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 |
|--------:|--------:|--------:|--------:|--------:|
|   39387 |  806.25 | 14.672  |  653.57 |  0.922  |
|   39722 |  806.62 | 10.4272 |  808.01 |  0.8891 |
|   40148 | 1134.72 | 17.6446 |  801.1  |  0.7468 |
|   39814 |  858.69 | 11.399  |  814.92 |  0.8577 |
|   39448 |  889.6  | 16.0591 |  679.8  |  1.0074 |

Candidate column headers:
Coffee_ Robusta
Silver
Month
Gold
Bananas



headers: Coffee_ Robusta
Silver
Month
Gold
Bananas
label: Month
Gold
Silver
Bananas
Coffee_ Robusta
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 |   Col_1 |   Col_2 |
|--------:|--------:|--------:|
|    0.09 |      11 |       1 |
|    0.07 |      10 |       1 |
|    0.16 |       6 |       1 |
|    0.12 |       9 |       1 |
|    0.1  |      15 |       1 |

Candidate column headers:
Gap Score (from Tabs)
Component # (RP 1175 Reference)
Component Weight



headers: Gap Score (from Tabs)
Component # (RP 1175 Reference)
Component Weight
label: Component Weight
Component # (RP 1175 Reference)
Gap Score (from Tabs)
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 |
|:----------|:--------|:--------|:--------|--------:|------------:|------------:|--------:|--------:|--------:|---------:|:---------|:-------------------|---------:|---------:|
| Chennai   | South   | Better  | RJ_corp |     453 | 1.34538e+06 | 1.33118e+06 |     322 | 2938.59 | 4134.1  |  1.40683 |          |                    |        0 |        1 |
| Hyderabad | South   | Better  | RJ_corp |     812 | 2.43094e+06 | 2.40081e+06 |     559 | 2956.67 | 4294.83 |  1.45259 | 6149.0   | 0.0909090909090908 |        0 |        1 |
| Mangalore | South   | Better  | RJ_corp |     420 | 1.11573e+06 | 1.09297e+06 |     279 | 2602.31 | 3917.46 |  1.50538 |          |                    |        0 |        1 |
| Lucknow   | North   | Better  | RJ_corp |     365 | 1.24364e+06 | 1.18042e+06 |     222 | 3234.02 | 5317.2  |  1.64414 |          |                    |        0 |        1 |
| Anand     | West    | Better  | Saffron |     375 | 1.21164e+06 | 1.14416e+06 |     217 | 3051.08 | 5272.61 |  1.72811 |          |                    |        0 |        1 |

Candidate column headers:
Unit
City
UPT
MRP Rev (M)
ASP
Door Classification
Bill Count
Traffic
Flag_1
ATV
Region
Account Name
Flag_2
Value Realised (M)
Conversion



headers: Unit
City
UPT
MRP Rev (M)
ASP
Door Classification
Bill Count
Traffic
Flag_1
ATV
Region
Account Name
Flag_2
Value Realised (M)
Conversion
label: City
Region
Door Classification
Account Name
Unit
MRP Rev (M)
Value Realised (M)
Bill Count
ASP
ATV
UPT
Traffic
Conversion
Flag_1
Flag_2
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 |
|--------:|--------:|--------:|
|       7 |   41680 |     521 |
|       1 |   41641 |     516 |
|       2 |   41649 |     523 |
|       2 |   41648 |     523 |
|       6 |   41677 |     523 |

Candidate column headers:
50
41620
523



headers: 50
41620
523
label: 50
41620
523
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 |
|:--------|--------:|--------:|--------:|--------:|--------:|--------:|
| OKS     |   20.22 |   18.59 |    1.34 |    2.2  |    6.8  |    6.8  |
| ETE     |   18.25 |   10.17 |    0.38 |  -13.98 |    7.52 |    7.52 |
| PAA     |   24.88 |   14.73 |    0.57 |    1.61 |   10.05 |    9.41 |
| WPZ     |   48.7  |   21.51 |    4.62 |    1.54 |    8.51 |    8.51 |
| WMB     |    0    |   29.15 |    2.68 |    2.67 |    3.59 |    4.31 |

