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 |
|:--------------|:-------------------------|--------:|--------:|
| CHHATTISGARH  | Dakshin Bastar Dantewada |   7.21  |      29 |
| UTTAR PRADESH | Chandauli                |   5.418 |      46 |
| KARNATAKA     | Raichur                  |  21.921 |       1 |
| JHARKHAND     | Khunti                   |  15.026 |       5 |
| JHARKHAND     | Garhwa                   |   5.42  |      45 |

Candidate column headers:
State Name
District Name
Delta
Rank



headers: State Name
District Name
Delta
Rank
label: State Name
District Name
Delta
Rank
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0     |   Col_1 |   Col_2 |   Col_3 |   Col_4 |
|:----------|--------:|--------:|--------:|--------:|
| September |      10 |      14 |      18 |       3 |
| April     |       7 |      15 |      66 |      99 |
| June      |       6 |      48 |      89 |      85 |
| May       |       7 |      13 |      41 |      21 |
| November  |       2 |      17 |       3 |      53 |

Candidate column headers:
South
West
North
East
Month/Region



headers: South
West
North
East
Month/Region
label: Month/Region
North
East
South
West
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    |
|:--------|:-----------|:--------|:--------|:---------|
| -       | CHE-BS     | MAJ     | UGRD    | BS       |
| 565     | CBUIS-BA   | MAJ     | CBUIS   | BA       |
| 356     | AEL-ADVCRT | MAJ     | ADVCT   | ADVCERPB |
| 441     | BUS-MS     | MAJ     | MS      | MS       |
| 229     | BUSX-BBA   | MAJ     | UGRD    | BBA      |

Candidate column headers:
CUNYfirst Academic Plan
SIMS MAJOR/MINOR CODE
Academic Program
Plan Type
Degree



headers: CUNYfirst Academic Plan
SIMS MAJOR/MINOR CODE
Academic Program
Plan Type
Degree
label: SIMS MAJOR/MINOR CODE
CUNYfirst Academic Plan
Plan Type
Academic Program
Degree
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   |
|----------------:|--------:|--------:|--------:|:--------|:--------|
| 270833601004027 |       1 |       0 |       1 | RURAL   | Yes     |
| 270833601004024 |       7 |       0 |       7 | RURAL   | Yes     |
| 270833601004003 |       0 |       0 |       0 | RURAL   |         |
| 270833601004013 |       0 |       0 |       0 | RURAL   |         |
| 270833601004045 |       0 |       0 |       0 | RURAL   |         |

Candidate column headers:
Total
Included
Census Block ID
Underserved
Unserved
Type



headers: Total
Included
Census Block ID
Underserved
Unserved
Type
label: Census Block ID
Underserved
Unserved
Total
Type
Included
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              |
|--------:|:--------|:----------|-----------:|---------:|:-------------------|:-------------------|
| 8087485 | UBA7    | NM_003335 | 0.00651708 | -3.55391 |                    |                    |
| 7914127 | IFI6    | NM_002038 | 0.0279904  | -4.27081 | 0.4808939999999999 | 1.177836294742701  |
| 8013341 | MFAP4   | NM_002404 | 0.0213033  | -6.792   | 0.901307           | 1.0694252431292868 |
| 7939546 | CD82    | NM_002231 | 0.00326389 | -4.07664 | 0.0702235          | 1.916510211014756  |
| 7937335 | IFITM1  | NM_003641 | 0.0177771  | -3.62439 |                    |                    |

Candidate column headers:
Transcript ID
RefSeq
Fold change  (tumor SSEA-1+ vs. tumor SSEA-1-)
p-value           (tumor SSEA-1+ vs. tumor SSEA-1-)
Gene Symbol
p-value                (tumor SSEA-1+ vs. transformed SSEA-1+)
Fold change           (tumor SSEA-1+ vs. transformed SSEA-1+)



