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   | Col_11   | Col_12          | Col_13   |
|:-----------|:--------|:--------|--------:|:--------|:--------|:--------|:--------------|:--------|:--------|:---------|:---------|:----------------|:---------|
|            |         |         |       0 |         |         |         |               |         |         | Major    |          |                 |          |
|            |         |         |       0 |         |         |         |               |         |         | Major    |          |                 |          |
|            |         |         |       0 |         |         |         |               |         |         | Major    |          |                 |          |
|            |         | 2.2     |   15000 | 15000.0 | 2.0     | US      | Non-Financial | 42979.0 | MCCDC   | Sub      | 43070.0  | Navy R&D Agency |          |
| 0603923D8Z |         | 1.1     |   70000 |         | 1.0     | US      | CWP           | 42389.0 | CWP     | Sub      | 42551.0  | Navy R&D Agency | 70000.0  |

Candidate column headers:
Total
Provider Country
Start (mm/dd/yyyy)
Resources Provider
End (mm/dd/yyyy)
Labor
Task Type
Task Number
Task Roll-up
Program Element (if US Financial)
Performing Organization
Facilities
Resource Type (CWP/Financial/ Non-financial)
Equipment



headers: Total
Provider Country
Start (mm/dd/yyyy)
Resources Provider
End (mm/dd/yyyy)
Labor
Task Type
Task Number
Task Roll-up
Program Element (if US Financial)
Performing Organization
Facilities
Resource Type (CWP/Financial/ Non-financial)
Equipment
label: Program Element (if US Financial)
Equipment
Task Number
Total
Facilities
Task Roll-up
Provider Country
Resource Type (CWP/Financial/ Non-financial)
Start (mm/dd/yyyy)
Resources Provider
Task Type
End (mm/dd/yyyy)
Performing Organization
Labor
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 |
|--------:|:--------|----------:|
|   40214 | CNC#3   | 0.0777778 |
|   40213 | CNC#2   | 0.0159722 |
|   40213 | CNC#4   | 0.225694  |
|   40214 | CNC#3   | 0.0458333 |
|   40212 | CNC#8   | 0.134028  |

Candidate column headers:
Dur
DDate
CNC



headers: Dur
DDate
CNC
label: DDate
CNC
Dur
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   |
|:----------|:----------|:--------|
| Professor | Chris     | Atton   |
| Dr        | Sara Jane | Bailes  |
| Dr        | Nicholas  | Baron   |
| Dr        | Charlotte | Alston  |
| Dr        | Maria     | Alvarez |

Candidate column headers:
Title
Last Name
First Name



headers: Title
Last Name
First Name
label: Title
First Name
Last 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   |
|--------:|--------:|--------:|:--------|
|     509 |    2002 |    3006 | VAL     |
|     311 |    1033 |     711 | PMC     |
|     318 |    1036 |     714 | SEQ     |
|     526 |    2011 |    3013 | DVX     |
|     321 |    1041 |     660 | PAX     |

Candidate column headers:
NDCInnerID
MVX
NDCOuterID
NDClinkerID



headers: NDCInnerID
MVX
NDCOuterID
NDClinkerID
label: NDClinkerID
NDCOuterID
NDCInnerID
MVX
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 |
|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|---------:|:---------|---------:|
|       2 |       2 |       2 |       1 |       2 |       2 |       2 |       2 |       2 |       2 |        1 |        2 |        2 |        1 | Ellis    |        7 |
|       2 |       2 |       4 |       2 |       2 |       2 |       2 |       2 |       2 |       2 |        2 |        2 |        2 |        2 | Ellis    |        7 |
|       2 |       2 |       2 |       1 |       2 |       3 |       2 |       2 |       2 |       2 |        3 |        2 |        2 |        2 | Ellis    |        7 |
|       2 |       2 |       2 |       1 |       2 |       1 |       2 |       2 |       2 |       2 |        2 |        2 |        2 |        2 | Ellis    |        7 |
|       2 |       1 |       2 |       2 |       2 |       1 |       2 |       2 |       1 |       1 |        2 |        2 |        2 |        2 | Ellis    |        7 |