Candidate column headers:
Price/_x000D_ Sales
%_x000D_ Dividend_x000D_ Yield TTM
Price/_x000D_ Book_x000D_ TTM
% Forward_x000D_ Dividend_x000D_ Yield
Price/_x000D_ Earnings_x000D_ Trailing
Price/_x000D_ Earnings_x000D_ Forward
Ticker



headers: Price/_x000D_ Sales
%_x000D_ Dividend_x000D_ Yield TTM
Price/_x000D_ Book_x000D_ TTM
% Forward_x000D_ Dividend_x000D_ Yield
Price/_x000D_ Earnings_x000D_ Trailing
Price/_x000D_ Earnings_x000D_ Forward
Ticker
label: Ticker
Price/_x000D_ Earnings_x000D_ Trailing
Price/_x000D_ Earnings_x000D_ Forward
Price/_x000D_ Sales
Price/_x000D_ Book_x000D_ TTM
%_x000D_ Dividend_x000D_ Yield TTM
% Forward_x000D_ Dividend_x000D_ 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 |
|:------------------|:---------------------|--------:|--------:|--------:|--------:|--------:|
| ACC Ltd           | Fixed Cost           |      30 |      27 |      26 |      29 |      29 |
| Ultratech Cement  | Power & Fuel         |      21 |      23 |      24 |      21 |      20 |
| Ambuja Cement     | Freight & Forwarding |      27 |      23 |      23 |      25 |      24 |
| Ultratech Cement  | Profit               |       9 |      17 |      17 |       8 |       9 |
| JK Lakshmi Cement | Power & Fuel         |      21 |      30 |      24 |      20 |      21 |

Candidate column headers:
2013
Company
2015
Variable
2014
2012
2011



headers: 2013
Company
2015
Variable
2014
2012
2011
label: Company
Variable
2015
2011
2012
2013
2014
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   |
|--------:|:---------------|:--------|
|      10 | KV HAFLONG     | A1      |
|      23 | KV KUNJABAN    | A1      |
|      12 | KV KAILASHAHAR | A1      |
|      40 | KV LAMPHELPAT  | A1      |
|       7 | KV BAGAFA      | A1      |

Candidate column headers:
Grade
Name of the KV
Sl. No.



headers: Grade
Name of the KV
Sl. No.
label: Sl. No.
Name of the KV
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 |
|--------:|--------:|--------:|
|       5 |    2000 |    1000 |
|     158 |    5000 |    4000 |
|     112 |    1000 |     800 |
|     146 |    4000 |    2500 |
|   98500 |   20000 |   15000 |

Candidate column headers:
tot_income_adj
job_id
tot_cost_adj



headers: tot_income_adj
job_id
tot_cost_adj
label: job_id
tot_income_adj
tot_cost_adj
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 |
|:---------|--------:|--------:|---------:|--------:|--------:|
| SKU86637 |   57.81 |     476 |  27517.6 |  712542 |    0.91 |
| SKU72812 |  444.16 |     110 |  48857.6 |  611208 |    0.78 |
| SKU65859 | 1217.41 |      87 | 105915   |  506129 |    0.64 |
| SKU15303 |   62.09 |     264 |  16391.8 |  777768 |    0.99 |
| SKU37892 |   97.08 |     357 |  34657.6 |  685025 |    0.87 |

Candidate column headers:
Part Number
Comulative Value
Percent Annual Value
Annual Demand
Annual Value
Price per unit



