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 |   Col_11 |    Col_12 |   Col_13 | Col_14   | Col_15   |
|--------:|--------:|--------:|:--------|:------------|:-----------------|--------:|:---------------------|:------------------|:---------------------|-----------:|---------:|----------:|---------:|:---------|:---------|
|    2011 |       9 |    1067 | W00706  | W00706.0040 | PRODUCTION STAFF |  555099 | CG COS Labor Summary | :FRINGE BENEFITS  | CG COS Labor Summary | 1700053710 |       28 | 118767096 |       54 | JU       | ALEUSER  |
|    2011 |       8 |    1067 | W00706  | W00706.0040 | PRODUCTION STAFF |  555099 | CG COS Labor Summary | :                 | Alloc-Labor Chgd Prd | 1701604819 |        5 | 118682337 |        6 | JV       | ALEUSER  |
|    2011 |       8 |    1067 | W00706  | W00706.0040 | PRODUCTION STAFF |  555099 | CG COS Labor Summary | :MILLER_ ROBERT C | CG COS Labor Summary | 1700053699 |      383 | 118701122 |      210 | JU       | ALEUSER  |
|    2011 |       8 |    1067 | W00706  | W00706.0040 | PRODUCTION STAFF |  555099 | CG COS Labor Summary | :                 | Alloc-Labor Chgd Prd | 1701604823 |        5 | 118682341 |        6 | JV       | ALEUSER  |
|    2011 |       8 |    1067 | W00706  | W00706.0040 | PRODUCTION STAFF |  555099 | CG COS Labor Summary | :                 | Alloc-Labor Chgd Prd | 1701604820 |        5 | 118682338 |        6 | JV       | ALEUSER  |

Candidate column headers:
CoCode
Cost Element
RefDocNo
CO object name
Year
Project Def.
Cost element name
Name
Name of offsetting account
DocTyp
Posting row
User
Per
PRw
DocumentNo
WBS Element



headers: CoCode
Cost Element
RefDocNo
CO object name
Year
Project Def.
Cost element name
Name
Name of offsetting account
DocTyp
Posting row
User
Per
PRw
DocumentNo
WBS Element
label: Year
Per
CoCode
Project Def.
WBS Element
CO object name
Cost Element
Cost element name
Name
Name of offsetting account
RefDocNo
PRw
DocumentNo
Posting row
DocTyp
User
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 |
|:--------|----------:|----------:|----------:|:--------|--------:|--------:|--------:|--------:|--------:|:---------|:---------|:---------|:---------|:---------|:---------|---------:|---------:|---------:|
| chr3    |  79216298 |  79216298 |  79216742 | plus    |    4720 |      23 |     438 |       1 | 40.376  | ROBO1    |          | YES      | YES      | YES      | YES      |     4720 |     2152 |       31 |
| chr3    |  80590203 |  80589926 |  80590203 | minus   |     244 |       7 |     273 |       1 | 30.5872 |          |          | YES      | YES      | YES      | YES      |        4 |      244 |        8 |
| chr4    |  66809094 |  66808813 |  66809094 | minus   |    3279 |       9 |     281 |       1 | 42.6634 |          |          | YES      | YES      | YES      | YES      |        3 |     3279 |       57 |
| chr4    |  27741049 |  27740728 |  27741049 | minus   |    4823 |      28 |     321 |       1 | 43.8338 |          |          | YES      | YES      | YES      | YES      |     4110 |     4312 |     4823 |
| chr2    | 221278245 | 221277924 | 221278245 | minus   |      53 |       3 |     321 |       1 | 38.0259 |          |          | YES      | YES      | YES      | YES      |       24 |       18 |       53 |

Candidate column headers:
Exon Annotation
colo4T Count
MapScore
Max Count
colo4T
End
Gene Annotation
Max Width
MapQual
colo4N
Strand
colo4P
Max Unique
colo4P Count
colo4N Count
Position
Chrom
colo4M
Start



headers: Exon Annotation
colo4T Count
MapScore
Max Count
colo4T
End
Gene Annotation
Max Width
MapQual
colo4N
Strand
colo4P
Max Unique
colo4P Count
colo4N Count
Position
Chrom
colo4M
Start
label: Chrom
Position
Start
End
Strand
Max Count
Max Unique
Max Width
MapScore
MapQual
Gene Annotation
Exon Annotation
colo4N
colo4P
colo4T
colo4M
colo4N Count
colo4P Count
colo4T 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 |
|:-----------------|:--------|:-----------------------|:----------|--------:|--------:|:--------|--------:|--------:|--------:|
| Becker_ Gretchen | West    | Facilities/Engineering | Full Time |   37960 |      10 | DMR     |   66890 |       5 |   68836 |
| Ayers_ Douglas   | Main    | Manufacturing          | Contract  |   40462 |       3 |         |   52940 |       4 |   54481 |
| Barker_ Heidi    | West    | Account Management     | Full Time |   40264 |       4 | D       |   29760 |       2 |   30626 |
| Anthony_ Robert  | Watson  | IT                     | Half-Time |   40393 |       3 | DMR     |   16925 |       1 |   17418 |
| Barnes_ Grant    | Main    | Quality Assurance      | Hourly    |   35861 |      16 |         |   12836 |       5 |   13210 |

