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

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

Table Data:
| Col_0         |   Col_1 |   Col_2 |   Col_3 |   Col_4 |   Col_5 |   Col_6 |   Col_7 |
|:--------------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|
| Seo Theory    | 46.3933 | 40.0875 |      14 |     192 |      13 |      47 |      19 |
| Miscellaneous | 46.3933 | 28.019  |       2 |      10 |       7 |       1 |       0 |
| Presentations | 46.3933 | 30.3212 |       3 |      40 |       2 |       1 |       2 |
| SEO Theory    | 46.3933 | 37.4917 |      10 |     188 |      23 |      42 |      32 |
| SEO Theory    | 46.3933 | 31.8859 |       4 |      37 |       1 |       5 |       0 |

Candidate column headers:
Twitter
DA
FB
LinkedIn
# LRDs
Google+
PA
Category/Type



headers: Twitter
DA
FB
LinkedIn
# LRDs
Google+
PA
Category/Type
label: Category/Type
DA
PA
# LRDs
Twitter
FB
Google+
LinkedIn
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   |
|--------:|--------:|:--------|--------:|--------:|--------:|:---------|:--------|--------:|--------:|:---------|:---------------------------|:---------------------------|:---------|:---------|
|   43372 |    3883 | Success |  360201 |    3495 |    4613 | IV15 9QF | Pass    |   41347 |     777 | Highland | http://www.highland.gov.uk | env.health@highland.gov.uk | FHIS     | False    |
|   43372 |    3883 | Success |  561497 |   34324 |    7841 | IV17 0PT | Pass    |   43053 |     777 | Highland | http://www.highland.gov.uk | env.health@highland.gov.uk | FHIS     | False    |
|   43372 |    3883 | Success |  562537 |    6966 |    7841 | KW1 5UN  | Pass    |   40469 |     777 | Highland | http://www.highland.gov.uk | env.health@highland.gov.uk | FHIS     | False    |
|   43372 |    3883 | Success |  561329 |    2683 |    7841 | PA37 1SE | Pass    |   40583 |     777 | Highland | http://www.highland.gov.uk | env.health@highland.gov.uk | FHIS     | False    |
|   43372 |    3883 | Success |  562596 |    9466 |    7842 | PH33 7AD | Pass    |   40960 |     777 | Highland | http://www.highland.gov.uk | env.health@highland.gov.uk | FHIS     | False    |

Candidate column headers:
LocalAuthorityEmailAddress
FHRSID
ItemCount
LocalAuthorityBusinessID
LocalAuthorityCode
LocalAuthorityName
BusinessTypeID
ExtractDate
PostCode
NewRatingPending
RatingDate
SchemeType
LocalAuthorityWebSite
ReturnCode
RatingValue



headers: LocalAuthorityEmailAddress
FHRSID
ItemCount
LocalAuthorityBusinessID
LocalAuthorityCode
LocalAuthorityName
BusinessTypeID
ExtractDate
PostCode
NewRatingPending
RatingDate
SchemeType
LocalAuthorityWebSite
ReturnCode
RatingValue
label: ExtractDate
ItemCount
ReturnCode
FHRSID
LocalAuthorityBusinessID
BusinessTypeID
PostCode
RatingValue
RatingDate
LocalAuthorityCode
LocalAuthorityName
LocalAuthorityWebSite
LocalAuthorityEmailAddress
SchemeType
NewRatingPending
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       |
|:-----------|:---------|:------------|
| Jacqueline | Alleman  | N3NP4CW8ZGQ |
| Derrick    | Barrow   | LXNVG2RRGQJ |
| Brandy     | Breechen | DKN7SLKZSLZ |
| Elke       | Aldridge | G8NH4BF6VWT |
| Nicole     | Alfonso  | ZHNDH2GHTG5 |

Candidate column headers:
First Name
Confirmation Number
Last Name



headers: First Name
Confirmation Number
Last Name
label: First Name
Last Name
Confirmation Number
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 | Col_1                    |   Col_2 |   Col_3 |   Col_4 |
|--------:|:-------------------------|--------:|--------:|--------:|
|   25033 | DMI oth coma uncontrold  |      17 |   0.344 |      14 |
|   25032 | DMII oth coma uncontrold |      17 |   0.344 |      14 |
|   25013 | DMI ketoacd uncontrold   |      17 |   0.344 |      14 |
|   25012 | DMII ketoacd uncontrold  |      17 |   0.344 |      14 |
|   25022 | DMII hprosmlr uncontrold |      17 |   0.344 |      14 |

