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 |
|:----------------------|--------:|--------:|--------:|--------:|
| Grasso Pietro         |    1232 |   41643 |   41687 |     274 |
| Costa Michele         |    1135 |   41742 |   41802 |     159 |
| Leone Caterina        |     309 |   41622 |   41755 |       6 |
| Martinelli Antonietta |     223 |   41787 |   41847 |      23 |
| Bianco Angelo         |    1247 |   41667 |   41764 |     197 |

Candidate column headers:
CLIENTE
SCADUTA (60)
EMISSIONE
TOTALE
RITARDO



headers: CLIENTE
SCADUTA (60)
EMISSIONE
TOTALE
RITARDO
label: CLIENTE
TOTALE
EMISSIONE
SCADUTA (60)
RITARDO
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             |
|--------:|-----------:|:-------------------|:------------------|
|   37987 | -0.610382  |                    | -0.61038182553073 |
|   31413 | -0.492422  |                    | -0.49242200363423 |
|   32874 |  0.439122  | 0.4391218144860499 |                   |
|   32509 |  0.547757  | 0.5477573218146    |                   |
|   35065 | -0.0546838 |                    | -0.05468375668068 |

Candidate column headers:
positif
BPBLTT01USA188S_CH1
observation_date
neg



headers: positif
BPBLTT01USA188S_CH1
observation_date
neg
label: observation_date
BPBLTT01USA188S_CH1
positif
neg
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 |
|--------:|--------:|--------:|--------:|--------:|--------:|
| 59.9695 | 56.75   | 63.35   | 71.7532 | 68.681  | 74.979  |
| 67.9304 | 65.765  | 70.204  | 74.1271 | 70.432  | 78.007  |
| 74.6732 | 71.6    | 77.9    | 82.3398 | 79.33   | 85.5    |
| 70.6801 | 67.644  | 73.868  | 73.5374 | 70.353  | 76.881  |
| 61.2741 | 59.2441 | 63.4056 | 70.9329 | 68.6037 | 73.3785 |

Candidate column headers:
1980_ Total
1980_ male
1980_ female
2008
Female
Male



headers: 1980_ Total
1980_ male
1980_ female
2008
Female
Male
label: 1980_ Total
1980_ male
1980_ female
2008
Male
Female
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         |
|:--------------------|:------------------------------|:------------------------------|:------------------------------|:----------------------------|:--------------|
| Other-Other 1       | Maintenance-Vehicle washing   | Maintenance-Vehicle washing   |                               | Other-Other 1               |               |
| Other-Other 2       | Maintenance-Pavement cleaning | Maintenance-Pavement cleaning |                               | Other-Other 2               |               |
| Maintenance-Boiler  | Maintenance-Boiler            | Maintenance-Boiler            | Other-Other 3                 | Maintenance-Vehicle washing |               |
| Restrooms-Faucets   | Restrooms-Faucets             | Restrooms-Faucets             | Maintenance-Pavement cleaning | Restrooms-Faucets           | Other-Other 1 |
| Break rooms-Faucets | Break rooms-Faucets           | Break rooms-Faucets           | Other-Other 1                 | Break rooms-Faucets         | Other-Other 2 |

Candidate column headers:
Ground Transportation
Office Buildings
Parking
Rental Car Center
Central Heating/Cooling Plant
Fire and Police Stations



headers: Ground Transportation
Office Buildings
Parking
Rental Car Center
Central Heating/Cooling Plant
Fire and Police Stations
label: Office Buildings
Rental Car Center
Ground Transportation
Parking
Fire and Police Stations
Central Heating/Cooling Plant
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 |
|--------:|--------:|--------:|--------:|-----------------:|--------:|-----------------:|--------:|-----------------:|
|    2555 |   52636 |  350449 |   62148 | 417318           |   42985 | 519199           |   35190 | 547555           |
|    4604 |   14098 |  965032 |   10975 |      1.00653e+06 |   11932 |      1.08167e+06 |   13384 |      1.01844e+06 |
|   10865 |   33368 |  462776 |   19759 | 516384           |   21513 | 574701           |   20023 | 532058           |
|    1002 |    8713 |  466459 |    2986 | 502619           |     886 | 567004           |    1063 | 542093           |
|    2526 |    4558 |  454803 |     536 | 472965           |    1153 | 500853           |    4031 | 467762           |

