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 |
|--------:|--------:|:--------|---------:|--------:|:--------|:--------|---------:|:--------|--------:|---------:|
|       5 |     410 | S1090   | 51404217 |       2 | VF      | PF3     | 19.2964  | BUKDS   |       1 |  0.60748 |
|       5 |      70 | Q1020   | 51406405 |       0 | CI      | CI2     |  4.29639 | BUKDS   |      12 |  1.56876 |
|       5 |     390 | G109K   | 51403637 |       0 | VK      | KOF     | 79.2964  | KOFEI   |       1 |  1.03514 |
|       5 |     410 | S1090   | 51306362 |       1 | VF      | PF3     | 36.2964  | BUKDS   |       2 |  1.21495 |
|       5 |     370 | Q1040   | 51405972 |       0 | RX      | RX1     | 64.2964  | AUBAE   |      23 |  0.63156 |

Candidate column headers:
GTYP
UP
QTOT
JL
OF8
MICRO
NbJour
sga
Valo
Ilo
I



headers: GTYP
UP
QTOT
JL
OF8
MICRO
NbJour
sga
Valo
Ilo
I
label: UP
sga
MICRO
OF8
I
JL
Ilo
NbJour
GTYP
QTOT
Valo
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 |
|:----------------|:----------|:---------------------|--------:|
| Relaxation CD   | Music     | Mix_ various artists |   39982 |
| Xbox 360        | Games     | Console              |   40172 |
| Classical Piano | Books     | Sheet music          |   38966 |
| Jewelry Box     | Keepsakes | Gift from mom        |   38686 |
| Sci-Fi          | Movies    | Sci-Fi history       |   37705 |

Candidate column headers:
Acquired
Name
Description
Type



headers: Acquired
Name
Description
Type
label: Name
Type
Description
Acquired
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 |
|--------:|:----------|:-----------|--------:|
|   42005 | Beverages | Cappuccino |  214.52 |
|   42036 | Beverages | Espresso   | 4496.5  |
|   42064 | Beverages | Iced Tea   | 1252.5  |
|   42064 | Beverages | Green Tea  |  219.37 |
|   42036 | Beverages | Chamomile  |  228    |

Candidate column headers:
Month
Category
Actual Sales
Product



headers: Month
Category
Actual Sales
Product
label: Month
Category
Product
Actual Sales
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0         |   Col_1 |   Col_2 | Col_3   |   Col_4 |
|:--------------|--------:|--------:|:--------|--------:|
| Golden Palm   |     756 |     600 |         |      15 |
| Santalum/Yasi |     256 |     200 | 2.0     |       5 |
| Coffee        |      90 |     450 | 3.0     |       5 |
| Soursop       |     480 |     200 | 2.0     |       3 |
| Pine          |   45152 |     700 | 4.0     |       1 |

Candidate column headers:
Avg. Diameter (MM)
Avg. Height (mm)
Species
Price From
Total Available



headers: Avg. Diameter (MM)
Avg. Height (mm)
Species
Price From
Total Available
label: Species
Total Available
Avg. Height (mm)
Avg. Diameter (MM)
Price From
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 |
|:--------|--------:|--------:|---------:|
| B       |   40548 |       1 | 0.73913  |
| B       |   40626 |      19 | 0.73913  |
| A       |   40594 |      18 | 0.478261 |
| B       |   40558 |      18 | 0.73913  |
| C       |   40563 |      20 | 1.38889  |

Candidate column headers:
Qty
Brand
Date
Bin



headers: Qty
Brand
Date
Bin
label: Brand
Date
Qty
Bin
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   |
|--------:|:---------------------|:--------|
|       6 | Directors Costs      |         |
|       1 | Sales                |         |
|       5 | Administration Costs |         |
|      10 | Interest             |         |
|      11 | Profit Before Tax    | 1.0     |

Candidate column headers:
Heading1_Name
Heading1_Code
Heading1_Summary



headers: Heading1_Name
Heading1_Code
Heading1_Summary
label: Heading1_Code
Heading1_Name
Heading1_Summary
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 |
|--------:|--------:|--------:|--------:|--------:|
|    5187 |    3224 |    0.05 |       1 |       1 |
|    4373 |     349 |    0.03 |       2 |       2 |
|    4513 |    2634 |    0.03 |       4 |       3 |
|    5284 |    3270 |    0.01 |       1 |       1 |
|    1724 |     334 |    0.01 |       2 |       2 |