Candidate column headers:
Building
Years
Status
Job Rating
New Salary
Benefits
Department
Salary
Employee Name
Hire Date



headers: Building
Years
Status
Job Rating
New Salary
Benefits
Department
Salary
Employee Name
Hire Date
label: Employee Name
Building
Department
Status
Hire Date
Years
Benefits
Salary
Job Rating
New Salary
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             |
|:---------------|:--------------|:------------|--------:|:--------|--------:|--------:|:---------|--------:|:-------------|:-------------------|
| snarfa         | educationweek | 0_ 12_ 96   |       1 | No      |     100 |      17 | Mentions | 41145.6 |              | 41145.631226851845 |
| HermosaSchools | educationweek | 0_ 100_ 50  |       1 | No      |     100 |      31 | Followed | 41145.7 |              |                    |
| ISSN           | educationweek | 0_ 100_ 50  |       1 | No      |     100 |      43 | Followed | 41145.7 |              |                    |
| collegeladydc  | collegeladydc | 0_ 136_ 227 |       1 | No      |     100 |      20 | Tweet    | 41145.6 |              | 41145.64806712963  |
| AdamsMath      | educationweek | 0_ 12_ 96   |       1 | No      |     100 |      16 | Mentions | 41145.6 | hr education | 41145.618946759256 |

Candidate column headers:
Width
Vertex 2
ID
Reciprocated?
Vertex 1
Opacity
Relationship
Tweet Date (UTC)
Color
Relationship Date (UTC)
Hashtags in Tweet



headers: Width
Vertex 2
ID
Reciprocated?
Vertex 1
Opacity
Relationship
Tweet Date (UTC)
Color
Relationship Date (UTC)
Hashtags in Tweet
label: Vertex 1
Vertex 2
Color
Width
Reciprocated?
Opacity
ID
Relationship
Relationship Date (UTC)
Hashtags in Tweet
Tweet Date (UTC)
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 |
|--------:|--------:|--------:|--------:|-----------:|--------:|--------:|--------:|--------:|--------:|
|  0.0014 |   0.465 |  0.2786 | 7.07644 | 0.00707644 | 3.80959 | 977.952 | 1.24567 | 4.1176  | 496.054 |
|  0.001  |   0.465 |  0.279  | 7.0866  | 0.0070866  | 3.80931 | 977.934 | 1.24744 | 4.12344 | 496.453 |
|  0.0026 |   0.465 |  0.2774 | 7.04596 | 0.00704596 | 3.81043 | 978.006 | 1.24038 | 4.10009 | 494.853 |
|  0.0012 |   0.465 |  0.2788 | 7.08152 | 0.00708152 | 3.80945 | 977.943 | 1.24656 | 4.12052 | 496.253 |
|  0.0018 |   0.465 |  0.2782 | 7.06628 | 0.00706628 | 3.80987 | 977.97  | 1.24391 | 4.11177 | 495.654 |

Candidate column headers:
width of cut
kc
woc (m)
woc (mm)
hm
deflection
Pc
Mc
force value
depth of cut



headers: width of cut
kc
woc (m)
woc (mm)
hm
deflection
Pc
Mc
force value
depth of cut
label: deflection
depth of cut
width of cut
woc (mm)
woc (m)
hm
kc
Pc
Mc
force 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   | Col_5   | Col_6   | Col_7   | Col_8   | Col_9   | Col_10   | Col_11   | Col_12   |
|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:---------|:---------|:---------|
|         |         | 3.0     | 8.0     | 8.0     |         |         | 6.0     |         |         | 2.0      | 7.0      | 8.0      |
| 4.0     | 1.0     | 1.0     |         |         | 8.0     | 6.0     |         | 2.0     | 4.0     | 4.0      | 10.0     |          |
|         |         |         | 6.0     |         | 5.0     | 5.0     | 4.0     |         | 10.0    | 8.0      |          |          |
|         |         | 6.0     |         |         |         | 10.0    |         |         |         | 6.0      | 6.0      | 5.0      |
|         |         |         |         |         |         |         |         | 7.0     | 4.0     |          |          |          |

