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 |
|--------:|:--------|:-------------------------------|--------:|--------:|--------:|
|       3 | HBMD    | Homeware Bread Machine -Deluxe |   89.95 |       6 |   539.7 |
|       4 | OGG     | Olson Gas Grill                |  159.95 |       4 |   639.8 |
|       3 | SPP     | Stone Pizza Pan                |   29.95 |       2 |    59.9 |
|       2 | SPP     | Stone Pizza Pan                |   29.95 |       6 |   179.7 |
|       4 | HBM     | Homeware Bread Machine         |   49.95 |       4 |   199.8 |

Candidate column headers:
Product Price
Units Sold
Total Sale
Product ID
Region
Product Name



headers: Product Price
Units Sold
Total Sale
Product ID
Region
Product Name
label: Region
Product ID
Product Name
Product Price
Units Sold
Total Sale
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 |
|:--------|:---------|:-----------|:--------|--------:|--------:|--------:|
| Chang   | Vanessa  | Cambridge  | PT      |   24000 |   15000 |   25000 |
| Lang    | Noah     | Cambridge  | FT      |   75000 |   77000 |   80000 |
| Huang   | Jennifer | Nashua     | FT      |   80000 |   83000 |   86000 |
| Mateev  | Rohan    | Framingham | FT      |   66000 |   70000 |   75000 |
| Walsh   | Michael  | Cambridge  | FT      |   80000 |   83000 |   86000 |

Candidate column headers:
2015
Status
2014
2016
Office
LastName
FirstName



headers: 2015
Status
2014
2016
Office
LastName
FirstName
label: LastName
FirstName
Office
Status
2014
2015
2016
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0     |   Col_1 | Col_2               |   Col_3 |
|:----------|--------:|:--------------------|--------:|
| 0014-0068 |  209866 | 2550 M ST NW        |       2 |
| 0028-2010 |   88255 | 0922 24TH ST NW     |       2 |
| 0031-0837 |  316550 | 2475 VIRGINIA AV NW |       2 |
| 0016-2006 |   77638 | 0955 26TH ST NW     |       2 |
| 0025-1646 |  112894 | 1111 25TH ST NW     |       2 |

Candidate column headers:
Ward
DC Real Property  Unique ID
Address
Gross Building Area



headers: Ward
DC Real Property  Unique ID
Address
Gross Building Area
label: DC Real Property  Unique ID
Gross Building Area
Address
Ward
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 |
|--------:|:--------|:--------|:--------|:--------|--------:|
|    1033 | FT-1    | CRT-1   | pHT-2   | IT-4    | 7.15561 |
|    1005 | FT-4    | CRT-2   | pHT-2   | IT-2    | 5.09187 |
|    1006 | FT-4    | CRT-1   | pHT-1   | IT-2    | 6.3245  |
|    1032 | FT-3    | CRT-3   | pHT-1   | IT-3    | 5.88222 |
|    1003 | FT-1    | CRT-1   | pHT-3   | IT-4    | 3.37244 |

Candidate column headers:
Irrigation
Fertilizer
pH
Yield
Sample ID
Rotation



headers: Irrigation
Fertilizer
pH
Yield
Sample ID
Rotation
label: Sample ID
Fertilizer
Rotation
pH
Irrigation
Yield
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   |
|:--------|:----------|--------:|---------:|---------:|:--------|:--------|:--------|
| BS7005  | Stratford |   42018 | 0.395833 | 0.479167 | 120     | RB1.04  |         |
| BS3270  | Stratford |   42009 | 0.583333 | 0.666667 | 2:00    | RB104   |         |
| BS6015T | Stratford |   42010 | 0.395833 | 0.479167 | 2:00    | RB117   |         |
| BS7018  | Stratford |   42016 | 0.583333 | 0.645833 | 90      | RB.G.16 |         |
| BS5008  | Stratford |   42011 | 0.583333 | 0.625    | 1:00    | UH303   | T-W     |

Candidate column headers:
Exam duration
Exam site
Exam start time
Exam end time
Room Split
Exam date
Module Code
Room Number



headers: Exam duration
Exam site
Exam start time
Exam end time
Room Split
Exam date
Module Code
Room Number
label: Module Code
Exam site
Exam date
Exam start time
Exam end time
Exam duration
Room Number
Room Split
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     |
|:--------|:------------|:----------|
|         | Operational | CAS_FB_09 |
|         | Operational | UGE_E_1   |
|         | Operational | OOR_E_2   |
|         | Operational | CAS_FB_02 |
|         | Operational | CAS_FB_16 |

