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

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

Table Data:
| Col_0   | Col_1       | Col_2   | Col_3   | Col_4   |   Col_5 | Col_6           | Col_7   | Col_8    | Col_9                     | Col_10   |
|:--------|:------------|:--------|:--------|:--------|--------:|:----------------|:--------|:---------|:--------------------------|:---------|
| HEP     | Fermilab AC | FNAL    | No      | No      |       2 | Nankang_ Taipei |         | 11529    | Taiwan_ Republic of China |          |
| NP      | CEBAF       | TJNAF   | No      | No      |       1 | Taipei City     |         | 11529    | Taiwan_ Republic of China |          |
| BER     | JGI         | LBNL    | No      | No      |       3 | Aberystwyth     | Wales   | SY23 3DA | United Kingdom            |          |
| BES     | ALS         | LBNL    | No      | No      |       5 | Aarhus C        |         | 8000     | Denmark                   |          |
| ASCR    | ALCF        | ANL     | No      | No      |       1 | Lincolnshire    | IL      | 60069    | United States             | IL-10    |

Candidate column headers:
Home Institution Postal Code/Zip
Home Institution State/Territory/Province
Industry?
Program Acronym
Congressional District
User Facility Acronym
Small Business?
Home Institution City
Home Institution Country
User Facility Host Institution Acronym
Number of users from that institution



headers: Home Institution Postal Code/Zip
Home Institution State/Territory/Province
Industry?
Program Acronym
Congressional District
User Facility Acronym
Small Business?
Home Institution City
Home Institution Country
User Facility Host Institution Acronym
Number of users from that institution
label: Program Acronym
User Facility Acronym
User Facility Host Institution Acronym
Small Business?
Industry?
Number of users from that institution
Home Institution City
Home Institution State/Territory/Province
Home Institution Postal Code/Zip
Home Institution Country
Congressional District
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   |
|:--------|--------:|:---------------------------|:--------|
| ICD-9   |  295.82 | Other schizophrenia        | F20.89  |
| ICD-9   |  295.3  | Paranoid schizophrenia     | F20.0   |
| ICD-9   |  295.03 | Other schizophrenia        | F20.89  |
| ICD-9   |  295.65 | Residual schizophrenia     | F20.5   |
| ICD-9   |  295.1  | Disorganized schizophrenia | F20.1   |

Candidate column headers:
Code Type
Code Description ICD-10
ICD-10 Code
Code



headers: Code Type
Code Description ICD-10
ICD-10 Code
Code
label: Code Type
Code
Code Description ICD-10
ICD-10 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 |
|:--------|:--------|:--------|--------:|:-------------|--------:|:--------|--------:|
| RUSSO   | BARBARA | TNT     |       6 | BUTTERFLY    |      60 | F       |   35682 |
| RUSSO   | PHILIP  | TNT     |       8 | BACKSTROKE   |      88 | M       |   34899 |
| BARNES  | ALICE   | FLST    |       8 | BREASTSTROKE |      81 | F       |   34983 |
| HARPER  | KELLY   | BOCA    |       7 | FREESTYLE    |      49 | F       |   35120 |
| SMITH   | SUZY    | CMSA    |       6 | BUTTERFLY    |      56 | F       |   35432 |

Candidate column headers:
GRADE
POINTS SCORED
USS TEAM
LAST NAME
FIRST
BIRTHDATE
GENDER
STROKE



headers: GRADE
POINTS SCORED
USS TEAM
LAST NAME
FIRST
BIRTHDATE
GENDER
STROKE
label: LAST NAME
FIRST
USS TEAM
GRADE
STROKE
POINTS SCORED
GENDER
BIRTHDATE
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   |
|:--------|:--------------|:--------|
| Yes     | Miscellaneous | Master  |
| No      | Miscellaneous | Master  |
| No      | Miscellaneous | Master  |
| No      | Miscellaneous | Master  |
| No      | Miscellaneous | Master  |

