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   | Col_8     | Col_9      | Col_10     |
|--------:|:-------------------|:------------------|:---------|:--------------|:--------------|:---------|:--------|:----------|:-----------|:-----------|
|   42789 | 1Z6722580392799199 | NEWCO ENTERPRISES | 655825.0 | PO 4619274865 | PO 4619274865 | 655825.0 |         | 26.00 LBS | UPS Ground | In Transit |
|   42789 | 1Z20214X0309281285 | AUTOTEX           |          |               |               |          |         | 17.00 LBS | UPS Ground | In Transit |
|   42789 | 1Z20214X0309281285 | AUTOTEX           |          |               |               |          |         | 17.00 LBS | UPS Ground | In Transit |
|   42789 | 1Z6722580392799199 | NEWCO ENTERPRISES | 655825.0 | PO 4619274865 | PO 4619274865 | 655825.0 |         | 26.00 LBS | UPS Ground | In Transit |
|   42789 | 1Z20214X0309281285 | AUTOTEX           |          |               |               |          |         | 17.00 LBS | UPS Ground | In Transit |

Candidate column headers:
Shipment Reference No. 2
Package Reference No. 1
Scheduled Delivery
Shipment Reference No. 1
Weight
Status
Service
Shipper Name
Tracking Number
Package Reference No. 2
Ship To Attention



headers: Shipment Reference No. 2
Package Reference No. 1
Scheduled Delivery
Shipment Reference No. 1
Weight
Status
Service
Shipper Name
Tracking Number
Package Reference No. 2
Ship To Attention
label: Scheduled Delivery
Tracking Number
Shipper Name
Package Reference No. 1
Package Reference No. 2
Shipment Reference No. 2
Shipment Reference No. 1
Ship To Attention
Weight
Service
Status
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 |
|:---------|--------:|:-------------|--------:|
| Summer 1 |    2015 | 51-64        |       8 |
| Fall     |    2014 | 51-64        |      35 |
| Fall     |    2014 | 31-35        |     248 |
| Spring   |    2014 | 16 and under |     476 |
| Spring   |    2014 | 65 and older |       1 |

Candidate column headers:
Year
Total
Age Group
Semester



headers: Year
Total
Age Group
Semester
label: Semester
Year
Age Group
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            | Col_6   | Col_7    |   Col_8 |   Col_9 |   Col_10 |   Col_11 |   Col_12 |   Col_13 |   Col_14 | Col_15   | Col_16   |   Col_17 |
|--------:|:-------------------------------|--------:|:--------|:-------------|:-----------------|:--------|:---------|--------:|--------:|---------:|---------:|---------:|---------:|---------:|:---------|:---------|---------:|
|     339 | Jefferson County North USD 339 |       1 | 2A      | Pony Express | Northeast Kansas | 2006-07 | 251-500  |     494 |   30350 |     3840 |   0.046  |        0 |        6 |        2 |          |          |        0 |
|     340 | Jefferson West USD 340         |       1 | 4A      | Pony Express | Big Seven        | 2002-03 | 501-1000 |     958 |   29914 |     2900 |   0.035  |        0 |        4 |        1 |          | 20.0     |        1 |
|     340 | Jefferson West USD 340         |       1 | 4A      | Pony Express | Big Seven        | 2016-17 | 501-1000 |     822 |   35710 |    10383 |   0.02   |        0 |        2 |        2 |          |          |        0 |
|     340 | Jefferson West USD 340         |       1 | 4A      | Pony Express | Big Seven        | 2015-16 | 501-1000 |     822 |   35410 |     7600 |   0.02   |        0 |        2 |        2 |          |          |        0 |
|     339 | Jefferson County North USD 339 |       1 | 2A      | Pony Express | Northeast Kansas | 2010-11 | 251-500  |     474 |   33720 |     3900 |   0.0001 |        0 |        5 |        2 |          |          |        0 |