Candidate column headers:
5
1
6
13
4
2
12
11
7
14
School
8
10
3
9
Grade



headers: 5
1
6
13
4
2
12
11
7
14
School
8
10
3
9
Grade
label: 1
2
3
4
5
6
7
8
9
10
11
12
13
14
School
Grade
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 |
|--------:|:--------|----------:|---------:|---------:|---------:|---------:|
|      11 | Sam     | 0.432906  | 0.408095 | 0.425569 | 0.013405 | 0.851103 |
|       7 | ham     | 0.400484  | 0.73582  | 0.133741 | 0.386983 | 0.479862 |
|      13 | them    | 0.757972  | 0.513442 | 0.436739 | 0.625151 | 0.231811 |
|       6 | green   | 0.0152618 | 0.254523 | 0.948661 | 0.645942 | 0.300744 |
|       2 | and     | 0.0549999 | 0.944165 | 0.984829 | 0.773789 | 0.69994  |

Candidate column headers:
3
4
Embeddings
Vocab
2
1
Word id



headers: 3
4
Embeddings
Vocab
2
1
Word id
label: Word id
Vocab
Embeddings
1
2
3
4
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    |
|:---------|:---------------|------------:|--------:|--------:|------------:|:---------|
| Adelaide | North Adelaide | 4.10021e+06 |     350 |     339 | -0.0314286  | Adelaide |
| Adelaide | Adelaide       | 4.10012e+06 |     364 |     470 |  0.291209   | Adelaide |
| Adelaide | Adelaide       | 4.10013e+06 |       0 |       0 |  0          | Adelaide |
| Adelaide | Adelaide       | 4.10011e+06 |     318 |     336 |  0.0566038  | Adelaide |
| Adelaide | North Adelaide | 4.10021e+06 |     201 |     200 | -0.00497512 | Adelaide |

Candidate column headers:
Growth (%)
SA1 Code (2011 SA1s)
Division
Proposed Division
Projected enrolment 20/01/2022
Actual enrolment 04/09/2017
SA2 Name (2011 SA2s)



headers: Growth (%)
SA1 Code (2011 SA1s)
Division
Proposed Division
Projected enrolment 20/01/2022
Actual enrolment 04/09/2017
SA2 Name (2011 SA2s)
label: Division
SA2 Name (2011 SA2s)
SA1 Code (2011 SA1s)
Actual enrolment 04/09/2017
Projected enrolment 20/01/2022
Growth (%)
Proposed Division
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 |
|--------:|:-------------|--------:|--------:|--------:|--------:|--------:|--------:|
|      12 | India        | 69.6846 | 60.4175 | 71.7458 | 83.3338 | 61.18   | 81.0345 |
|      32 | Gabon        | 45.9642 | 60.4175 | 51.1114 | 39.1969 | 27.9838 | 46.5517 |
|      34 | Malaysia     | 45.8174 | 39.4438 | 45.4337 | 38.8887 | 59.8872 | 43.1034 |
|      35 | Sierra Leone | 45.7142 | 52.3814 | 46.581  | 59.2598 | 23.7676 | 41.3793 |
|      37 | Yemen        | 43.1546 | 57.4078 | 45.5356 | 51.7774 | 15.5164 | 37.931  |

Candidate column headers:
Column1
Country
Safeguards & Quality Controls
Reporting Practices
Enabling Environment
Institutional & Legal Setting
Rank
Composite Score



headers: Column1
Country
Safeguards & Quality Controls
Reporting Practices
Enabling Environment
Institutional & Legal Setting
Rank
Composite Score
label: Rank
Country
Composite Score
Institutional & Legal Setting
Reporting Practices
Safeguards & Quality Controls
Enabling Environment
Column1
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 |
|:--------|:---------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|---------:|
| RTQ     | No harm  |     219 |     248 |     370 |     495 |     330 |     389 |     487 |      86 |      191 |      231 |      169 |        0 |
| RVN     | Severe   |       0 |       0 |       2 |       4 |       1 |       8 |       4 |       2 |        0 |        0 |        0 |        1 |
| RTQ     | Low      |     108 |      85 |      96 |     196 |     172 |     121 |     204 |      26 |       93 |       99 |       89 |        1 |
| RF4     | Moderate |      22 |      19 |      15 |       9 |       0 |       0 |      85 |       0 |       85 |       42 |       31 |       55 |
| RCF     | No harm  |     423 |     295 |     309 |     278 |     309 |     372 |     553 |     308 |      318 |      472 |      346 |      634 |