Candidate column headers:
ICD-9-CM Description
RxHCC Model Category
ICD-9-CM Code
CMS-HCC Model Category (ESRD_ PACE)
Comm Factor



headers: ICD-9-CM Description
RxHCC Model Category
ICD-9-CM Code
CMS-HCC Model Category (ESRD_ PACE)
Comm Factor
label: ICD-9-CM Code
ICD-9-CM Description
CMS-HCC Model Category (ESRD_ PACE)
Comm Factor
RxHCC Model Category
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   |
|:--------|:--------|--------:|:--------|:--------|:---------------|:--------|:--------|:---------|:--------|
| Milan   | ZPMIDE  | 6124035 | M.      | Milan   | Dekker         | ZP      | Man     | Dekker   |         |
| Sander  | ZPSABO  | 6123613 | S.      | Sander  | de Boer        | ZP      | Man     | Boer     | de      |
| Bart    | VIBALI  | 6123840 | B.W.D.  | Bart    | van der Linden | VI      | Man     | Linden   | van der |
| Ruben   | VIRUFO  | 6115648 | R.J.    | Ruben   | Fontaine       | VI      | Man     | Fontaine |         |
| Maurits | ZPMARO  | 6115790 | M.      | Maurits | de Rooij       | ZP      | Man     | Rooij    | de      |

Candidate column headers:
Geslacht
DisplayName
Voorletters
Club
tussenvoegsel
Naam2
Code
Naam
Voornaam
Lic.Nr.



headers: Geslacht
DisplayName
Voorletters
Club
tussenvoegsel
Naam2
Code
Naam
Voornaam
Lic.Nr.
label: DisplayName
Code
Lic.Nr.
Voorletters
Voornaam
Naam
Club
Geslacht
Naam2
tussenvoegsel
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 |
|:-----------|--------:|--------:|:-------------------|:----------|--------:|:---------|:--------------------------|:--------|:--------|:---------|:---------|:---------|:---------|---------:|
| QC15100630 |   42195 |   42397 | CRIM CODE C'WEALTH | 272.19(1) |       1 | SENTENCE | Discontinued (indictment) |         |         |          |          |          |          |    42396 |
| DC15100004 |   42016 |   42314 | CRIM CODE C'WEALTH | 272.9(2)  |       1 | SENTENCE | Proven                    | 1.0     | GAOL    | 10y4m    | 5y3m     |          | Y        |    42314 |
| MC14100056 |   41669 |   41864 | CRIM CODE C'WEALTH | 272.8(2)  |       5 | SENTENCE | Proven                    | 1.0     | GAOL    | 11y      | 8y       |          | Y        |    41864 |
| QC13100261 |   41393 |   41743 | CRIM CODE C'WEALTH | 272.14(1) |       1 | SENTENCE | Proven                    | 3.0     | SELF    |          |          | 2000.0   |          |    41743 |
| PC13100426 |   41606 |   41925 | CRIM CODE C'WEALTH | 272.8(1)  |       1 | SENTENCE | Proven                    | 4.0     | OTHER   |          |          |          |          |    41925 |

Candidate column headers:
id_file
DATE_RECEIVED
act
max_duration
PENALTY_SEQ_NO
act_section
phase_type
penalty_type
Date Sentenced/Finalised
link_indicator
No. of offences
min_duration
END_DATE
AMOUNT
Outcome