Candidate column headers:
02 Base Salary
Student FTE
08 Negotiation Cost
04 Packet Percent Change
KNEA Uniserv
High School League
KSHSAA Class
06 Negotiaiton Session
School Year
KASB Region
03 Board Paid Fringe
09 Negotiation Time
05 Bonus
USD
07 Negotiation Length
Dist Size
USDName
10a Provisions Enroll



headers: 02 Base Salary
Student FTE
08 Negotiation Cost
04 Packet Percent Change
KNEA Uniserv
High School League
KSHSAA Class
06 Negotiaiton Session
School Year
KASB Region
03 Board Paid Fringe
09 Negotiation Time
05 Bonus
USD
07 Negotiation Length
Dist Size
USDName
10a Provisions Enroll
label: USD
USDName
KASB Region
KSHSAA Class
KNEA Uniserv
High School League
School Year
Dist Size
Student FTE
02 Base Salary
03 Board Paid Fringe
04 Packet Percent Change
05 Bonus
06 Negotiaiton Session
07 Negotiation Length
08 Negotiation Cost
09 Negotiation Time
10a Provisions Enroll
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   |
|:--------|:-----------|:--------|
| ANTH    | ANTH       | False   |
| GIAR    | GIAR       | False   |
| ANTH    | AGGR       | True    |
| DIPH    | DIPH       | False   |
| AH1N1   | AH1N1HAGGR | True    |

Candidate column headers:
Aggregated reporting
Allowed record type for reporting
Subject



headers: Aggregated reporting
Allowed record type for reporting
Subject
label: Subject
Allowed record type for reporting
Aggregated reporting
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 | Brown    | Carolyn | 0.0801968 |    2005 | Walker female |
|       5 | Lynch    | Vinny   | 0.0896759 |    2008 | Walker male   |
|       4 | Lynch    | Vinny   | 0.0907176 |    2007 | Walker male   |
|       6 | Hempsall | Mark    | 0.090625  |    2008 | Walker male   |
|       1 | George   | Michael | 0.0818634 |    2006 | Walker male   |

Candidate column headers:
Man/Woman
Christian Name
Time
Position
Surname
Year



headers: Man/Woman
Christian Name
Time
Position
Surname
Year
label: Position
Surname
Christian Name
Time
Year
Man/Woman
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                      |
|:--------|--------:|--------:|:---------------------------|
| Alachua |   43281 |       1 | BMW 7-Series Plug in       |
| Alachua |   43281 |       1 | Volkswagen e-Golf          |
| Alachua |   43281 |       1 | Porsche Cayenne S E-Hybrid |
| Alachua |   43281 |       1 | Chevrolet Bolt EV          |
| Bay     |   43281 |       1 | Porsche Cayenne S E-Hybrid |

Candidate column headers:
DMV ID
Vehicle Name
Registration Valid Date
County



headers: DMV ID
Vehicle Name
Registration Valid Date
County
label: County
Registration Valid Date
DMV ID
Vehicle Name
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0                         | Col_1   | Col_2   | Col_3   |
|:------------------------------|:--------|:--------|:--------|
| Salaries                      | 800.0   | 1200.0  | 1200.0  |
| Website Development & Hosting |         |         | 1300.0  |
| Radio Advertising             | 2000.0  |         |         |
| Event Attendance              | 200.0   | 300.0   | 175.0   |
| Print Media                   | 700.0   | 600.0   | 2045.0  |

Candidate column headers:
Q1
Q2
Expense / Cost
Q4



headers: Q1
Q2
Expense / Cost
Q4
label: Expense / Cost
Q1
Q2
Q4
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0                    |   Col_1 | Col_2         |   Col_3 |   Col_4 |   Col_5 |   Col_6 |   Col_7 |   Col_8 |   Col_9 |   Col_10 |   Col_11 |   Col_12 |   Col_13 |   Col_14 |   Col_15 |   Col_16 |   Col_17 |   Col_18 |   Col_19 |
|:-------------------------|--------:|:--------------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
| Region I - Ilocos Region |    2013 | Urdaneta City |    1358 |    1285 |    2643 |    1628 |    1383 |    3011 |    1677 |     1478 |     3155 |     1759 |     1478 |     3237 |     1635 |     1466 |     3101 |     1558 |     1466 |
| Region I - Ilocos Region |    2011 | Candon City   |     499 |     451 |     950 |     679 |     592 |    1271 |     571 |      555 |     1126 |      573 |      464 |     1037 |      542 |      472 |     1014 |      530 |      491 |
| Region I - Ilocos Region |    2012 | Laoag City    |     773 |     717 |    1490 |    1095 |    1001 |    2096 |    1069 |     1019 |     2088 |     1022 |      922 |     1944 |      928 |      903 |     1831 |      873 |      815 |
| Region I - Ilocos Region |    2014 | La Union      |    6035 |    5576 |   11611 |    6911 |    6218 |   13129 |    6779 |     5930 |    12709 |     6973 |     6025 |    12998 |     7120 |     6295 |    13415 |     6736 |     6193 |
| Region I - Ilocos Region |    2013 | Ilocos Norte  |    4839 |    4459 |    9298 |    5124 |    4380 |    9504 |    5401 |     4602 |    10003 |     5099 |     4745 |     9844 |     4936 |     4358 |     9294 |     4609 |     4327 |