Candidate column headers:
2015 Denial Nature.com platform
2014 Denial Nature.com platform
2013 Denial Nature.com platform
2016 Download Nature.com platform
Mps Site ID
2015 Download Nature.com platform
2013 Download Nature.com platform
2014 Download Nature.com platform
2016 Denial Nature.com platform



headers: 2015 Denial Nature.com platform
2014 Denial Nature.com platform
2013 Denial Nature.com platform
2016 Download Nature.com platform
Mps Site ID
2015 Download Nature.com platform
2013 Download Nature.com platform
2014 Download Nature.com platform
2016 Denial Nature.com platform
label: Mps Site ID
2013 Denial Nature.com platform
2013 Download Nature.com platform
2014 Denial Nature.com platform
2014 Download Nature.com platform
2015 Denial Nature.com platform
2015 Download Nature.com platform
2016 Denial Nature.com platform
2016 Download Nature.com platform
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 |
|--------:|:--------|--------:|--------:|
|    7372 | FALL    |       2 |     6   |
|    5273 | FALL    |       2 |     4.5 |
|    5294 | FALL    |       4 |     6   |
|    5660 | SPRING  |       3 |     6   |
|    7367 | SPRING  |       1 |     3   |

Candidate column headers:
CREDITS
CODE
SEMESTER
YEAR



headers: CREDITS
CODE
SEMESTER
YEAR
label: CODE
SEMESTER
YEAR
CREDITS
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 |
|:---------------|--------:|--------:|--------:|--------:|
| Belgium        |   0.45  |   0.402 |    0.34 |    0.34 |
| Sweden         |   0.566 |   0.28  |    0.28 |    0.22 |
| United Kingdom |   0.35  |   0.33  |    0.3  |    0.2  |
| Malta          |   0.325 |   0.35  |    0.35 |    0.35 |
| Slovakia       |   0.45  |   0.4   |    0.19 |    0.22 |

Candidate column headers:
Country
1986
2016
1996
2006



headers: Country
1986
2016
1996
2006
label: Country
1986
1996
2006
2016
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0   | Col_1   | Col_2   | Col_3   | Col_4         | Col_5   | Col_6              | Col_7   | Col_8   | Col_9   | Col_10   | Col_11   | Col_12   | Col_13   | Col_14   |
|:--------|:--------|:--------|:--------|:--------------|:--------|:-------------------|:--------|:--------|:--------|:---------|:---------|:---------|:---------|:---------|
| IPICS   | LAO01   | B       | RG      | Laos          |         |                    |         |         | 0.625   |          |          | 0.55     |          |          |
| IPICS   | ALNAP   | B       | NW      | Africa        |         | 0.5799999999999998 |         |         | 0.675   |          |          |          |          |          |
| IPICS   | BUF01   | A       | RG      | Burkina Faso  |         | 0.5                | 0.625   | 0.625   |         |          | 0.66     |          |          | 0.48     |
| IPICS   | LAO03   | A       | RG      | Laos          | 0.33    |                    | 0.15    |         |         |          |          |          |          |          |
| IPICS   | AFASSA  | A       | NW      | Interregional |         | 0.33               |         |         |         |          |          |          |          |          |

Candidate column headers:
2010
2015
2014
2008
RG/NW
2012
2011
CODE
Country
2017
2009
2016
2013
PROG
Reviewed by



headers: 2010
2015
2014
2008
RG/NW
2012
2011
CODE
Country
2017
2009
2016
2013
PROG
Reviewed by
label: PROG
CODE
Reviewed by
RG/NW
Country
2008
2009
2010
2011
2012
2013
2014
2015
2016
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                |
|--------:|:----------------------|:---------------------|
|   42753 | Scholarships & Prizes |                      |
|   42769 | Student Finance       |                      |
|   42786 | Student Finance       | Study Period 1       |
|   42743 | Student Finance       | OUA - Study Period 4 |
|   42787 | Scholarships & Prizes |                      |