Candidate column headers:
Type
#
DG_Code



headers: Type
#
DG_Code
label: #
Type
DG_Code
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0               | Col_1                   | Col_2     | Col_3   |   Col_4 | Col_5   |   Col_6 |   Col_7 |   Col_8 |   Col_9 |   Col_10 |   Col_11 |   Col_12 |   Col_13 |   Col_14 |
|:--------------------|:------------------------|:----------|:--------|--------:|:--------|--------:|--------:|--------:|--------:|---------:|---------:|---------:|---------:|---------:|
| PO BOX 359          |                         | HARPSWELL | ME      |    4079 | B2248R  |  755400 |  100100 |  855500 |      26 |        0 |        0 |        0 |        0 |        0 |
| 54 BARNES POINT RD  |                         | HARPSWELL | ME      |    4079 | S3645R  |  340800 |  321000 |   20000 |      50 |        0 |        0 |        0 |        0 |   641800 |
| 50 WALINI WAY       |                         | HARPSWELL | ME      |    4079 | C0095R  |  341800 |  416900 |   20000 |      50 |        0 |        0 |        0 |        0 |   738700 |
| 12A SKOLFIED PLACE  |                         | HARPSWELL | ME      |    4079 | B0870R  |    4700 |       0 |       0 |       0 |        0 |        0 |        0 |        0 |     4700 |
| DSM II LIVING TRUST | 270 HARPSWELL NECK ROAD | HARPSWELL | ME      |    4079 | W2150R  |   50100 |  203900 |       0 |       0 |        0 |        0 |        0 |        0 |   254000 |

Candidate column headers:
Exemption1
Address1
Building_Value
Exemption3
Address2
Exemption_Code3
City
Total Assessed 
Zip
Land_Value
Exemption_Code1
State
AccountNumber
Exemption2
Exemption_Code2



headers: Exemption1
Address1
Building_Value
Exemption3
Address2
Exemption_Code3
City
Total Assessed 
Zip
Land_Value
Exemption_Code1
State
AccountNumber
Exemption2
Exemption_Code2
label: Address1
Address2
City
State
Zip
AccountNumber
Land_Value
Building_Value
Exemption1
Exemption_Code1
Exemption2
Exemption_Code2
Exemption3
Exemption_Code3
Total Assessed
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 |
|:-----------|--------:|--------:|--------:|
| Task Force |   69241 |    2666 |   71907 |
| Local      |       0 |   19600 |   19600 |
| Local      |  223899 |   65501 |  289400 |
| Local      |  331968 |  185973 |  517941 |
| Local      |    9765 |       0 |    9765 |

Candidate column headers:
Agency Type
Sales Proceeds
Totals
Cash Value



headers: Agency Type
Sales Proceeds
Totals
Cash Value
label: Agency Type
Cash Value
Sales Proceeds
Totals
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 |
|:---------|--------:|--------:|--------:|
| Npath    |    9483 |    9678 |    -195 |
| Photobug |    1052 |    4594 |   -3542 |
| Quaxo    |    3129 |    1487 |    1642 |
| Midel    |     497 |    9623 |   -9126 |
| Riffwire |    4638 |    9838 |   -5200 |

Candidate column headers:
Profit
Job Cost
Invoice Amount
Region



headers: Profit
Job Cost
Invoice Amount
Region
label: Region
Invoice Amount
Job Cost
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 |
|:------------|--------:|--------:|----------:|
| Bulqize     |    2921 |  184501 | 0.0158319 |
| Kruje       |    2546 |   87578 | 0.0290712 |
| Gjirokaster |    8823 |  227955 | 0.038705  |
| Polican     |    1230 |  116729 | 0.0105372 |
| Corovode    |    2439 |  124401 | 0.019606  |

Candidate column headers:
Bashkia
Vlera e demit ekonomik
Transferta e pakushtezuar
Raporti



headers: Bashkia
Vlera e demit ekonomik
Transferta e pakushtezuar
Raporti
label: Bashkia
Vlera e demit ekonomik
Transferta e pakushtezuar
Raporti
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 |     340 |    2.03 |
|    2000 |      67 |    0.39 |
|    2011 |     240 |    1.42 |
|    2007 |     180 |    1.07 |
|    2009 |     240 |    1.42 |