Candidate column headers:
Grade 4 Male
Kinder Total
Grade 3 Total
Grade 3 Male
Grade 4 Female
Grade 4 Total
Grade 1 Total
Grade 3 Female
Grade 5 Male
Kinder Female
Year
Region
Grade 1 Female
Grade 1 Male
Grade 5 Female
Grade 2 Female
Grade 2 Total
Region/Division
Kinder Male
Grade 2 Male



headers: Grade 4 Male
Kinder Total
Grade 3 Total
Grade 3 Male
Grade 4 Female
Grade 4 Total
Grade 1 Total
Grade 3 Female
Grade 5 Male
Kinder Female
Year
Region
Grade 1 Female
Grade 1 Male
Grade 5 Female
Grade 2 Female
Grade 2 Total
Region/Division
Kinder Male
Grade 2 Male
label: Region
Year
Region/Division
Kinder Male
Kinder Female
Kinder Total
Grade 1 Male
Grade 1 Female
Grade 1 Total
Grade 2 Male
Grade 2 Female
Grade 2 Total
Grade 3 Male
Grade 3 Female
Grade 3 Total
Grade 4 Male
Grade 4 Female
Grade 4 Total
Grade 5 Male
Grade 5 Female
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       |
|:--------------|--------:|:--------|--------:|:------------|
| 0102210000 10 |   40909 | NL      |       6 | -           |
| 0102219000 80 |   40909 | NL      |       8 | - - -       |
| 0102291020 80 |   40909 | NL      |      10 | - - - - -   |
| 0102292930 80 |   40909 | NL      |      10 | - - - - - - |
| 0102291000 10 |   41275 | NL      |       8 | - - -       |

Candidate column headers:
HIER_POS
LANG_COD
SUBSTR('---------------'_1_2*NOM_ITEM_IND.QUANT_IND)
DAT_START
NOM_ITEM.GOODS_NOM_ITEM_ID||''||NOM_ITEM.PROD_LIN



headers: HIER_POS
LANG_COD
SUBSTR('---------------'_1_2*NOM_ITEM_IND.QUANT_IND)
DAT_START
NOM_ITEM.GOODS_NOM_ITEM_ID||''||NOM_ITEM.PROD_LIN
label: NOM_ITEM.GOODS_NOM_ITEM_ID||''||NOM_ITEM.PROD_LIN
DAT_START
LANG_COD
HIER_POS
SUBSTR('---------------'_1_2*NOM_ITEM_IND.QUANT_IND)
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             |
|:---------|:--------|:--------|:------------------|:-------------------|:-------------------|:--------|:--------|:--------|:--------|:---------|:---------|:---------|:---------|:---------|:---------|:-------------------|
|          | 567     | 2100.3  |                   |                    | 567                |         |         |         |         |          |          |          |          |          |          | 3234.3             |
| 11144.98 |         |         |                   |                    |                    |         |         |         |         |          |          |          |          |          |          |                    |
|          |         |         |                   | 1175               | 500                |         |         |         |         |          |          |          |          |          |          | 1675.0             |
|          |         |         |                   |                    |                    |         |         |         |         | 641.9    |          |          |          |          |          | 641.9              |
| 354.08   | 628.73  | 2721.42 | 6423.150000000001 | 8097.3899999999994 | 4436.6000000000004 |         |         |         |         | 641.9    | 7277.5   | 1437.9   | 2402.5   | 5461.0   | 550.0    | 40432.170000000006 |