Candidate column headers:
Sep-18
May-18
Apr-18
Mar-18
Jun-18
Aug-18
Feb-18
Jul-18
Jan-18
Organisation code
Degree of harm
Oct-18
Nov-17
Dec-17



headers: Sep-18
May-18
Apr-18
Mar-18
Jun-18
Aug-18
Feb-18
Jul-18
Jan-18
Organisation code
Degree of harm
Oct-18
Nov-17
Dec-17
label: Organisation code
Degree of harm
Nov-17
Dec-17
Jan-18
Feb-18
Mar-18
Apr-18
May-18
Jun-18
Jul-18
Aug-18
Sep-18
Oct-18
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 |
|:----------|--------:|:--------|--------:|--------:|--------:|--------:|--------:|
| Sunday    |   43205 | Grave   |      34 |       0 |       0 |      34 |       6 |
| Monday    |   43199 | Grave   |      29 |       1 |       0 |      30 |       7 |
| Sunday    |   43198 | Day     |      27 |       3 |       1 |      29 |       6 |
| Thursday  |   43202 | Day     |      31 |       4 |       4 |      31 |       7 |
| Wednesday |   43201 | Grave   |      30 |       1 |       0 |      31 |       7 |

Candidate column headers:
Patient-Admit
Weekday
Day
Shift
Patient-Discharge
Patient-End
Nurses 
Patient-Start



headers: Patient-Admit
Weekday
Day
Shift
Patient-Discharge
Patient-End
Nurses 
Patient-Start
label: Weekday
Day
Shift
Patient-Start
Patient-Admit
Patient-Discharge
Patient-End
Nurses
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 |
|:--------------|:--------------------|--------:|--------:|--------:|--------:|
| Europe        | England             |   27459 |     544 |    0.34 |  8879.1 |
| North America | Western US & Mexico |   91279 |     610 |    0.27 | 24175.6 |
| Europe        | Western Europe      |   14940 |      71 |    0.9  | 13346.6 |
| North America | Eastern US          |   43105 |    1052 |    0.83 | 34378   |
| Europe        | England             |   15967 |     262 |    0.26 |  3952.3 |

Candidate column headers:
Books Distributed
Profit
Gildrose's Royalty/book
Distribution Locale
Returns
Continent



headers: Books Distributed
Profit
Gildrose's Royalty/book
Distribution Locale
Returns
Continent
label: Continent
Distribution Locale
Books Distributed
Returns
Gildrose's Royalty/book
Profit
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   |
|:--------|-----------:|:--------|--------:|-----------:|--------:|:-----------|:--------|:--------|:--------|
| 1986-87 |  257337000 |         | 2202000 |  259539000 | 2661000 |            |         |         |         |
| 2012-13 |  782115000 |         | 6705000 |  788820000 |  498000 | 82548000.0 |         | 0.3068  |         |
| 1974-75 |  264182000 |         | 2262000 |  266444000 |  745000 |            |         |         |         |
| 2003-04 | 1021366000 |         | 8755000 | 1030121000 | 4721000 | 44166000.0 |         | 0.4676  |         |
| 1967-68 |  208125000 | i/      | 1862000 |  209987000 |  328000 |            |         |         |         |

Candidate column headers:
Ref6
Refunds
OTP Revenue
Rate
Fiscal year
Revenue a/
Distributors' discounts b/
Ref1
Gross value of tax indicia c/
Ref5



headers: Ref6
Refunds
OTP Revenue
Rate
Fiscal year
Revenue a/
Distributors' discounts b/
Ref1
Gross value of tax indicia c/
Ref5
label: Fiscal year
Revenue a/
Ref1
Distributors' discounts b/
Gross value of tax indicia c/
Refunds
OTP Revenue
Ref5
Rate
Ref6
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 |
|:--------|:--------|--------:|
| BLR     | CNY     |      78 |
| BEL     | KRW     |      17 |
| BRA     | EEK     |      21 |
| BAN     | MXN     |      28 |
| ARG     | LBP     |      80 |