Candidate column headers:
Lew (BGN) per month
on 1 January
Lew (BGN) per hour



headers: Lew (BGN) per month
on 1 January
Lew (BGN) per hour
label: on 1 January
Lew (BGN) per month
Lew (BGN) per hour
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 |
|--------:|--------:|--------:|--------:|:--------|--------:|--------:|
|       4 |      11 |    2016 |   42678 | Yes     |       3 | 750.535 |
|       4 |       7 |    2016 |   42555 | Yes     |       2 | 112.186 |
|       4 |       4 |    2016 |   42464 | Yes     |       1 | 815.3   |
|       4 |       6 |    2016 |   42525 | Yes     |       1 | 649.473 |
|       4 |       4 |    2016 |   42464 | Yes     |       1 | 496.835 |

Candidate column headers:
Month
Visible
Position
Date
Value
Day
Year



headers: Month
Visible
Position
Date
Value
Day
Year
label: Day
Month
Year
Date
Visible
Position
Value
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0        |   Col_1 |   Col_2 | Col_3   |   Col_4 |   Col_5 | Col_6   |   Col_7 |   Col_8 |   Col_9 |   Col_10 | Col_11   |   Col_12 | Col_13            |   Col_14 |
|:-------------|--------:|--------:|:--------|--------:|--------:|:--------|--------:|--------:|--------:|---------:|:---------|---------:|:------------------|---------:|
| SP10         |    5612 |     805 | 894.0   |    1901 |    3356 | 9361.0  |    7131 |    3417 |   12369 |     7745 | 2018.0   |     5013 | 1569              |     8117 |
| SU11         |     148 |     156 |         |      40 |     186 | 281.0   |     390 |     352 |     312 |       83 | 69.0     |       95 |                   |      188 |
| Winter 09/10 |       0 |       0 | 4.0     |       0 |       5 |         |       0 |      81 |      22 |       20 |          |       51 | included in SP #s |        0 |
| Winter 10/11 |       0 |       0 | 0.0     |       0 |     400 |         |       0 |    2259 |     824 |      560 |          |      501 | 0                 |        0 |
| Winter 10/11 |       0 |       0 | 0.0     |       0 |      31 |         |       0 |     100 |      32 |       31 |          |       30 | 0                 |        0 |

Candidate column headers:
MassArt
Salem U
Emmanuel
BHCC
Wheelock
UMD
QCC
UMB
WIT
Column2
MCLA
Westfield
UMW
UMA
Simmons



headers: MassArt
Salem U
Emmanuel
BHCC
Wheelock
UMD
QCC
UMB
WIT
Column2
MCLA
Westfield
UMW
UMA
Simmons
label: Column2
BHCC
Emmanuel
MassArt
MCLA
QCC
Salem U
Simmons
UMA
UMB
UMD
UMW
Westfield
Wheelock
WIT
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   |
|--------:|:---------------------|:--------|
|  521035 | Meals Taxable        | N       |
|  533000 | Food and Clothing    | Y       |
|  532121 | Electronic Resources | N       |
|  532010 | Athletic Supplies    | N       |
|  522015 | Intl Lodging         | N       |

Candidate column headers:
Account Descr
Bud. Only
Account



headers: Account Descr
Bud. Only
Account
label: Account
Account Descr
Bud. Only
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   |
|:---------|:------------|:--------|--------:|:--------|:--------|:--------|:--------|:--------|:--------|:---------|
| NCR10351 | New England |         |       2 |         | GO      | GOP     |         |         |         |          |
| NCR07028 | New York    | DP      |       4 |         |         |         | TO      | TOP     | TP      |          |
| NCR11791 | New York    |         |       2 |         |         |         | TO      | TOP     |         |          |
| NCR11647 | New England |         |       1 |         |         | GOP     |         |         |         |          |
| NCR07055 | New England |         |       2 |         |         |         | TO      |         |         | TSP      |

Candidate column headers:
TP
NERC ID
GOP
GO
DPUF
TSP
TO
TOP
DP
Sub-Region
Total



headers: TP
NERC ID
GOP
GO
DPUF
TSP
TO
TOP
DP
Sub-Region
Total
label: NERC ID
Sub-Region
DP
Total
DPUF
GO
GOP
TO
TOP
TP
TSP
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        |
|--------:|:--------|--------:|:-------------|
|      14 | D       |       7 | Stock        |
|      20 | C       |       3 | Forecast     |
|       7 | D       |       8 | Sales        |
|       9 | A       |      10 | Stock        |
|      26 | C       |       5 | Net Forecast |