Candidate column headers:
May
April
December
March3
August
November
May5
June
January
October
March
April4
September
Total
February2
July
June6



headers: May
April
December
March3
August
November
May5
June
January
October
March
April4
September
Total
February2
July
June6
label: July
August
September
October
November
December
March
April
May
June
January
February2
March3
April4
May5
June6
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 |   Col_6 |   Col_7 |   Col_8 |   Col_9 | Col_10             |
|--------:|--------:|:----------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|:-------------------|
|    3007 |      89 | Govar     | 38195   | 38195   |       0 |       1 |       0 |      52 |      24 | 1.3611111111094942 |
|    2997 |      82 | Anonymous | 38176.9 | 38176.9 |       0 |       1 |       0 |     203 |      14 |                    |
|    3001 |      83 | Anonymous | 38179.2 | 38179.2 |       0 |       1 |       0 |      70 |      18 | 0.03125            |
|    3030 |     105 | Chandoo   | 38223.8 | 38223.8 |       0 |       1 |       0 |     161 |      47 | 0.930555555562023  |
|    3026 |     105 | Chandoo   | 38222.4 | 38222.4 |       0 |       1 |       0 |     111 |      43 | 0.0402777777781011 |

Candidate column headers:
comment_date
comment_date_gmt
comment_ID
Order
comment_approved
Previous Comment DTS
comment_parent
@ Reply?
Length
comment_author
comment_post_ID



headers: comment_date
comment_date_gmt
comment_ID
Order
comment_approved
Previous Comment DTS
comment_parent
@ Reply?
Length
comment_author
comment_post_ID
label: comment_ID
comment_post_ID
comment_author
comment_date
comment_date_gmt
@ Reply?
comment_approved
comment_parent
Length
Order
Previous Comment DTS
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 |
|:--------|:------------|:--------|--------:|---------:|
| USA     | New York    | East    |    2649 |  9271.5  |
| USA     | New York    | East    |    7803 | 27310.5  |
| Canada  | Nova Scotia | East    |    2431 |  6150.43 |
| USA     | New York    | East    |     812 |  2842    |
| Canada  | Nova Scotia | East    |    5133 | 12986.5  |

Candidate column headers:
Region
Units Sold
State/Province
Country
Revenue



headers: Region
Units Sold
State/Province
Country
Revenue
label: Country
State/Province
Region
Units Sold
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 |   Col_8 |   Col_9 |   Col_10 |
|--------:|--------:|:--------|:---------------|--------:|:------------|--------:|--------:|--------:|--------:|---------:|
|     102 |   18    |         | 30 PARKVIEW DR |     129 | Ranch       |    1982 |    1692 |   0.574 |  250900 |   413500 |
|     102 |   37    |         | 70 PARKVIEW DR |     130 | Bi Level    |    1979 |    2400 |   0.574 |  231300 |   369300 |
|     101 |    3.08 |         | 25 REGER RD    |     129 | Colonial    |    2006 |    3214 |   0.932 |  340900 |   604800 |
|     102 |   38    |         | 72 PARKVIEW DR |     130 | Split Level |    1979 |    2134 |   0.574 |  229300 |   370100 |
|     101 |    3.1  |         | 21 REGER RD    |     129 | Colonial    |    2005 |    3188 |   0.924 |  386500 |   575600 |

Candidate column headers:
Proposed 2019 Assessment
2018 Assessment
NBHD
Block
Style
Location
Qual
Year Built
Livable Area
Lot Size (AC)
Lot



headers: Proposed 2019 Assessment
2018 Assessment
NBHD
Block
Style
Location
Qual
Year Built
Livable Area
Lot Size (AC)
Lot
label: Block
Lot
Qual
Location
NBHD
Style
Year Built
Livable Area
Lot Size (AC)
2018 Assessment
Proposed 2019 Assessment
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 |
|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|
|     100 |    0.16 |    0.1  |   30    |    3.4  |    0.04 |    4.12 |   62.18 |
|     100 |    0.14 |    0.23 |   38.91 |    4.71 |    0.04 |    6.12 |   49.85 |
|     100 |    0.2  |    0.09 |   93.81 |    4.48 |    0    |    0.83 |    0.58 |
|     100 |    0.38 |    0.11 |   61.29 |    3.53 |    0.08 |    3.29 |   31.31 |
|     100 |    0.11 |    0.11 |   70.52 |    1.25 |    0    |    1.63 |   26.38 |

