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   |
|--------:|:--------|--------:|:--------|
|       8 | Emp50   |   42219 |         |
|       2 | Emp11   |   42228 | Done    |
|       9 | Emp31   |   42224 |         |
|       1 | Emp12   |   42228 |         |
|      23 | Emp49   |   42234 | Done    |

Candidate column headers:
Assigned to
Done?
ID
Due by date



headers: Assigned to
Done?
ID
Due by date
label: ID
Assigned to
Due by date
Done?
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 |       4 | NSWN/EAPHSeason         | Early   |         |         |         |         |         |         |
|       1 |       4 | NSWS/WFEEDSeason        | Early   |         |         |         |         |         |         |
|       1 |       4 | VICNorth EastFEEDSeason | Early   |         |         |         |         |         |         |
|       1 |       4 | NSWS/WAPHSeason         | Early   |         |         |         |         |         |         |
|       1 |       4 | VICSouth WestASF1Season | Early   |         |         |         |         |         |         |

Candidate column headers:
5
7
Entries
2
Concatenated
Sort Order
4
3
6
1



headers: 5
7
Entries
2
Concatenated
Sort Order
4
3
6
1
label: Entries
Sort Order
Concatenated
1
2
3
4
5
6
7
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   |
|--------:|--------:|:--------|
|   39818 |    2009 | 01/05   |
|   39852 |    2009 | 02/08   |
|   39836 |    2009 | 01/23   |
|   39851 |    2009 | 02/07   |
|   39855 |    2009 | 02/11   |

Candidate column headers:
Day
Date
Year



headers: Day
Date
Year
label: Date
Year
Day
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   |
|:------------|--------:|:--------|--------:|:-------------|--------:|--------:|:--------|
| 6 SD3393021 |       6 | SD3393  |      21 | SD3367093575 |  333668 |  493635 | Yes     |
| -           |       1 | SD3893  |      26 | -            |  338275 |  493913 | No      |
| 2 SD3393021 |       2 | SD3393  |      21 | SD3344393804 |  333434 |  493850 | Yes     |
| -           |       1 | SD3893  |      26 | -            |  338216 |  493953 | No      |
| -           |       1 | SD3893  |      26 | -            |  338171 |  493988 | No      |

Candidate column headers:
EASTING
GRIDREF1KM
GRIDREF
NORTHING
HEDGEID
GRIDSQNO
CELLNO
SURVEYED



headers: EASTING
GRIDREF1KM
GRIDREF
NORTHING
HEDGEID
GRIDSQNO
CELLNO
SURVEYED
label: HEDGEID
CELLNO
GRIDREF1KM
GRIDSQNO
GRIDREF
EASTING
NORTHING
SURVEYED
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 |
|--------:|--------:|--------:|--------:|:--------|--------:|--------:|--------:|--------:|--------:|---------:|---------:|
|      18 |       5 |       0 |       0 | E       |     8.4 |     363 |     0   |     0   |       0 |        0 |      8.4 |
|       2 |       1 |       0 |       0 | A       |     5.6 |      52 |     5.6 |     0   |       0 |        0 |      0   |
|       8 |       3 |       1 |       1 | C       |     1.2 |     117 |     0   |     0   |       0 |        0 |      0   |
|       6 |       2 |       0 |       0 | B       |    10.1 |     104 |     0   |    10.1 |       0 |        0 |      0   |
|       0 |       1 |       1 |       1 | A       |     5.6 |       0 |     0   |     0   |       0 |        0 |      0   |

Candidate column headers:
IsLeft
C
X
B
IsBottom
ItemName
ItemHeight
A
ItemId
Index
E
D



headers: IsLeft
C
X
B
IsBottom
ItemName
ItemHeight
A
ItemId
Index
E
D
label: Index
ItemId
IsLeft
IsBottom
ItemName
ItemHeight
X
A
B
C
D
E
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 |
|:---------|--------:|--------:|
| Robinson |   40522 |      51 |
| Garcia   |   40509 |      78 |
| Garcia   |   40459 |      62 |
| Garcia   |   40484 |      77 |
| Clark    |   40514 |      86 |