Candidate column headers:
Heading
Quantity
SaleID
Product



headers: Heading
Quantity
SaleID
Product
label: SaleID
Product
Quantity
Heading
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 |
|:--------|:----------|--------:|:--------|--------:|--------:|
| Jan     | STATEWIDE |    2005 | Total   |  576294 |   38353 |
| Jan     | STATEWIDE |    1998 | Total   |  547595 |   35796 |
| Jan     | STATEWIDE |    1993 | Total   |  529625 |   33970 |
| Jan     | STATEWIDE |    2004 | Total   |  518105 |   37987 |
| Jan     | STATEWIDE |    1991 | Total   |  535445 |   33239 |

Candidate column headers:
Year
Destination
Visitors
Source
Month
Date



headers: Year
Destination
Visitors
Source
Month
Date
label: Month
Destination
Year
Source
Visitors
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   |
|--------:|:--------|:---------------------------|:---------------|:--------|
|     978 | 0-87587 | Abbeville Publishing Group | Abbeville Kids | ABBKD   |
|     978 | 0-7896  | Abbeville Publishing Group | Abbeville Kids | ABBKD   |
|     978 | 0-7892  | Abbeville Publishing Group | Abbeville Kids | ABBKD   |
|     978 | 2-02    | Abbeville Publishing Group | Abbeville Kids | ABBKD   |
|     978 | 1-4229  | A&E Home Entertainment     | Lifetime       | AEHLF   |

Candidate column headers:
ISBNPrefix
ISBN13
Publisher
ImpShort
Imprint



headers: ISBNPrefix
ISBN13
Publisher
ImpShort
Imprint
label: ISBN13
ISBNPrefix
Publisher
Imprint
ImpShort
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    |
|--------:|:--------|--------:|:---------|
|   42895 | Yanaki  |  274.6  | In Store |
|   42889 | Quad    |  889.59 | In Store |
|   42894 | Yanaki  | 1765.85 | Dealer   |
|   42893 | Aspen   | 1538.44 | Dealer   |
|   42885 | Quad    |  734.16 | In Store |

Candidate column headers:
Product
Channel
Sales
Date



headers: Product
Channel
Sales
Date
label: Date
Product
Sales
Channel
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             |
|:-----------------|:---------------|--------:|--------:|---------:|---------:|---------:|----------:|---------:|----------:|:-------------------|:-------------------|:-------------------|:-------------------|:-------------------|
| Philadelphia PMC | North Atlantic | 42402.8 |     310 | 0.96983  | 0.952621 | 0.871007 | 0.0500693 | 0.887835 | 0.0527465 | 0.9113678804855276 | 0.9345937167946428 | 0.0422618780477084 | 0.9215577582562126 | 0.0507142431582818 |
| Cheyenne VSC     | Continental    | 42402.8 |     442 | 0.956279 | 0.915385 | 0.873791 | 0.0541255 | 0.880115 | 0.0576718 |                    |                    |                    |                    |                    |
| Continental      | Continental    | 42402.8 |      33 | 0.963858 | 0.898324 | 0.906931 | 0.0168998 | 0.926697 | 0.0164352 |                    |                    |                    |                    |                    |
| Nashville VSC    | Southeast      | 42402.8 |     320 | 0.951217 | 0.944447 | 0.94161  | 0.0365206 | 0.946999 | 0.0360914 |                    |                    |                    |                    |                    |
| Newark VSC       | North Atlantic | 42402.8 |     309 | 0.953547 | 0.910159 | 0.882996 | 0.0441613 | 0.862488 | 0.0489708 |                    |                    |                    |                    |                    |

Candidate column headers:
COMP3_RTNG_CLM_WGHTED_ACC
DEF_DIST
COMP12_AUTH_CLM_WGHTED_ACC
RELEASE_DATE
PMC12_RTNG_CLM_WGHTED_ACC
PMC12_AUTH_CLM_WGHTED_ACC
COMP12_RTNG_CLM_WGHTED_ACC
COMP12_AUTH_CLM_MOE
COMP12_RTNG_CLM_MOE
PMC12_RTNG_CLM_MOE
PMC3_RTNG_CLM_WGHTED_ACC
PMC12_AUTH_CLM_MOE
COMP3_ISSUES_WGHTED_ACC
RO
MMWR_ACCURACY_RO