Candidate column headers:
TableGroup
EntityCategory
Public



headers: TableGroup
EntityCategory
Public
label: Public
TableGroup
EntityCategory
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   |
|--------:|:-------------------------|:----------------------|:-------------|:--------|:------------------------|:--------|:--------|:--------|
|      17 | 45-09-07-130-035.000-004 | 1142 GIBSON PL        | 1ST DISTRICT |         | UNSAFE BUILDING HEARING |         |         |         |
|      36 | 45-09-06-277-003.000-004 | 315 S. HENRY STREET   | 1ST DISTRICT |         | UNSAFE BUILDING HEARING |         |         |         |
|      41 | 45-08-12-251-018.000-004 | 4034 EAST 13TH AVENUE | 1ST DISTRICT | /       | UNSAFE BUILDING HEARING |         |         |         |
|      34 | 45-05-31-457-034.000-004 | 218 HENRY STREET      | 1ST DISTRICT | /       | UNSAFE BUILDING HEARING |         |         |         |
|      10 | 45-09-07-102-017.000-004 | 1059 DECATUR STREET   | 1ST DISTRICT | /       | CITY OWNED              |         |         |         |

Candidate column headers:
Parcel_Number
TITLE_ORDERED_RECEIVED
NIPSCO_RETIRED
ID
WATER_RETIRED
COUNCIL_DISTRICT
DNR_APPROVED
ADDRESS
RECOMMENDATION



headers: Parcel_Number
TITLE_ORDERED_RECEIVED
NIPSCO_RETIRED
ID
WATER_RETIRED
COUNCIL_DISTRICT
DNR_APPROVED
ADDRESS
RECOMMENDATION
label: ID
Parcel_Number
ADDRESS
COUNCIL_DISTRICT
TITLE_ORDERED_RECEIVED
RECOMMENDATION
NIPSCO_RETIRED
DNR_APPROVED
WATER_RETIRED
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 |
|:--------|:---------------------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|
| NSW     | Dapto - Port Kembla  |    6180 |     880 |    6376 |    9992 |    3027 |   75354 |   60944 |   46839 |    14108 |
| NSW     | Kiama - Shellharbour |    6131 |     949 |    3453 |   10207 |    2268 |   89839 |   72632 |   56130 |    16501 |
| NSW     | Dubbo                |    4935 |    3246 |     740 |    8944 |    1782 |   69411 |   55311 |   42440 |    12881 |
| NSW     | Tamworth - Gunnedah  |    5822 |    2643 |     661 |   10713 |    2073 |   80498 |   64230 |   48850 |    15380 |
| NSW     | Upper Hunter         |    2409 |     609 |     275 |    4165 |     758 |   30198 |   23667 |   18966 |     4703 |

Candidate column headers:
Population (65 and over)
NLAS(65+)
SA3
State/Territory
Population (15-64)
NLAS(ATSI)
NLAS($52K)
Population (15 and over)
Population (all)
NLAS(Capability)
NLAS(CALD)



headers: Population (65 and over)
NLAS(65+)
SA3
State/Territory
Population (15-64)
NLAS(ATSI)
NLAS($52K)
Population (15 and over)
Population (all)
NLAS(Capability)
NLAS(CALD)
label: State/Territory
SA3
NLAS(Capability)
NLAS(ATSI)
NLAS(CALD)
NLAS($52K)
NLAS(65+)
Population (all)
Population (15 and over)
Population (15-64)
Population (65 and over)
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0   | Col_1   | Col_2   | Col_3   | Col_4   |
|:--------|:--------|:--------|:--------|:--------|
| 0       | 2       | 0       | 2       | 0       |
| 13      | 3       | 1       | 0       | 3       |
| 0       | 5       | 0       | 3       | 2       |
| 10      | 240     | 174     | 0       | 67      |
| *       | *       | *       | *       | *       |

Candidate column headers:
Number of children removed to an IAES1 by hearing officer for likely injury
Number of removals to an IAES1 by school personnel for serious bodily injury
Number of removals to an IAES1 by school personnel for weapons
Number of removals to an IAES1 by school personnel for drugs
Number of children removed to an IAES1 by school personnel



headers: Number of children removed to an IAES1 by hearing officer for likely injury
Number of removals to an IAES1 by school personnel for serious bodily injury
Number of removals to an IAES1 by school personnel for weapons
Number of removals to an IAES1 by school personnel for drugs
Number of children removed to an IAES1 by school personnel
label: Number of children removed to an IAES1 by hearing officer for likely injury
Number of children removed to an IAES1 by school personnel
Number of removals to an IAES1 by school personnel for drugs
Number of removals to an IAES1 by school personnel for serious bodily injury
Number of removals to an IAES1 by school personnel for weapons
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 |
|--------:|:--------|--------:|
|    2014 | N       |    1413 |
|    2007 | N       |    1613 |
|    2005 | Y       |     651 |
|    2011 | N       |    1243 |
|    2005 | N       |    1698 |