Candidate column headers:
CntB
Var Count
Rate
B_ID
S_ID



headers: CntB
Var Count
Rate
B_ID
S_ID
label: S_ID
B_ID
Rate
CntB
Var 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 |   Col_15 |   Col_16 |   Col_17 |   Col_18 |   Col_19 |
|:------------------|:--------------|:--------------|:--------|:--------|:--------|:--------|:--------|:--------|--------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
| Proportion  15-59 | Male & Female | Agincourt     | 49.76   | 50.62   | 51.11   | 51.67   | 52.31   | 52.9    |   53.43 |    54.18 |    54.95 |    55.71 |    56.62 |    57.29 |    57.91 |    58.41 |    58.59 |    59.1  |    59.37 |
| Proportion 60+    | Male          | Africa Centre |         |         |         |         |         |         |    4.67 |     4.44 |     4.39 |     4.43 |     4.48 |     4.45 |     4.44 |     4.33 |     4.29 |     4.33 |     4.32 |
| Proportion 60+    | Male & Female | Africa Centre |         |         |         |         |         |         |    4.67 |     4.44 |     4.39 |     4.43 |     4.48 |     4.45 |     4.44 |     4.33 |     4.29 |     4.33 |     4.32 |
| Proportion 60+    | Female        | Dikgale       |         | 4.63    | 4.75    | 4.84    | 5.11    | 5.23    |    5.39 |     5.5  |     5.63 |     5.76 |     5.97 |     6.07 |     6.1  |     6.2  |     6.47 |     6.76 |     6.79 |
| Crude Death Rate  | Female        | Dikgale       |         | 4.28    | 7.15    | 5.0     | 5.95    | 5.77    |    8.91 |     7.72 |     7.59 |     7.68 |     7.41 |     9.26 |    12.63 |     9.24 |     9.81 |     8.53 |     5.55 |

Candidate column headers:
2004
1998
1997
2010
1999
2000
Measure
1996
2001
2006
1994
2008
2009
2002
2007
2005
Sex
2003
1995
Site 



headers: 2004
1998
1997
2010
1999
2000
Measure
1996
2001
2006
1994
2008
2009
2002
2007
2005
Sex
2003
1995
Site 
label: Measure
Sex
Site 
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0                         | Col_1                  | Col_2      | Col_3   |   Col_4 |   Col_5 | Col_6                |   Col_7 |
|:------------------------------|:-----------------------|:-----------|:--------|--------:|--------:|:---------------------|--------:|
| RIVER MEDLOCK AT POTATO WHARF | 1/18/2000 11:40:00 AM  | BOD ATU    | mg/l    | 36543.5 |      85 | 0085 BOD ATU mg/l    |   1.53  |
| RIVER MEDLOCK AT POTATO WHARF | 1/18/2000 11:40:00 AM  | Ammonia(N) | mg/l    | 36543.5 |     111 | 0111 Ammonia(N) mg/l |   0.46  |
| RIVER MEDLOCK AT POTATO WHARF | 5/10/2000 11:15:00 AM  | BOD ATU    | mg/l    | 36656.5 |      85 | 0085 BOD ATU mg/l    |   2.38  |
| RIVER MEDLOCK AT POTATO WHARF | 10/10/2000 12:00:00 PM | BOD ATU    | mg/l    | 36809.5 |      85 | 0085 BOD ATU mg/l    |   9.13  |
| RIVER MEDLOCK AT POTATO WHARF | 12/7/2000 11:45:00 AM  | Ammonia(N) | mg/l    | 36867.5 |     111 | 0111 Ammonia(N) mg/l |   0.573 |