Candidate column headers:
Study Period
Due Date
Area



headers: Study Period
Due Date
Area
label: Due Date
Area
Study Period
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 |
|--------:|:-----------|:--------|--------:|:----------------|:---------|:---------|:--------|:---------|--------:|:---------|:---------|---------:|
|      45 | Issue # 45 | Ram     |   40317 | Payments        | Critical | Prasoon  | 40338.0 | Comments |      21 | 24       | Closed   |       21 |
|      47 | Issue # 47 | Ram     |   40307 | Payments        | High     | Trisha   |         | Comments |      20 | #VALUE!  | Open     |       16 |
|      28 | Issue # 28 | Ramesh  |   40303 | Policy Admin    | Critical | Prashant |         | Comments |      19 | #VALUE!  | Open     |       20 |
|      27 | Issue # 27 | Shyam   |   40269 | Payments        | Critical | Trisha   | 40273.0 | Comments |      14 | 15       | Closed   |        4 |
|      38 | Issue # 38 | Ramesh  |   40276 | Policy Creation | Low      | Prashant | 40295.0 | Comments |      15 | 18       | Closed   |       19 |

Candidate column headers:
Additional Comments
Created on (w)
Assigned to
Fixed on (w)
Priority
Created by
Bug  #
Age
Created on
Module
Bug Details
Fixed on
Status



headers: Additional Comments
Created on (w)
Assigned to
Fixed on (w)
Priority
Created by
Bug  #
Age
Created on
Module
Bug Details
Fixed on
Status
label: Bug  #
Bug Details
Created by
Created on
Module
Priority
Assigned to
Fixed on
Additional Comments
Created on (w)
Fixed on (w)
Status
Age
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 |
|--------:|--------:|--------:|--------:|--------:|
|      23 |     471 |      80 |   83.23 |   16.77 |
|      34 |     715 |     180 |   75.52 |   24.48 |
|     248 |    5206 |     709 |   83.86 |   16.14 |
|       7 |     139 |      51 |   62.59 |   37.41 |
|      49 |    1037 |     151 |   85.44 |   14.56 |

Candidate column headers:
[300] GG.Ass.Tot.
[507] Percentuale Ass.
[102] Num.Dip.Fine Periodo
[402] Tasso Presenza
[200] GG.Lavorativi



headers: [300] GG.Ass.Tot.
[507] Percentuale Ass.
[102] Num.Dip.Fine Periodo
[402] Tasso Presenza
[200] GG.Lavorativi
label: [102] Num.Dip.Fine Periodo
[200] GG.Lavorativi
[300] GG.Ass.Tot.
[402] Tasso Presenza
[507] Percentuale Ass.
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   |
|:--------------------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|
| Danville City       | VA      | A1234   | B1234   | C1234   | D1234   | G1234   | ABCDG   |
| Carroll             | VA      | A34     | B1234   | C1234   | D1234   | G1234   | A34BCDG |
| Fredericksburg City | VA      | A1234   | B1234   | C1234   | Full    | G1234   | ABC G   |
| Alexandria City     | VA      | Full    | Full    | Full    | Full    | Full    | Full    |
| Arlington           | VA      | Full    | Full    | Full    | Full    | Full    | Full    |

Candidate column headers:
C Group Channels Available
A Group Channels Available
State
County
Empty Channel Blocks
D Group Channels Available
B Group Channels Available
G Group Channels Available



headers: C Group Channels Available
A Group Channels Available
State
County
Empty Channel Blocks
D Group Channels Available
B Group Channels Available
G Group Channels Available
label: County
State
A Group Channels Available
B Group Channels Available
C Group Channels Available
D Group Channels Available
G Group Channels Available
Empty Channel Blocks
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               |
|:-------------------|:---------------|:----------|:--------------------|
| SCRIM Safety First | 61 2 9977 1077 |           |                     |
| EHSInsight         | 8775717475     |           |                     |
| IndustrySafe       | 2155469110     |           |                     |
| RCI Safety         | 8008889596     | Web-Based | info@rci-safety.com |
| Intelex            | 4165996009     |           |                     |