headers: Part Number
Comulative Value
Percent Annual Value
Annual Demand
Annual Value
Price per unit
label: Part Number
Price per unit
Annual Demand
Annual Value
Comulative Value
Percent Annual 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 |   Col_15 |
|--------:|--------:|--------:|--------:|:--------|:---------|:--------|--------:|--------:|--------:|---------:|---------:|---------:|---------:|---------:|---------:|
|  106306 |   40737 |       4 |  106305 | OE43    | Mehdi MO | Luddo   |   40737 |  909377 | 34013.9 |    40620 |  20357.3 |  20357.3 |      248 |        0 |        0 |
|  106278 |   40709 |       4 |  106277 | OE15    | Mehdi MO | Luddo   |   40709 |  339373 | 34013.9 |    40592 |  20357.3 |  20357.3 |      248 |        0 |        0 |
|  106283 |   40714 |       4 |  106282 | OE20    | Mehdi MO | Luddo   |   40714 |  441160 | 34013.9 |    40597 |  20357.3 |  20357.3 |      248 |        0 |        0 |
|  106290 |   40721 |       4 |  106289 | OE27    | Mehdi MO | Luddo   |   40721 |  583660 | 34013.9 |    40604 |  20357.3 |  20357.3 |      248 |        0 |        0 |
|  106307 |   40738 |       4 |  106306 | OE44    | Mehdi MO | Luddo   |   40738 |  929734 | 34013.9 |    40621 |  20357.3 |  20357.3 |      248 |        0 |        0 |

Candidate column headers:
Dosslitige2
DOSSCDRA
DOSSDATEMD
DOSSCLI
DOSSsolde2
DOSSNUM
DOSSAG
DOSSOPEN
DOSSECHU2
DOSSPEC
DOSSNBJ2
DOSSECHU1
DOSSENC2
DOSSPHASEENC
DOSSENC1
DOSSCDRC



headers: Dosslitige2
DOSSCDRA
DOSSDATEMD
DOSSCLI
DOSSsolde2
DOSSNUM
DOSSAG
DOSSOPEN
DOSSECHU2
DOSSPEC
DOSSNBJ2
DOSSECHU1
DOSSENC2
DOSSPHASEENC
DOSSENC1
DOSSCDRC
label: DOSSNUM
DOSSOPEN
DOSSPHASEENC
DOSSCLI
DOSSAG
DOSSCDRA
DOSSCDRC
DOSSDATEMD
DOSSECHU1
DOSSENC1
DOSSPEC
DOSSECHU2
DOSSENC2
DOSSNBJ2
Dosslitige2
DOSSsolde2
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   |
|--------:|:--------|:--------|
|      61 | INT     | INT     |
|      55 | LI+     | LI+     |
|      43 | E+      | E       |
|      67 | UI      | INT+    |
|      72 | UI      | UI      |

Candidate column headers:
Speaking
Column1
Writing



headers: Speaking
Column1
Writing
label: Column1
Writing
Speaking
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 |
|--------:|--------:|--------:|
|    2011 |   96.44 |  26.02  |
|    2001 |   38.85 |  15.741 |
|    2002 |   46.04 |  16.08  |
|    2007 |   82.54 |  27.73  |
|    2009 |   56.86 |  22.31  |

Candidate column headers:
Earnings
Dividends
Year



headers: Earnings
Dividends
Year
label: Year
Earnings
Dividends
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 |
|:--------|:---------------------|:---------|--------:|--------:|--------:|--------:|--------:|--------:|
| UNI     | Union Dues Local 32  | Union    | 1375.18 |   18    |  144    |    0    |    0    |    1357 |
| MED     | Medicare             | Medicare |  469.25 |    6.8  |   92.11 |    6.8  |   92.11 |     462 |
| KEY     | Medical EPO After Ta | Medical  | 2364.8  |  156.59 | 1252.72 |  524.23 | 4193.84 |    2208 |
| AS2     | TIAA/CREF SRA        | Savings  | 2314.4  |   50    |  150    |    0    |    0    |    2264 |
| AS3     | TIAA/CREF SRA Age Fi | Savings  | 2169.18 |  118.09 |  944.72 |    0    |    0    |    2051 |

Candidate column headers:
Employer YTD
Description
Category
Employer Amt. 
YTD
Employee Ded Amt.
Employee Net
Employee Appl Gross
Deduction



headers: Employer YTD
Description
Category
Employer Amt. 
YTD
Employee Ded Amt.
Employee Net
Employee Appl Gross
Deduction
label: Deduction
Description
Category
Employee Appl Gross
Employee Ded Amt.
YTD
Employer Amt. 
Employer YTD
Employee Net
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 |
|--------:|:--------|:--------|-----------:|-----------:|
|      25 | y       | FED     |          0 |     422469 |
|       9 | i       | FED     |    1486013 |    1803464 |
|       7 | g       | FED     |   32408000 |   30370000 |
|       5 | zzz     | FED     | 1417660000 | 1505506000 |
|      13 | m       | FED     |      48500 |     160000 |