Candidate column headers:
result
determinand.label
determinand.notation
Determinand
determinand.unit.label
SamplingPoint.label
Date.Time
sample.sampleDateTime



headers: result
determinand.label
determinand.notation
Determinand
determinand.unit.label
SamplingPoint.label
Date.Time
sample.sampleDateTime
label: SamplingPoint.label
sample.sampleDateTime
determinand.label
determinand.unit.label
Date.Time
determinand.notation
Determinand
result
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 |
|:------------------|--------:|:--------|--------:|--------:|--------:|--------:|
| Gabriella Collins |       1 | Silver  |       1 |       0 |       0 |       2 |
| Gerrit Straatsma  |       0 | Bronze  |       1 |       0 |       0 |       1 |
| Ronald Kapoor     |       0 | Bronze  |       1 |       0 |       0 |       1 |
| Kaitlyn Henderson |       1 | Bronze  |       1 |       1 |       1 |       4 |
| Jasmine Stewart   |       0 | Gold    |       1 |       0 |       0 |       1 |

Candidate column headers:
Program
TotalScore
Name
Likes Cake
Likes Mousse
Likes Bread Pudding
Likes Pie



headers: Program
TotalScore
Name
Likes Cake
Likes Mousse
Likes Bread Pudding
Likes Pie
label: Name
Likes Bread Pudding
Program
Likes Cake
Likes Pie
Likes Mousse
TotalScore
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 |
|:--------|:---------|:--------|--------:|
| Bill W  | Downtown | Book    |      81 |
| Ahmed L | Uptown   | Book    |      53 |
| Jenny A | Uptown   | CD      |      81 |
| Helen M | Uptown   | Book    |      66 |
| Fred B  | Uptown   | DVD     |      41 |

Candidate column headers:
Price
Store
Product
Customer



headers: Price
Store
Product
Customer
label: Customer
Store
Product
Price
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0   |   Col_1 |   Col_2 |
|:--------|--------:|--------:|
| WMJP    |  1.0202 |  0.9945 |
| WMHA    |  1.0268 |  1.0007 |
| WGNI    |  1.0281 |  1.0007 |
| TMSK    |  1.0261 |  1.0011 |
| WKOJ    |  1.0284 |  1.0026 |

Candidate column headers:
MU_refnode
Code
ST_refnode



headers: MU_refnode
Code
ST_refnode
label: Code
MU_refnode
ST_refnode
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 |
|--------:|:--------------|:---------------|:---------------------|--------:|--------:|--------:|
|   41690 | Personal      | Entertaintment |                      |      25 |       2 |    2014 |
|   41640 | Health        | Gym            | Monthly Subscription |      50 |       1 |    2014 |
|   41662 | Income        | Web            |                      |      50 |       1 |    2014 |
|   41644 | Communication | Phone1         |                      |      40 |       1 |    2014 |
|   41657 | Pet           | Vet            |                      |      30 |       1 |    2014 |

Candidate column headers:
Sub-Category
Amount
Date
Description
Month
Year
Category



headers: Sub-Category
Amount
Date
Description
Month
Year
Category
label: Date
Category
Sub-Category
Description
Amount
Month
Year
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                      |
|:--------|:----------------|--------:|:----------------------|--------:|--------:|:--------|--------:|:---------|------------:|:---------|:-----------------|:----------|:----------|:----------------------------|
| SND422  | SUPPORT WORKERS |    5771 | CARE AGENCY STAFF     |    2015 |       5 | AP5103  |   27.6  | SS114610 | 1.00194e+06 | 15S23230 | MED CK MONTE BRE | 4239839.0 | 1001941.0 | CARING HANDS                |
| SND422  | SUPPORT WORKERS |    4580 | CONTRACT/AGENCY STAFF |    2015 |       2 | AP5024  |   24.15 | SS113920 | 1.00194e+06 | 1549126  | MED CHECK MON BR | 4201312.0 | 1001941.0 | CARING HANDS                |
| SND422  | SUPPORT WORKERS |    4580 | CONTRACT/AGENCY STAFF |    2015 |       3 | AP5058  |  116.75 | SS114517 | 1.20767e+06 | 15S29169 | DAY SER PN       | 4217492.0 | 1207667.0 | DENBIGHSHIRE COUNTY COUNCIL |
| SND422  | SUPPORT WORKERS |    4580 | CONTRACT/AGENCY STAFF |    2015 |      13 | JFAY24  |   24.15 | SS115666 | 1.00194e+06 | 16S29110 | CRED AP6012 6013 |           |           |                             |
| SND422  | SUPPORT WORKERS |    4580 | CONTRACT/AGENCY STAFF |    2015 |      13 | JFAY24  |   24.15 | SS115654 | 1.00194e+06 | 16S29110 | CRED AP6012 6013 |           |           |                             |