headers: Transcript ID
RefSeq
Fold change  (tumor SSEA-1+ vs. tumor SSEA-1-)
p-value           (tumor SSEA-1+ vs. tumor SSEA-1-)
Gene Symbol
p-value                (tumor SSEA-1+ vs. transformed SSEA-1+)
Fold change           (tumor SSEA-1+ vs. transformed SSEA-1+)
label: Transcript ID
Gene Symbol
RefSeq
p-value           (tumor SSEA-1+ vs. tumor SSEA-1-)
Fold change  (tumor SSEA-1+ vs. tumor SSEA-1-)
p-value                (tumor SSEA-1+ vs. transformed SSEA-1+)
Fold change           (tumor SSEA-1+ vs. transformed SSEA-1+)
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0   |   Col_1 |   Col_2 |   Col_3 |   Col_4 | Col_5   | Col_6   | Col_7   |   Col_8 |   Col_9 |   Col_10 |   Col_11 |   Col_12 |   Col_13 |   Col_14 |    Col_15 |
|:--------|--------:|--------:|--------:|--------:|:--------|:--------|:--------|--------:|--------:|---------:|---------:|---------:|---------:|---------:|----------:|
| user 1  |      10 |      49 |       0 |       0 | LARGE   | B       | in-in   |       1 |       0 |        0 |      363 |      151 |      120 |        0 | 0.010929  |
| user 1  |      31 |      50 |       1 |       0 | LARGE   | B       | in-in   |       1 |       0 |        0 |      288 |      168 |     -165 |       49 | 0.0338798 |
| user 1  |      29 |      50 |       1 |       0 | LARGE   | B       | in-in   |       1 |       0 |        0 |      288 |      168 |      165 |       49 | 0.031694  |
| user 1  |      28 |      50 |       1 |       0 | LARGE   | B       | in-in   |       1 |       0 |        0 |      288 |      168 |      150 |       49 | 0.0306011 |
| user 1  |      20 |      50 |       0 |       0 | LARGE   | B       | in-in   |       1 |       0 |        0 |      288 |      168 |       30 |        0 | 0.0218579 |

Candidate column headers:
angle
shape ID
collision? 
nudge movements?
shape type
y
nudge rotation?
move ID
rotation?
shape size
moves percentage count
user
drag moves
collision ID
x
direction



headers: angle
shape ID
collision? 
nudge movements?
shape type
y
nudge rotation?
move ID
rotation?
shape size
moves percentage count
user
drag moves
collision ID
x
direction
label: user
move ID
shape ID
collision? 
drag moves
shape size
shape type
direction
rotation?
nudge rotation?
nudge movements?
x
y
angle
collision ID
moves percentage count
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 |   Col_1 | Col_2   |   Col_3 | Col_4        |   Col_5 | Col_6         | Col_7   | Col_8   |   Col_9 |   Col_10 | Col_11   | Col_12                     | Col_13            | Col_14   |
|--------:|--------:|:--------|--------:|:-------------|--------:|:--------------|:--------|:--------|--------:|---------:|:---------|:---------------------------|:------------------|:---------|
|   43372 |    1268 | Success |  549197 | PI/000030469 |    4613 | Falkirk       | FK2 9DD | Pass    |   43110 |      774 | Falkirk  | http://www.falkirk.gov.uk/ | fs@falkirk.gov.uk | FHIS     |
|   43372 |    1268 | Success |  549778 | PI/000080584 |       1 | Stenhousemuir | FK5 4BL | Pass    |   43199 |      774 | Falkirk  | http://www.falkirk.gov.uk/ | fs@falkirk.gov.uk | FHIS     |
|   43372 |    1268 | Success |  896117 | PI/000098811 |       1 |               |         | Pass    |   43089 |      774 | Falkirk  | http://www.falkirk.gov.uk/ | fs@falkirk.gov.uk | FHIS     |
|   43372 |    1268 | Success |  730940 | PI/000097902 |    4613 | Allandale     | FK4 2HG | Pass    |   42972 |      774 | Falkirk  | http://www.falkirk.gov.uk/ | fs@falkirk.gov.uk | FHIS     |
|   43372 |    1268 | Success |  548929 | PI/000005648 |       1 | Head Of Muir  | FK6 5JZ | Exempt  |   42945 |      774 | Falkirk  | http://www.falkirk.gov.uk/ | fs@falkirk.gov.uk | FHIS     |