Candidate column headers:
SPEEDING INVOLVED
SERIOUS INJURIES
YEAR



headers: SPEEDING INVOLVED
SERIOUS INJURIES
YEAR
label: YEAR
SPEEDING INVOLVED
SERIOUS INJURIES
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 |
|:-----------|:---------------|--------:|
| Cambodia   | Asia           |    20   |
| Cyprus     | Western Europe |    12.5 |
| Bangladesh | Asia           |    25   |
| Botswana   | Africa         |    22   |
| Argentina  | Latin America  |    35   |

Candidate column headers:
LOCATION
Continent
Average



headers: LOCATION
Continent
Average
label: LOCATION
Continent
Average
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 |
|----------:|------------:|--------:|
| 0.0366467 | 1.0267e+07  |  238260 |
| 0.01295   | 3.62807e+06 |   64670 |
| 0.0181862 | 5.09504e+06 |   16833 |
| 0.059765  | 1.67438e+07 |  115494 |
| 0.0219477 | 6.14887e+06 |   18922 |

Candidate column headers:
Statistical Value IN USD
QTY
Statistical Value IN USD %



headers: Statistical Value IN USD
QTY
Statistical Value IN USD %
label: Statistical Value IN USD %
Statistical Value IN USD
QTY
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                   |
|:----------|:----------------|:--------|:------------|:------------|:--------|:------------|:------------------|:--------|:--------|:---------|:-----------------|:-----------|:-------------------------|
| AELAC-SEA | CCM Afghanistan | CCM     | Afghanistan | Male        |         | Afghanistan | Non-public sector | NGO     |         |          | 17.02.2013 02:00 | Member     |                          |
| AELAC-SEA | CCM Afghanistan | CCM     | Afghanistan | Not Defined |         |             |                   | GOV     |         |          |                  | Non Member |                          |
| AELAC-SEA | CCM Afghanistan | CCM     | Afghanistan | Male        | Malaria |             | Non-public sector | PLWD    |         |          | 10.07.2014 04:00 | Member     | Non-Recipient            |
| AELAC-SEA | CCM Afghanistan | CCM     | Afghanistan | Female      |         | Afghanistan | Public sector     | GOV     |         |          | 03.04.2007 04:00 | Member     | Non-Recipient            |
| AELAC-SEA | CCM Afghanistan | CCM     | Afghanistan | Male        |         | Afghanistan |                   | GOV     |         |          | 26.04.2010 04:00 | Member     | PR - Principal recipient |

Candidate column headers:
Account Level
Role
Other Sector Represented
Member Since
Public Sector or non-Public Sector
Organization Coding
Principal Component
Gender
Implementer Role (PR_ SR_ Sub-SR)
Regional Team
Organization Represents
Sector Represented
Organization Name
Countries



headers: Account Level
Role
Other Sector Represented
Member Since
Public Sector or non-Public Sector
Organization Coding
Principal Component
Gender
Implementer Role (PR_ SR_ Sub-SR)
Regional Team
Organization Represents
Sector Represented
Organization Name
Countries
label: Regional Team
Organization Name
Account Level
Countries
Gender
Principal Component
Organization Represents
Public Sector or non-Public Sector
Sector Represented
Other Sector Represented
Organization Coding
Member Since
Role
Implementer Role (PR_ SR_ Sub-SR)
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   |
|:-----------------|:--------|:----------|:----------------|:--------|
| Gwen Sieswerda   | X       |           | X               | X       |
| A-selectie blauw | B-comp. | Min./Jun. | Jun./Jeugd/Sen. | Limiet  |
| Sven Harskamp    | X       |           | X               | X       |
| Jip Bakker       | X       | X         | X               | X       |
| Sam Derckx       | X       | X         | X               | X       |

Candidate column headers:
Limiet
Jun./Jeugd/Sen.
B-comp.
A-selectie rood
Min./Jun.



headers: Limiet
Jun./Jeugd/Sen.
B-comp.
A-selectie rood
Min./Jun.
label: A-selectie rood
B-comp.
Min./Jun.
Jun./Jeugd/Sen.
Limiet
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 |
|--------:|:------------|--------:|--------:|--------:|
|      13 | Bangladeshi |       4 |       6 |      10 |
|       8 | Indian      |      46 |      41 |      87 |
|       7 | Indonesian  |       1 |       0 |       1 |
|      15 | Yemeni      |       2 |       1 |       3 |
|       4 | Malaysian   |       1 |       0 |       1 |