Candidate column headers:
TRP Jump
400 Dash
1600 Relay
400 Relay
800 Relay
1600 Run
200 Dash
Shot Put
800 Run
Long Jump
100 Dash
DIS
High Jump



headers: TRP Jump
400 Dash
1600 Relay
400 Relay
800 Relay
1600 Run
200 Dash
Shot Put
800 Run
Long Jump
100 Dash
DIS
High Jump
label: Shot Put
DIS
Long Jump
High Jump
TRP Jump
400 Relay
800 Relay
1600 Relay
100 Dash
200 Dash
400 Dash
800 Run
1600 Run
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       |
|--------:|:----------|:--------------------------|:-------------------|--------:|:------------|
|     519 | BKC0428 : | PAEDIATRIC SURGERIES      | Surgical Procedure |   16000 | CGHS_GW_BBS |
|     512 | BKC0313:  | PLASTIC SURGERY           | Surgical Procedure |   20500 | CGHS_GW_BBS |
|     535 | BKC0750 : | SURGICAL GASTROENTEROLOGY | Surgical Procedure |   14000 | CGHS_GW_BBS |
|     520 | BKC0429 : | PAEDIATRIC SURGERIES      | Surgical Procedure |   16000 | CGHS_GW_BBS |
|     517 | BKC0338 : | ORTHOPEDIC TRAUMA         | Surgical Procedure |   19000 | CGHS_GW_BBS |

Candidate column headers:
SPECIALITY
TYPE
S No.
ICs Proposed Rates
BKKY CODE
ICs_Rate Bucket



headers: SPECIALITY
TYPE
S No.
ICs Proposed Rates
BKKY CODE
ICs_Rate Bucket
label: S No.
BKKY CODE
SPECIALITY
TYPE
ICs Proposed Rates
ICs_Rate Bucket
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 |
|:-----------------------------|--------:|--------:|--------:|
| Education Savings (529_ Etc) |       0 |       0 |       0 |
| Retirement Contributions     |       0 |       0 |       0 |
| Other #2                     |       0 |       0 |       0 |
| Other #7                     |       0 |       0 |       0 |
| Clothing Expense             |       0 |       0 |       0 |

Candidate column headers:
Bill Type
Amount
Avg/Week
Avg/Day



headers: Bill Type
Amount
Avg/Week
Avg/Day
label: Bill Type
Amount
Avg/Week
Avg/Day
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 | Col_1    |      Col_2 |   Col_3 |    Col_4 |       Col_5 |     Col_6 |    Col_7 |       Col_8 |    Col_9 |   Col_10 |           Col_11 |      Col_12 |   Col_13 |      Col_14 |   Col_15 |   Col_16 |
|--------:|:---------|-----------:|--------:|---------:|------------:|----------:|---------:|------------:|---------:|---------:|-----------------:|------------:|---------:|------------:|---------:|---------:|
|       9 | Navajo   |  214837537 |   20947 | 10256.2  | 6.27478e+07 | 0.292071  | 2995.55  | 1.06962e+08 | 0.497876 |  5106.33 | 162264           | 0.000755287 |  7.74641 | 4.49651e+07 | 0.209298 |  2146.61 |
|       0 | ~Total-D | 8953120539 |  935219 |  9573.29 | 9.55251e+08 | 0.106695  | 1021.42  | 4.36583e+09 | 0.487633 |  4668.25 |      4.07485e+06 | 0.000455132 |  4.35711 | 3.62796e+09 | 0.405217 |  3879.26 |
|       8 | Mohave   |  179327654 |   23158 |  7743.66 | 2.18588e+07 | 0.121893  |  943.898 | 8.54946e+07 | 0.476751 |  3691.8  | 488878           | 0.00272617  | 21.1105  | 7.14854e+07 | 0.39863  |  3086.85 |
|      11 | Pinal    |  437783091 |   40439 | 10825.8  | 3.62082e+07 | 0.082708  |  895.378 | 2.70879e+08 | 0.618752 |  6698.47 | 154352           | 0.000352576 |  3.81691 | 1.30541e+08 | 0.298187 |  3228.1  |
|       7 | Maricopa | 5616885964 |  587562 |  9559.65 | 4.52851e+08 | 0.0806232 |  770.729 | 2.61599e+09 | 0.465736 |  4452.27 |      1.46551e+06 | 0.000260912 |  2.49423 | 2.54658e+09 | 0.45338  |  4334.15 |