Candidate column headers:
Phone
Name
Comment
Contact



headers: Phone
Name
Comment
Contact
label: Name
Phone
Comment
Contact
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   |
|--------:|--------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|:---------|
|    1976 |  129347000000 | 1.21143e+11 | 1.21709e+11 | 1.22275e+11 | 1.22842e+11 | 1.23408e+11 | 1.22275e+11 | 1.23408e+11 | 1.21143e+11 | C        |
|    2006 | 1798820000000 | 9.22173e+11 | 1.06556e+12 | 1.23042e+12 | 1.41983e+12 | 1.63733e+12 | 1.25506e+12 | 1.63733e+12 | 9.22173e+11 | B        |
|    1995 |  872727000000 | 4.38118e+11 | 4.80933e+11 | 5.27704e+11 | 5.78776e+11 | 6.3452e+11  | 5.3201e+11  | 6.3452e+11  | 4.38118e+11 | C        |
|    1991 |  677397000000 | 3.34238e+11 | 3.60123e+11 | 3.87878e+11 | 4.1763e+11  | 4.4951e+11  | 3.89876e+11 | 4.4951e+11  | 3.34238e+11 | C        |
|    1994 |  822982000000 | 4.09456e+11 | 4.4738e+11  | 4.88615e+11 | 5.33434e+11 | 5.82129e+11 | 4.92203e+11 | 5.82129e+11 | 4.09456e+11 | C        |

Candidate column headers:
8.0% Rate
Maximum
Average
7.5% Rate
Year
Expenditures
Class
Minimum
7.0% Rate
8.5% Rate
9.0% Rate



headers: 8.0% Rate
Maximum
Average
7.5% Rate
Year
Expenditures
Class
Minimum
7.0% Rate
8.5% Rate
9.0% Rate
label: Year
Expenditures
7.0% Rate
7.5% Rate
8.0% Rate
8.5% Rate
9.0% Rate
Average
Maximum
Minimum
Class
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 |
|:--------|:----------|:--------|:---------|--------:|--------:|--------:|
| OR      | Wholesale | Mid     | Kids     |     325 |  710.34 |  71.034 |
| WA      | Wholesale | High    | Art      |     410 | 1062.13 | 106.213 |
| NV      | Retail    | Mid     | Kids     |     476 | 1977    | 197.7   |
| CA      | Wholesale | Low     | Humorous |     435 |  738.58 |  73.858 |
| OR      | Wholesale | High    | Art      |     900 | 2196.42 | 219.642 |

Candidate column headers:
Category
Price
Qty
Dollars
State
Channel
Bonus



headers: Category
Price
Qty
Dollars
State
Channel
Bonus
label: State
Channel
Price
Category
Qty
Dollars
Bonus
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   |
|--------:|:------------|:-------------------|:--------|
|    4650 | Maharashtra | Affiliated College | NO      |
|    4972 | Maharashtra | Affiliated College | NO      |
|    4848 | Maharashtra | Affiliated College | NO      |
|    4780 | Maharashtra | Affiliated College | NO      |
|    4532 | Maharashtra | Affiliated College | NO      |

Candidate column headers:
Sr.No.
College Type
Name of the State
Registered and Approved 



headers: Sr.No.
College Type
Name of the State
Registered and Approved 
label: Sr.No.
Name of the State
College Type
Registered and Approved
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            |
|--------:|--------:|:--------|--------:|:-------------|--------:|:------------|:---------------------|:--------|:--------------------|:---------|---------:|:------------------|
|   43371 |    2615 | Success |  614811 | G01GNL92GL/1 |    4613 | Cumbernauld | Glasgow_ Lanarkshire | G67 2LA | Pass                | 43213.0  |      779 | North Lanarkshire |
|   43371 |    2615 | Success |  613292 | C12HNLMOSS/1 |    7843 | Cumbernauld | Glasgow_ Lanarkshire | G67 3NZ | Pass                | 43129.0  |      779 | North Lanarkshire |
|   43371 |    2615 | Success |  614645 | G01GNL23GL/1 |    4613 | Wishaw      | Lanarkshire          | ML2 7PG | Pass                | 43137.0  |      779 | North Lanarkshire |
|   43371 |    2615 | Success |  613227 | C12HNL263S/1 |    7843 | Shotts      | Lanarkshire          | ML7 5EB | Pass                | 42836.0  |      779 | North Lanarkshire |
|   43371 |    2615 | Success | 1073748 | C01HNL541M/6 |       1 | Bellshill   | North Lanarkshire    | ML4 1DG | Awaiting Inspection |          |      779 | North Lanarkshire |