Candidate column headers:
Qty
Country
Currency



headers: Qty
Country
Currency
label: Country
Currency
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 |    Col_14 |     Col_15 |   Col_16 |   Col_17 |   Col_18 |
|:--------|:------------------------------|---------:|---------:|--------:|----------:|----------:|--------:|---------:|----------:|---------:|-----------:|----------:|---------:|----------:|-----------:|---------:|---------:|---------:|
| ES      | ES                            | 0.947674 | 1.71578  |     212 | 0.113539  |  0.386391 |     177 | 0.131783 | -0.34462  |       77 | 0.00193798 | -0.243726 |    157   | 0.0852713 |  0.0245514 |    137   | 0.868941 |       24 |
| EC      | EC(elementary_ middle)        | 0.767722 | 0.983502 |     178 | 0.130719  |  0.656591 |     188 | 0.18552  |  0.140049 |      143 | 0.0573152  |  2.14062  |    205   | 0.307692  |  2.56396   |    204.5 | 1.05185  |       16 |
| EC      | EC (elementary_ middle_ high) | 0.795775 | 1.09765  |     184 | 0.118196  |  0.459624 |     182 | 1        |  7.486    |      212 | 0          | -0.327169 |     75.5 | 0.028169  | -0.627393  |     74   | 1.76853  |        6 |
| EC      | EC(middle_ high)              | 0.737463 | 0.860369 |     166 | 0.0657926 | -0.364554 |      87 | 0.230088 |  0.542017 |      180 | 0          | -0.327169 |     75.5 | 0.117994  |  0.398151  |    162   | 0.52612  |       50 |
| MS      | MS                            | 0.777027 | 1.02137  |     181 | 0.105202  |  0.255265 |     172 | 0.256757 |  0.782544 |      195 | 0.00337838 | -0.181708 |    170   | 0.192568  |  1.24957   |    188   | 0.891912 |       23 |

Candidate column headers:
Avg Z
absent_z
absent_pct
outsusp_z
iep_pct
osse_rank
Original Category
iep_rank
inschoolsusp_pct
Category
outsusp_rank
osse_z
iep_z
Rank
osse_pct
absent_rank
outschoolsusp_pct
insusp_z
insusp_rank



headers: Avg Z
absent_z
absent_pct
outsusp_z
iep_pct
osse_rank
Original Category
iep_rank
inschoolsusp_pct
Category
outsusp_rank
osse_z
iep_z
Rank
osse_pct
absent_rank
outschoolsusp_pct
insusp_z
insusp_rank
label: Original Category
Category
osse_pct
osse_z
osse_rank
absent_pct
absent_z
absent_rank
iep_pct
iep_z
iep_rank
inschoolsusp_pct
insusp_z
insusp_rank
outschoolsusp_pct
outsusp_z
outsusp_rank
Avg Z
Rank
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 |
|:-----------|:-------------------|--------:|--------:|
| Seafood    | Banana Prawns      | 3810.16 |   41579 |
| Beverages  | Apple Juice        | 1008    |   41306 |
| Beverages  | Apple Juice        | 1829.52 |   41518 |
| Beverages  | Apple Juice        | 1273.5  |   41365 |
| Condiments | Boysenberry Spread |  750    |   41365 |

Candidate column headers:
Month
Product
Sales
Category



headers: Month
Product
Sales
Category
label: Category
Product
Sales
Month
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 |
|--------:|:-----------|--------:|
|       5 | N17-P64B   |   41655 |
|      23 | N16-P77E   |   41655 |
|      26 | 0433668-11 |   41655 |
|      25 | N17-P21B   |   41655 |
|      34 | SL227B3    |   41655 |