Candidate column headers:
Column3
Column1
Ethnicity Distribution of pupils 2013/14
Column4
Column2



headers: Column3
Column1
Ethnicity Distribution of pupils 2013/14
Column4
Column2
label: Column1
Ethnicity Distribution of pupils 2013/14
Column2
Column3
Column4
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 |
|--------:|:-----------|:--------|:------------|:---------|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|
|    1626 | Outlet1626 | Europe  | Spain       | Madrid   |   43373 | 2695700 | 2129600 |   89700 |  175810 |    59090 |    24530 |    53350 |
|    1447 | Outlet1447 | Europe  | Belarus     | Minsk    |   43220 | 3069000 | 2455200 |   79200 |  178200 |    52800 |    16266 |    92070 |
|    1339 | Outlet1339 | Europe  | Switzerland | Bern     |   43434 | 4695600 | 3474700 |  204000 |  264890 |    78030 |    30990 |   135180 |
|    1107 | Outlet1107 | Europe  | Hungary     | Budapest |   43282 | 3264000 | 2350080 |   78200 |  173400 |    71400 |    23501 |    97920 |
|    1043 | Outlet1043 | Europe  | Italy       | Rome     |   43404 | 1435200 | 1033300 |   90000 |  224910 |    87210 |    13060 |    15300 |

Candidate column headers:
Utilities
Personnel Cost
Outlet Name
Sales
Geography
Consumables
Month
Cost of Goods Sold
State
Rent
City
Store
Misc Exp



headers: Utilities
Personnel Cost
Outlet Name
Sales
Geography
Consumables
Month
Cost of Goods Sold
State
Rent
City
Store
Misc Exp
label: Store
Outlet Name
Geography
State
City
Month
Sales
Cost of Goods Sold
Rent
Personnel Cost
Utilities
Consumables
Misc Exp
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 |
|:--------|--------:|:--------|--------:|:--------|--------:|--------:|:------------------|--------:|--------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
| N138    |    67.4 | 33.2    |    56.3 | 39.2    | 54.489  |    44.7 | 46.6              | 42.6407 |    40.6 |  43.8281 |  51.2    |     41.5 |  46.8048 |     0.89 |  41.6563 |      100 |     0.95 |  44.4646 |
| N129    |    45.7 | 34.0    |    31.1 |         | 33.6    |    19.7 | 29.5324283045977  | 30.6    |    37.9 |  34.9    |  37.9    |     35.7 |  33.6939 |     0.89 |  29.9875 |       91 |     0.95 |  32.0092 |
| N102    |    52.5 | 57.0    |    37.1 | 27.7    | 32.7708 |    29.9 |                   | 27.7386 |    32.4 |  35.2984 |  39.9    |     32.3 |  36.7825 |     0.89 |  32.7364 |       91 |     0.95 |  34.9434 |
| N124    |    53.6 |         |    38.5 | 37.9    | 42.8    |    36.4 | 38.8097225308642  | 35.9151 |    36   |  41.0576 |  47.7265 |     39.9 |  40.7826 |     0.89 |  36.2965 |       91 |     0.95 |  38.7435 |
| N120    |    61.6 | 42.4    |    44.8 | 47.3    | 44.4    |    43   | 41.80086997126437 | 43      |    41.8 |  47.1    |  49.1    |     45.2 |  45.9584 |     0.89 |  40.903  |      100 |     0.95 |  43.6605 |

Candidate column headers:
National BIAS ADJ FACTOR  6/15 update
DECEMBER
Local Bias Adj factor
NOVEMBER 
JUNE
MAY
AUGUST
DC(%)
OCTOBER
Site ID
FEBRUARY
AVERAGE
SEPTEMBER
local adjusted
March
JULY
JANUARY
ADJUSTED
APRIL