Candidate column headers:
PostCode
LocalAuthorityCode
BusinessTypeID
RatingDate
ReturnCode
ExtractDate
LocalAuthorityBusinessID
FHRSID
ItemCount
RatingValue
AddressLine3
AddressLine2
LocalAuthorityName



headers: PostCode
LocalAuthorityCode
BusinessTypeID
RatingDate
ReturnCode
ExtractDate
LocalAuthorityBusinessID
FHRSID
ItemCount
RatingValue
AddressLine3
AddressLine2
LocalAuthorityName
label: ExtractDate
ItemCount
ReturnCode
FHRSID
LocalAuthorityBusinessID
BusinessTypeID
AddressLine2
AddressLine3
PostCode
RatingValue
RatingDate
LocalAuthorityCode
LocalAuthorityName
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              |
|--------:|:-----------------------|:--------|:------------|:------------------|:-------------------|
|       4 | Bilthoven              | Utrecht | Netherlands | Voluntary         | Johan van Leeuwen  |
|       6 | Camden                 | NJ      | USA         | NJDEP Brownfields | Melissa Harclerode |
|       7 | Port Jefferson Station | NY      | USA         | CERCLA            | Ellen Gallerie     |
|       5 |                        | CT      | USA         | CTDEEP            | Michael E. Miller  |
|      11 | Minneapolis            | MN      | USA         | MPCA              | Amanda McNally     |

Candidate column headers:
Location
State
#
Regulatory Program
Primary Contact
Country



headers: Location
State
#
Regulatory Program
Primary Contact
Country
label: #
Location
State
Country
Regulatory Program
Primary Contact
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   |
|--------:|:----------|:--------|:--------|--------:|--------:|--------:|:--------|
|   39539 | kigali    | rwanda  | beans   | 621.01  |    2008 |       4 | MT      |
|   40634 | kimironko | rwanda  | maize   | 201.99  |    2011 |       4 | MT      |
|   40269 | rwanda    | rwanda  | sorghum | 379     |    2010 |       4 | MT      |
|   40634 | rwanda    | rwanda  | maize   | 315.737 |    2011 |       4 | MT      |
|   40269 | kimironko | rwanda  | maize   | 254.98  |    2010 |       4 | MT      |

Candidate column headers:
Country
Retail(USD/1000)
End Date
Product
Yr
Unit
mon
Market



headers: Country
Retail(USD/1000)
End Date
Product
Yr
Unit
mon
Market
label: End Date
Market
Country
Product
Retail(USD/1000)
Yr
mon
Unit
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   |
|:---------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:---------|
| Caitlyn  | P       | P       | P       | P       | P       | P       | P       | P       | P       | P        |
| Lisa     | P       | M       | M       | P       | M       | P       | P       | M       | P       | M        |
| Bryan    | E       | P       | P       | E       | P       | E       | E       | P       | E       | P        |
| Kathleen | M       | E       | E       | M       | E       | M       | M       | E       | M       | E        |
| Larry    | M       | M       | M       | M       | M       | M       | M       | M       | M       | M        |