Candidate column headers:
Field 1
Ref 2
Ref 1
Det Desc
Cst Ctr
Ref 3
Field 2
Amount
Pd
Detail
Year
Ref 4
CC Desc
Key Ref
Jnl ID



headers: Field 1
Ref 2
Ref 1
Det Desc
Cst Ctr
Ref 3
Field 2
Amount
Pd
Detail
Year
Ref 4
CC Desc
Key Ref
Jnl ID
label: Cst Ctr
CC Desc
Detail
Det Desc
Year
Pd
Jnl ID
Amount
Ref 1
Ref 2
Ref 3
Ref 4
Key Ref
Field 1
Field 2
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0   |   Col_1 |   Col_2 |
|:--------|--------:|--------:|
| Hall    |    2010 |   34.46 |
| Lee     |    2006 |   68.1  |
| Allen   |    2010 |   19.4  |
| Allen   |    2008 |   99.24 |
| Lee     |    2010 |   61.38 |

Candidate column headers:
Rep
Year
Amt



headers: Rep
Year
Amt
label: Rep
Year
Amt
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   |
|:--------|:--------|:--------|:--------|:--------|
| 201102  | B744    | 42479.0 | 42491.0 |         |
| 9MXXS   | A333    |         | 42484.0 |         |
| 9VSWK   | B77W    |         | 42481.0 |         |
| A7BCF   | B788    |         |         | 42500.0 |
| 753252  | GLF4    |         | 42478.0 | 42489.0 |

Candidate column headers:
A/C ICAO
Last successful date  (Niigata HMU)
Reg No
Last successful date  (Setouchi HMU)
Last successful date  (Sendai HMU)



headers: A/C ICAO
Last successful date  (Niigata HMU)
Reg No
Last successful date  (Setouchi HMU)
Last successful date  (Sendai HMU)
label: Reg No
A/C ICAO
Last successful date  (Sendai HMU)
Last successful date  (Niigata HMU)
Last successful date  (Setouchi HMU)
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 |
|--------:|:------------------|:--------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|:---------|---------:|
|   13515 | Dance Cate        | U14     |    2005 |      80 |       0 |       0 |       0 |       0 |       0 |        0 |        0 | 50.0     |      130 |
|   17046 | Madeleine Hooker  | U14     |    2005 |       0 |       0 |       0 |       0 |       0 |       0 |       32 |        0 |          |       32 |
|   26017 | Anika Whitby      | U14     |    2005 |       0 |       0 |       0 |       0 |       0 |       0 |        5 |        0 |          |        5 |
|   16367 | Hardman Catherine | U14     |    2004 |       0 |       0 |       0 |       0 |       0 |       0 |       22 |        0 | 12.0     |       34 |
|   26880 | Papworth Lulu     | U14     |    2005 |       5 |       0 |       0 |       0 |       0 |       0 |       10 |        0 | 13.0     |       28 |

Candidate column headers:
SL4
Name
SL2
GS3
Category
WC POINTS TOTAL
GS2
SX1
GS1
SL1
GS4
SnowID
YOB
SL3



headers: SL4
Name
SL2
GS3
Category
WC POINTS TOTAL
GS2
SX1
GS1
SL1
GS4
SnowID
YOB
SL3
label: SnowID
Name
Category
YOB
SL1
SL2
SL3
SL4
GS1
GS2
GS3
GS4
SX1
WC POINTS 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   |
|:--------|:--------|:--------|--------:|:----------|:--------|:------------|:--------|:--------|:--------|:---------|:---------|:---------|:---------|:---------|:---------|:---------|:---------|
| Sort    | M       | PRI     |       1 | ME0001872 | E       | Baileyville | 001A    | SRB     | Zn      | Zn       | Zn       | Zn       | Zn       | Zn       |          | Zn       |          |
| Sort    | M       | PUB     |       1 | ME0100218 | S       | Falmouth    | 001A    | MRH     |         |          |          |          | WET      |          |          |          |          |
| Sort    | M       | PUB     |       1 | ME0101117 | S       | Saco        | 001A    | SMR     |         |          | TRC      |          |          |          |          |          |          |
| Sort    | M       | PUB     |       1 | ME0101478 | S       | Lewiston    | 001C    | SMR     |         |          |          |          |          |          |          |          |          |
| Sort    | M       | PUB     |       1 | ME0100285 | S       | Kittery     | 001A    | MRH     |         |          |          | SS       |          |          |          |          |          |