Candidate column headers:
REGULATOR
ID_RSSD
BHCKC969
BHCKC968
NM_LGL



headers: REGULATOR
ID_RSSD
BHCKC969
BHCKC968
NM_LGL
label: ID_RSSD
NM_LGL
REGULATOR
BHCKC968
BHCKC969
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 | Col_1        | Col_2     |   Col_3 |   Col_4 |   Col_5 |   Col_6 |   Col_7 |   Col_8 |   Col_9 |   Col_10 |   Col_11 |   Col_12 |   Col_13 |   Col_14 |   Col_15 |   Col_16 |   Col_17 |   Col_18 |   Col_19 |
|--------:|:-------------|:----------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
|    2008 | Roy Halladay | Blue Jays |    1902 |      20 |      11 |       0 |      34 |      33 |   246   |     7.54 |     1.43 |     0.66 |    0.284 |    0.745 |    0.537 |    0.094 |     2.78 |     3.03 |     3.11 |
|    2008 | Roy Oswalt   | Astros    |    1903 |      17 |      10 |       0 |      32 |      32 |   208.2 |     7.12 |     2.03 |     0.99 |    0.285 |    0.746 |    0.503 |    0.127 |     3.54 |     3.8  |     3.51 |
|    2008 | Kevin Slowey | Twins     |    1394 |      12 |      11 |       0 |      27 |      27 |   160.1 |     6.9  |     1.35 |     1.23 |    0.29  |    0.727 |    0.361 |    0.099 |     3.99 |     3.91 |     3.96 |
|    2008 | Jon Lester   | Red Sox   |    1204 |      16 |       6 |       0 |      33 |      33 |   210.1 |     6.5  |     2.82 |     0.6  |    0.297 |    0.774 |    0.475 |    0.07  |     3.21 |     3.64 |     4.03 |
|    2008 | Josh Beckett | Red Sox   |    1271 |      12 |      10 |       0 |      27 |      27 |   174.1 |     8.88 |     1.76 |     0.93 |    0.315 |    0.713 |    0.408 |    0.105 |     4.03 |     3.24 |     3.19 |

Candidate column headers:
GB%
W
Player_ID
HR/9
xFIP
LOB%
IP
BB/9
HR/FB
Team
GS
L
ERA
K/9
BABIP
SV
Name
FIP
G
YEAR



headers: GB%
W
Player_ID
HR/9
xFIP
LOB%
IP
BB/9
HR/FB
Team
GS
L
ERA
K/9
BABIP
SV
Name
FIP
G
YEAR
label: YEAR
Name
Team
Player_ID
W
L
SV
G
GS
IP
K/9
BB/9
HR/9
BABIP
LOB%
GB%
HR/FB
ERA
FIP
xFIP
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 |
|:---------|--------:|--------:|--------:|--------:|
| Williams |   41670 |       0 |       0 |       0 |
| Williams |   41833 |    3000 |       0 |       0 |
| Williams |   41679 |    2800 |       0 |       0 |
| Williams |   41728 |       0 |       0 |       0 |
| Williams |   41724 |       0 |     200 |     460 |

Candidate column headers:
Name
LateFee
CourtFee
Rent
Date



headers: Name
LateFee
CourtFee
Rent
Date
label: Name
Date
Rent
LateFee
CourtFee
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   |
|-------------:|:--------|--------:|--------:|:----------------|:-------------------------------|--------:|:--------|:--------|:--------|
|   1.2009e+06 |         |       0 |       0 | System required | Refunds Clearing               |       5 | No      | No      | No      |
|   1.2092e+06 | 1360.0  |       0 |       0 |                 | AR - Retirement Contributions  |       5 | No      | No      | No      |
| 112          | 1200.0  |       0 |       1 |                 | Temporary Investments          |       3 | Yes     | No      | No      |
|   1.2052e+06 | 1352.0  |       0 |       0 |                 | AR - PP Prop Tax - Interest    |       5 | No      | No      | No      |
|   1.2095e+06 | 1390.0  |       0 |       0 |                 | Allowance for Uncollectable AR |       5 | No      | No      | No      |