Candidate column headers:
DDate
Prod
Qty



headers: DDate
Prod
Qty
label: Prod
DDate
Qty
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0       | Col_1       |   Col_2 |
|:------------|:------------|--------:|
| -=Not Set=- | -=Not Set=- |       0 |
| Important   | This Month  |      15 |
| -=Not Set=- | -=Not Set=- |       0 |
| -=Not Set=- | -=Not Set=- |       0 |
| -=Not Set=- | -=Not Set=- |       0 |

Candidate column headers:
Urgency
Score
Importance



headers: Urgency
Score
Importance
label: Importance
Urgency
Score
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 | Col_1              | Col_2    | Col_3   | Col_4   | Col_5         | Col_6      |   Col_7 |   Col_8 |
|--------:|:-------------------|:---------|:--------|:--------|:--------------|:-----------|--------:|--------:|
|   42225 | Smileys            | Dallas   | TX      | Online  | Blaster Blend | Economical |      42 |  1033.2 |
|   42109 | Smileys            | Portland | OR      | Catalog | Blaster Blend | Economical |      49 |  1205.4 |
|   42130 | Food Blender_ Inc. | Ripley   | NY      | Catalog | Blaster Blend | Economical |      51 |  1254.6 |
|   42196 | Smileys            | Dallas   | TX      | Online  | Blaster Blend | Economical |      91 |  2238.6 |
|   42041 | Smileys            | Portland | OR      | Online  | Blaster Blend | Economical |      46 |  1131.6 |

Candidate column headers:
State
Total Sales
Quantity
Customer
Source
City
Category
Product
Date



headers: State
Total Sales
Quantity
Customer
Source
City
Category
Product
Date
label: Date
Customer
City
State
Source
Product
Category
Quantity
Total Sales
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                  |
|--------:|--------:|--------:|:--------------------------|--------:|--------:|--------:|--------:|--------:|--------:|---------:|:------------------------|:------------------------|
|       5 |       3 |    9913 | Department of Agriculture |      50 |       3 |      10 |    3090 |       7 |       8 |       -1 | Office of the Secretary | Office of the Secretary |
|       5 |       3 |    9913 | Department of Agriculture |      50 |       3 |      10 |    1000 |       1 |       1 |        0 | Office of the Secretary | Office of the Secretary |
|       5 |       3 |    9913 | Department of Agriculture |      50 |       3 |      10 |    1941 |       1 |       1 |        0 | Office of the Secretary | Office of the Secretary |
|       5 |       3 |    9913 | Department of Agriculture |      50 |       3 |      10 |    3051 |       1 |       2 |       -1 | Office of the Secretary | Office of the Secretary |
|       5 |       3 |    8203 | Department of Agriculture |      50 |       3 |      20 |    1000 |       3 |       3 |        0 | Office of the Secretary | Gifts and Bequests      |

Candidate column headers:
MXAMT
AGELT
BURUP
AGEUP
ACCT
ACCTSEQ
LINENO
AGESEQ
BUREAU
BURSEQ
PFAMT
DIFF
ACCTLT



headers: MXAMT
AGELT
BURUP
AGEUP
ACCT
ACCTSEQ
LINENO
AGESEQ
BUREAU
BURSEQ
PFAMT
DIFF
ACCTLT
label: AGEUP
BURUP
ACCT
AGELT
AGESEQ
BURSEQ
ACCTSEQ
LINENO
PFAMT
MXAMT
DIFF
BUREAU
ACCTLT
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 |
|:--------------------|--------:|:---------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|
| AMC Javelin         |       2 | AMC      |    15.2 |       8 |   304   |     150 |    3.15 |   3.435 |   17.3  |        0 |        0 |        3 |
| Camaro Z28          |       4 | Camaro   |    13.3 |       8 |   350   |     245 |    3.73 |   3.84  |   15.41 |        0 |        0 |        3 |
| Honda Civic         |       2 | Honda    |    30.4 |       4 |    75.7 |      52 |    4.93 |   1.615 |   18.52 |        1 |        1 |        4 |
| Maserati Bora       |       8 | Maserati |    15   |       8 |   301   |     335 |    3.54 |   3.57  |   14.6  |        0 |        1 |        5 |
| Lincoln Continental |       4 | Lincoln  |    10.4 |       8 |   460   |     215 |    3    |   5.424 |   17.82 |        0 |        0 |        3 |