Candidate column headers:
Skill 3
Skill 7
Skill 10
Skill 2
Skill 4
Skill 9
Skill 1
Skill 6
Skill 5
Column1
Skill 8



headers: Skill 3
Skill 7
Skill 10
Skill 2
Skill 4
Skill 9
Skill 1
Skill 6
Skill 5
Column1
Skill 8
label: Column1
Skill 1
Skill 2
Skill 3
Skill 4
Skill 5
Skill 6
Skill 7
Skill 8
Skill 9
Skill 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                    |
|:---------------|:---------------|:---------------|:-------------------------|
| Castlegar      | Lorna Ferguson | (250) 365-4488 | Lorna.Ferguson@gov.bc.ca |
| Campbell River | Diana Vaton    | (250) 830-6517 | Diana.Vaton@gov.bc.ca    |
| Abbotsford     | Ingrid Cameron | (604) 870-5411 | Ingrid.Cameron@gov.bc.ca |
| Burnaby        | Mike Kastelein | (604) 660-2402 | Mike.Kastelein@gov.bc.ca |
| Burnaby        | Lola Oloyede   | (604) 660-7031 | Lola.Oloyede@gov.bc.ca   |

Candidate column headers:
Contact Name
Ministry City
Contact Phone #
Contact Email



headers: Contact Name
Ministry City
Contact Phone #
Contact Email
label: Ministry City
Contact Name
Contact Phone #
Contact Email
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   |
|--------:|--------:|--------:|:------------------|:--------|
|   82020 |     111 |    9635 | Wayne RESA        | No      |
|   63901 |     126 |   10937 | Royal Oak Schools | No      |
|   13904 |       1 |      87 |                   | Yes     |
|   63915 |      90 |    7812 | Wayne RESA        | No      |
|   81010 |    1798 |  156072 |                   | No      |

Candidate column headers:
District Code
Current Consortium Fiscal Agent
Preliminary Allocation
UIC Count
Consortium Required?



headers: District Code
Current Consortium Fiscal Agent
Preliminary Allocation
UIC Count
Consortium Required?
label: District Code
UIC Count
Preliminary Allocation
Current Consortium Fiscal Agent
Consortium Required?
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   |
|:--------|:---------|---------:|:----------|:--------|
| Kauai   | October  |  60645.8 | Dominquez | John    |
| Oahu    | November |  77040.8 | Hassell   | Bobbie  |
| Lanai   | October  | 172204   | Crawford  | Katie   |
| Kauai   | December |  51525.5 | Morrison  | Kate    |
| Kauai   | December |  55404.1 | Guthrie   | Gambino |

Candidate column headers:
Last Name
First Name
Region
Sales
Month



headers: Last Name
First Name
Region
Sales
Month
label: Region
Month
Sales
Last Name
First Name
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 |
|:--------|:-----------|:-----------|:--------|:--------|:--------|--------:|--------:|
| NAPOLI  | CAM0000022 | NAPC350003 | A008    | NORMALE | NORMALE |       1 |       0 |
| NAPOLI  | CAM0000019 | NAIS078002 | A010    | NORMALE | NORMALE |       1 |       1 |
| NAPOLI  | CAM0000019 | NAIS10900C | A008    | NORMALE | NORMALE |       0 |       1 |
| NAPOLI  | CAM0000018 | NAPS43000T | A007    | NORMALE | NORMALE |       1 |       0 |
| SALERNO | CAM0000023 | SASL040008 | A007    | NORMALE | NORMALE |       1 |       0 |

Candidate column headers:
PROVINCIA
TIPO POSTO
DISPONIBILITA' CATTEDRE ESTERNE
CODICE AMBITO
TIPO SCUOLA
CODICE SCUOLA
DISPONIBILITA' CATTEDRE INTERNE
CODICE CLASSE DI CONCORSO