Candidate column headers:
LocalAuthorityWebSite
RatingValue
ItemCount
ReturnCode
FHRSID
LocalAuthorityEmailAddress
BusinessTypeID
RatingDate
LocalAuthorityName
LocalAuthorityBusinessID
LocalAuthorityCode
ExtractDate
PostCode
AddressLine4
SchemeType



headers: LocalAuthorityWebSite
RatingValue
ItemCount
ReturnCode
FHRSID
LocalAuthorityEmailAddress
BusinessTypeID
RatingDate
LocalAuthorityName
LocalAuthorityBusinessID
LocalAuthorityCode
ExtractDate
PostCode
AddressLine4
SchemeType
label: ExtractDate
ItemCount
ReturnCode
FHRSID
LocalAuthorityBusinessID
BusinessTypeID
AddressLine4
PostCode
RatingValue
RatingDate
LocalAuthorityCode
LocalAuthorityName
LocalAuthorityWebSite
LocalAuthorityEmailAddress
SchemeType
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 |
|:---------------------------|------------:|------------:|------------:|
| Current Share Price        |     36.63   |     36.63   |     36.63   |
| Implied Enterprise Value   | 171269      | 192590      | 173735      |
| Implied Market Value       | 175597      | 196917      | 178062      |
| - "b" from linear equation |      0.8861 |      1.7283 |     -1.2463 |
| Implied Share Price        |     41.7965 |     46.8714 |     42.3833 |

Candidate column headers:
Peer Group
S&P
Implied Valuation Of ORCL
Large Cap Tech



headers: Peer Group
S&P
Implied Valuation Of ORCL
Large Cap Tech
label: Implied Valuation Of ORCL
S&P
Large Cap Tech
Peer Group
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 |
|:--------|--------:|--------:|
| H5459   |    1716 |   40210 |
| H1021   |    1931 |   40422 |
| H1021   |    1634 |   40330 |
| F9417   |    1313 |   40360 |
| F9417   |    1481 |   40179 |

Candidate column headers:
DDate
Usage
Meter



headers: DDate
Usage
Meter
label: Meter
Usage
DDate
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     |
|--------:|--------:|--------:|--------:|--------:|:------------|:----------|
|   42548 |     214 | 9405.3  |       1 | 4739.03 | Qd1-1-Ge10  | Quad      |
|   42370 |     251 | 6764.45 |       2 | 3099.85 | St1-4-Ge50  | Sunset    |
|   42965 |      28 | 1230.6  |       3 |  620.06 | Qd2-1-Ge20  | Quad      |
|   42979 |      22 |  438.9  |       2 |  241.45 | Fe1-6-In120 | Frog Kite |
|   43001 |      96 | 1723.2  |       4 |  696.48 | Ee1-3-Ch60  | Eagle     |

Candidate column headers:
ProKey
Product
Cost
Date
Units
Customer Key
Sales



headers: ProKey
Product
Cost
Date
Units
Customer Key
Sales
label: Date
Units
Sales
Customer Key
Cost
ProKey
Product
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 |
|--------:|:-----------|:---------|--------:|--------:|--------:|--------:|
|      10 | Cortina    | Antonio  |      30 |      10 |      10 |      10 |
|      15 | Di Luch    | Dimitri  |      23 |      10 |       8 |       5 |
|      17 | Frigo      | Corinne  |      18 |      10 |       5 |       3 |
|      38 | Tancon     | Federico |      20 |      10 |      10 |       0 |
|      25 | Piacentini | Giorgia  |      22 |       4 |      10 |       8 |

Candidate column headers:
Cognome
Nome
n.
Geodinamica
Voto totale
Sismica/Isostasia
Sismologia



headers: Cognome
Nome
n.
Geodinamica
Voto totale
Sismica/Isostasia
Sismologia
label: n.
Cognome
Nome
Voto totale
Geodinamica
Sismologia
Sismica/Isostasia
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 |
|--------:|--------:|--------:|--------:|
|   36740 |  114673 |   80250 |   23807 |
|   37319 |  179068 |   83923 |   21218 |
|   35053 |  130348 |   80356 |   28095 |
|   37129 |  168784 |   88011 |   29886 |
|   36069 |  118283 |   70861 |   20396 |

Candidate column headers:
Date
Expenses
Cost of Sales
Sales



headers: Date
Expenses
Cost of Sales
Sales
label: Date
Sales
Cost of Sales
Expenses
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 |
|---------:|--------:|--------:|--------:|--------:|--------:|--------:|
| 0.380208 |       2 |       1 |       1 |       1 |       0 |       0 |
| 0.385417 |       2 |       1 |       1 |       1 |       0 |       0 |
| 0.416667 |       0 |       1 |       3 |       1 |       1 |       1 |
| 0.494792 |       2 |       4 |       1 |       1 |       1 |       1 |
| 0.447917 |       1 |       0 |       0 |       0 |       0 |       0 |