Candidate column headers:
DSCG
Jan-12
TYPO
May-12
INSP
12MoRecord
Oct-12
Apr-12
TYP
MADI
Feb-12
Jun-12
MUNI
SUBR
NPID
Mar-12
Aug-12
Sep-12



headers: DSCG
Jan-12
TYPO
May-12
INSP
12MoRecord
Oct-12
Apr-12
TYP
MADI
Feb-12
Jun-12
MUNI
SUBR
NPID
Mar-12
Aug-12
Sep-12
label: 12MoRecord
MADI
TYPO
TYP
NPID
SUBR
MUNI
DSCG
INSP
Jan-12
Feb-12
Mar-12
Apr-12
May-12
Jun-12
Aug-12
Sep-12
Oct-12
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   |
|--------:|--------:|:--------|
|      20 |    1739 | E12     |
|      48 |    1976 | I11     |
|      15 |    3392 | F06     |
|      25 |    3365 | R05     |
|      21 |    3086 | R05     |

Candidate column headers:
Order ID
Trip ID
Customer ID



headers: Order ID
Trip ID
Customer ID
label: Order ID
Customer ID
Trip 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   | Col_3   | Col_4    | Col_5    | Col_6                | Col_7   | Col_8   | Col_9   |   Col_10 | Col_11   | Col_12                    |
|:-----------|:--------|:--------|:--------|:---------|:---------|:---------------------|:--------|:--------|:--------|---------:|:---------|:--------------------------|
|            |         |         | #DIV/0! |          |          |                      |         | 0.0     |         |        0 | #DIV/0!  |                           |
|            |         |         | #DIV/0! |          |          |                      |         | 0.0     |         |        0 | #DIV/0!  |                           |
| BIA        |         | 57.0    | 0       | 63552.0  | 0.0      | Sept.2017-Sept 2018  |         |         | Pueblo  |    63552 | 0        | retreat/pile/wood harvest |
| USFS       | 239.0   | 239.0   | 1       | 200000.0 | 200000.0 | Completed            | -       | 0.0     | USFS    |   200000 | 1        | Thin and pile             |
| BIA (2017) | 0.0     | 62.0    | 0       | 327028.0 | 0.0      | Sept. 2017-Sept.2018 |         |         | Pueblo  |   327028 | 0        | retreat/thin/pile         |

Candidate column headers:
Amount Funded2
Funding Source #1
Project Type
Amount Expended
Total Funding
Target Acres
Project Status 
Percent Completed
Funding Source #2
Completed Acres
Percent Expended
Ownership
Amount Funded 



headers: Amount Funded2
Funding Source #1
Project Type
Amount Expended
Total Funding
Target Acres
Project Status 
Percent Completed
Funding Source #2
Completed Acres
Percent Expended
Ownership
Amount Funded 
label: Funding Source #1
Completed Acres
Target Acres
Percent Expended
Amount Funded 
Amount Expended
Project Status 
Funding Source #2
Amount Funded2
Ownership
Total Funding
Percent Completed
Project 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 |
|--------:|:--------|:--------|:--------|--------:|--------:|
|      17 | LPC     | TLSE    | L       |   40433 |   40302 |
|      16 | LPC     | TLSE    | L       |   38121 |   41520 |
|      82 | MP      | TLSE    | C       |   40433 |   39725 |
|      46 | MP      | TLSE    | C       |   40433 |   39725 |
|      81 | MP      | TLSE    | C       |   40433 |   39725 |