Candidate column headers:
hp
am
mpg
maker
cyl
disp
gear
model
qsec
drat
carb
vs
wt



headers: hp
am
mpg
maker
cyl
disp
gear
model
qsec
drat
carb
vs
wt
label: model
carb
maker
mpg
cyl
disp
hp
drat
wt
qsec
vs
am
gear
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                        |
|--------:|:--------|:---------|:--------|:-----------------------------|
|    4969 | Cathy   | Rein     | 101B    | crein@hinghamschools.org     |
|    4914 | Emily   | Day      | 114     | eday@hinghamschools.org      |
|    4956 | Vera    | Neville  | 209A    | vneville@hinghamschools.org  |
|    4904 | Marian  | Francis  | 104     | mfrancis@hinghamschools.org  |
|    4946 | Julie   | Chandler | 211A    | jchandler@hinghamschools.org |

Candidate column headers:
Last Name
Email
First Name
Ext
Room #



headers: Last Name
Email
First Name
Ext
Room #
label: Ext
First Name
Last Name
Room #
Email
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0                      |    Col_1 | Col_2   |     Col_3 |     Col_4 |     Col_5 |     Col_6 |     Col_7 |     Col_8 |     Col_9 |    Col_10 |    Col_11 |    Col_12 |    Col_13 |    Col_14 |    Col_15 |    Col_16 |
|:---------------------------|---------:|:--------|----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|
| Total : East South Central | 78656669 | Region  | 157126323 | 148544587 | 139182693 | 142987262 | 147844800 | 146279214 | 140807322 | 146108375 | 131570330 | 130658741 | 132130979 | 114226008 | 103673302 |  98274017 |
| Total : West South Central | 40228355 | Region  |  50486353 |  50469210 |  53117647 |  51467875 |  51958701 |  51682749 |  46805604 |  44392847 |  39711527 |  45969255 |  51046309 |  49301448 |  46854765 |  47257201 |
| Total : New Mexico         | 19678796 | NM      |  29617552 |  28916344 |  26388865 |  27249951 |  28518728 |  25912523 |  24450829 |  25644851 |  25124096 |  20990588 |  21922457 |  22452275 |  21968641 |  21963311 |
| Total : West Virginia      | 95632900 | WV      | 162415815 | 150077736 | 139711310 | 147993201 | 153649647 | 152373615 | 153479515 | 157778087 | 137127322 | 135219749 | 134661745 | 120424758 | 112786287 | 112187373 |
| Total : Virginia           | 13914269 | VA      |  32774024 |  29956063 |  31596312 |  31420320 |  27743396 |  29739969 |  25346083 |  24712406 |  21019178 |  22385402 |  22522668 |  18965105 |  16619072 |  15059178 |

Candidate column headers:
2008
2002
2009
2010
State-Region
2011
2003
2012
2007
2001
2005
2004
2015
2013
2014
State
2006



headers: 2008
2002
2009
2010
State-Region
2011
2003
2012
2007
2001
2005
2004
2015
2013
2014
State
2006
label: State
2015
State-Region
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
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   | Col_3           | Col_4   | Col_5   |   Col_6 |       Col_7 |   Col_8 |   Col_9 |
|:--------|--------:|:--------|:----------------|:--------|:--------|--------:|------------:|--------:|--------:|
| ID0012  | 42370.5 | Greg    | Toaster         | Y       | Y       |      50 | 0.000367288 |       4 |       0 |
| ID0034  | 42370.6 | Jim     | Toaster         | Y       | N       |      53 | 0.0011987   |       2 |       0 |
| ID0031  | 42370.6 | Joe     | Fridge          | Y       | Y       |      22 | 0.00356944  |       3 |       0 |
| ID0004  | 42370.4 | Greg    | Washing Machine | Y       | Y       |      53 | 0.000433813 |       2 |       0 |
| ID0041  | 42370.6 | Jim     | Air Conditioner | Y       | Y       |      27 | 0.00149727  |       5 |       0 |