headers: PROVINCIA
TIPO POSTO
DISPONIBILITA' CATTEDRE ESTERNE
CODICE AMBITO
TIPO SCUOLA
CODICE SCUOLA
DISPONIBILITA' CATTEDRE INTERNE
CODICE CLASSE DI CONCORSO
label: PROVINCIA
CODICE AMBITO
CODICE SCUOLA
CODICE CLASSE DI CONCORSO
TIPO POSTO
TIPO SCUOLA
DISPONIBILITA' CATTEDRE INTERNE
DISPONIBILITA' CATTEDRE ESTERNE
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   |
|:----------|--------:|:--------|--------:|--------:|:--------|:-------------|:---------------|:------------------------|:------------------------|:-------------------------|:-------------------------------|:---------------------------|:---------|
| Fall 2014 |  389052 | M       |       2 |      29 | 25-29   | Assessed     | No Orientation | Attempted Registration  | Survived Drop Date      | Survived Withdrawal Date | Withdrew or failed all classes | Not Registered Next Spring | Yes      |
| Fall 2014 |  389071 | F       |       2 |      23 | 20-24   | Assessed     | No Orientation | No Registration Attempt | Droped before drop date | Withdrew before deadline | Withdrew or failed all classes | Not Registered Next Spring | No       |
| Fall 2014 |  389013 | F       |       1 |      51 | 30+     | Not Assessed | No Orientation | No Registration Attempt | Droped before drop date | Withdrew before deadline | Withdrew or failed all classes | Not Registered Next Spring | No       |
| Fall 2014 |  389051 | F       |       2 |      23 | 20-24   | Assessed     | No Orientation | No Registration Attempt | Droped before drop date | Withdrew before deadline | Withdrew or failed all classes | Registered for Spring      | No       |
| Fall 2014 |  389028 | M       |       1 |      46 | 30+     | Not Assessed | No Orientation | No Registration Attempt | Droped before drop date | Withdrew before deadline | Withdrew or failed all classes | Not Registered Next Spring | No       |

Candidate column headers:
Attempted Registration
Survived Drop Date
Survived Withdrawal deadline
Gender
Age Group
SPBPERS_ETHN_CDE
Pell Grant Offered
Assessed
Attended Orientation
Registered next Spring
APPL_TERM_DESC
Survived to the end
PIDM
Age



headers: Attempted Registration
Survived Drop Date
Survived Withdrawal deadline
Gender
Age Group
SPBPERS_ETHN_CDE
Pell Grant Offered
Assessed
Attended Orientation
Registered next Spring
APPL_TERM_DESC
Survived to the end
PIDM
Age
label: APPL_TERM_DESC
PIDM
Gender
SPBPERS_ETHN_CDE
Age
Age Group
Assessed
Attended Orientation
Attempted Registration
Survived Drop Date
Survived Withdrawal deadline
Survived to the end
Registered next Spring
Pell Grant Offered
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   |
|--------:|:----------|--------:|--------:|--------:|:--------|
|       3 | Student 3 |    0.75 |    0.75 |    0    | Term 4  |
|       3 | Student 3 |    0.8  |    0.8  |    0    | Term 1  |
|       4 | Student 4 |    0.68 |    0.66 |   -0.02 | Term 4  |
|       3 | Student 3 |    0.78 |    0.78 |    0    | Term 2  |
|       1 | Student 1 |    0.59 |    0.58 |   -0.01 | Term 4  |

Candidate column headers:
Difference %
Target %
Mark %
Number
Name
Term



headers: Difference %
Target %
Mark %
Number
Name
Term
label: Number
Name
Target %
Mark %
Difference %
Term
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   |
|--------:|:--------|--------:|--------:|:--------|:--------|--------:|:--------|--------:|--------:|---------:|:---------|---------:|---------:|---------:|---------:|:-------------------|---------:|---------:|:---------|
|  252701 |         |    1.53 |    1.53 | H       | 1.53    |    7.78 |         |    0.77 |   640   |     0.77 |          |    513.5 |     6.73 |     11.6 |      297 | 5.0849673202614376 |  36.13   | -102.622 | 121OGLL  |
|  333505 | <       |    0.1  |    0    | H       |         |    8.51 |         |    2.3  |   583.5 |     2.3  |          |    229   |    10.7  |     22.3 |      338 |                    |  36.4183 | -101.924 | 121OGLL  |
|  242904 |         |    0.27 |    0.27 | H       | 0.27    |   34.9  |         |    2.44 |   265   |     2.44 |          |    212   |    15.85 |     86.9 |      372 | 129.25925925925924 |  36.2744 | -102.768 | 121OGLL  |
|  253702 |         |    1.55 |    1.55 | I       | 1.55    |   15.3  |         |    0.62 |   650.5 |     0.62 |          |    538.5 |    10.8  |     10.1 |      308 | 9.870967741935484  |  36.1336 | -102.473 | 121OGLL  |
|  264406 | <       |    0.1  |    0    | H       |         |   11.3  |         |    1.12 |   332   |     1.12 |          |    448.5 |     7.43 |     67.9 |      281 |                    |  36.0672 | -102.1   | 121OGLL  |