headers: COMP3_RTNG_CLM_WGHTED_ACC
DEF_DIST
COMP12_AUTH_CLM_WGHTED_ACC
RELEASE_DATE
PMC12_RTNG_CLM_WGHTED_ACC
PMC12_AUTH_CLM_WGHTED_ACC
COMP12_RTNG_CLM_WGHTED_ACC
COMP12_AUTH_CLM_MOE
COMP12_RTNG_CLM_MOE
PMC12_RTNG_CLM_MOE
PMC3_RTNG_CLM_WGHTED_ACC
PMC12_AUTH_CLM_MOE
COMP3_ISSUES_WGHTED_ACC
RO
MMWR_ACCURACY_RO
label: MMWR_ACCURACY_RO
DEF_DIST
RELEASE_DATE
RO
COMP3_ISSUES_WGHTED_ACC
COMP3_RTNG_CLM_WGHTED_ACC
COMP12_RTNG_CLM_WGHTED_ACC
COMP12_RTNG_CLM_MOE
COMP12_AUTH_CLM_WGHTED_ACC
COMP12_AUTH_CLM_MOE
PMC3_RTNG_CLM_WGHTED_ACC
PMC12_RTNG_CLM_WGHTED_ACC
PMC12_RTNG_CLM_MOE
PMC12_AUTH_CLM_WGHTED_ACC
PMC12_AUTH_CLM_MOE
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 |
|:--------|:--------|:-----------|--------:|:--------|---------:|
| ANTH    | 151**   | Briggs     |     120 | MWF     | 0.649306 |
| ANTH    | 711     | Babb       |      12 | M       | 0.645833 |
| ANTH    | 421     | Shore      |       9 | T/TH    | 0.458333 |
| ANTH    | 897     | Leslie     |      15 | F       | 0.395833 |
| ANTH    | 138     | Mora-Marin |       5 | MWF     | 0.559028 |

Candidate column headers:
Time
Cap
Subject
Professor
Number
Days



headers: Time
Cap
Subject
Professor
Number
Days
label: Subject
Number
Professor
Cap
Days
Time
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 |
|:-----------------------|:--------------------|:------------------|:-----------------------|--------:|:--------|:--------|:------------------|:--------|:--------|---------:|---------:|---------:|---------:|:---------|:-------------|---------:|
| 10000000               | 01 020 B0051 34.51  | 020-US-0051  -000 | 15000000               |       1 |         |         | Reconstruction    | 5.0     | N       |    4.396 |       11 |    8.259 |   12.655 | Carlisle | PURCHASE ADD |  155.316 |
| 3500000                | 01 020 B0062 1.00   | 020-US-0062  -000 | 5350000                |       1 |         |         | Reconstruction    |         | N       |    1.043 |       10 |    1.062 |    2.105 | Carlisle | PURCHASE ADD |  198.94  |
| 3000000                | 01 018 D0822 10.00  | 018-KY-0822  -000 | 6750000                |       1 |         |         | Reconstruction    |         | N       |    0.73  |       10 |    0     |    0.73  | Calloway | PURCHASE ADD |  618.432 |
| See Highway Plan Costs | 01 018 D1327 955.00 | 018-KY-1327  -000 | See Highway Plan Costs |       1 | 1.0     | 121.0   | RECONSTRUCTION(O) |         | N       |    0.02  |       12 |    1.49  |    1.51  | Calloway | PURCHASE ADD | 1121.84  |
| 4500000                | 01 018 E9999 2001   | 018-CR-1401  -000 | 9500000                |       1 |         |         | Minor widening    |         | N       |    1.026 |        9 |    0.811 |    1.837 | Calloway | PURCHASE ADD |  278.28  |

Candidate column headers:
Lane Width Avg (ft)
ITEM NO
Evaluated EMP
DHV = K*AADT
ADD  MPO
TOTAL PIF
Control_No
Project Type
L  (Miles)
NHS
Deck Rating
Evaluated Route
PIF C
Evaluated BMP
District
County
Dist Item



headers: Lane Width Avg (ft)
ITEM NO
Evaluated EMP
DHV = K*AADT
ADD  MPO
TOTAL PIF
Control_No
Project Type
L  (Miles)
NHS
Deck Rating
Evaluated Route
PIF C
Evaluated BMP
District
County
Dist Item
label: PIF C
Control_No
Evaluated Route
TOTAL PIF
District
Dist Item
ITEM NO
Project Type
Deck Rating
NHS
L  (Miles)
Lane Width Avg (ft)
Evaluated BMP
Evaluated EMP
County
ADD  MPO
DHV = K*AADT
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 |
|--------:|--------:|:--------|--------:|--------:|
|      37 |   40319 | Q2      |      45 |    2010 |
|      70 |   40185 | Q1      |       4 |    2010 |
|      68 |   40314 | Q2      |      43 |    2010 |
|      31 |   40194 | Q1      |       8 |    2010 |
|      35 |   40197 | Q1      |       9 |    2010 |