headers: National BIAS ADJ FACTOR  6/15 update
DECEMBER
Local Bias Adj factor
NOVEMBER 
JUNE
MAY
AUGUST
DC(%)
OCTOBER
Site ID
FEBRUARY
AVERAGE
SEPTEMBER
local adjusted
March
JULY
JANUARY
ADJUSTED
APRIL
label: Site ID
JANUARY
FEBRUARY
March
APRIL
MAY
JUNE
JULY
AUGUST
SEPTEMBER
OCTOBER
NOVEMBER 
DECEMBER
AVERAGE
National BIAS ADJ FACTOR  6/15 update
ADJUSTED
DC(%)
Local Bias Adj factor
local adjusted
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 |  133.61 |  140.59 |  132.33 |  138.26 | 1.91957e+08 | 115.599  |  0.0476623   |
|   40452 |  114.99 |  119.76 |  113.18 |  118.49 | 1.86276e+08 | 104.768  |  0.038202    |
|   40119 |  104.13 |  111.74 |  103.08 |  109.94 | 1.79587e+08 |  95.3249 |  0.0616069   |
|   40695 |  134.51 |  134.92 |  126.19 |  131.97 | 2.24262e+08 | 118.394  | -0.0168698   |
|   39965 |   93.67 |   96.11 |   88.85 |   91.95 | 2.4017e+08  |  79.3485 | -0.000654933 |

Candidate column headers:
Open
Monthly Return
High
Volume
Low
Date
Close
Adj Close



headers: Open
Monthly Return
High
Volume
Low
Date
Close
Adj Close
label: Date
Open
High
Low
Close
Volume
Adj Close
Monthly Return
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 |
|:-------------------|:--------|:------------------------|:----------|--------:|:--------|--------:|:--------|--------:|--------:|
| Pitts_ Dana        | Taft    | Engineering/Maintenance | Contract  |   33678 | Mar     |      19 |         |   64460 |       1 |
| Wyatt_ Kelly       | West    | Audit Services          | Full Time |   33551 | Nov     |      19 | DM      |   41350 |       2 |
| Gallagher_ Johnson | West    | Admin Training          | Hourly    |   40126 | Nov     |       1 |         |   10636 |       4 |
| Chase_ Troy        | Main    | Admin Training          | Full Time |   38142 | Jun     |       7 | DMR     |   49350 |       4 |
| Cunningham_ Denise | Main    | Engineering/Maintenance | Full Time |   35086 | Jan     |      15 | M       |   77350 |       5 |

Candidate column headers:
Employee Name
Status
Building
Job Rating
Salary
Years
Benefits
Month
Department
Hire Date



headers: Employee Name
Status
Building
Job Rating
Salary
Years
Benefits
Month
Department
Hire Date
label: Employee Name
Building
Department
Status
Hire Date
Month
Years
Benefits
Salary
Job Rating
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 |
|:----------------------|--------:|--------:|:--------|:--------|:--------|:--------|:--------|:----------------|:---------------|:---------|:----------|---------:|---------:|---------:|
| Ofsted School Webpage |  138779 | 9266006 |         |         |         | Open    | Ofsted  | East of England | Norfolk        | NR30 1DX | 10012967  |    42647 |    42649 |    42692 |
| Ofsted School Webpage |  131940 | 3126063 | 2.0     |         |         | Open    | Ofsted  | London          | Hillingdon     | UB8 3HD  | ITS454263 |    42066 |    42068 |    42114 |
| Ofsted School Webpage |  135001 | 3926011 | 9.0     |         |         | Open    | Ofsted  | North East      | North Tyneside | NE27 0AB | ITS463009 |    42122 |    42124 |    42151 |
| Ofsted School Webpage |  117048 | 8846010 | 8.0     |         |         | Open    | Ofsted  | West Midlands   | Herefordshire  | HR6 8LL  | ITS408715 |    41235 |    41236 |    41255 |
| Ofsted School Webpage |  130913 | 3576056 |         |         |         | Open    | Ofsted  | North West      | Tameside       | PR2 2YQ  | 10008892  |    42661 |    42662 |    42702 |