Candidate column headers:
UCM_Acct
Visible in Hyperion
Is_Bud_Acct
Online_Descr
Acct_Final
Comments
Curr_Level
Budgetary
Control
Is_Heading



headers: UCM_Acct
Visible in Hyperion
Is_Bud_Acct
Online_Descr
Acct_Final
Comments
Curr_Level
Budgetary
Control
Is_Heading
label: Acct_Final
UCM_Acct
Is_Heading
Is_Bud_Acct
Comments
Online_Descr
Curr_Level
Budgetary
Control
Visible in Hyperion
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 |
|:--------|---------:|----------:|----------:|----------:|
| DCLG    | 0.733333 | 0.15      | 0.0333333 | 0.0833333 |
| DfT     | 0.827068 | 0.075188  | 0.0150376 | 0.0827068 |
| HO      | 0.579805 | 0.117264  | 0.211726  | 0.0912052 |
| HMT     | 0.728395 | 0.0740741 | 0.17284   | 0.0246914 |
| DfE     | 0.906977 | 0.0465116 | 0.0232558 | 0.0232558 |

Candidate column headers:
Upheld in full
Overturned
Upheld in part
Unknown
Row Labels



headers: Upheld in full
Overturned
Upheld in part
Unknown
Row Labels
label: Row Labels
Upheld in full
Upheld in part
Unknown
Overturned
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   |
|--------:|:--------|:--------|
|      36 | UHN     | 2       |
|      30 | CHTN    | 2       |
|      22 | CHTN    | Normal  |
|       7 | UHN     | 3       |
|      15 | CHTN    | 2       |

Candidate column headers:
Sample ID
Grade
Site



headers: Sample ID
Grade
Site
label: Sample ID
Site
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 | Col_7   |
|:-----------------------------|:------------|:-------------|:---------|:----------|--------:|--------:|:--------|
| Soil Testing and Engineering | Rich Evans  | 614-761-4700 | Troxler  | 3430      |   64753 |   41346 | Y       |
| CTL Engineering              | Bill Sharpe | 614-595-4080 | Troxler  | 3430      |   65938 |   41346 | Y       |
| TTL                          | John Rust   |              | Troxler  | 3430      |   25638 |   41353 | Y       |
| Solar Testing                |             |              | Humboldt | HS5001-EZ |     191 |   41359 | Y       |
| Soil Testing and Engineering | Rich Evans  | 614-761-4700 | Troxler  | 3430      |   64761 |   41346 | Y       |

Candidate column headers:
Phone Number
ODOT Certification Date*
Contact Name
Serial Number
Model Number
Active              (Y / N)
Je
Manufacturer Name



headers: Phone Number
ODOT Certification Date*
Contact Name
Serial Number
Model Number
Active              (Y / N)
Je
Manufacturer Name
label: Je
Contact Name
Phone Number
Manufacturer Name
Model Number
Serial Number
ODOT Certification Date*
Active              (Y / N)
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 |
|--------:|--------:|------------:|------------:|----------:|--------:|------------:|------------:|------------:|------------:|-------------:|
|       7 |   31.3  | -0.0133293  | 2.94398e-05 | 0.0440342 |  715.19 | 7.82357e-06 |  0.00542584 | 0.00017767  | 1.29636e-06 | -3.18467e-06 |
|      21 |   27.41 |  0.00182582 | 1.3295e-05  | 0.0185174 |  708.87 | 6.17299e-08 |  0.00364623 | 3.33361e-06 | 2.4619e-07  |  1.23277e-07 |
|       5 |   30.95 |  0.0278478  | 5.43157e-06 | 0.049835  |  704.51 | 3.86471e-05 |  0.00233057 | 0.000775501 | 2.70682e-07 |  3.23436e-06 |
|      14 |   26.62 | -0.0505417  | 3.90225e-06 | 0.0285552 |  734.75 | 7.29433e-05 |  0.00197541 | 0.00255446  | 1.11429e-07 | -2.85097e-06 |
|       3 |   29.85 | -0.00966835 | 0.000288853 | 0.0564    |  741.5  | 5.27211e-06 | -0.0169957  | 9.34771e-05 | 1.62913e-05 |  9.26766e-06 |