Candidate column headers:
Date
Qtr
Seq
Year
Val



headers: Date
Qtr
Seq
Year
Val
label: Val
Date
Qtr
Seq
Year
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 |
|--------:|-----------:|:-----------------------|:---------|:-----------------------|:------------|:--------|:--------|:-----------|:--------------------|:--------------|:---------|:---------|:---------|---------:|
|   13685 | 1111122818 | ACE Cash Express_ Inc. |          | 2300 N Main St         | Las Cruces  | NM      | US      | 88001-1117 | 1239 Greenway Drive | Suite 608     | Irving   | TX       | US       |    75046 |
| 1583628 | 1111228551 | Allsup's 123           | Allsup's | 1401 N Norris St       | Clovis      | NM      | US      | 88101-5009 | PO Box 1907         | 2112 Thornton | Clovis   | NM       | US       |    88102 |
|   13685 | 1111122812 | ACE Cash Express_ Inc. |          | 4001 San Mateo Blvd NE | Albuquerque | NM      | US      | 87110-1474 | 1233 Greenway Drive | Suite 602     | Irving   | TX       | US       |    75040 |
|   13685 | 1111122810 | Ace Cash Express_ INC. |          | 4720 Central Ave SE    | Albuquerque | NM      | US      | 87108-1225 | 1231 Greenway Drive | Suite 600     | Irving   | TX       | US       |    75038 |
|   13685 | 1111446014 | Ace Cash Express_ INC. |          | 111 Coors Blvd NW      | Albuquerque | NM      | US      | 87121-2008 | 1231 Greenway Drive | Suite 600     | Irving   | TX       | US       |    75038 |

Candidate column headers:
Company ID
Mailing Address Line 1
Doing Business As
Country
Authorized Delegate ID
State or Province
City
City2
Mailing Address Line 2
Authorized Delegate Legal Name
Country4
State or Province3
Postal Code
Postal Code5
Physical Address Line 1



headers: Company ID
Mailing Address Line 1
Doing Business As
Country
Authorized Delegate ID
State or Province
City
City2
Mailing Address Line 2
Authorized Delegate Legal Name
Country4
State or Province3
Postal Code
Postal Code5
Physical Address Line 1
label: Company ID
Authorized Delegate ID
Authorized Delegate Legal Name
Doing Business As
Physical Address Line 1
City
State or Province
Country
Postal Code
Mailing Address Line 1
Mailing Address Line 2
City2
State or Province3
Country4
Postal Code5
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   |
|:--------|--------:|:--------|:--------|
| DIS     |      34 | AK      | Brown   |
| RIH     |      83 | MO      | Brown   |
| GIM     |      83 | TX      | Magenta |
| GIM     |      61 | KY      | Brown   |
| DIS     |      11 | KY      | Cyan    |

Candidate column headers:
Qty
Color
Item
Loc



headers: Qty
Color
Item
Loc
label: Item
Qty
Loc
Color
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 |
|--------:|:-------------|--------:|:---------------------------|--------:|--------:|:---------|----------------:|-----------:|:--------|:---------|---------:|---------:|-----------:|
| 1100000 | General Fund | 1101000 | New General Appropriations | 1101100 | 1101101 | Programs | 103001000100000 | 1000000000 | GAS     | PS       | 50100000 | 50103040 | 5010304001 |
| 1100000 | General Fund | 1101000 | New General Appropriations | 1101100 | 1101101 | Programs | 103001000100000 | 1000000000 | GAS     | MOOE     | 50200000 | 50203040 | 5020304000 |
| 1100000 | General Fund | 1101000 | New General Appropriations | 1101100 | 1101101 | Programs | 103001000100000 | 1000000000 | GAS     | PS       | 50100000 | 50102130 | 5010213002 |
| 1100000 | General Fund | 1101000 | New General Appropriations | 1101100 | 1101101 | Programs | 103001000100000 | 1000000000 | GAS     | MOOE     | 50200000 | 50204010 | 5020401000 |
| 1100000 | General Fund | 1101000 | New General Appropriations | 1101100 | 1101101 | Programs | 103001000100000 | 1000000000 | GAS     | MOOE     | 50200000 | 50203010 | 5020301000 |