Candidate column headers:
Hispanic/Latino (%)
Black or African American (%)
Native Hawaiian or Other Pacific Islander (%)
All Race/Ethnicities2 (%)
White (%)
American Indian or Alaska Native (%)
Two or more races (%)
Asian (%)



headers: Hispanic/Latino (%)
Black or African American (%)
Native Hawaiian or Other Pacific Islander (%)
All Race/Ethnicities2 (%)
White (%)
American Indian or Alaska Native (%)
Two or more races (%)
Asian (%)
label: All Race/Ethnicities2 (%)
American Indian or Alaska Native (%)
Asian (%)
Black or African American (%)
Hispanic/Latino (%)
Native Hawaiian or Other Pacific Islander (%)
Two or more races (%)
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 |
|--------:|:--------|--------:|--------:|--------:|--------:|:---------|---------:|----------:|--------:|---------:|---------:|----------:|---------:|-----------:|---------:|
|    2008 | AHEALTH |   39813 | -196668 | 1302.65 | 17713.3 | 332.315  | -46161.2 |  2321.41  |       0 |    39813 |   259240 | -1015.23  |  19019.5 |  -3911.13  |  14776.1 |
|    2010 | AHEALTH |   40543 | -230208 | 1370.5  | 29490   | 5398.494 | -55391.1 | 15957.3   |       0 |    40543 |   313718 |  -353.395 |  45093.9 |  -6753.88  |  32941.6 |
|    2005 | AHEALTH |   38717 | -142070 | 5662.23 | 19780.1 |          | -33996.9 |   503.668 |       0 |    38717 |   190184 |  -612.106 |  19671.6 |  -3886.84  |  15784.8 |
|    2013 | AHEALTH |   41639 | -312888 | 2528    | 41436   | 101.0    | -66655   |  -778     |       0 |    41639 |   418451 |   -43     |  40615   | -10492     |  31875   |
|    2007 | AHEALTH |   39447 | -179891 | 4209.37 | 18788.5 |          | -41467.5 |  1238.23  |       0 |    39447 |   235938 |  -621.37  |  19405.3 |   -172.279 |  19233   |

Candidate column headers:
Financial Year
Interest Expense
Year End Date
Stock Code
Other Operating Income
Operating Income
Quarter End Date
Total Revenue
NET PROFIT/LOSS TO SHAREHOLDERS
Operating Expenses
PROFIT/LOSS BEFORE TAX
Income Tax Expense
Cumulative Period
Share of profits from associates/JCE
Non-controlling interests
Cost of Goods Sold



headers: Financial Year
Interest Expense
Year End Date
Stock Code
Other Operating Income
Operating Income
Quarter End Date
Total Revenue
NET PROFIT/LOSS TO SHAREHOLDERS
Operating Expenses
PROFIT/LOSS BEFORE TAX
Income Tax Expense
Cumulative Period
Share of profits from associates/JCE
Non-controlling interests
Cost of Goods Sold
label: Financial Year
Stock Code
Year End Date
Cost of Goods Sold
Other Operating Income
Operating Income
Non-controlling interests
Operating Expenses
Share of profits from associates/JCE
Cumulative Period
Quarter End Date
Total Revenue
Interest Expense
PROFIT/LOSS BEFORE TAX
Income Tax Expense
NET PROFIT/LOSS TO SHAREHOLDERS
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     |
|:----------|:------------|--------:|--------:|--------:|--------:|--------:|--------:|:----------|
| 1ST GRADE | Classroom   |     800 |       1 |     800 |      10 |      10 |      10 | Cyan      |
| ALCOVE    | Circulation |     100 |       1 |     100 |      10 |      10 |      10 | Yellow    |
| 6TH GRADE | Classroom   |     850 |       1 |     850 |      10 |      10 |      10 | Cyan      |
| COATS     | Facility    |     120 |       1 |     120 |      10 |      10 |      10 | LightBlue |
| COATS     | Facility    |     100 |       1 |     100 |      10 |      10 |      10 | LightBlue |