Candidate column headers:
Rx2*w
Rx*Ry*w
Returns X
(Return Y)2
w
Ry2*w
Stock X
(Return X)2
Returns Y
Day
Stock Y



headers: Rx2*w
Rx*Ry*w
Returns X
(Return Y)2
w
Ry2*w
Stock X
(Return X)2
Returns Y
Day
Stock Y
label: Day
Stock X
Returns X
(Return Y)2
w
Stock Y
Rx2*w
Returns Y
(Return X)2
Ry2*w
Rx*Ry*w
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 |
|--------:|:--------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
|   36775 | 22/00   |   36822 |     400 |   400   |       0 |   400   |  1534   |   3.835 |    6.25 |     6.3  |    6.285 |     6.38 |   6.3177 |       17 |        6 |        4 |       10 |
|   36978 | 7/01    |   37095 |     500 |   500.5 |       0 |   500.5 |  2660   |   5.32  |    4.99 |     5.01 |    5.003 |     5.09 |   5.0399 |       37 |       13 |        8 |       29 |
|   36754 | 21/00   |   36822 |     300 |   300   |       0 |   300   |  1419   |   4.73  |    6.2  |     6.39 |    6.346 |     6.5  |   6.4134 |       13 |        8 |        7 |       27 |
|   36908 | 2/01    |   37004 |     500 |   500   |       0 |   500   |  2455.8 |   4.912 |    5.76 |     5.81 |    5.791 |     5.94 |   5.8729 |       42 |        8 |        5 |        3 |
|   36950 | 5/01    |   37095 |     300 |   300   |       0 |   300   |  1395   |   4.65  |    5.22 |     5.27 |    5.253 |     5.37 |   5.2959 |       21 |        5 |        3 |       18 |

Candidate column headers:
Lowest Yield Accepted %
Date Held
Number of Bids
Weighted Average Issue Yield %
Amount Offered $m
Maturity
Total Amount Allotted $m
Number of Successful Bids
Amount of Bids Received $m
Coverage Ratio
Amount Allotted to Public $m
Tender Number
Highest Bid    %
Highest Yield Accepted %
% Allotted at Highest Yield
Weighted Average Bid %
RBA Take-up $m
Bids Allocated in Full



headers: Lowest Yield Accepted %
Date Held
Number of Bids
Weighted Average Issue Yield %
Amount Offered $m
Maturity
Total Amount Allotted $m
Number of Successful Bids
Amount of Bids Received $m
Coverage Ratio
Amount Allotted to Public $m
Tender Number
Highest Bid    %
Highest Yield Accepted %
% Allotted at Highest Yield
Weighted Average Bid %
RBA Take-up $m
Bids Allocated in Full
label: Date Held
Tender Number
Maturity
Amount Offered $m
Amount Allotted to Public $m
RBA Take-up $m
Total Amount Allotted $m
Amount of Bids Received $m
Coverage Ratio
Lowest Yield Accepted %
Highest Yield Accepted %
Weighted Average Issue Yield %
Highest Bid    %
Weighted Average Bid %
Number of Bids
Number of Successful Bids
Bids Allocated in Full
% Allotted at Highest 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 | Col_8    |   Col_9 |
|:--------|--------:|:-------------------|:---------|:--------|--------:|:--------|--------:|:---------|--------:|
| P23     |   40830 | SEDAN              | 131000   | SN3554  |   25000 | POOR    |       4 | 32500.0  |       3 |
| 30      |   39975 | 2003 FORD EXPLORER | 102420   | SN7248  |    3300 | AVERAGE |      12 |          |      19 |
| P117    |   41456 | MURRAY MOWER       | 578 HRS. | SN0001  |    1900 | FAIR    |       8 | 4700.0   |      13 |
| P101    |   39806 | LIGHT DUMP TRUCK   | 112015   | SN5465  |   77000 | POOR    |      10 | 96000.0  |       8 |
| PW65    |   38404 | HEAVY DUMP TRUCK   | 87000    | SN7182  |  110000 | FAIR    |      10 | 150000.0 |       7 |