Candidate column headers:
Date de fin
Date lanc
Etat
DEPT
GROUPEMENT
ZONE



headers: Date de fin
Date lanc
Etat
DEPT
GROUPEMENT
ZONE
label: DEPT
ZONE
GROUPEMENT
Etat
Date de fin
Date lanc
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 |
|:--------------|--------:|--------:|--------:|
| Sample Task 4 |   42772 |   42774 |       2 |
| Sample Task 6 |   42774 |   42779 |       5 |
| Sample Task 7 |   42775 |   42783 |       8 |
| Sample Task 1 |   42767 |   42769 |       2 |
| Sample Task 8 |   42776 |   42777 |       1 |

Candidate column headers:
Task Name
Start  (Date)
End  (Date)
Duration (Days)



headers: Task Name
Start  (Date)
End  (Date)
Duration (Days)
label: Task Name
Start  (Date)
End  (Date)
Duration (Days)
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   |
|--------:|:----------|:---------------|:--------|
|    2001 | AllAround | Wade Kreutzer  | M       |
|    2011 | AllAround | Jesse Jolly    | M       |
|    1991 | AllAround | Connie Harris  | F       |
|    2002 | AllAround | Josh Peek      | M       |
|    1997 | AllAround | Jim Bob Custer | M       |

Candidate column headers:
gender
year
event
name1



headers: gender
year
event
name1
label: year
event
name1
gender
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 |
|:-----------|:-----------|:-------------|:--------------|--------:|--------:|---------:|--------:|---------:|
| Arthropoda | Insecta    | Coleoptera   | Elateridae    |       2 | 90.925  | 1.05359  | 134     | 31.1127  |
| Arthropoda | Arachnida  | Araneae      | Araneidae     |      16 | 90.945  | 0.861642 | 111.062 |  7.0377  |
| Arthropoda | Arachnida  | Araneae      | Gnaphosidae   |      14 | 91.0029 | 1.04608  | 119.286 | 17.661   |
| Amoebozoa  | Tubulinea  | Arcellinida  | Nebelidae     |       2 | 90.53   | 0.749533 | 126.5   |  4.94975 |
| Annelida   | Clitellata | Lumbriculida | Lumbriculidae |      29 | 93.4331 | 3.63724  | 138.931 | 67.3609  |

Candidate column headers:
Average of % of identical matches
Order
Average reference alignment length
Phylum
Class
S.D.2
Family
S.D.
Number of Sequences



headers: Average of % of identical matches
Order
Average reference alignment length
Phylum
Class
S.D.2
Family
S.D.
Number of Sequences
label: Phylum
Class
Order
Family
Number of Sequences
Average of % of identical matches
S.D.
Average reference alignment length
S.D.2
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 |   Col_1 | Col_2            |   Col_3 |   Col_4 |   Col_5 |   Col_6 |
|--------:|--------:|:-----------------|--------:|--------:|--------:|--------:|
|   10302 |      17 | Alice Mutton     |    31.2 |      40 |    0    | 1248    |
|   10922 |      17 | Alice Mutton     |    39   |      15 |    0    |  585    |
|   10267 |      40 | Boston Crab Meat |    14.7 |      50 |    0    |  735    |
|   10795 |      17 | Alice Mutton     |    39   |      35 |    0.25 | 1023.75 |
|   10764 |       3 | Aniseed Syrup    |    10   |      20 |    0.1  |  180    |

Candidate column headers:
Unt Price
Product ID
Product
Ext Price
Disc
Order ID
Qty



headers: Unt Price
Product ID
Product
Ext Price
Disc
Order ID
Qty
label: Order ID
Product ID
Product
Unt Price
Qty
Disc
Ext Price
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0   |   Col_1 |   Col_2 |   Col_3 |   Col_4 |   Col_5 |   Col_6 |   Col_7 |   Col_8 |   Col_9 |   Col_10 |   Col_11 |   Col_12 |   Col_13 |
|:--------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|---------:|
| Szkola  |   25.1  |    0    |    0    |    0    |    0    |    0    |    0    |    0    |    0    |   599.82 |   655.2  |     0    |     0    |
| Wyjazd  | 1053.09 | 2639.92 | 1502.24 | 1315.58 | 2166.88 | 1900.77 | 2017.48 | 1801.66 |  903.08 |  2654.99 |  1966.65 |   944.52 |  1376.46 |
| Miasto  | 2270.98 | 2482.15 | 1385.39 | 1801.51 | 2141.3  | 1218.36 | 2233.09 | 4024.54 | 3017.85 |  3502.61 |  2383.84 |  2458.85 |   677.41 |
| Pepsi   |    0    |    0    |    0    |    0    |    0    |    0    |    0    |    0    |    0    |     0    |     0    |     0    |   153.96 |
| Piwo    |    0    |    0    |    0    |   11.69 |    0    |    0    |    0    |    0    |    0    |     0    |     0    |     9.7  |    85.16 |