Candidate column headers:
1 Department
3 Quantity
7 Width
0 Name
6 Length
2 Program Area
5 Height
4 Total Area
8 Color



headers: 1 Department
3 Quantity
7 Width
0 Name
6 Length
2 Program Area
5 Height
4 Total Area
8 Color
label: 0 Name
1 Department
2 Program Area
3 Quantity
4 Total Area
5 Height
6 Length
7 Width
8 Color
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0              |   Col_1 |   Col_2 |
|:-------------------|--------:|--------:|
| GB_GCA_900116905.1 |   98.22 |    1521 |
| GB_GCA_001769655.1 |   86.09 |    1531 |
| RS_GCF_000696115.1 |   97.76 |    1788 |
| GB_GCA_001950395.1 |   89.82 |    1513 |
| GB_GCA_001567165.1 |   90.78 |    1529 |

Candidate column headers:
Genome accession
Alignment length
Percent identity of BLAST match 



headers: Genome accession
Alignment length
Percent identity of BLAST match 
label: Genome accession
Percent identity of BLAST match 
Alignment length
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 |
|:----------------|--------:|--------:|--------:|--------:|--------:|--------:|
| LP ZE           |    3000 |      93 |      71 |      95 |      92 |     102 |
| Dim to Warm     |    2000 |      96 |      84 |      91 |      90 |     105 |
| Xicato Artist   |    3000 |      98 |      98 |      98 |      96 |     103 |
| Xicato Standard |    2700 |      83 |      16 |      76 |      78 |      99 |
| Xicato Standard |    3500 |      81 |      16 |      75 |      76 |     101 |

Candidate column headers:
TM-30-15 Rf
TM-30-15 Rg
CCT
CIE Ra
TM-30-15 Rf_ skin
LED Module
CIE R9



headers: TM-30-15 Rf
TM-30-15 Rg
CCT
CIE Ra
TM-30-15 Rf_ skin
LED Module
CIE R9
label: LED Module
CCT
CIE Ra
CIE R9
TM-30-15 Rf_ skin
TM-30-15 Rf
TM-30-15 Rg
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               |
|:------------|:-------------------------------|:--------------|:----------------------|:--------------------|
| 2016U LAC04 | Liberal Arts and Communication | Communication |                       | Course Revision     |
| 2016U LAC03 | Liberal Arts and Communication | Communication |                       | Bulletin Change     |
| 2016U LAC10 | Liberal Arts and Communication | Music         | Fundamentals of Music | New Course Proposal |
| 2016U LAC07 | Liberal Arts and Communication | Theatre       | Performance Practicum | New Course Proposal |
| 2016U NHP25 | Nursing and Health Professions | Nursing       |                       | Bulletin Change     |

Candidate column headers:
Department
Proposal Type
College
Code #
Course Title



headers: Department
Proposal Type
College
Code #
Course Title
label: Code #
College
Department
Course Title
Proposal 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   | Col_3   | Col_4     | Col_5   |
|--------:|:---------|:--------|:--------|:----------|:--------|
|   40323 | Robert K | Spieler | j       | 2010/2011 | Sommer  |
|   40323 | Thomas   | Spieler |         | 2010/2011 | Sommer  |
|   40309 | Konrad   | Spieler | j       | 2010/2011 | Sommer  |
|   40309 | Helmut   | Spieler | j       | 2010/2011 | Sommer  |
|   40323 | Helmut   | Spieler | j       | 2010/2011 | Sommer  |

Candidate column headers:
J/N
Spieltag
Spieler
Saison
Art
Jahr



headers: J/N
Spieltag
Spieler
Saison
Art
Jahr
label: Spieltag
Spieler
Art
J/N
Jahr
Saison
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                   |
|:--------|:--------|:--------|:-----------------|:-----------|:--------|:------------------------|
| 200m    | FR      | 25-29   | NICHOLAS GRINTER | 14.10.2017 | 2.08.57 | NSW State Championships |
| 100m    | BU      | 18-24   | KADE BURGESS     | 10.04.2015 | 1:03.06 | Nationals_ Hobart       |
| 1500m   | FR      | 18-24   |                  |            |         |                         |
| 100m    | FR      | 18-24   | KADE BURGESS     | 11.04.2015 | 58.50   | Nationals_ Hobart       |
| 100m    | FR      | 25-29   | NICHOLAS GRINTER | 14.10.2017 | 58.53   | NSW State Championships |