headers: id_file
DATE_RECEIVED
act
max_duration
PENALTY_SEQ_NO
act_section
phase_type
penalty_type
Date Sentenced/Finalised
link_indicator
No. of offences
min_duration
END_DATE
AMOUNT
Outcome
label: id_file
DATE_RECEIVED
END_DATE
act
act_section
No. of offences
phase_type
Outcome
PENALTY_SEQ_NO
penalty_type
max_duration
min_duration
AMOUNT
link_indicator
Date Sentenced/Finalised
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 |
|:--------------------------|:--------------------------|--------:|--------:|--------:|:--------|:-----------|------------:|:-------------|:-------------|---------:|---------:|---------:|
| Pubblicazione 1 legge 190 | Pubblicazione 1 legge 190 |   42028 |   42028 |    2014 | IODL    | Z4C0EE528B | 96005660160 | 1631170162.0 |              |   418.92 |    41751 |    41771 |
| Pubblicazione 1 legge 190 | Pubblicazione 1 legge 190 |   42028 |   42028 |    2014 | IODL    | Z1910D8E3F | 96005660160 |              | 3034840169.0 |   500    |    41901 |    41901 |
| Pubblicazione 1 legge 190 | Pubblicazione 1 legge 190 |   42028 |   42028 |    2014 | IODL    | 5598752C4C | 96005660160 |              | 1558890164.0 | 73733.8  |    41615 |    41678 |
| Pubblicazione 1 legge 190 | Pubblicazione 1 legge 190 |   42028 |   42028 |    2014 | IODL    | Z2811FE6D0 | 96005660160 |              | 1063120222.0 |    38.1  |    41972 |    41972 |
| Pubblicazione 1 legge 190 | Pubblicazione 1 legge 190 |   42028 |   42028 |    2014 | IODL    | Z0D0EE2EEF | 96005660160 |              | 2940920164.0 |  3093.8  |    41747 |    41769 |

Candidate column headers:
dataUltimazione
codiceFiscale2
titolo
annoRiferimento
codiceFiscale
dataPubbicazioneDataset
codiceFiscaleProp
importoAggiudicazione
dataUltimoAggiornamentoDataset
abstract
licenza
cig
dataInizio



headers: dataUltimazione
codiceFiscale2
titolo
annoRiferimento
codiceFiscale
dataPubbicazioneDataset
codiceFiscaleProp
importoAggiudicazione
dataUltimoAggiornamentoDataset
abstract
licenza
cig
dataInizio
label: titolo
abstract
dataPubbicazioneDataset
dataUltimoAggiornamentoDataset
annoRiferimento
licenza
cig
codiceFiscaleProp
codiceFiscale
codiceFiscale2
importoAggiudicazione
dataInizio
dataUltimazione
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   |
|-----------:|--------:|:-----------------------------|--------:|--------:|---------:|--------:|--------:|------------:|:-------------|:---------|---------:|:-------------|---------:|:---------|:---------------|:---------|
| 5000241877 | 4000504 | NE Health and Human Services | 5002794 |     100 | 20150731 |  100209 |     304 | 6.1572e+08  | BELLEVUE     | NE       |     0.82 | 4100008521.0 |        1 | OREC     | Order Received | E        |
| 5000241916 | 4000504 | NE Health and Human Services | 5002892 |     100 | 20150831 |  100239 |     950 | 5.39476e+08 | OMAHA        | NE       |     1.05 | 4100008790.0 |        3 | OREC     | Order Received | E        |
| 5000241907 | 4000504 | NE Health and Human Services | 5002794 |     200 | 20150831 |  100225 |     228 | 6.91292e+07 | BELLEVUE     | NE       |     0.83 | 4100008786.0 |       74 | OREC     | Order Received | E        |
| 5000241893 | 4000504 | NE Health and Human Services | 5004570 |     300 | 20150831 |  100212 |     456 | 3.04181e+08 | LINCOLN      | NE       |     0.96 | 4100008782.0 |      161 | OREC     | Order Received | E        |
| 5000241888 | 4000504 | NE Health and Human Services | 5002779 |     100 | 20150930 |  100209 |     456 | 5.48364e+08 | GRAND ISLAND | NE       |     0    |              |        0 | CANC     | Cancelled      | E        |