Candidate column headers:
Authorization Code
Fund Category Code
Funding Source Code
GL Acct Code
Main MFO/PAP Code
MFO/PAP Category1 Code
Funding Source
MFO/PAP Category2
Major Acct Group Code
Major Acct Group Shortname
Fund Sub-Category Code
Authorization
MFO/PAP Group
Object Code



headers: Authorization Code
Fund Category Code
Funding Source Code
GL Acct Code
Main MFO/PAP Code
MFO/PAP Category1 Code
Funding Source
MFO/PAP Category2
Major Acct Group Code
Major Acct Group Shortname
Fund Sub-Category Code
Authorization
MFO/PAP Group
Object Code
label: Funding Source Code
Funding Source
Authorization Code
Authorization
Fund Category Code
Fund Sub-Category Code
MFO/PAP Group
Main MFO/PAP Code
MFO/PAP Category1 Code
MFO/PAP Category2
Major Acct Group Shortname
Major Acct Group Code
GL Acct Code
Object Code
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 | Col_1    |   Col_2 | Col_3                     |   Col_4 | Col_5                        | Col_6   | Col_7   |   Col_8 |   Col_9 |   Col_10 |   Col_11 | Col_12   |   Col_13 |   Col_14 |   Col_15 |   Col_16 |   Col_17 |
|--------:|:---------|--------:|:--------------------------|--------:|:-----------------------------|:--------|:--------|--------:|--------:|---------:|---------:|:---------|---------:|---------:|---------:|---------:|---------:|
| 7587494 | HS391227 | 40185.9 | 037XX W HARRISON ST       |     486 | DOMESTIC BATTERY SIMPLE      | False   | True    |    1133 |      11 |       24 |       27 | 08B      |  1151741 |  1897101 |     2010 |  42462.3 |  41.8735 |
| 7587491 | HS391185 | 40216.8 | 060XX W GRAND AVE         |     820 | $500 AND UNDER               | False   | False   |    2512 |      25 |       29 |       19 | 6        |  1135614 |  1914236 |     2010 |  42462.3 |  41.9208 |
| 7587486 | HS390881 | 40216.7 | 007XX N MONTICELLO AVE    |     460 | SIMPLE                       | False   | False   |    1112 |      11 |       27 |       23 | 08B      |  1151917 |  1904742 |     2010 |  42462.3 |  41.8945 |
| 7587505 | HS391291 | 40216.8 | 009XX S RACINE AVE        |    1310 | TO PROPERTY                  | False   | False   |    1213 |      12 |        2 |       28 | 14       |  1168521 |  1895964 |     2010 |  42462.3 |  41.8701 |
| 7587507 | HS391204 | 40216.9 | 065XX S COTTAGE GROVE AVE |    1811 | POSS: CANNABIS 30GMS OR LESS | True    | False   |     321 |       3 |       20 |       42 | 18       |  1182687 |  1861807 |     2010 |  42462.3 |  41.776  |

Candidate column headers:
Y Coordinate
Domestic
Community Area
X Coordinate
Block
Arrest
Beat
IUCR
ID
Description
Date
FBI Code
Ward
Year
Updated On
Case Number
District
Latitude



headers: Y Coordinate
Domestic
Community Area
X Coordinate
Block
Arrest
Beat
IUCR
ID
Description
Date
FBI Code
Ward
Year
Updated On
Case Number
District
Latitude
label: ID
Case Number
Date
Block
IUCR
Description
Arrest
Domestic
Beat
District
Ward
Community Area
FBI Code
X Coordinate
Y Coordinate
Year
Updated On
Latitude
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   |
|:--------|--------:|---------:|----------:|---------:|----------:|--------:|------------------:|:--------|
| COTE    |   41266 | -77.8059 |  161.998  | -7748.36 |  16159.9  | -774821 |       1.61595e+06 | 1901.0  |
| FOS1    |   42997 | -71.313  |  -68.321  | -7118.78 |  -6819.26 | -711847 | -681916           |         |
| FRKI    |   40571 | -76.1644 |  168.383  | -7609.86 |  16823    | -760952 |       1.68226e+06 | 175.0   |
| CRDI    |   41266 | -82.8615 |  -53.1987 | -8251.69 |  -5311.92 | -825141 | -531155           | 963.0   |
| CLRK    |   41188 | -77.3401 | -141.874  | -7720.41 | -14152.4  | -772024 |      -1.41523e+06 | 1025.0  |