Candidate column headers:
2002
Subcategory
2009
2007
2006
2003
2008
2012
2000
2010
2005
2004
2011
2001



headers: 2002
Subcategory
2009
2007
2006
2003
2008
2012
2000
2010
2005
2004
2011
2001
label: Subcategory
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
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 |
|--------:|--------:|--------:|:--------|:--------------|--------:|---------:|--------:|--------:|--------:|------------:|-----------:|
|  268820 | 1373190 |       1 | RFE13GM | 151-1SD179272 | 42809.7 | 21248906 |   42809 |   42811 |   42811 | 1.02906e+08 | 3903518136 |
|  268820 | 2842839 |       1 | RFE13GM | 151-1SD177079 | 42789.7 | 21237107 |   42790 |   42794 |   42794 | 1.02897e+08 | 3903529646 |
|  268820 | 2943892 |       1 | RFE13GM | 151-1SD178634 | 42803.7 | 21245665 |   42803 |   42807 |   42807 | 1.02903e+08 | 3903528945 |
|  268820 | 1674852 |       1 | RFE13GM | 151-1SD177063 | 42789.7 | 21236940 |   42789 |   42793 |   42793 | 1.02896e+08 | 3903529521 |
|  268820 | 1576263 |       1 | RFE13GM | 151-1SD177681 | 42795.7 | 21240631 |   42795 |   42797 |   42797 | 1.02899e+08 | 3903529075 |

Candidate column headers:
USBNumber
CreateDate
ShipToPONumber
ShipDate
OrderNumber
InvoiceNumber
EntryDate
PartNum
TrackingNumber
Quantity
ShipToCustomer
InvoiceDate



headers: USBNumber
CreateDate
ShipToPONumber
ShipDate
OrderNumber
InvoiceNumber
EntryDate
PartNum
TrackingNumber
Quantity
ShipToCustomer
InvoiceDate
label: ShipToCustomer
USBNumber
Quantity
PartNum
ShipToPONumber
CreateDate
OrderNumber
EntryDate
ShipDate
InvoiceDate
InvoiceNumber
TrackingNumber
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 |
|--------:|---------:|:--------|:--------|:--------|:--------------|:-----------|:--------|--------:|--------:|
|   42217 | 0.504051 | Uten    | Male    | North   | Self-employed | Snacks     | Medium  |       2 |    11.2 |
|   42217 | 0.433704 | Xinhua  | Male    | East    | Retired       | Chocolates | Large   |       5 |    47   |
|   42217 | 0.425683 | Nancy   | Female  | South   | Salaried      | Chocolates | Large   |       3 |    28.2 |
|   42217 | 0.534838 | Vaughn  | Male    | Middle  | Self-employed | Biscuits   | Medium  |       3 |    21   |
|   42217 | 0.496088 | Mindy   | Female  | Middle  | Professional  | Snacks     | Large   |       1 |     2.5 |