Candidate column headers:
CorrelationId
CostPerlb
OrderQty
EntitlementBonusFlag
ScheduleLineDt
DestinationState
Status
StatusCode
SoldToDesc
SoldToParty
PONumber
DestinationCity
SalesOrderNo
ShipToParty
SalesOrderitem
MaterialNo
POLineNo



headers: CorrelationId
CostPerlb
OrderQty
EntitlementBonusFlag
ScheduleLineDt
DestinationState
Status
StatusCode
SoldToDesc
SoldToParty
PONumber
DestinationCity
SalesOrderNo
ShipToParty
SalesOrderitem
MaterialNo
POLineNo
label: SalesOrderNo
SoldToParty
SoldToDesc
ShipToParty
SalesOrderitem
ScheduleLineDt
MaterialNo
OrderQty
CorrelationId
DestinationCity
DestinationState
CostPerlb
PONumber
POLineNo
StatusCode
Status
EntitlementBonusFlag
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 |
|--------:|--------:|--------:|--------:|:------------|----------:|----------:|
|    4442 |    2000 |     500 |       0 | 1   (0)     |   5097.83 |  12039.8  |
|   32300 |    1110 |    3300 |       0 | 3   (0.01)  |  15293.5  |  52003.5  |
|  164464 |    2916 |     750 |       0 | 25   (0.11) | 127447    | 295577    |
|       0 |       0 |       0 |       0 | 0.5   (0)   |   2548.91 |   2548.91 |
|       0 |       0 |       0 |       0 | 0.5   (0)   |   2548.91 |   2548.91 |

Candidate column headers:
Number of Users (Percentage of County)
Total
Supplies
Enterprise
Staff
Infrastructure
Training



headers: Number of Users (Percentage of County)
Total
Supplies
Enterprise
Staff
Infrastructure
Training
label: Staff
Supplies
Training
Infrastructure
Number of Users (Percentage of County)
Enterprise
Total
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        |
|--------:|--------:|:-----------------------|:-----------|--------:|:-------------|
| 3960466 |   40547 | FIRST CENTENNIAL TITLE | 023-472-08 |  168750 | CONVENTIONAL |
| 3959602 |   40546 | WESTERN TITLE          | 522-132-20 |  263300 | CONVENTIONAL |
| 3959676 |   40546 | WESTERN TITLE          | 152-183-18 |  123000 | CONVENTIONAL |
| 3960424 |   40547 | COMMERCE TITLE         | 204-661-01 |  265000 | CONVENTIONAL |
| 3959805 |   40546 | TICOR TITLE            | 089-423-02 |  114200 | CONVENTIONAL |

Candidate column headers:
RECDATE
LOAN AMOUNT
TITLECOMPANY
APN
DOCNUM
TYPELOAN



headers: RECDATE
LOAN AMOUNT
TITLECOMPANY
APN
DOCNUM
TYPELOAN
label: DOCNUM
RECDATE
TITLECOMPANY
APN
LOAN AMOUNT
TYPELOAN
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 |
|:---------------------|--------:|--------:|
| Oman                 |   90000 | 1236700 |
| Syria                |   30000 | 3935000 |
| Lebanon              |  300000 | 1000000 |
| United Arab Emirates |  735000 | 3777900 |
| Qatar                |   30000 |  436000 |

Candidate column headers:
2000
2010
Country



headers: 2000
2010
Country
label: Country
2000
2010
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 |
|:-----------|--------:|--------:|--------:|--------:|--------:|-----------:|
| East Lyme  |      60 |      75 |      78 |      69 |      64 |  0.0666667 |
| Brooklyn   |      31 |      54 |      53 |      27 |      51 |  0.645161  |
| Eastford   |       7 |       1 |       2 |       1 |       3 | -0.571429  |
| Chaplin    |       6 |      19 |      16 |      16 |      16 |  1.66667   |
| Bloomfield |      20 |      27 |      24 |      27 |      35 |  0.75      |

Candidate column headers:
Town
2017
2013
2016
% change/5 yrs
2014
2015



headers: Town
2017
2013
2016
% change/5 yrs
2014
2015
label: Town
2013
2014
2015
2016
2017
% change/5 yrs
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 |
|--------:|--------:|:--------|--------:|
|       5 |   40736 | 40758.0 |    0.17 |
|       2 |   40816 |         |    0.21 |
|       5 |   40925 |         |    0.23 |
|       3 |   40950 |         |    0.15 |
|       1 |   40742 | 40791.0 |    0.24 |