Candidate column headers:
Age Gp
Name
Time
Meet
Stroke
Distance
Date



headers: Age Gp
Name
Time
Meet
Stroke
Distance
Date
label: Distance
Stroke
Age Gp
Name
Date
Time
Meet
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    |
|:------------|:---------------|--------:|:---------------------|--------:|--------:|:---------|
| Z18-0178    |                | 3231101 | 1337 EASTGATE AVE NE |   43283 |       0 | ISSUED   |
| CMR18-0196  | WITH TRADES    | 1460101 | 2102 GRANDIN RD SW   |   43284 |   10000 | APPROVED |
| ELEV18-0399 |                | 1012103 | 220 CHURCH AVE SW    |   43283 |       0 | ISSUED   |
| CMR18-0200  | WITHOUT TRADES | 1020426 | 121 ELM AVE SW       |   43286 |    4133 | REVIEW   |
| RADD18-0027 |                | 1530515 | 2307 LINCOLN AVE SW  |   43283 |   11000 | REVIEW   |

Candidate column headers:
Permit Number
Declared Value
Site Address
Applied Date
Tax Map Number
Status
Permit Subtype



headers: Permit Number
Declared Value
Site Address
Applied Date
Tax Map Number
Status
Permit Subtype
label: Permit Number
Permit Subtype
Tax Map Number
Site Address
Applied Date
Declared Value
Status
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    |
|:--------|:--------|:---------------|:---------|------------:|:---------|
|         |         |                | U-238    | 5.44063e-06 | pCi/cu m |
|         |         | GFPC: SR-89/90 | Sr-89/90 | 4.56596     | pCi/sq m |
|         |         | LSC: TC-99     | Tc-99    | 0.00258748  | pCi/mL   |
|         |         |                | U-238    | 3.60327e-05 | pCi/mL   |
|         |         | HPGE: I-129    | I-129    | 0.00155646  | pCi/cu m |

Candidate column headers:
METHOD
UNITS
TYPE_OF_MEDIA
ANALYTE
MATERIAL_TYPE
CY2013 AVERAGE MDC



headers: METHOD
UNITS
TYPE_OF_MEDIA
ANALYTE
MATERIAL_TYPE
CY2013 AVERAGE MDC
label: MATERIAL_TYPE
TYPE_OF_MEDIA
METHOD
ANALYTE
CY2013 AVERAGE MDC
UNITS
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 |
|:-----------|--------:|:--------|--------:|
| Incomplete |   39859 | CatA    |       1 |
| Complete   |   39985 | CatB    |       0 |
| Incomplete |   40046 | CatC    |       1 |
| Complete   |   39985 | CatA    |       0 |
| Evaluate   |   39918 | CatC    |       1 |

Candidate column headers:
Status
Value
DDate
Cat



headers: Status
Value
DDate
Cat
label: Status
DDate
Cat
Value
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 |   Col_1 |    Col_2 |   Col_3 |   Col_4 |
|--------:|--------:|---------:|--------:|--------:|
|       2 |   41016 | 0.516655 | 41016.5 |       1 |
|       3 |   41017 | 0.581238 | 41017.6 |       4 |
|       1 |   41016 | 0.411794 | 41016.4 |       9 |
|       3 |   41016 | 0.574988 | 41016.6 |       1 |
|       2 |   41016 | 0.515266 | 41016.5 |       8 |

Candidate column headers:
Colonne1
sign_time
mark
Colonne2
emp_id



headers: Colonne1
sign_time
mark
Colonne2
emp_id
label: mark
Colonne2
Colonne1
sign_time
emp_id
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 |
|--------:|--------:|--------:|
|   42373 |      34 |       0 |
|   42375 |     280 |       0 |
|   42383 |       7 |       1 |
|   42371 |     327 |       0 |
|   42380 |     310 |       1 |