Candidate column headers:
Local authority
First day of inspection
Inspectorate
Last day of inspection
Religious ethos
Postcode
Early years provision
URN
Religious character
Inspection number
Region
Publication date
LAESTAB
Provider open / closed status
Report



headers: Local authority
First day of inspection
Inspectorate
Last day of inspection
Religious ethos
Postcode
Early years provision
URN
Religious character
Inspection number
Region
Publication date
LAESTAB
Provider open / closed status
Report
label: Report
URN
LAESTAB
Early years provision
Religious character
Religious ethos
Provider open / closed status
Inspectorate
Region
Local authority
Postcode
Inspection number
First day of inspection
Last day of inspection
Publication date
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 | Col_1     | Col_2   | Col_3   | Col_4     | Col_5   | Col_6   | Col_7     | Col_8   | Col_9   |
|--------:|:----------|:--------|:--------|:----------|:--------|:--------|:----------|:--------|:--------|
|    2012 | Sociology |         |         |           |         |         | City Year | Boston  | MA      |
|    2013 | Sociology |         |         | St Louis  | MO      | M.A.    |           |         |         |
|    2012 | Sociology |         |         | Arlington | VA      | J.D.    |           |         |         |
|    2013 | Sociology | Chicago | IL      |           |         |         |           |         |         |
|    2012 | Sociology |         |         | Cambridge | MA      | J.D.    |           |         |         |

Candidate column headers:
Class Year
Volunteer Site's State/Country
Degree
Employer's State/Country
Institution's City
Volunteer Site's City
Institution's State/Country
Major
Employer's City
Volunteer Site



headers: Class Year
Volunteer Site's State/Country
Degree
Employer's State/Country
Institution's City
Volunteer Site's City
Institution's State/Country
Major
Employer's City
Volunteer Site
label: Class Year
Major
Employer's City
Employer's State/Country
Institution's City
Institution's State/Country
Degree
Volunteer Site
Volunteer Site's City
Volunteer Site's State/Country
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 |
|---------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|
| 0.78125  |       0 |       0 |       0 |       0 |       0 |       0 |       0 |
| 0.520833 |       0 |       0 |       0 |       0 |       0 |       0 |       0 |
| 0.614583 |       0 |       0 |       0 |       0 |       0 |       0 |       0 |
| 0.604167 |       0 |       0 |       0 |       0 |       0 |       0 |       0 |
| 0.71875  |       0 |       0 |       0 |       0 |       0 |       0 |       0 |

Candidate column headers:
UHRZEIT
MITTWOCH
MONTAG
SONNTAG
DIENSTAG
DONNERSTAG
FREITAG
SAMSTAG



headers: UHRZEIT
MITTWOCH
MONTAG
SONNTAG
DIENSTAG
DONNERSTAG
FREITAG
SAMSTAG
label: UHRZEIT
MONTAG
DIENSTAG
MITTWOCH
DONNERSTAG
FREITAG
SAMSTAG
SONNTAG
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   |
|:--------|--------:|:---------|:------------------|:-----------------|---------:|:--------|
| u18s    |   42106 | League   | Golborne Parkside | Cadishead Rhinos | 0.489583 | HOME    |
| u10s    |   42085 | Friendly | Golborne Parkside | Chorley          | 0.4375   | HOME    |
| u7s     |   42071 | Friendly | Golborne Parkside | Orrell St James  | 0.4375   | HOME    |
| u10s    |   42092 | Friendly | Golborne Parkside | Folly Lane       | 0.4375   | HOME    |
| u12s    |   42113 | CUP      | TBC               | TBC              | 0.4375   | TBC     |

Candidate column headers:
Team
Away Team
Home Team
Date
Venue
Kickoff
Type



headers: Team
Away Team
Home Team
Date
Venue
Kickoff
Type
label: Team
Date
Type
Home Team
Away Team
Kickoff
Venue
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   |
|:--------|--------:|:-----------|--------:|:--------|:--------|:--------|:--------|:--------|:--------|:---------|:---------|:---------|:---------|:---------|:---------|:---------|:---------|
| 1990-91 |       9 | Mayurbhanj |  215    |         |         | 349.0   |         |         | 900.0   | 667.0    |          |          |          | 1002.0   |          |          |          |
| 1990-91 |       5 | Ganjam     |  184    |         | 180.0   |         |         |         |         |          | 762.0    | 664.0    |          |          | 566.0    |          |          |
| 1990-91 |       1 | Balasore   |  257.65 | 316.25  |         |         |         |         |         |          | 976.0    | 700.8    |          | 815.0    | 739.4    |          | 799.3    |
| 1990-91 |      10 | Phulbani   |  140    |         |         | 329.28  |         |         |         | 750.0    |          |          | 333.33   | 1200.0   |          |          |          |
| 1990-91 |       8 | Koraput    |  184    | 263.0   | 150.0   | 166.0   |         |         |         | 885.0    |          |          | 297.0    | 968.0    |          |          | 621.0    |