Candidate column headers:
White
Harris
Jackson
Anderson
Min:15
Thomas
Martin



headers: White
Harris
Jackson
Anderson
Min:15
Thomas
Martin
label: Min:15
Anderson
Harris
Jackson
Martin
Thomas
White
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 |   Col_1 | Col_2   | Col_3   | Col_4   | Col_5     | Col_6   |   Col_7 | Col_8                     |    Col_9 |   Col_10 |   Col_11 |   Col_12 |   Col_13 |   Col_14 |   Col_15 |   Col_16 |   Col_17 |   Col_18 |
|--------:|--------:|:--------|:--------|:--------|:----------|:--------|--------:|:--------------------------|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
|   20122 |    2122 | CB      | DO      |         | Hoboken   | NJ      |       1 | New York-Newark_ NY-NJ-CT | 18351295 |     3450 |     5319 |  3777761 |        0 |  8694568 |   244784 |    14271 | 37501705 | 40003948 |
|   20126 |    2126 | CB      | DO      |         | Mahwah    | NJ      |       1 | New York-Newark_ NY-NJ-CT | 18351295 |     3450 |     5319 |  4360967 |        0 |  9447524 |   210984 |    15325 | 48382672 | 53344905 |
|   20160 |    2160 | CB      | DO      |         | Paramus   | NJ      |       1 | New York-Newark_ NY-NJ-CT | 18351295 |     3450 |     5319 |   534692 |        0 |   822075 |    26094 |        0 |  4717616 |  4717616 |
|      19 |      19 | CB      | DO      |         | Olympia   | WA      |     195 | Olympia-Lacey_ WA         |   176617 |      106 |     1673 |   191157 |  4813977 |   540214 |    21743 |      659 |   255059 |  2565367 |
|      41 |      41 | AR      | DO      |         | Anchorage | AK      |     149 | Anchorage_ AK             |   251243 |       85 |     2956 |   178350 | 21842570 |  1060672 |    38865 |     1150 | 19932525 | 43306054 |

Candidate column headers:
UZA name
Trips
Density
Wkday Trips
State
Nickname
TOS
City
PM
Area
UZA pop
Fares
UZA
VRH
VRM
Mode
Old ID
Op Costs
ID



headers: UZA name
Trips
Density
Wkday Trips
State
Nickname
TOS
City
PM
Area
UZA pop
Fares
UZA
VRH
VRM
Mode
Old ID
Op Costs
ID
label: ID
Old ID
Mode
TOS
Nickname
City
State
UZA
UZA name
UZA pop
Area
Density
Trips
PM
VRM
VRH
Wkday Trips
Fares
Op Costs
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 |
|--------:|:-----------|:---------|:-------------|--------:|------------:|------------:|
|   41311 | Theon      | Jon Snow | Air Bus A380 |       3 | 3.77843e+06 | 3.77843e+06 |
|   41293 | Tyrion     | Robb     | Air Bus A340 |      10 | 2.68536e+06 | 1.87975e+07 |
|   41296 | Jaimie     | Robb     | Boeing 737   |      14 | 9.08641e+06 | 7.26913e+07 |
|   41306 | Ned Stark  | Bran     | Air Bus A350 |       9 | 4.5622e+06  | 1.82488e+07 |
|   41323 | Khal Drogo | Sansa    | Boeing 737   |      25 | 6.78148e+06 | 5.42519e+07 |

Candidate column headers:
Units Sold
Customer
Total Revenue
Price per unit
Product
Date
Sales person



headers: Units Sold
Customer
Total Revenue
Price per unit
Product
Date
Sales person
label: Date
Customer
Sales person
Product
Units Sold
Price per unit
Total Revenue
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 | Europe  | Country4 | Shirts     |   11243 |  505935 |
|       1 | Asia    | Country6 | Shirts     |   18696 |  841320 |
|       1 | Europe  | Country2 | Acessories |   28913 | 1069781 |
|       1 | Africa  | Country8 | Shoes      |   33916 | 2102792 |
|       1 | Africa  | Country1 | Acessories |   15620 |  577940 |