Candidate column headers:
Agent
Call Id
Speed of Answer
Satisfaction rating
Column1
AvgTalkDuration
Department
Resolved
Answered (Y/N)
Date



headers: Agent
Call Id
Speed of Answer
Satisfaction rating
Column1
AvgTalkDuration
Department
Resolved
Answered (Y/N)
Date
label: Call Id
Date
Agent
Department
Answered (Y/N)
Resolved
Speed of Answer
AvgTalkDuration
Satisfaction rating
Column1
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 |   Col_1 | Col_2   | Col_3   | Col_4   | Col_5   | Col_6   | Col_7   | Col_8   | Col_9   |   Col_10 | Col_11   | Col_12   | Col_13      | Col_14     |
|--------:|--------:|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|---------:|:---------|:---------|:------------|:-----------|
|      36 |      24 | 36.0    | 24.0    | 6.0     | 20.0    | 36.0    | 24.0    | 36.0    | 24.0    |  8910267 | SCI      | Wajir    | Wajir South | Biyamathow |
|      61 |      51 |         |         |         |         |         |         | 57.0    | 41.0    |  9334890 |          |          |             |            |
|      56 |      56 |         |         |         |         |         |         |         |         |  9336501 | DOL      |          |             |            |
|      56 |      56 |         |         |         |         |         |         |         |         |  9336661 | DOL      |          |             |            |
|      25 |      15 | 18.0    | 15.0    | 27.0    | 13.0    | 25.0    | 15.0    | 18.0    | 15.0    |  8736731 | SCI      |          |             |            |

Candidate column headers:
_id
End of Term III 2016 attendance girls ECD
org
County
Sub-county
End of Term I 2017 attendance girls ECD
Term I 2017 ECD enrolment girls
End of Term III 2016 attendance boys ECD
Start of Term II 2017 (today) attendance boys ECD
Term II 2017 ECD enrolment girls
End of Term I 2017 attendance boys ECD
Term I 2017 ECD enrolment boys
Start of Term II 2017 (today) attendance girls ECD
Village or sub-location
Term II 2017 ECD enrolment boys



headers: _id
End of Term III 2016 attendance girls ECD
org
County
Sub-county
End of Term I 2017 attendance girls ECD
Term I 2017 ECD enrolment girls
End of Term III 2016 attendance boys ECD
Start of Term II 2017 (today) attendance boys ECD
Term II 2017 ECD enrolment girls
End of Term I 2017 attendance boys ECD
Term I 2017 ECD enrolment boys
Start of Term II 2017 (today) attendance girls ECD
Village or sub-location
Term II 2017 ECD enrolment boys
label: Term I 2017 ECD enrolment boys
Term I 2017 ECD enrolment girls
Term II 2017 ECD enrolment boys
Term II 2017 ECD enrolment girls
End of Term III 2016 attendance boys ECD
End of Term III 2016 attendance girls ECD
End of Term I 2017 attendance boys ECD
End of Term I 2017 attendance girls ECD
Start of Term II 2017 (today) attendance boys ECD
Start of Term II 2017 (today) attendance girls ECD
_id
org
County
Sub-county
Village or sub-location
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 |
|--------:|:--------|--------:|
|     487 | Cus31   |    9574 |
|     427 | Cus10   |    3611 |
|     402 | Cus28   |    9141 |
|     195 | Cus15   |    4404 |
|     230 | Cus2    |    3798 |