Candidate column headers:
Groundnut
Maize
Mung
Gram
Wheat
Bajra
Kulthi
Arhar
Name of District
Sl No 
Jowar
Mustard
Castor
Biri
Til
Ragi
Year
Paddy



headers: Groundnut
Maize
Mung
Gram
Wheat
Bajra
Kulthi
Arhar
Name of District
Sl No 
Jowar
Mustard
Castor
Biri
Til
Ragi
Year
Paddy
label: Year
Sl No 
Name of District
Paddy
Wheat
Ragi
Maize
Jowar
Bajra
Gram
Arhar
Mung
Biri
Kulthi
Mustard
Groundnut
Til
Castor
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   |
|:-----------|:---------------|--------:|:-----------|:-----------|:-----------|:----------|:----------|--------:|:--------|:---------|
| 1A5.g205   | Cluster_000201 |      26 | 1A5.g10866 | 1E4.g10673 |            |           | Accessory |       2 |         |          |
| 1A5.g2200  | Cluster_000200 |      33 | 1A5.g10867 | 1E4.g10672 | 3D1.g11534 | 3D7.g2524 | Accessory |       4 |         |          |
| 1A5.g8835  | Cluster_000001 |      21 | 1A5.g1     | 1E4.g11617 | 3D1.g1     | 3D7.g4628 | Accessory |       4 |         |          |
| 1A5.g2199  | Cluster_000201 |      26 | 1A5.g10866 | 1E4.g10673 |            |           | Accessory |       2 |         |          |
| 1A5.g11527 | Cluster_000200 |      33 | 1A5.g10867 | 1E4.g10672 | 3D1.g11534 | 3D7.g2524 | Accessory |       4 |         |          |

Candidate column headers:
GeneID
Pangenome_category
Homolog_1E4_geneID
Number_of_orthologs_in_pangenome
Homolog_3D7_geneID
Pangenome_cluster_ID
Phobius_Secr
Phobius_extracellular_dom.
Homolog_3D1_geneID
Homolog_1A5_geneID
Pangenome_cluster_size



headers: GeneID
Pangenome_category
Homolog_1E4_geneID
Number_of_orthologs_in_pangenome
Homolog_3D7_geneID
Pangenome_cluster_ID
Phobius_Secr
Phobius_extracellular_dom.
Homolog_3D1_geneID
Homolog_1A5_geneID
Pangenome_cluster_size
label: GeneID
Pangenome_cluster_ID
Pangenome_cluster_size
Homolog_1A5_geneID
Homolog_1E4_geneID
Homolog_3D1_geneID
Homolog_3D7_geneID
Pangenome_category
Number_of_orthologs_in_pangenome
Phobius_Secr
Phobius_extracellular_dom.
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 |
|:------------------------|--------:|--------:|--------:|--------:|
| Centro Comm.            |       1 |       1 |       1 |       3 |
| Rome Around You         |       1 |       3 |       3 |       7 |
| Porta di Roma           |       3 |       3 |       2 |       8 |
| Stadio Roma             |       2 |       3 |       3 |       8 |
| Centro Com. Evangelisti |       1 |       1 |       1 |       3 |

Candidate column headers:
Project
Sum
user view
presentation
business view



headers: Project
Sum
user view
presentation
business view
label: Project
user view
business view
presentation
Sum
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   |
|--------:|:------------------|:--------|:--------|:------------|:--------------|:---------------|--------:|--------:|--------:|:---------|---------:|:---------|:---------|:---------|:---------|:---------|---------:|:---------|:---------|
|      29 | Master's Degree   |         | Own     | Dial-Up     |               | Me             |     2.5 |       3 |       1 |          |        3 | Never    | Rarely   | DVD      | Never    | Monthly  |   882738 | Monthly  | Weekly   |
|      47 | Doctorate         | Male    | Own     | Cable Modem | Married       | Spouse/Partner |     3.5 |       4 |       3 | 2.0      |        5 | Rarely   | Rarely   | DVD      | Rarely   | Rarely   |   885129 | Rarely   | Rarely   |
|      40 | Bachelor's Degree | Male    | Own     | DSL         | Married       | Me             |     3.5 |       5 |       5 | 6.0      |        4 | Never    | Monthly  | DVD      | Monthly  | Monthly  |   882364 | Rarely   | Never    |
|      38 | Some College      | Male    | Own     | Cable Modem | Married       | Me             |     3   |       5 |       2 | 4.0      |        4 | Rarely   | Monthly  | DVD      | Monthly  | Weekly   |   882368 | Rarely   | Daily    |
|      38 | Bachelor's Degree | Male    | Own     | DSL         | Never Married | Me             |     2   |       2 |       1 | 0.0      |        2 | Monthly  | Monthly  | DVD      | Monthly  | Weekly   |   882500 | Monthly  | Weekly   |