Candidate column headers:
Rate
End
Client
Start



headers: Rate
End
Client
Start
label: Client
Start
End
Rate
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 |
|--------:|:-----------------|:--------|--------:|--------:|--------:|
|   42372 | Steve Masters    | Fitness |      95 |       9 |     855 |
|   42370 | Wayne Connolly   | Rugby   |      80 |       4 |     320 |
|   42370 | Dave Black       | Rugby   |      80 |       8 |     640 |
|   42371 | Mike Whitehead   | Rugby   |     110 |       9 |     990 |
|   42370 | Nate Christensen | Rugby   |      80 |       3 |     240 |

Candidate column headers:
Duration
Date
RPE
Load
Training Type
Athlete



headers: Duration
Date
RPE
Load
Training Type
Athlete
label: Date
Athlete
Training Type
Duration
RPE
Load
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 |
|:--------|--------:|--------:|--------:|:--------|--------:|
| f27     |       0 |       2 |       0 | -       |       6 |
| f34     |       0 |       2 |       0 | -       |       6 |
| f24     |       2 |       4 |       8 | -       |      12 |
| f33     |       2 |       3 |       6 | -       |       9 |
| f25     |       0 |       4 |       0 | -       |      12 |

Candidate column headers:
P
S
Qualitative Evaluation
max. S
Nr.
W



headers: P
S
Qualitative Evaluation
max. S
Nr.
W
label: Nr.
P
W
S
Qualitative Evaluation
max. S
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 | Col_1   | Col_2      | Col_3   |     Col_4 |       Col_5 |   Col_6 |        Col_7 |
|--------:|:--------|:-----------|:--------|----------:|------------:|--------:|-------------:|
|      12 | (12)    | Kazakhstan | metr. t |  10632570 |  0.675031   | 96.1054 |   0.455667   |
|      39 | (37)    | Laos       | metr. t |     69040 |  0.00438315 | 99.9965 |   1.9212e-05 |
|       2 | (  2)   | China      | metr. t | 345841000 | 21.9564     | 55.6729 | 482.085      |
|       5 | (  5)   | Russia     | metr. t |  55550000 |  3.52671    | 84.3486 |  12.4377     |
|       4 | (  4)   | India      | metr. t | 124852650 |  7.92653    | 80.8219 |  62.8299     |

Candidate column headers:
Share in %
Rank 2015
Share cum.%
Share HHI
Production 2016
Country
Rank 2016
unit



headers: Share in %
Rank 2015
Share cum.%
Share HHI
Production 2016
Country
Rank 2016
unit
label: Rank 2016
Rank 2015
Country
unit
Production 2016
Share in %
Share cum.%
Share HHI
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 | Tyrone Smithe       | NE      |
|      18 | Dominica Ordonez    | W       |
|      12 | Alysha Dewitt       | W       |
|       9 | Ghislaine Stidham   | SW      |
|       1 | Sioux Radcoolinator | NW      |

Candidate column headers:
SalesRep
SalesRepID
Region



headers: SalesRep
SalesRepID
Region
label: SalesRepID
SalesRep
Region
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     |
|:--------------------------|--------:|:----------|:--------|:--------|:----------|:--------------------|:-------------|:----------|
| LOT 4 - Thazin Gone Young |       2 | Completed | Rakhine | Sittwe  | (2 units) | Ohn Taw Gyi (North) | MMR012CMP115 | MPB Large |
| LOT 5 - Thazin Gone Young |       1 | Completed | Rakhine | Sittwe  |           | Say Tha Mar Gyi     | MMR012CMP045 | MPB Large |
| LOT 6 - Thazin Gone Young |       1 | Completed | Rakhine | Sittwe  |           | Baw Du Pha 2        | MMR012CMP114 | MPB Large |
| LOT 5 - Thazin Gone Young |       2 | Completed | Rakhine | Sittwe  | (2 units) | Khaung Doke Khar    | MMR012CMP042 | MPB Small |
| LOT 6 - Thazin Gone Young |       1 | Cancelled | Rakhine | Sittwe  |           | Baw Du Pha 1        | MMR012CMP113 | MPB Large |

Candidate column headers:
Pcode
Type
Infrastructure
Township
State
Status
Lot
Camp
Total



headers: Pcode
Type
Infrastructure
Township
State
Status
Lot
Camp
Total
label: Lot
Total
Status
State
Township
Type
Camp
Pcode
Infrastructure
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 |
|--------:|:----------------------|--------:|
|      26 | Celana Aladin         |    4500 |
|      46 | Celana Kulot Diana    |   32000 |
|      14 | BT Katun ABG/ set 3/4 |   25000 |
|      36 | Celana Hotpan ABG     |   15000 |
|      29 | Celana Basket Anak    |   15000 |