Candidate column headers:
Days Past Due
Customer
Amount Owed



headers: Days Past Due
Customer
Amount Owed
label: Days Past Due
Customer
Amount Owed
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 |
|--------:|:--------|:-----------|:--------|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|
|       3 | Punjab  | GR03000092 | G       |     199 |       0 |     199 |     170 |      29 |     170 |       29 |      170 |       29 |
|       3 | Punjab  | GR03000047 | G       |      57 |       0 |      57 |      56 |       1 |      36 |       21 |       57 |        0 |
|       3 | Punjab  | GR03000037 | G       |     171 |       0 |     171 |     161 |      10 |     165 |        6 |      160 |       11 |
|       3 | Punjab  | GR03000011 | G       |      66 |       0 |      66 |       0 |      66 |       0 |       66 |        0 |       66 |
|       3 | Punjab  | GR03000022 | G       |      89 |       0 |      89 |      82 |       7 |      89 |        0 |       89 |        0 |

Candidate column headers:
Q1 Marks Updated
Trainee Count Total
State Name
Q2 Marks Remaining
Category
Trainee Count Pvt
ITI Code
Q2 Marks Updated
Q1 Marks Remaining
Trainee Count Reg
Trainee Update Remaining
State Id
Trainee Updated



headers: Q1 Marks Updated
Trainee Count Total
State Name
Q2 Marks Remaining
Category
Trainee Count Pvt
ITI Code
Q2 Marks Updated
Q1 Marks Remaining
Trainee Count Reg
Trainee Update Remaining
State Id
Trainee Updated
label: State Id
State Name
ITI Code
Category
Trainee Count Reg
Trainee Count Pvt
Trainee Count Total
Trainee Updated
Trainee Update Remaining
Q1 Marks Updated
Q1 Marks Remaining
Q2 Marks Updated
Q2 Marks Remaining
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 |
|--------:|--------:|--------:|--------:|
|     555 |      21 |  387650 |  372800 |
|     409 |      49 |  621000 |  596000 |
|     389 |      16 |  567348 |  549600 |
|     589 |      28 |  251300 |  242500 |
|     633 |      74 |  267600 |  261300 |

Candidate column headers:
  Avg. List Price
   Avg Sold Price
# of Listings
#New Listings



headers:   Avg. List Price
   Avg Sold Price
# of Listings
#New Listings
label: # of Listings
#New Listings
  Avg. List Price
   Avg Sold Price
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   |
|--------:|--------:|:--------|:----------------|--------:|--------:|:------------------|:--------|:--------|:--------|:---------|:---------|:---------|:---------|:---------|
|   91534 |       1 | Ach     | Humanities      |       3 |       3 | Chinese           |         |         |         |          |          | IN       |          |          |
|   91404 |       2 | Ach     | Business        |       3 |       4 | Accounting        | 4.0     |         |         | 4.0      |          | EX       |          |          |
|   91397 |       2 | Ach     | Social Sciences |       3 |       6 | Classical Studies | 6.0     |         |         | 6.0      |          | IN       |          |          |
|   91391 |       2 | Ach     | Sciences        |       3 |       5 | Chemistry         |         |         |         | 5.0      |          | EX       | R        | R        |
|   91396 |       2 | Ach     | Social Sciences |       3 |       6 | Classical Studies | 6.0     | 6.0     |         | 6.0      |          | EX       |          |          |

Candidate column headers:
Primary Industries
Field Description
NCEA Numeracy (need 10 credits)
Credits
UE Numeracy (need 10 credits)
Standard Level
UE Reading (need 5 credits)
Latest Standard Version
UE Writing (need 5 credits)
Standard Type
Internal or External
NCEA Literacy (need 10 credits)
Manufacturing & Technology
UE Approved subject
Standard Number



