (Name)
Path
MenuItemType



headers: LicenseCodeLabel (Name)
Path
MenuItemType
label: LicenseCodeLabel (Name)
Path
MenuItemType
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 |
|--------:|--------:|:--------------------|--------:|
|   38442 |   38443 | 5.75% June 2011     |    52   |
|   38404 |   38405 | 6.75% Nov 2006      |     7.9 |
|   38405 |   38406 | 5.75% June 2011     |    50   |
|   38463 |   38464 | 5.25% Aug 2010      |    51   |
|   38404 |   38405 | 10.00% October 2007 |    13.7 |

Candidate column headers:
 Face Value Amount Lent ($m)
Start Date
Stock
End Date



headers:  Face Value Amount Lent ($m)
Start Date
Stock
End Date
label: Start Date
End Date
Stock
 Face Value Amount Lent ($m)