Candidate column headers:
DESCRIPTION
CONDITION
LIFE EXP
PURCHASE DATE
SN#
AMOUNT
REPL. RANK
VEHICLE #
REPL. COSTS
ODOMETER/HRS



headers: DESCRIPTION
CONDITION
LIFE EXP
PURCHASE DATE
SN#
AMOUNT
REPL. RANK
VEHICLE #
REPL. COSTS
ODOMETER/HRS
label: VEHICLE #
PURCHASE DATE
DESCRIPTION
ODOMETER/HRS
SN#
AMOUNT
CONDITION
LIFE EXP
REPL. COSTS
REPL. 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 | Col_4   |
|--------:|:------------|:----------|--------:|:--------|
|   41069 | Subway      | Lunch     |    7.5  |         |
|   41078 | Sam's Club  | Gas       |   24.11 |         |
|   41078 | Costco      | Groceries |  120.44 |         |
|   41086 | Hampton Inn | Hotel     |  112.44 | Yes     |
|   41078 | Sam's Club  | Books     |   18.44 |         |

Candidate column headers:
Business
Description
Date
Store
Amount



headers: Business
Description
Date
Store
Amount
label: Date
Store
Description
Amount
Business
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 |
|--------:|:---------|--------:|
|    2011 | December |    1699 |
|    2011 | October  |    1172 |
|    2011 | March    |    1228 |
|    2012 | June     |     819 |
|    2013 | May      |    1483 |

Candidate column headers:
Attendances
Month
Year 



headers: Attendances
Month
Year 
label: Year 
Month
Attendances
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 |
|--------:|--------:|--------:|--------:|-----------:|--------:|--------:|--------:|
|     700 |    7244 |  0.0114 |  0.0097 | 0.00836546 |    3748 |      10 |    3496 |
|   11855 |    3059 |  0.0036 |  0.0041 | 0.00448902 |    1183 |      38 |    1876 |
|     749 |   62822 |  0.1599 |  0.0841 | 0.0244264  |   52614 |       1 |   10208 |
|     141 |    3527 |  0.0048 |  0.0047 | 0.00469959 |    1563 |      30 |    1964 |
|     931 |    8829 |  0.0094 |  0.0118 | 0.0136896  |    3108 |       8 |    5721 |

Candidate column headers:
Not GHS
ITV001_NRI
%Total Docs
Total Docs
%Not GHS
TopDown Ranking 
%GHS
GHS



headers: Not GHS
ITV001_NRI
%Total Docs
Total Docs
%Not GHS
TopDown Ranking 
%GHS
GHS
label: ITV001_NRI
Total Docs
%GHS
%Total Docs
%Not GHS
GHS
TopDown Ranking 
Not GHS
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 |
|:---------|:--------|--------:|
| Ramirez  | IA      |      17 |
| Thompson | HI      |      20 |
| Lopez    | GA      |       1 |
| Stewart  | GA      |      16 |
| Young    | GA      |      28 |

Candidate column headers:
User
State
Ins



headers: User
State
Ins
label: User
State
Ins
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 |
|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|
|   26506 |   35339 |       0 |   34660 |    9909 |       0 |       0 |       0 |       0 |       0 |
|  388339 |  391466 |  440548 |  510751 |  516538 |  502747 |  422536 |  372909 |  338347 |  311059 |
|  130250 |  158289 |  170008 |  199609 |  228766 |  265838 |  211394 |  228141 |  217735 |  214742 |
|       0 |       0 |   35981 |       0 |       0 |     906 |     977 |     974 |     935 |     877 |
|    3689 |    4008 |    4114 |    1564 |       0 |       0 |       0 |       0 |       0 |       0 |

Candidate column headers:
2006
2011
2010
2013
2007
2015
2009
2012
2008
2015(1)



headers: 2006
2011
2010
2013
2007
2015
2009
2012
2008
2015(1)
label: 2006
2007
2008
2009
2010
2011
2012
2013
2015
2015(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   |
|--------:|--------:|:--------|
|    1983 |    20.8 |         |
|    1985 |    19.1 |         |
|    1985 |    23.2 |         |
|    1978 |    26.3 |         |
|    1986 |    26.3 |         |