headers: Primary Industries
Field Description
NCEA Numeracy (need 10 credits)
Credits
UE Numeracy (need 10 credits)
Standard Level
UE Reading (need 5 credits)
Latest Standard Version
UE Writing (need 5 credits)
Standard Type
Internal or External
NCEA Literacy (need 10 credits)
Manufacturing & Technology
UE Approved subject
Standard Number
label: Standard Number
Latest Standard Version
Standard Type
Field Description
Standard Level
Credits
UE Approved subject
UE Reading (need 5 credits)
UE Writing (need 5 credits)
UE Numeracy (need 10 credits)
NCEA Literacy (need 10 credits)
NCEA Numeracy (need 10 credits)
Internal or External
Manufacturing & Technology
Primary Industries
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 |
|--------:|:----------|--------:|:-----------|--------:|
|   42412 | Friday    |       7 | Gifts      |   36.82 |
|   42406 | Saturday  |       6 | Gifts      |   68.8  |
|   42403 | Wednesday |       6 | Coffee     |  177.55 |
|   42405 | Friday    |       6 | Spec Drink |  149.62 |
|   42409 | Tuesday   |       7 | Coffee     |  164.35 |

Candidate column headers:
Trn Date
Segment
Week #
Day
Amount



headers: Trn Date
Segment
Week #
Day
Amount
label: Trn Date
Day
Week #
Segment
Amount
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 |
|:---------|--------:|--------:|--------:|
| Clatsop  |    1933 |      64 | 3.452   |
| Douglas  |    1990 |      24 | 4.6332  |
| Baker    |    1897 |       0 | 0       |
| Columbia |    1944 |      56 | 2.4295  |
| Coos     |    1966 |      68 | 1.60795 |

Candidate column headers:
District ID
County
Percent of District Enrollment
2014-15 Homeless K-12



headers: District ID
County
Percent of District Enrollment
2014-15 Homeless K-12
label: County
District ID
2014-15 Homeless K-12
Percent of District Enrollment
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 |
|--------:|--------:|--------:|--------:|--------:|----------:|----------:|--------:|--------:|----------:|
|   41038 | 68.7022 | 65.1165 | 63.7684 | 64.5542 |   5.82661 |  4.14802  | 60.3527 | 62.8756 |   4.76378 |
|   41033 | 24.0007 | 28.1341 | 36.7526 | 29.016  | -14.0998  | -5.01532  | 40.3495 | 38.1005 | -12.2154  |
|   41022 | 35.2555 | 34.022  | 31.9536 | 34.674  |   1.55597 |  0.581503 | 36.7961 | 33.6995 |  -2.77415 |
|   41047 | 47.8193 | 52.5014 | 41.5875 | 52.4317 |   5.28491 | -4.61242  | 43.9294 | 42.5344 |   8.57204 |
|   41049 | 40.1634 | 44.4227 | 50.8936 | 44.6685 | -10.9052  | -4.50511  | 50.8198 | 51.0686 |  -6.39713 |

Candidate column headers:
R_23
C1
C3
R_13
C2
T_13
T_23
R_12
T_12
DATE 



headers: R_23
C1
C3
R_13
C2
T_13
T_23
R_12
T_12
DATE 
label: DATE 
C1
C2
C3
T_12
R_13
R_12
T_23
T_13
R_23
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 |
|--------:|--------:|--------:|
|       2 |      50 |      10 |
|       4 |      10 |      30 |
|       6 |      10 |      28 |
|       0 |      49 |       8 |
|       2 |      17 |      29 |

Candidate column headers:
Tentti (max 36)
Poissaolo (max 24)
Puuttuvat harjoitukset (max 60)



headers: Tentti (max 36)
Poissaolo (max 24)
Puuttuvat harjoitukset (max 60)
label: Poissaolo (max 24)
Puuttuvat harjoitukset (max 60)
Tentti (max 36)
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            |
|:--------|:--------|:--------|:-------------------|--------:|:-----------------|
| 0058B   | $13.99  | $22.65  | 750 mL (Fifth)     |    25.4 | ABSOLUT 100      |
| 0083B   | $9.99   | $16.06  | 750 mL (Fifth)     |    25.4 | ANTICA SAMBUCA   |
| 0142B   | $16.49  | $27.13  | 750 mL (Fifth)     |    25.4 | ALASKA OUTLAW    |
| 0319H   | $3.49   | $4.43   | 200 mL (Half Pint) |     6.8 | PARAMOUNT GIN 90 |
| 0241L   | $6.99   | $11.09  | 1 Liter            |    33.8 | HIGHLAND MIST    |