Candidate column headers:
NAMA BARANG
HARGA
NO



headers: NAMA BARANG
HARGA
NO
label: NO
NAMA BARANG
HARGA
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 |
|----------:|--------:|--------:|
| 2.012e+09 |   42049 |      21 |
| 2.012e+09 |   42057 |      17 |
| 2.012e+09 |   42112 |      24 |
| 2.012e+09 |   42120 |      22 |
| 2.012e+09 |   42049 |       5 |

Candidate column headers:
Date
ID
Score



headers: Date
ID
Score
label: ID
Date
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   |
|--------:|:--------|:------------|:-------------|--------:|:--------|
|   41298 | Th      | 01-January  |              |    2013 |         |
|   41307 | Sa      | 02-February |              |    2013 |         |
|   41308 | Su      | 02-February | Construction |    2013 | 1.0     |
|   41304 | We      | 01-January  |              |    2013 |         |
|   41316 | Mo      | 02-February |              |    2013 |         |

Candidate column headers:
Month
Weekday
Year
Date
Event
Value



headers: Month
Weekday
Year
Date
Event
Value
label: Date
Weekday
Month
Event
Year
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 |
|--------:|:---------------|--------:|
|      25 | Subassembly 3A |       0 |
|      34 | Subassembly 3A |       0 |
|      41 | Subassembly 3A |       0 |
|      40 | Subassembly 3A |       0 |
|       4 | Subassembly 3A |       0 |

Candidate column headers:
Part / Component or Subassembly
FMEA ID
RPN Score



headers: Part / Component or Subassembly
FMEA ID
RPN Score
label: FMEA ID
Part / Component or Subassembly
RPN 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 |
|:--------|:--------|:--------|:--------|--------:|--------:|
| 1.0     |         |         |         |       2 |       3 |
| 3.0     | 1.0     |         |         |       2 |       6 |
| 6.0     | 5.0     | 1.0     |         |       9 |      21 |
| 8.0     | 1.0     | 1.0     | 1.0     |       1 |      12 |
|         | 2.0     |         | 3.0     |       1 |       6 |

Candidate column headers:
Defensive
Total
Campaign/Research Reporting
Critical
Name Check
Congratulatory



headers: Defensive
Total
Campaign/Research Reporting
Critical
Name Check
Congratulatory
label: Campaign/Research Reporting
Congratulatory
Critical
Defensive
Name Check
Total
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   |
|:----------------------------|:--------|:-----------|:----------|:--------|
| ../xml/abk0/921/abk0921.xml | abk0921 | AfterVisit | updated   | content |
| ../xml/abk0/887/abk0887.xml | abk0887 | AfterVisit | unchanged |         |
| ../xml/abj9/913/abj9913.xml | abj9913 | AfterVisit | updated   | content |
| ../xml/abk1/361/abk1361.xml | abk1361 | AfterVisit | updated   | content |
| ../xml/abk1/996/abk1996.xml | abk1996 | PI-Pre-Op  | unchanged |         |

Candidate column headers:
status
hwid-content
filename
type
update-type



headers: status
hwid-content
filename
type
update-type
label: filename
hwid-content
type
status
update-type
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 |
|:--------|--------:|--------:|
| I       |       2 |       1 |
| E       |       5 |       3 |
| A       |       4 |       1 |
| E       |       2 |       3 |
| E       |       4 |       2 |

Candidate column headers:
Req
Team
Project



headers: Req
Team
Project
label: Project
Team
Req
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 |
|:-------------------------|:-----------|--------:|--------:|
| N2K_MTRGL_38_vlgnr06.png | Gelderland |       6 |      38 |
| N2K_MTRGL_38_vlgnr19.png | Gelderland |      19 |      38 |
| N2K_MTRGL_30_vlgnr1.png  | Drenthe    |       1 |      30 |
| N2K_MTRGL_38_vlgnr05.png | Gelderland |       5 |      38 |
| N2K_MTRGL_38_vlgnr07.png | Gelderland |       7 |      38 |