Candidate column headers:
Lot #
Index
Date Entered



headers: Lot #
Index
Date Entered
label: Index
Lot #
Date Entered
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0   | Col_1       | Col_2    | Col_3        |
|:--------|:------------|:---------|:-------------|
| A       | District    | Selected | Valid        |
| R       | P1 - Sector | Selected | Valid        |
| C       | Ward        | Manual   | As per entry |
| U       | P2 - Trade  | Selected | Valid        |
| S       | P1 - Trade  | Selected | Valid        |

Candidate column headers:
Data  Quality
Entry Mode
Column Name
Ref  Column



headers: Data  Quality
Entry Mode
Column Name
Ref  Column
label: Ref  Column
Column Name
Entry Mode
Data  Quality
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 |
|--------:|--------:|--------:|--------:|:-------------------|------------:|
|   47801 |       1 |     8   |       0 | 15.99              | 6.34084e+11 |
|   33610 |       1 |    47.5 |       0 | $80.99 - $94.99    | 6.34084e+11 |
|   27006 |       1 |     8.5 |       0 | 16.989999999999998 | 6.34084e+11 |
|   33425 |       1 |     8.5 |       0 | 16.989999999999998 | 6.34084e+11 |
|   13034 |       1 |     9   |       0 | 17.989999999999998 | 6.34084e+11 |

Candidate column headers:
SRP
Case Cost
Total Cost
Case Pack Count
ITEM #
UPC Code



headers: SRP
Case Cost
Total Cost
Case Pack Count
ITEM #
UPC Code
label: ITEM #
Case Pack Count
Case Cost
Total Cost
SRP
UPC 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 |
|:--------|--------:|--------:|
| ESP     |   41091 |   18.15 |
| ESP     |   40981 |   72.84 |
| ESP     |   41195 |   92.21 |
| RUS     |   41048 |   87.82 |
| POR     |   41208 |   62.22 |

Candidate column headers:
Val
Country
Date



headers: Val
Country
Date
label: Country
Date
Val
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 |
|:--------|--------:|:--------|:-------------|:--------|:--------|:----------------|--------:|--------:|---------:|-----------------:|---------:|---------:|:---------------------------|---------:|---------:|---------:|
| Crt     |   40909 | DC01    | DC01AP000027 | P000027 | GMK     | 5 Kg/Crtn       |     5   |       1 | 8        | 224773           |        1 | 8        | Upper 50% dari TOR         |        1 |        0 |        0 |
| Crt     |   40909 | DC02    | DC02AP000114 | P000114 | Colatta | 4x6x150 Gr/Crtn |     3.6 |       0 | 2        | 750375           |        0 | 2        | Ending Stock ada_ No Sales |        0 |        0 |        1 |
| Crt     |   40940 | DC02    | DC02AP000113 | P000113 | Colatta | 4x6x150 Gr/Crtn |     3.6 |       0 | 0.833333 |      1.05052e+06 |        0 | 0.833333 | Ending Stock ada_ No Sales |        0 |        0 |        1 |
| Crt     |   40909 | DC01    | DC01AP000110 | P000110 | Colatta | 4x6x150 Gr/Crtn |     3.6 |       0 | 0.958333 | 875438           |        0 | 0.958333 | Ending Stock ada_ No Sales |        0 |        0 |        1 |
| Crt     |   40940 | DC02    | DC02AP000110 | P000110 | Colatta | 4x6x150 Gr/Crtn |     3.6 |       0 | 0.958333 | 875438           |        0 | 0.958333 | Ending Stock ada_ No Sales |        0 |        0 |        1 |

Candidate column headers:
Item ID
Rem2
periode
End Stock
Out SO
area
Packaging
rec_id
Weight (Kg)
Remarks
Rem3
Price
Item Brand
Out SO dt
out_type
Rem1
End Stock dt