Candidate column headers:
Bottle Size
Retail Price Effective 1/1/2018
Brand
Bottle Size (Ounces)
Name
Current Retail Price



headers: Bottle Size
Retail Price Effective 1/1/2018
Brand
Bottle Size (Ounces)
Name
Current Retail Price
label: Brand
Retail Price Effective 1/1/2018
Current Retail Price
Bottle Size
Bottle Size (Ounces)
Name
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 |   Col_1 |   Col_2 |   Col_3 |
|--------:|--------:|--------:|--------:|
|      75 |      80 |      90 |      85 |
|      35 |      95 |      98 |      85 |
|      25 |      95 |      98 |      85 |
|      30 |      95 |      98 |      85 |
|      60 |      90 |      95 |      85 |

Candidate column headers:
Aspect 
P
LT
MRT



headers: Aspect 
P
LT
MRT
label: Aspect 
P
LT
MRT
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 |
|:----------------|--------:|--------:|
| Chrome 30.0     |   24.05 |       1 |
| Chrome 29.0     |    9.06 |       1 |
| IE 7.0          |    0.28 |       0 |
| Chrome for Andr |    0.46 |       1 |
| Other           |    4.33 |       0 |

Candidate column headers:
Browser Version
Support
Market Share Perc. (Oct 2013)



headers: Browser Version
Support
Market Share Perc. (Oct 2013)
label: Browser Version
Market Share Perc. (Oct 2013)
Support
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 |
|--------:|:-----------|--------:|:-------------|---------:|---------:|---------:|
|       9 | Eldr       |       2 | Eldr_2       | 37002600 | 33337100 | 1.12678  |
|       6 | Crnde      |       2 | Crnde_2      | 28630200 | 33337100 | 0.872202 |
|      50 | linc_Brn1b |       1 | linc_Brn1b_1 | 30294500 | 33337100 | 0.929538 |
|      20 | Peril      |       1 | Peril_1      | 27635500 | 33337100 | 0.827599 |
|      45 | WT         |      16 | WT_16        | 38948900 | 33337100 | 1.17837  |

Candidate column headers:
rep_name
total_mass
norm_mass
replicate
sample_name
internal_scale
Index



headers: rep_name
total_mass
norm_mass
replicate
sample_name
internal_scale
Index
label: Index
sample_name
replicate
rep_name
total_mass
norm_mass
internal_scale
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 |
|--------:|--------:|--------:|--------:|---------:|---------:|---------:|---------:|---------:|---------:|
|   40108 |      90 |      11 |      85 | 10.6667  |  7.66667 | 11.1667  | 10.2222  |  7.86111 | 10.4722  |
|   40103 |      43 |      56 |       8 |  4.27273 |  5.09091 |  4.90909 |  4.57851 |  5.33058 |  4.78512 |
|   40104 |      18 |      37 |      72 |  6       | 13.6667  | 24       |  7.77778 | 15.3333  | 21.1111  |
|   40108 |      55 |      17 |      56 | 10.6667  |  7.66667 | 11.1667  | 10.2222  |  7.86111 | 10.4722  |
|   40105 |      66 |      63 |      14 |  4.35    |  6.7     |  6       |  4.7     |  6.07    |  6.05    |

Candidate column headers:
Avg2
Item3
Avg3
Median1
Median3
Avg1
Median2
Item1
DDate
Item2



headers: Avg2
Item3
Avg3
Median1
Median3
Avg1
Median2
Item1
DDate
Item2
label: DDate
Item1
Item2
Item3
Median1
Median2
Median3
Avg1
Avg2
Avg3
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   |
|--------:|--------:|--------:|--------:|:--------|:--------|:--------|
|     586 |   41517 |      11 |      22 | YES     |         | YES     |
|     586 |   41545 |       7 |      20 |         | YES     | YES     |
|     588 |   41573 |       9 |      24 |         | YES     | YES     |
|     586 |   41503 |      12 |      19 | YES     |         | YES     |
|     586 |   41510 |      10 |      16 |         | YES     | YES     |