Candidate column headers:
Product
Sales Revenue
Country
Month
Region
Sales Amount



headers: Product
Sales Revenue
Country
Month
Region
Sales Amount
label: Month
Region
Country
Product
Sales Amount
Sales Revenue
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 |
|:--------|----------:|---------:|---------:|----------:|-----------:|----------:|--------:|
| HO      | 0.297922  | 0.404491 | 0.22252  | 0.0680295 | 0.00703753 | 0.075067  |      14 |
| Defra   | 0.0809524 | 0.152381 | 0.461905 | 0.257143  | 0.047619   | 0.304762  |      12 |
| MoJ     | 0.640179  | 0.16588  | 0.156444 | 0.033772  | 0.00372486 | 0.0374969 |      17 |
| DfE     | 0.0429185 | 0.197425 | 0.397711 | 0.320458  | 0.0414878  | 0.361946  |       9 |
| DH      | 0.0445545 | 0.163366 | 0.351485 | 0.356436  | 0.0841584  | 0.440594  |       6 |

Candidate column headers:
EO
SCS
Dept
Percentage SCS_ 6&7
SEO/HEO
AO/AA
Grades 6 and 7
RANK



headers: EO
SCS
Dept
Percentage SCS_ 6&7
SEO/HEO
AO/AA
Grades 6 and 7
RANK
label: Dept
AO/AA
EO
SEO/HEO
Grades 6 and 7
SCS
Percentage SCS_ 6&7
RANK
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0     |   Col_1 | Col_2               |   Col_3 |
|:----------|--------:|:--------------------|--------:|
| 0073-0080 |  235326 | 2120 L ST NW        |       2 |
| 0031-0837 |  316550 | 2475 VIRGINIA AV NW |       2 |
| 0040-0036 |  450927 | 900 23RD ST         |       2 |
| 0050-0085 |  454961 | 1250 22ND ST NW     |       2 |
| 0024-0113 |  241362 | 2400 N ST NW        |       2 |

Candidate column headers:
Address
UBI
GBA_Liv
WARD



headers: Address
UBI
GBA_Liv
WARD
label: UBI
GBA_Liv
Address
WARD
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0                   |   Col_1 |   Col_2 |   Col_3 |   Col_4 |   Col_5 |   Col_6 |   Col_7 |   Col_8 |   Col_9 |   Col_10 |   Col_11 |
|:------------------------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|
| 2014-02-09 03:40:01.390 |   1e+15 |       0 |     100 |  158696 |      24 |      15 |       0 |       4 |       0 |        3 |        0 |
| 2014-02-09 01:35:01.337 |   1e+15 |       0 |     100 |  151196 |      18 |      11 |       0 |       4 |       0 |        3 |        0 |
| 2014-02-09 01:15:01.843 |   1e+15 |       0 |     100 |  149997 |      42 |      27 |       1 |       4 |       0 |        4 |        0 |
| 2014-02-09 02:45:01.770 |   1e+15 |       0 |     100 |  155397 |      47 |      31 |       1 |       4 |       0 |        4 |        0 |
| 2014-02-09 01:05:01.733 |   1e+15 |       0 |     100 |  149397 |      36 |      22 |       0 |       4 |       0 |        3 |        0 |

Candidate column headers:
LockWaits/sec
DateStamp
Compilations/sec
BufferCacheHit(%)
BatchRequests/sec
PageSplits/sec
ProcessBlocked
Recompilations/sec
UserConnections
CHKPT / sec
PLE
BufferCacheHit



headers: LockWaits/sec
DateStamp
Compilations/sec
BufferCacheHit(%)
BatchRequests/sec
PageSplits/sec
ProcessBlocked
Recompilations/sec
UserConnections
CHKPT / sec
PLE
BufferCacheHit
label: DateStamp
BufferCacheHit
CHKPT / sec
BufferCacheHit(%)
PLE
BatchRequests/sec
Compilations/sec
Recompilations/sec
UserConnections
LockWaits/sec
PageSplits/sec
ProcessBlocked
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 |
|:--------------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|--------:|
| CA-300064-GLA | Yes     | Yes     | Yes     | Yes     | Yes     | Yes     | Yes     | Yes     |   25863 |
| CA-300043-GLA | No      | No      | Yes     | Yes     | Yes     | No      | No      | No      |   30122 |
| CA-300030-GLA | Yes     | No      | No      | No      | No      | No      | No      | No      |   33025 |
| CA-300011-GLA | No      | No      | Yes     | Yes     | No      | No      | No      | No      |   29647 |
| CA-300026-GLA | No      | No      | No      | No      | No      | Yes     | Yes     | No      |   32174 |