headers: Item ID
Rem2
periode
End Stock
Out SO
area
Packaging
rec_id
Weight (Kg)
Remarks
Rem3
Price
Item Brand
Out SO dt
out_type
Rem1
End Stock dt
label: out_type
periode
area
rec_id
Item ID
Item Brand
Packaging
Weight (Kg)
Out SO dt
End Stock dt
Price
Out SO
End Stock
Remarks
Rem1
Rem2
Rem3
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 |
|:--------|:--------|:---------|:-------------|--------:|--------:|---------:|---------:|--------:|
| Bajwa   | Guhan   | Westview | Register     |      25 |   10.5  | 11845.3  | 12468.8  | 13125   |
| Walters | Morgan  | Westview | Floor        |      15 |    8.25 |  5584.22 |  5878.12 |  6187.5 |
| Harris  | Zachary | Westview | Asst Manager |      35 |   18.75 | 29613.3  | 31171.9  | 32812.5 |
| Rosich  | Mallory | Westview | Floor        |      12 |    8.25 |  4467.38 |  4702.5  |  4950   |
| Khatri  | Samir   | Westview | Manager      |      40 |   25    | 45125    | 47500    | 50000   |

Candidate column headers:
2016 Wages
2017 Wages
2018 Weekly Hours
2018 Hourly Rate
FirstName
LastName
Position
2018 Wages
Store



headers: 2016 Wages
2017 Wages
2018 Weekly Hours
2018 Hourly Rate
FirstName
LastName
Position
2018 Wages
Store
label: LastName
FirstName
Store
Position
2018 Weekly Hours
2018 Hourly Rate
2016 Wages
2017 Wages
2018 Wages
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   |
|:--------|:--------|:--------|:-------------------|:--------|:--------|
| HAT     | I       |         | 17.999999999999996 |         |         |
| GID     | H       |         |                    | Passed  | Passed  |
| NIS     |         |         | 19.000000000000004 |         |         |
| RIH     |         |         |                    | Passed  |         |
| DUL     | F       | JES     | 1.0                |         | Passed  |

Candidate column headers:
Amend
ID
Status.1
D_Left
ID.1
Status



headers: Amend
ID
Status.1
D_Left
ID.1
Status
label: ID
Amend
ID.1
D_Left
Status
Status.1
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0   | Col_1   | Col_2   | Col_3   |
|:--------|:--------|:--------|:--------|
| BANDUNG | SAKINAH | LILI    | PISANG  |
| BANDUNG | SAMSUL  | LILI    | PISANG  |
| BANDUNG | 123     | LILI    | PISANG  |
| BANDUNG | MENTARI | LILI    | PISANG  |
| BANDUNG | MADANI  | LILI    | PISANG  |

Candidate column headers:
PRODUK DESC
SALESMAN DESC
CUST NAME
CABANG



headers: PRODUK DESC
SALESMAN DESC
CUST NAME
CABANG
label: CABANG
CUST NAME
SALESMAN DESC
PRODUK DESC
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 |
|:--------|--------:|--------:|--------:|--------:|--------:|:--------|--------:|
| 2008/09 |    2409 |    0.37 |    2047 |    0.85 |    0.12 | -       |     297 |
| 2013/14 |    1461 |    0.75 |    1276 |    0.87 |    0.09 | -       |     165 |
| 2007/08 |    2394 |    0.39 |    2065 |    0.86 |    0.11 | -       |     252 |
| 2012/13 |    1683 |    0.81 |    1436 |    0.85 |    0.11 | -       |     186 |
| 2014/15 |    1512 |    0.74 |    1317 |    0.87 |    0.1  | -       |     143 |

Candidate column headers:
Percentage of applications refused
Number of appeal requests
Number of applications determined
Percentage of applications approved
Percentage Applications determined within 8 weeks
Number of applications approved 
Financial year
Number of review requests



headers: Percentage of applications refused
Number of appeal requests
Number of applications determined
Percentage of applications approved
Percentage Applications determined within 8 weeks
Number of applications approved 
Financial year
Number of review requests
label: Financial year
Number of applications determined
Percentage Applications determined within 8 weeks
Number of applications approved 
Percentage of applications approved
Percentage of applications refused
Number of review requests
Number of appeal requests
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 |
|:-----------|:----------|:-----------|--------:|
| Sales      | Full-time | PC laptop  |       5 |
| R&D        | Full-time | PC desktop |       7 |
| Accounting | Full-time | Apple Mac  |       5 |
| Sales      | Part-time | PC laptop  |       3 |
| Accounting | Part-time | Apple Mac  |       2 |