Candidate column headers:
Date
Qty
Prod Size
Cust Area
Cust Name
Time
Amount
Cust Gender
Cust Profession
Prod Category



headers: Date
Qty
Prod Size
Cust Area
Cust Name
Time
Amount
Cust Gender
Cust Profession
Prod Category
label: Date
Time
Cust Name
Cust Gender
Cust Area
Cust Profession
Prod Category
Prod Size
Qty
Amount
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0               | Col_1   | Col_2   | Col_3   | Col_4        | Col_5      | Col_6   | Col_7   | Col_8   | Col_9            | Col_10                         |
|:--------------------|:--------|:--------|:--------|:-------------|:-----------|:--------|:--------|:--------|:-----------------|:-------------------------------|
| NE 148th Apartments | R144050 |         |         |              | Apartments | No      | No      | No      | Residential Only | Yes                            |
| Ivanhoe 4           | R192155 |         |         | Kathy Romero | Apartments | No      | No      | No      | Residential Only | No- Under 20 Units (Voluntary) |
| L&L PDX Hostel      | R225683 |         |         |              |            | No      |         |         |                  | No- Commercial Only            |
| 32 Division         | R150788 |         |         |              | Apartments | No      | No      | No      |                  | Yes                            |
| Ellis Apartments    | R122529 |         |         |              | Apartments | No      | No      | No      |                  | Yes                            |

Candidate column headers:
Within the N/NE Neighborhood Housing Strategy area or the Inters
Subject to IH?
Central City or Gateway Plan District?
PHB Project?
Closer
Project/Building Name
Property Type
Mixed-Use or Residential Only?
Construction Coordinator
Tax ID #(s)
Underwriter



headers: Within the N/NE Neighborhood Housing Strategy area or the Inters
Subject to IH?
Central City or Gateway Plan District?
PHB Project?
Closer
Project/Building Name
Property Type
Mixed-Use or Residential Only?
Construction Coordinator
Tax ID #(s)
Underwriter
label: Project/Building Name
Tax ID #(s)
Underwriter
Construction Coordinator
Closer
Property Type
PHB Project?
Central City or Gateway Plan District?
Within the N/NE Neighborhood Housing Strategy area or the Inters
Mixed-Use or Residential Only?
Subject to IH?
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   |
|--------:|:------------|:--------|:--------|
| 10000   | Education   | Uganda  | Q2-2018 |
| 35415.7 | Livelihoods | India   | Q2-2016 |
| 28850   | Education   | Uganda  | Q2-2019 |
|     0   | Livelihoods | Nepal   | Q2-2019 |
| 30000   | Livelihoods | Uganda  | Q2-2019 |

Candidate column headers:
Grant Made
Theme of Work
Award Amount
Project Country



headers: Grant Made
Theme of Work
Award Amount
Project Country
label: Award Amount
Theme of Work
Project Country
Grant Made
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 |
|:--------|:--------|---------:|---------:|--------:|--------:|---------:|---------:|---------:|----------:|---------:|
| andere  | O&O     |  59.1488 |  97.3347 | 199.389 | 165.802 |  75.1941 |  93.2208 |  134.569 |   59.7251 |   93.712 |
| EWI     | O&V     |  15      |   0      |   0     |   0     |   0      |   0      |    0     |    0      |    5.5   |
| andere  | WIB     | 109.123  | 144.189  | 245.444 | 207.178 | 124.595  | 148.767  |  187.747 |  121.107  |  150.988 |
| OV      | WIB     | 869.852  | 879.724  | 881.361 | 898.579 | 843.29   | 868.93   | 1027.47  | 1128.94   | 1030.27  |
| OV      | O&O     | 303.741  | 311.271  | 311.793 | 317.485 | 239.198  | 259.358  |  306.091 |  338.743  |  314.96  |

Candidate column headers:
2015
2008
2014
2010
2011
2016i
2013
beleidsdomein
wetenschap en innovatie
2012
2009



headers: 2015
2008
2014
2010
2011
2016i
2013
beleidsdomein
wetenschap en innovatie
2012
2009
label: beleidsdomein
wetenschap en innovatie
2008
2009
2010
2011
2012
2013
2014
2015
2016i
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   |
|:--------|:--------|:--------|:--------|
| Yes     | Yes     | Yes     | Yes     |
| No      | Yes     | No      | No      |
| No      | Yes     | No      | Yes     |
| No      | Yes     | No      | No      |
| Yes     | No      | No      | No      |

Candidate column headers:
Discovery Days 
Exploring Aspirations 
Learning Journeys
Learning Leaders 



headers: Discovery Days 
Exploring Aspirations 
Learning Journeys
Learning Leaders 
label: Learning Leaders 
Discovery Days 
Learning Journeys
Exploring Aspirations