Candidate column headers:
Northeastern Newfoundland
Eastern Newfoundland
Western Newfoundland and Labrador
Data Release Date
Southeastern Newfoundland
Southern Newfoundland
Jeanne d'Arc 
Program number
Labrador South
Labrador North



headers: Northeastern Newfoundland
Eastern Newfoundland
Western Newfoundland and Labrador
Data Release Date
Southeastern Newfoundland
Southern Newfoundland
Jeanne d'Arc 
Program number
Labrador South
Labrador North
label: Program number
Eastern Newfoundland
Jeanne d'Arc 
Labrador North
Labrador South
Northeastern Newfoundland
Southeastern Newfoundland
Southern Newfoundland
Western Newfoundland and Labrador
Data Release Date
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 |   Col_1 |   Col_2 |   Col_3 |   Col_4 |   Col_5 |   Col_6 |   Col_7 |
|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|
|    0.5  |   1.117 |   0.996 |   0.922 |   1.117 |   0.928 |   0.909 |   1.119 |
|    1    |   1.038 |   0.876 |   0.688 |   0.991 |   0.802 |   0.732 |   0.919 |
|    3    |   0.795 |   0.496 |   0.117 |   0.599 |   0.423 |   0.282 |   0.328 |
|    0.25 |   1.143 |   1.06  |   1.049 |   1.183 |   0.996 |   1.008 |   1.204 |
|    0    |   1.88  |   1.142 |   1.204 |   1.272 |   1.081 |   1.139 |   1.329 |

Candidate column headers:
F3
F4
F1
FPA
Tiempo (min)
F2
FPB
FPC



headers: F3
F4
F1
FPA
Tiempo (min)
F2
FPB
FPC
label: Tiempo (min)
F1
F2
F3
F4
FPA
FPB
FPC
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   |
|:--------|:--------|--------:|:--------|
| Mgmt    | PEP     |      46 | CCC     |
| Mgmt    | KKK     |      94 | ANZ     |
| Mkt     | IGS     |     100 | ANZ     |
| Sales   | IGS     |     106 | ANZ     |
| Engr    | SWG     |      73 | BBB     |

Candidate column headers:
Rept
Dept
Revn
Div



headers: Rept
Dept
Revn
Div
label: Dept
Rept
Revn
Div
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 |
|:--------|:---------------------|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|---------:|---------:|---------:|
| 240     | CLEVELAND OUTPOST    |    2500 |    1000 |   39846 |   39875 | 1595.97 |  918    |  5605.62 |    41    |        0 |        0 |  4940.65 |    28    |
| 241     | INDEPENDENCE OUTPOST |    5600 |    1000 |   39862 |   39874 | 1631.98 |  701.29 |  9424.54 |    84    |        0 |        0 |  8535.35 |    42.5  |
| 245     | MUNSON OUTPOST       |    2500 |     900 |   39871 |   39867 | 1030.18 |  200    |  4490.18 |   258    |        0 |        0 |     0    |  3918    |
| 237     | WARRENSVILLE OUTPOST |   10000 |    5000 |   39855 |   39873 |  957.57 |  734.91 | 15564.9  |  3204.74 |        0 |        0 |  9969.5  |  8577.5  |
| GEA     | GEAUGA COUNTY GARAGE |    2500 |     500 |   39869 |   39867 | 1054.33 |  800    |  4644.72 | 15315.2  |        0 |        0 | 18705.4  |  1000.25 |

Candidate column headers:
MAX_LEVEL
RE_ORDER
LAST_USED_DT
GAR_NBR
YTD_QTY_DIRECT_USE
YTD_QTY_OHD_USE
YTD_QTY_RECEIVED
YTD_QTY_MISC_USE
QTY_IN_STK
YTD_QTY_TRANSFER_OUT
LAST_PURCH_DT
GAR_NAME
SFY_QTY_IN_STOCK
YTD_QTY_TRANSFER_IN



headers: MAX_LEVEL
RE_ORDER
LAST_USED_DT
GAR_NBR
YTD_QTY_DIRECT_USE
YTD_QTY_OHD_USE
YTD_QTY_RECEIVED
YTD_QTY_MISC_USE
QTY_IN_STK
YTD_QTY_TRANSFER_OUT
LAST_PURCH_DT
GAR_NAME
SFY_QTY_IN_STOCK
YTD_QTY_TRANSFER_IN
label: GAR_NBR
GAR_NAME
MAX_LEVEL
RE_ORDER
LAST_PURCH_DT
LAST_USED_DT
QTY_IN_STK
SFY_QTY_IN_STOCK
YTD_QTY_RECEIVED
YTD_QTY_TRANSFER_IN
YTD_QTY_DIRECT_USE
YTD_QTY_OHD_USE
YTD_QTY_MISC_USE
YTD_QTY_TRANSFER_OUT
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   |
|:-------------|--------:|--------:|:--------|
| dhcfp.nv.gov |     350 |       0 |         |
| dhcfp.nv.gov |     397 |       0 |         |
| dhcfp.nv.gov |     204 |       0 |         |
| dhcfp.nv.gov |     211 |       0 |         |
| dhcfp.nv.gov |     240 |     240 |         |