Candidate column headers:
PPV Freq
Marital Status
Theater Freq
Home Ownership
Prerec Format
CustomerID
Movie Selector
Education Level
Num Bathrooms
Num TVs
Prerec Viewing Freq
Age
Num Bedrooms
Num Cars
Internet Connection
TV Movie Freq
Num Children
Prerec Renting Freq
Prerec Buying Freq
Gender



headers: PPV Freq
Marital Status
Theater Freq
Home Ownership
Prerec Format
CustomerID
Movie Selector
Education Level
Num Bathrooms
Num TVs
Prerec Viewing Freq
Age
Num Bedrooms
Num Cars
Internet Connection
TV Movie Freq
Num Children
Prerec Renting Freq
Prerec Buying Freq
Gender
label: Age
Education Level
Gender
Home Ownership
Internet Connection
Marital Status
Movie Selector
Num Bathrooms
Num Bedrooms
Num Cars
Num Children
Num TVs
PPV Freq
Prerec Buying Freq
Prerec Format
Prerec Renting Freq
Prerec Viewing Freq
CustomerID
Theater Freq
TV Movie Freq
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   |
|:--------|--------:|:--------|
| Junio   |     746 | M       |
| Abril   |     516 | F       |
| Abril   |     533 | M       |
| Marzo   |     589 | M       |
| Enero   |     554 | F       |

Candidate column headers:
Sexo
Cantidad
Mes



headers: Sexo
Cantidad
Mes
label: Mes
Cantidad
Sexo
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   |
|:----------|:--------|:--------------------|:--------------------|--------:|:--------|
| 8/15/2018 |         | 0.52083333333333337 | 0.60416666666666663 |     2   | ?       |
| 43306     | X       | 0.41666666666666669 | 0.5                 |     1   |         |
| 8/13/2018 | X       | 0.54166666666666663 | 0.58333333333333337 |     1   |         |
| 43325     | X       | Any                 | Any                 |     0.5 | Y       |
| 43313     | X       | 0.33333333333333331 | 0.66666666666666663 |     7.5 |         |

Candidate column headers:
Int. Req'd
Mandatory
Date
End Time
Start Time
# of Hours



headers: Int. Req'd
Mandatory
Date
End Time
Start Time
# of Hours
label: Date
Mandatory
Start Time
End Time
# of Hours
Int. Req'd
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   |
|:--------|:----------------|--------:|:--------|:--------|--------:|:--------|
| ash     | Fraxinus spp.   |     9.7 | Fair    | S       |     4.1 | N       |
| ash     | Fraxinus spp.   |     7.4 | Fair    | S       |     4.1 | N       |
| apple   | Malus domestica |     3   | Fair    | CNS     |     0   | N       |
| apple   | Malus domestica |     2.4 | Fair    | S       |     4.8 | N       |
| alder   | Alnus spp.      |     7   | Fair    | NCNS    |     0   | N       |

Candidate column headers:
DBH
Cond.
Common Name
Scientific Name
Site Type
Strip Size
Wires



headers: DBH
Cond.
Common Name
Scientific Name
Site Type
Strip Size
Wires
label: Common Name
Scientific Name
DBH
Cond.
Site Type
Strip Size
Wires
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 |
|:----------|:----------|---------:|---------:|
| 0 - 3     | Metro     | 70954    | 0.058378 |
| 4 or more | Non-Metro |  3576.02 | 0.258186 |
| 4 or more | Metro     | 11165.8  | 0.137583 |
| 4 or more | Non-Metro |  1082.58 | 0.423303 |
| 0 - 3     | Non-Metro | 10562    | 0.158758 |

Candidate column headers:
Total Changeable Traditional Globes
RSE
Estimate
Metro Status



headers: Total Changeable Traditional Globes
RSE
Estimate
Metro Status
label: Total Changeable Traditional Globes
Metro Status
Estimate
RSE