Candidate column headers:
LAT (DD MM)
LONG (DD)
STATION
HEIGHT
LONG (DD MM SS)
LAT (DD MM SS)
LONG (DD MM)
COORD
LAT (DD)



headers: LAT (DD MM)
LONG (DD)
STATION
HEIGHT
LONG (DD MM SS)
LAT (DD MM SS)
LONG (DD MM)
COORD
LAT (DD)
label: STATION
COORD
LAT (DD)
LONG (DD)
LAT (DD MM)
LONG (DD MM)
LAT (DD MM SS)
LONG (DD MM SS)
HEIGHT
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   |
|:------------|:--------------------|--------:|--------:|--------:|--------:|--------:|:--------|
| Acme Inc 19 | Manufacturing       |       0 |    8739 |    5685 |    2137 |   16561 | Carol   |
| Acme Inc 3  | Accounting          |    2762 |   11982 |   15747 |    8042 |   38533 | Mark    |
| Acme Inc 11 | Non-Profit          |    1828 |   20585 |    7905 |   10605 |   40923 | Sue     |
| Acme Inc 2  | Construction / Dev. |    5920 |   12750 |   16472 |       0 |   35142 | Carol   |
| Acme Inc 16 | Prof Services       |   10441 |    3071 |    7495 |    1526 |   22533 | Carol   |

Candidate column headers:
P&C
Total Premium
Gen Liablity
Rep
Client Name
Worker's Comp
Industry
E&O



headers: P&C
Total Premium
Gen Liablity
Rep
Client Name
Worker's Comp
Industry
E&O
label: Client Name
Industry
Gen Liablity
P&C
Worker's Comp
E&O
Total Premium
Rep
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 |
|--------:|--------:|:--------|:-------------|--------:|
|   71821 |   43013 | OH      | OH0176147138 | 1846935 |
|   75525 |   42914 | OH      | OH0176095931 | 1845204 |
|  282577 |   42955 | GA      | GA0006347400 | 1844911 |
|   82866 |   43035 | IN      | IN0903025717 | 1844458 |
|  282577 |   42913 | GA      | GA0006292569 | 1844933 |

Candidate column headers:
CRASH REPORT STATE
CRASH DATE
RDR NUMBER
CRASH REPORT NUMBER
DOT NUMBER



headers: CRASH REPORT STATE
CRASH DATE
RDR NUMBER
CRASH REPORT NUMBER
DOT NUMBER
label: DOT NUMBER
CRASH DATE
CRASH REPORT STATE
CRASH REPORT NUMBER
RDR NUMBER
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 |   Col_1 | Col_2   |
|--------:|--------:|:--------|
|       6 | 8000    |         |
|    4990 | 7000    | 7000.0  |
|     985 | 5003.27 | 5003.27 |
|     775 | 6010    |         |
|     362 | 9095    | 9095.0  |

Candidate column headers:
Total Award_ Per CE
CE ID
Award Amount_ Per Item



headers: Total Award_ Per CE
CE ID
Award Amount_ Per Item
label: CE ID
Award Amount_ Per Item
Total Award_ Per CE
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 |
|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|
|      20 |     4.7 |     1.3 |     9.9 |     6.7 |     3   |     2.6 |     6.8 |     6   |
|      36 |     1.8 |     3.3 |     7.5 |     4.5 |     2.5 |     2.4 |     7.6 |     3.6 |
|       8 |     1.3 |     4.2 |     6.2 |     5.1 |     2.8 |     2.2 |     6.9 |     4.3 |
|       2 |     1.8 |     3   |     6.3 |     6.6 |     2.5 |     4   |     8.4 |     4.3 |
|      17 |     3.2 |     4.1 |     5.7 |     5.1 |     3.6 |     2.9 |     6.2 |     4.4 |

Candidate column headers:
Price Level
Service
id
Satisfaction Level
Salesforce Image
Delivery Speed
Product Quality
Manufacturer Image
Price Flexibility



headers: Price Level
Service
id
Satisfaction Level
Salesforce Image
Delivery Speed
Product Quality
Manufacturer Image
Price Flexibility
label: id
Delivery Speed
Price Level
Price Flexibility
Manufacturer Image
Service
Salesforce Image
Product Quality
Satisfaction Level