Candidate column headers:
Num Pages
Site
Fillable
Num Images



headers: Num Pages
Site
Fillable
Num Images
label: Site
Num Pages
Num Images
Fillable
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 |
|:--------|:---------------------------|----------:|----------:|----------:|---------:|---------:|---------:|--------:|
| AF      | AFGHANISTAN ROSHAN MOBILE  | 0.087814  | 0.102145  | 0.0768372 | 1.28345  | 0.655733 | 11.1313  | -0.1525 |
| AM      | ARMENIA NON GEO            | 0.164289  | 0.191101  | 0.143753  | 2.40119  | 1.2268   | 20.8253  |  0.0498 |
| AG      | ANTIGUA AND BARBUDA MOBILE | 0.189779  | 0.220751  | 0.166056  | 2.77373  | 1.41713  | 24.0564  |  0.0237 |
| AO      | ANGOLA FIXED               | 0.0295579 | 0.0343817 | 0.0258631 | 0.432006 | 0.220718 |  3.74676 |  0      |
| AM      | ARMENIA ORANGE MOBILE      | 0.189854  | 0.220838  | 0.166122  | 2.77482  | 1.41769  | 24.0658  |  0.0354 |

Candidate column headers:
Tariff ZAR
Tariff GBP
Tariff USD
Tariff JPY
Destination
Tariff EUR
Tariff CNY
Change %
Country ISO code



headers: Tariff ZAR
Tariff GBP
Tariff USD
Tariff JPY
Destination
Tariff EUR
Tariff CNY
Change %
Country ISO code
label: Country ISO code
Destination
Tariff EUR
Tariff USD
Tariff GBP
Tariff ZAR
Tariff CNY
Tariff JPY
Change %
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   |
|--------:|:--------|:--------|
|       3 | ACLANM  | Mother  |
|      28 | MORENOM |         |
|       2 | ABNERK  |         |
|      30 | OSILIAA |         |
|      36 | REAPORY |         |

Candidate column headers:
ParentsCode
Relationship
StudentID



headers: ParentsCode
Relationship
StudentID
label: ParentsCode
StudentID
Relationship
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   |
|:--------------------------|:--------|:--------|:--------|
| Gain a basic awareness of | Req'd   |         | Req'd   |
| Gain a basic awareness of | Req'd   |         | Req'd   |
| Gain a basic awareness of | Req'd   |         |         |
| Gain a basic awareness of | Req'd   |         | Req'd   |
| Gain an awareness of      | Req'd   |         | Req'd   |

Candidate column headers:
OPEP 2
Skill
Kdge
Level Descriptor



headers: OPEP 2
Skill
Kdge
Level Descriptor
label: Level Descriptor
OPEP 2
Skill
Kdge
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 | BARNES  |   0.224 | .224 40-GR VMTR         |   0.175 |    0.67 |      40 |   22480 |
|      23 | BARNES  |   0.224 | .224 55-GR  TAC RRLP FB |   0.225 |    0.96 |      55 |   22414 |
|      15 | BARNES  |   0.224 | .224 50-GR  SOLID       |   0.235 |    0.78 |      50 |   22402 |
|      40 | BARNES  |   0.243 | .243 85-GR  XLC BT      |   0.401 |    1.08 |      85 |   24352 |
|      18 | BARNES  |   0.224 | .224 50-GR VG           |   0.183 |    0.86 |      50 |   22486 |