Candidate column headers:
Investe- ringsrate
Gjennom- snitt
                               Kolonne1



headers: Investe- ringsrate
Gjennom- snitt
                               Kolonne1
label:                                Kolonne1
Investe- ringsrate
Gjennom- snitt
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 |
|:------------|--------:|:---------|:----------|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|
| New Visitor |       1 | (direct) | (not set) |   41658 |       2 |       2 |     100 |       2 |     100 |        0 |        0 |
| New Visitor |       1 | (direct) | (not set) |   41697 |       1 |       1 |     100 |       0 |       0 |      132 |      132 |
| New Visitor |       1 | (direct) | (not set) |   41871 |       1 |       1 |     100 |       1 |     100 |        0 |        0 |
| New Visitor |       1 | (direct) | (not set) |   41647 |       1 |       1 |     100 |       0 |       0 |       13 |       13 |
| New Visitor |       1 | (direct) | (not set) |   41841 |       1 |       1 |     100 |       0 |       0 |     1111 |     1111 |

Candidate column headers:
entrance bounce rate
percent new visits
date
bounces
visit count
visitors
country
time on site
new visits
avg time on site
visitor type
source



headers: entrance bounce rate
percent new visits
date
bounces
visit count
visitors
country
time on site
new visits
avg time on site
visitor type
source
label: visitor type
visit count
source
country
date
visitors
new visits
percent new visits
bounces
entrance bounce rate
time on site
avg time on site
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 |
|:-----------|-----------:|---------:|
| At least 1 |   5713.5   | 0.171439 |
|            |   3006.77  | 0.270261 |
|            | 230348     | 0.019296 |
|            |    921.671 | 0.478676 |
|            |   6569.05  | 0.146524 |

Candidate column headers:
RSE
Estimate
Number of nests of electronic equipment for IT usage



headers: RSE
Estimate
Number of nests of electronic equipment for IT usage
label: Number of nests of electronic equipment for IT usage
Estimate
RSE
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 |
|:-----------|--------:|:-------------------|-----------:|
| Diyala     |   15875 |                    | 0.057878   |
| Dahuk1     |   42784 |                    | 0.155984   |
| Kadhimia   |    8691 | 0.1142857142857142 | 0.0316861  |
| Hilla      |     877 | 0.0847588673045327 | 0.00319742 |
| Abu Ghraib |       0 | 0.0                | 0          |

Candidate column headers:
governorate level weighting
Iraq-wide weighting
Governorate
Total HH #



headers: governorate level weighting
Iraq-wide weighting
Governorate
Total HH #
label: Governorate
Total HH #
governorate level weighting
Iraq-wide weighting
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 |
|--------:|:--------------------------|--------:|:--------|:--------|--------:|---------:|
|      28 | 621 Westchester Dr        |     710 |         |         | 29.5608 | -98.2653 |
|       5 | 1049 Live Oak Rd          |     764 | 50.0    | 814.0   | 29.5734 | -98.2648 |
|      23 | 1400 Schertz Pkwy Bldg. 9 |     710 |         |         | 29.5659 | -98.2682 |
|      17 | 1400 Schertz Pkwy Bldg. 3 |     710 |         |         | 29.5647 | -98.2689 |
|      33 | 18726 IH 35 N             |     840 |         |         | 29.6135 | -98.2611 |

Candidate column headers:
Longitude
Base Elevation
Full Address/Facility ID
Tank Height
Total Height
ID
Latitude



headers: Longitude
Base Elevation
Full Address/Facility ID
Tank Height
Total Height
ID
Latitude
label: ID
Full Address/Facility ID
Base Elevation
Tank Height
Total Height
Latitude
Longitude
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   |
|:--------|:--------|:--------|
| A-002   | X2      | B       |
| A-003   | X2      | B       |
| A-001   | X2      | B       |
| A-001   | X2      | G       |
| A-003   | X1      | G       |

Candidate column headers:
EntityID
ScoreName
GroupName



headers: EntityID
ScoreName
GroupName
label: EntityID
GroupName
ScoreName