Candidate column headers:
Bestand
Nr
Voortouwnemer
Volgnr



headers: Bestand
Nr
Voortouwnemer
Volgnr
label: Bestand
Voortouwnemer
Volgnr
Nr
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   |
|:------------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:---------|:---------|
| Utah        | 50      | 8       | 5       | 0       | 6       | 1       | 0       | 7       | -       | 18       | 5        |
| Delaware    | x       | x       | x       | x       | x       | x       | x       | x       | x       | x        | x        |
| Illinois    | 13      | 1       | 0       | 0       | 2       | 0       | 1       | 1       | 0       | 5        | 3        |
| New York    | 69      | 4       | 7       | 0       | 1       | 3       | 4       | 12      | 13      | 21       | 4        |
| Puerto Rico | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 0       | -       | 0        | 0        |

Candidate column headers:
Attempts to contact unsuccessful
Withdrawal by parent
Deceased
Not eligible for Part B_ exit with referrals to other programs
Not eligible for Part B_ exit with no referrals
Part B eligible_ continuing in Part C
Part B eligibility not determined
Complete prior to reaching max age for Part C
State
Moved out of state
Part B eligible_ exiting Part C
Exiting total2



headers: Attempts to contact unsuccessful
Withdrawal by parent
Deceased
Not eligible for Part B_ exit with referrals to other programs
Not eligible for Part B_ exit with no referrals
Part B eligible_ continuing in Part C
Part B eligibility not determined
Complete prior to reaching max age for Part C
State
Moved out of state
Part B eligible_ exiting Part C
Exiting total2
label: State
Exiting total2
Attempts to contact unsuccessful
Complete prior to reaching max age for Part C
Deceased
Moved out of state
Not eligible for Part B_ exit with no referrals
Not eligible for Part B_ exit with referrals to other programs
Part B eligibility not determined
Part B eligible_ continuing in Part C
Part B eligible_ exiting Part C
Withdrawal by parent
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 |
|:----------------------|:------------------------|:----------------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|---------:|---------:|
| Dekalb                | DKC63-33RIB             | GENSSRIB        |   38.17 |   25.47 |    7.82 |   36.63 |   38.78 |    2.96 |    0.79 |    49.07 |  3278.89 |    29.33 |  33150   |      113 |
| DuPont Pioneer        | P0825AMXT               | AcreMax XTreme  |   35.95 |   23.02 |    8.05 |   37.19 |   34.74 |    2.83 |    0.77 |    52.48 |  3246    |    28.93 |  31750   |      113 |
| Prairie Hybrids       | Prairie 8229            | Conventional    |   34.38 |   24.26 |    8.26 |   38.2  |   32.89 |    3.13 |    0.76 |    51.29 |  3317.67 |    29.99 |  33111.1 |      114 |
| Channel               | 215-83STXRIB            | GENSSRIB        |   35.23 |   24.04 |    7.7  |   39.13 |   33.88 |    3.11 |    0.77 |    50.23 |  3282.22 |    29.82 |  33444.4 |      115 |
| King's Agriseeds Inc. | Masters Choice MCT 6363 | Agrisure 3000GT |   38.31 |   24.34 |    7.36 |   36.11 |   38.41 |    2.84 |    0.78 |    50.21 |  3298.44 |    28.74 |  32166.7 |      113 |

Candidate column headers:
FS 62R43VT2P
0.78
7.5
33_056
39.4
36.3
3.0
3_274
48.6
25.4
112
30.5
GENVT2PRIB
38.6
Growmark FS



headers: FS 62R43VT2P
0.78
7.5
33_056
39.4
36.3
3.0
3_274
48.6
25.4
112
30.5
GENVT2PRIB
38.6
Growmark FS
label: Growmark FS
FS 62R43VT2P
GENVT2PRIB
38.6
25.4
7.5
36.3
39.4
3.0
0.78
48.6
3_274
30.5
33_056
112
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 |
|:--------|--------:|:--------|:---------|:-------------|:--------|:----------------------|--------:|
|         |       1 | U7      | Maccabi  | Green United |         | Maccabi Green United  |  0.375  |
|         |       1 | U7      | Maccabi  | Barcelona    |         | Maccabi Barcelona     |  0.4375 |
|         |       1 | U6      | Maccabi  | Lions 2      |         | Maccabi Lions 2       |  0.375  |
|         |       1 | U6      | Maroubra | Dynamos Bibs | Strong  | Maroubra Dynamos Bibs |  0.4375 |
|         |       1 | U6      | Maccabi  | Sharks 2     |         | Maccabi Sharks 2      |  0.375  |