Candidate column headers:
Date
3 ou 7
Nombre



headers: Date
3 ou 7
Nombre
label: Date
Nombre
3 ou 7
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0                |   Col_1 |     Col_2 |
|:---------------------|--------:|----------:|
| artikels kranten     |       0 | 0         |
| boeken               |     117 | 0.154354  |
| statistieken         |      13 | 0.0171504 |
| eindwerken           |      45 | 0.0593668 |
| onderzoeksliteratuur |     539 | 0.711082  |

Candidate column headers:
soort bron
procent
aantal



headers: soort bron
procent
aantal
label: soort bron
aantal
procent
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    |
|:--------------------|:-----------------|:---------|
| M.H. Naseem Mushtaq | Proprietor       | Karachi  |
| Asif Farooq         | Chief Executive  | Lahore   |
| Aurangzeb           | Director         | Karachi  |
| Zubair Aziz         | Proprietor       | Peshawar |
| M. Rmzan Ansari     | Managing Partner | Sialkot  |

Candidate column headers:
City
Full Name
Designation



headers: City
Full Name
Designation
label: Full Name
Designation
City
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 |
|--------:|--------:|------------:|--------:|----------:|--------:|
|      37 |       8 | 3.70008e+09 |      10 | 127346    |  138994 |
|      32 |       8 | 3.20008e+09 |      10 | 200058    |  155881 |
|      36 |       5 | 3.60005e+09 |      10 |    615.64 |    1319 |
|      40 |       9 | 4.00009e+09 |      10 |  19141.4  |   17500 |
|      31 |       7 | 3.10007e+09 |      10 |  15231.5  |   30000 |

Candidate column headers:
Vs1BTD
Vs1Grp
Vs1Svt
Vs1Mvt
Vs1Vte
Vs1ATD



headers: Vs1BTD
Vs1Grp
Vs1Svt
Vs1Mvt
Vs1Vte
Vs1ATD
label: Vs1Mvt
Vs1Svt
Vs1Vte
Vs1Grp
Vs1ATD
Vs1BTD
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 |
|--------:|--------:|--------:|--------:|--------:|------------:|--------:|
|   42632 |  115.19 |  116.18 |  113.25 |  113.58 | 4.7023e+07  |  113.58 |
|   42612 |  105.8  |  106.5  |  105.5  |  106    | 2.48639e+07 |  106    |
|   42608 |  107.41 |  107.95 |  106.31 |  106.94 | 2.77663e+07 |  106.94 |
|   42605 |  108.59 |  109.32 |  108.53 |  108.85 | 2.12577e+07 |  108.85 |
|   42611 |  106.62 |  107.44 |  106.29 |  106.82 | 2.49703e+07 |  106.82 |

Candidate column headers:
Open
High
Close
Date
Low
Volume
Adj Close



headers: Open
High
Close
Date
Low
Volume
Adj Close
label: Date
Open
High
Low
Close
Volume
Adj Close
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 |
|--------:|-----------------:|-----------------:|----------:|--------:|--------:|-----------------:|-----------------:|--------:|
|      29 | 231458           | 231458           |      0    |       0 |       0 | 231458           | 231458           |    0    |
|      28 |      1.45428e+07 |      1.44269e+07 | 115968    |       0 |  115968 |      1.45428e+07 |      1.45428e+07 |    0.48 |
|       1 | 179904           | 179904           |      0    |       0 |       0 | 179904           | 179904           |    0    |
|       5 | 374256           | 374256           |      0    |       0 |       0 | 374256           | 374256           |    0    |
|      42 |      2.3238e+06  |      2.3238e+06  |     -0.24 |       0 |       0 |      2.3238e+06  |      2.3238e+06  |   -0.24 |

Candidate column headers:
Government -US$
Government-US$
Taxpayer-US$4
Government-US$2
Difference -US$
Taxpayer-US$3
Column1
Difference-US$
Taxpayer-US$



headers: Government -US$
Government-US$
Taxpayer-US$4
Government-US$2
Difference -US$
Taxpayer-US$3
Column1
Difference-US$
Taxpayer-US$
label: Column1
Government-US$
Taxpayer-US$
Difference -US$
Government-US$2
Taxpayer-US$3
Government -US$
Taxpayer-US$4
Difference-US$