Candidate column headers:
Department
Count
Computer
Job type



headers: Department
Count
Computer
Job type
label: Department
Job type
Computer
Count
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 |
|:--------|--------:|:------------------|--------:|--------:|--------:|---------:|---------:|----------:|-----------:|-----------:|----------:|---------:|---------:|---------:|---------:|---------:|---------:|
| 9-12    | 5801002 | High School Level |   57.77 |   79.13 |     279 | 0.498208 | 0.942652 | 0.0215054 | 0.00716846 | 0.0286738  | 0.0573477 |        1 |      130 |    77.2  |      131 |  5801000 |    81.05 |
| K-4     | 5801001 | Elementary Level  |   59.2  |   74.76 |     411 | 0.737226 | 0.965937 | 0.0194647 | 0.00973236 | 0.00486618 | 0.0340633 |        1 |      156 |    74.05 |      157 |  5801000 |    75.8  |
| 9-12    |  440703 | High School Level |   89.77 |   84.59 |     221 | 0.253394 | 0.78733  | 0.104072  | 0.0226244  | 0.0859729  | 0.21267   |        1 |       88 |    88.05 |       88 |   440700 |    81.4  |
| 7-12    | 7401003 | High School Level |   46.6  |   83.01 |     168 | 0.839286 | 0.380952 | 0.0416667 | 0.571429   | 0.00595238 | 0.619048  |        2 |      102 |    83.15 |      102 |  7401000 |    82.8  |
| 7-12    |  444703 | High School Level |   54.27 |   75.89 |     163 | 0.564417 | 0.680982 | 0.0981595 | 0.128834   | 0.0920245  | 0.319018  |        1 |       74 |    73    |       74 |   444700 |    78.6  |

Candidate column headers:
Region
Number of Students Tested in ELA
Final Weighted Achievement
District % Hispanic
Overall Combined Content Growth Score
District % White
District % Other Races
School Level
Number of Students Tested in Math
District % Black
District Overall % Minority
Combined Math Growth Score
Distict % FRL
District Enrollment
School LEA
Grades
Combined ELA Growth Score
District LEA



headers: Region
Number of Students Tested in ELA
Final Weighted Achievement
District % Hispanic
Overall Combined Content Growth Score
District % White
District % Other Races
School Level
Number of Students Tested in Math
District % Black
District Overall % Minority
Combined Math Growth Score
Distict % FRL
District Enrollment
School LEA
Grades
Combined ELA Growth Score
District LEA
label: Grades
School LEA
School Level
Final Weighted Achievement
Overall Combined Content Growth Score
District Enrollment
Distict % FRL
District % White
District % Hispanic
District % Black
District % Other Races
District Overall % Minority
Region
Number of Students Tested in Math
Combined Math Growth Score
Number of Students Tested in ELA
District LEA
Combined ELA Growth Score
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0                | Col_1               | Col_2     | Col_3               | Col_4   | Col_5   | Col_6   | Col_7   |
|:---------------------|:--------------------|:----------|:--------------------|:--------|:--------|:--------|:--------|
| Julia Stegman        | TSIA                | Monday    | 3:00 PM to 4:00 PM  | no      | no      | yes     | no      |
| Jeanne Beliveau-Dunn | Cisco Systems       | Monday    | 4:15 PM to 5:15 PM  | no      | no      | no      | no      |
| Phil Chouinard       | Bentley Systems     | Wednesday | 9:45 AM to 10:45 AM | yes     | no      | no      | no      |
| Tim Albright         | Jive Software_ Inc. | Tuesday   | 12:20 PM to 1:00 PM | yes     | yes     | yes     | yes     |
| Susan Merriman       | HP Software (HPSW)  | Tuesday   | 8:30 AM to 9:30 AM  | no      | no      | no      | yes     |

Candidate column headers:
Time
Speaker Name
Speaker Company
SS
SRG
Day
ES
PS



headers: Time
Speaker Name
Speaker Company
SS
SRG
Day
ES
PS
label: Speaker Name
Speaker Company
Day
Time
SS
PS
SRG
ES
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 |
|--------:|--------:|--------:|
|   42278 |  425.63 |  1.734  |
|   41579 |  536.88 |  3.1084 |
|   42186 |  448.13 |  1.8114 |
|   41091 |  585    |  2.8974 |
|   42248 |  442.5  |  1.593  |