Candidate column headers:
start
teamname
Age Group
Note
Team
Club
Strength
in use



headers: start
teamname
Age Group
Note
Team
Club
Strength
in use
label: Note
in use
Age Group
Club
Team
Strength
teamname
start
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 |
|:---------------|:----------------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|
| Central Region | Hanover         |     155 |      46 |      36 |       9 |       3 |       0 |       6 |
| Central Region | King William    |      54 |      16 |      13 |       1 |       0 |       0 |       1 |
| Central Region | Richmond County |       9 |       3 |       3 |       0 |       0 |       0 |       0 |
| Eastern Region | Prince George   |      88 |      41 |      30 |      10 |       5 |       0 |       5 |
| Eastern Region | Surry           |       8 |       8 |       8 |       0 |       0 |       0 |       0 |

Candidate column headers:
Locality
Region
Number Referrals
Number Investigated
Number Unfounded
Number Family Assessment
Number Founded
Number Appealed
Number Accepted



headers: Locality
Region
Number Referrals
Number Investigated
Number Unfounded
Number Family Assessment
Number Founded
Number Appealed
Number Accepted
label: Region
Locality
Number Referrals
Number Accepted
Number Family Assessment
Number Investigated
Number Founded
Number Appealed
Number Unfounded
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 |
|:--------|--------:|-----------:|----------:|----------:|
| Lehah   |   40019 | 0.242369   | 0.549658  | 0.307289  |
| Faset   |   40051 | 0.422274   | 0.805206  | 0.382932  |
| Rekid   |   40033 | 0.441793   | 0.930743  | 0.48895   |
| Faset   |   40002 | 0.217632   | 0.327646  | 0.110014  |
| Lehah   |   40018 | 0.00285615 | 0.0842065 | 0.0813503 |

Candidate column headers:
DDate
NName
Start
End
Dur



headers: DDate
NName
Start
End
Dur
label: NName
DDate
Start
End
Dur
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0    |   Col_1 | Col_2   |   Col_3 | Col_4   |
|:---------|--------:|:--------|--------:|:--------|
| Arapahoe |   41934 | 7.0     |      51 | 12.0    |
| Alamosa  |   41929 |         |       2 |         |
| Adams    |   41937 | 7.0     |     165 | 5.0     |
| Adams    |   41925 |         |      53 |         |
| Arapahoe |   41936 | 19.0    |      66 | 6.0     |

Candidate column headers:
UPDATE WITHIN STATE-ISSUED BALLOT
County/Date
County/Date2
NEW REGISTRATION-ISSUED BALLOT
UPDATE WITHIN COUNTY-ISSUED BALLOT



headers: UPDATE WITHIN STATE-ISSUED BALLOT
County/Date
County/Date2
NEW REGISTRATION-ISSUED BALLOT
UPDATE WITHIN COUNTY-ISSUED BALLOT
label: County/Date
County/Date2
NEW REGISTRATION-ISSUED BALLOT
UPDATE WITHIN COUNTY-ISSUED BALLOT
UPDATE WITHIN STATE-ISSUED BALLOT
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                  |
|--------:|:------------|:------------------|:-----------------------|
|     820 | MOO D001.12 | Other Grave Goods | Qoz Nasra; Marangan    |
|     823 | MOO D001.15 | Surface Finds     | Qoz Nasra; Marangan    |
|     855 | MOO D004.02 | Correspondence    |                        |
|     851 | MOO D003.22 | Necklace Finds    | Karim's Garden; Sennar |
|     841 | MOO D003.12 | Grave Pottery     | Karim's Garden; Sennar |

Candidate column headers:
SITE
SUBJECT
REF No
ID1



headers: SITE
SUBJECT
REF No
ID1
label: ID1
REF No
SUBJECT
SITE