Candidate column headers:
Date  (DD-MM-YY)
Flight #
1st Bag w/i 5mins
1st Bag Time
1st Bag SLA met
Last Bag SLA met
Last Bag Time



headers: Date  (DD-MM-YY)
Flight #
1st Bag w/i 5mins
1st Bag Time
1st Bag SLA met
Last Bag SLA met
Last Bag Time
label: Flight #
Date  (DD-MM-YY)
1st Bag Time
Last Bag Time
1st Bag w/i 5mins
1st Bag SLA met
Last Bag SLA met
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 |
|:--------|:--------|:--------|--------:|-----------:|--------:|--------:|--------:|
| B       | Watson  | GIP     |      88 | 0.22662    |       1 |       1 |       1 |
| C       | Kelly   | NIL     |      75 | 0.00656998 |       0 |       0 |       1 |
| E       | Cole    | WIM     |      55 | 0.504716   |       0 |       0 |       1 |
| D       | White   | HIM     |      22 | 0.932404   |       0 |       0 |       1 |
| C       | Torres  | HIB     |      98 | 0.555512   |       0 |       0 |       1 |

Candidate column headers:
Err_3
Err_2
Mach
TTime
Empl
Err_1
Ops
Qty



headers: Err_3
Err_2
Mach
TTime
Empl
Err_1
Ops
Qty
label: Mach
Empl
Ops
Qty
TTime
Err_1
Err_2
Err_3
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   |
|:----------|:--------|:--------|:--------|
| Galactose | Lactaid | Yeast   | 1.5     |
| 4 ml      | 2 ml    | 10 ml   | ml      |
| Lactose   | Water   | Yeast   | 0.5     |
| Galactose | Beano   | Yeast   | 1.5     |
| Melibiose | Beano   | Yeast   | 2       |

Candidate column headers:
Substrate (Sugar)
CO2
Enzyme Prep
Yeast



headers: Substrate (Sugar)
CO2
Enzyme Prep
Yeast
label: Substrate (Sugar)
Enzyme Prep
Yeast
CO2
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    |
|:--------|:--------|:-------------|:---------|
| Mouse   | --      | R&D Systems  | 1:100    |
| Mouse   | --      | R&D Systems  | 1:500    |
| Mouse   | 13H5    | Custom MSKCC | 1:10     |
| Human   | --      | Biodesign    | 1:10_000 |
| Mouse   | Mec14.7 | Biolegend    | 1:200    |

Candidate column headers:
Reactivity
Dilution
Vendor
Clone



headers: Reactivity
Dilution
Vendor
Clone
label: Reactivity
Clone
Vendor
Dilution
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                |
|:-----------|:--------|:--------|:--------|:------------------------|:--------------|:---------------------|
| LIONS 4B   |         |         |         | u10 evaluations 6 - 8pm | NOT AVAILABLE | u8 evaluation 6-8 pm |
| LIONS 6    |         |         |         |                         | NOT AVAILABLE |                      |
| LIONS 2    |         |         |         |                         | NOT AVAILABLE |                      |
| CENTRE 5/6 |         |         |         |                         | NOT AVAILABLE |                      |
| LIONS 5    |         |         |         |                         | NOT AVAILABLE |                      |

Candidate column headers:
FRIDAY
WEDNESDAY
THURSDAY
SUNDAY
Time
MONDAY
TUESDAY



headers: FRIDAY
WEDNESDAY
THURSDAY
SUNDAY
Time
MONDAY
TUESDAY
label: Time
MONDAY
TUESDAY
WEDNESDAY
THURSDAY
SUNDAY
FRIDAY