Candidate column headers:
Date
OPIS
KAPI



headers: Date
OPIS
KAPI
label: Date
KAPI
OPIS
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 |
|--------:|--------:|--------:|--------:|--------:|
|       4 |  190.72 |       0 |  190.72 |       1 |
|     131 |  258.01 |       0 |  258.01 |       1 |
|     136 |  258.01 |       0 |  258.01 |       1 |
|     138 |  123.36 |       0 |  123.36 |       1 |
|     137 |   59.5  |       0 |   59.5  |       1 |

Candidate column headers:
Project ID
Margin %
Cost
Margin
Amount



headers: Project ID
Margin %
Cost
Margin
Amount
label: Project ID
Amount
Cost
Margin
Margin %
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           |
|:-------------------|:------------------|--------:|:------------|--------:|:----------------|
| Carmina Burana     | Orff_ Carl        |       2 | 13674       |   35524 | New York_ NY    |
| Autumn Valentine   | Gordon_ Ricky Ian |       1 | 16-Sep-1992 |   33867 | Omaha_ NE       |
| A Song in the Wind | Baltzer_ Suzi     |       1 | ?           |   35265 | Albuquerque_ NM |
| Adriana Lecouvreur | Cilea_ Francesco  |       3 | 06-Nov-1902 |   28574 | New York_ NY    |
| Bluebeard's Castle | Bartok_ Bela      |       1 | 6719        |   32543 | New York_ NY    |

Candidate column headers:
First Wiess Viewing
Opera
Number of Performances Attended
Location (City)
Composer
First Ever Performed



headers: First Wiess Viewing
Opera
Number of Performances Attended
Location (City)
Composer
First Ever Performed
label: Opera
Composer
Number of Performances Attended
First Ever Performed
First Wiess Viewing
Location (City)
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 |
|:---------------|:----------|--------:|---------:|--------:|
| Duplin County  | 12-C-2414 |       7 | 10650    |   42313 |
| New Bern_ City | 12-C-2471 |       6 | 96021.1  |   42313 |
| Pamlico County | 09-C-2074 |      39 |  5521.36 |   42313 |
| Ayden_ Town    | 12-C-2466 |      22 | 11300    |   42313 |
| Goldston_ Town | 12-C-2499 |      32 |  2979.66 |   42313 |

Candidate column headers:
Grant #
Amount
Req #
Grantee
Pymt Date



headers: Grant #
Amount
Req #
Grantee
Pymt Date
label: Grantee
Grant #
Req #
Amount
Pymt 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 |
|:-------------------------|--------:|---------:|:-------------------|:---------|---------:|
| Communications           |    5000 |    88.79 |                    | 4961.21  |   -50    |
| Cons/Prof Serv-Interdept |  154400 |     0    |                    | 153284.0 |  1116    |
| Training                 |    1000 |     0    |                    |          |  1000    |
| General Expense          |   24500 |     0    | 22500.0            |          |  2000    |
| Salaries/ Wages          |  365400 | 30791.3  | 338703.97000000003 |          | -4095.24 |

Candidate column headers:
Year to Date
Projected
Budget Plan
           Description
Available Balance
Encumbrances



headers: Year to Date
Projected
Budget Plan
           Description
Available Balance
Encumbrances
label:            Description
Budget Plan
Year to Date
Projected
Encumbrances
Available Balance
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 |
|--------:|--------:|--------:|--------:|--------:|
|    2013 | 52158.7 | 22734.8 | 2812.93 | 21446.9 |
|    2006 | 57804   | 22401.5 | 7127.55 | 22754.2 |
|    2014 | 57592.9 | 22857.8 | 2022.25 | 17609.9 |
|    2012 | 49448.5 | 18261.7 | 1998.35 | 21171.6 |
|    2016 | 34012.5 | 15782   | 2110.37 | 14895.7 |

Candidate column headers:
Summer
Fall
Winter
Column1
Spring



headers: Summer
Fall
Winter
Column1
Spring
label: Column1
Winter
Spring
Summer
Fall