Candidate column headers:
BC
Diameter
Manufacturer
Length
CatalogNum
Description
ID
Weight



headers: BC
Diameter
Manufacturer
Length
CatalogNum
Description
ID
Weight
label: ID
Manufacturer
Diameter
Description
BC
Length
Weight
CatalogNum
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   |
|--------:|--------:|:---------|:-------------|:-------------------|:------------------------|:--------|:--------|:--------|:--------|
|       9 |       2 | 0R       |              |                    | RES 0R Imax 2A JUMPER   |         |         |         |         |
|      13 |       1 | BGM11S   | Silicon Labs | BGM11S22F256GA-V2R |                         |         |         |         |         |
|      11 |       1 | 10K      |              |                    | RES 10K 1/16W 1% 100ppm |         |         |         |         |
|       3 |       1 | 10U      |              |                    |                         |         |         | X5R     | NM      |
|       4 |       1 | PCB4303A |              | PCB4303A Rev. A04  |                         |         |         |         |         |

Candidate column headers:
Technology
Manufacturer
Tolerance
Value
Rating
Description
Not Mounted
Quantity
Item
Manufacturers Part Number



headers: Technology
Manufacturer
Tolerance
Value
Rating
Description
Not Mounted
Quantity
Item
Manufacturers Part Number
label: Item
Quantity
Value
Manufacturer
Manufacturers Part Number
Description
Tolerance
Rating
Technology
Not Mounted
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              |
|:-----------|--------:|:--------|--------:|--------:|--------:|-----------:|---------:|:-------------------|
| A2A        |   41675 | LONG    |   0.816 |   41710 |   1.009 |  0.23652   | 2365.2   |                    |
| CAMPARI    |   41683 | LONG    |   6.04  |   41710 |   6.165 |  0.0206954 |  206.954 | Stop reverse 6_02  |
| ENI        |   41680 | LONG    |  16.54  |   41710 |  17.45  |  0.0550181 |  550.181 |                    |
| PIRELLI    |   41653 | LONG    |  12.57  |   41710 |  12.33  | -0.0190931 | -190.931 | Stop reverse 12_30 |
| UNIPOL SAI |   41661 | LONG    |   2.28  |   41661 |   2.618 |  0.148246  | 1482.46  |                    |

Candidate column headers:
TITOLO
Profit/ loss
DATA OPEN
DATE
GAIN/LOSS
PREZZO
CLOSE
Segnali operativi
LONG/SHORT



headers: TITOLO
Profit/ loss
DATA OPEN
DATE
GAIN/LOSS
PREZZO
CLOSE
Segnali operativi
LONG/SHORT
label: TITOLO
DATA OPEN
LONG/SHORT
PREZZO
DATE
CLOSE
GAIN/LOSS
Profit/ loss
Segnali operativi
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 |
|:--------|:--------|:---------------------|:--------|:-----------|:--------|--------:|--------:|:--------------|--------:|
| Nikon   | Red     | 7/10/2012 5:22:57 PM |         | 2083092.0  | 26299   |   96.95 |  106.95 | DigitalCamera |     100 |
| Nikon   | Red     | 7/3/2012 1:48:29 AM  |         | 33477972.0 | 26299   |  109    |  119    | DigitalCamera |     100 |
| Nikon   | Red     | 7/10/2012 5:23:00 PM |         | 33477972.0 | 26299   |   99    |  109    | DigitalCamera |     100 |
| Nikon   | Red     | 7/14/2012 2:42:41 PM |         |            | 26299   |   85.99 |   84.99 | DigitalCamera |      74 |
| Nikon   | Red     | 7/6/2012 4:31:45 PM  |         | 6038.0     | 26299-1 |  106.95 |  119.95 | DigitalCamera |       0 |

Candidate column headers:
NewPrice
Rating
Model
OldPrice
Brand
Color
InStockQuantity
MarketplaceRetailerId
ProductType
DateTimeRecorded



headers: NewPrice
Rating
Model
OldPrice
Brand
Color
InStockQuantity
MarketplaceRetailerId
ProductType
DateTimeRecorded
label: Brand
Color
DateTimeRecorded
InStockQuantity
MarketplaceRetailerId
Model
NewPrice
OldPrice
ProductType
Rating
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   |
|:--------------------|--------:|:-------------|--------:|:--------|
| Arceneaux_ Terry    |   40279 | The Falls    |     350 | Place   |
| Block_ Michael      |   40370 | Quail Valley |      17 | Place   |
| Arredondo_ Humberto |   40237 | Bear Creek   |     155 | Place   |
| Arredondo_ Humberto |   40342 | Wilderness   |      25 | Place   |
| Barnum_ Ken         |   40209 | Longwood     |     215 | Place   |

Candidate column headers:
Type
Name
POY
Date
Course



headers: Type
Name
POY
Date
Course
label: Name
Date
Course
POY
Type