Candidate column headers:
Br_map
F_map
TDS
Lat_dec
Br Fl
F Fl
F_map rank
NO3 Fl
Li rank
Cl
Li
ID
aquifer_code
Long_dec
NO3
F
Br_ratio
primary_water_use
Br
Cl/Br



headers: Br_map
F_map
TDS
Lat_dec
Br Fl
F Fl
F_map rank
NO3 Fl
Li rank
Cl
Li
ID
aquifer_code
Long_dec
NO3
F
Br_ratio
primary_water_use
Br
Cl/Br
label: ID
Br Fl
Br
Br_map
primary_water_use
Br_ratio
Cl
F Fl
F
Li rank
F_map
NO3 Fl
F_map rank
NO3
Li
TDS
Cl/Br
Lat_dec
Long_dec
aquifer_code
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0    |   Col_1 | Col_2   | Col_3   | Col_4   | Col_5   | Col_6   | Col_7   | Col_8   | Col_9   | Col_10   | Col_11   | Col_12   | Col_13   | Col_14   |
|:---------|--------:|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:---------|:---------|:---------|:---------|:---------|
| Mercer   |    0.4  | Y       | Y       | N       | N       | N       | N       | N       | N       | N        | N        | N        | N        | N        |
| Preston  |    0.44 | Y       | Y       | N       | Y       | N       | N       | N       | N       | N        | N        | N        | N        | N        |
| Preston  |    5.44 | Y       | Y       | N       | Y       | N       | N       | N       | N       | N        | N        | N        | N        | N        |
| Multiple |  202.75 | Y       | Y       | N       | N       | Y       | N       | N       | N       | N        | N        | N        | N        | N        |
| Preston  |    2.58 | Y       | Y       | N       | Y       | N       | N       | N       | N       | N        | N        | N        | N        | N        |

Candidate column headers:
Inter-pretive
Bike
ATV
OHV
County
Backpack
Ski
Motorized
Water
ADA
Length(mi)
Dirtbike
Hike
Horses
Fitness



headers: Inter-pretive
Bike
ATV
OHV
County
Backpack
Ski
Motorized
Water
ADA
Length(mi)
Dirtbike
Hike
Horses
Fitness
label: County
Length(mi)
Hike
Bike
Horses
Ski
Backpack
Inter-pretive
Fitness
ADA
Water
Motorized
ATV
OHV
Dirtbike
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   |
|:--------|:--------|--------:|:--------|:--------|:--------|:--------|
| 250     | OK      |      72 | 71      | 55      | OK      | SGC     |
| 250     | OK      |      30 | 29      | 81      | OK      | SGC     |
| 250     | OK      |      68 | 67      | 140     | OK      | SGC     |
| 250     | OK      |     144 | 108     | 91      | OK      | SGC     |
| -       | OK      |     130 | -       | -       | -       | -       |

Candidate column headers:
BD
Sistema
ID IIS
WSConfig
Conteudo
ID CF
63.143.46.118



headers: BD
Sistema
ID IIS
WSConfig
Conteudo
ID CF
63.143.46.118
label: WSConfig
63.143.46.118
ID IIS
ID CF
Conteudo
BD
Sistema
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 |
|--------:|--------:|--------:|--------:|--------:|
|       5 |       7 |       8 |       0 |       0 |
|       3 |       4 |      15 |       1 |      15 |
|       2 |       4 |      12 |       0 |       0 |
|       1 |       2 |      16 |       0 |       0 |
|       4 |       6 |      17 |       0 |       0 |

Candidate column headers:
used flow
from
use
to
flow



headers: used flow
from
use
to
flow
label: from
to
flow
use
used flow