Candidate column headers:
N
B
J
P
L
O
D
M
F
K
A
C
Q
G
I
E
H



headers: N
B
J
P
L
O
D
M
F
K
A
C
Q
G
I
E
H
label: A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
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     |
|--------:|--------:|--------:|:----------|
| 74.0359 | 87.2059 | 81.6368 | Very good |
| 33.2967 | 36.1197 | 29.5943 | Poor      |
| 32.5208 | 31.1197 | 30.3451 | Poor      |
| 32.8538 | 40.7526 | 42.8054 | Fair      |
| 43.6197 | 33.34   | 18.4169 | Poor      |

Candidate column headers:
Activity
Organisation
Column1
Commitment



headers: Activity
Organisation
Column1
Commitment
label: Commitment
Organisation
Activity
Column1
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 |   Col_1 |   Col_2 | Col_3       | Col_4   | Col_5         | Col_6           | Col_7   | Col_8   |   Col_9 |   Col_10 |
|--------:|--------:|--------:|:------------|:--------|:--------------|:----------------|:--------|:--------|--------:|---------:|
|       1 |    1111 |   48514 | RSCA        | Nguyen  | Budget Upload | RSCA grant      | 7997.0  |         | 7997    |  7997    |
|       2 |    1111 |   48500 | DO Prof Dev | Johnson | Budget Upload | Prof Dev        | 500.0   |         |  500    |   500    |
|       4 |    1222 |   48500 | O&E         | DEPT    | Patino        | Ind Cont_SpCons |         | 200.0   | -200    |  -200    |
|       2 |    1111 |   48500 | O&E         | DEPT    | US Bank       | Supplies        |         | 29.04   |  -29.04 |   -29.04 |
|       3 |    1111 |   48500 | O&E         | DEPT    | US Bank       | Supplies        |         | 68.36   |  -68.36 |   -68.36 |

Candidate column headers:
Budget
Dept
Fund
Manager of Funds
Actuals
Period
Projected Balance Avail
Tag
Payee
Fund Source
Balance Avail



headers: Budget
Dept
Fund
Manager of Funds
Actuals
Period
Projected Balance Avail
Tag
Payee
Fund Source
Balance Avail
label: Period
Dept
Fund
Fund Source
Manager of Funds
Payee
Tag
Budget
Actuals
Balance Avail
Projected Balance Avail
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               |
|:----------|:-------------------|:----------------------|:--------------------|
| Muse      | 293                | 0.54545454545454541   | 24.454545454545453  |
| Momauk    | 3619               | 9.4848732624693372E-2 | 1106.9051512673752  |
| Mogaung   | #VALUE!            | no coverage           | #VALUE!             |
| Hpakant   | 762.5              | 0                     | 0.5                 |
| Myitkyina | 1438.7531055900622 | 0                     | 0.24689440993788819 |

Candidate column headers:
Township
V1
V2
V3



headers: Township
V1
V2
V3
label: Township
V1
V2
V3
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 |
|:--------|:--------|:--------|--------:|--------:|
| SVC1    | A       | K4      |      40 |      39 |
| SVC     | A       | K3      |      36 |      35 |
| SVC3    | A       | K1      |      28 |      27 |
| SVC     | C       | DF      |      18 |      17 |
| SVC3    | D       | XO      |      12 |      11 |

Candidate column headers:
type
centre
service
CONSO AE
CONSO CP



headers: type
centre
service
CONSO AE
CONSO CP
label: service
centre
type
CONSO AE
CONSO CP
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           |
|--------:|:----------|--------:|:----------------|
|   40182 | (01)  Jan |    2010 | 2010  (01)  Jan |
|   40201 | (01)  Jan |    2010 | 2010  (01)  Jan |
|   40207 | (01)  Jan |    2010 | 2010  (01)  Jan |
|   40194 | (01)  Jan |    2010 | 2010  (01)  Jan |
|   40185 | (01)  Jan |    2010 | 2010  (01)  Jan |

Candidate column headers:
Date
YrMos
Year
Month



headers: Date
YrMos
Year
Month
label: Date
Month
Year
YrMos
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 |
|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|:---------|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
|   41699 |       0 |    1402 |       0 |     506 |    1253 |       0 |     808 |      12 | 6163.33 |          |        3 |        0 |        0 |      232 |        3 |     2579 |     1402 |      232 |     4216 |
|   41518 |       0 |     591 |       0 |     233 |     244 |       0 |      31 |      33 | 1809.5  |          |        0 |        0 |        0 |        2 |        0 |      541 |      591 |        2 |     1134 |
|   41852 |      10 |    1601 |      14 |    1128 |      88 |       0 |    3094 |      13 | 5183.67 | 0.0      |        1 |        0 |        0 |     1616 |        1 |     4323 |     1625 |     1616 |     7565 |
|   41640 |       0 |    1052 |       0 |    8548 |     620 |       0 |      18 |      24 | 6266.67 |          |        4 |        0 |        0 |      446 |        4 |     9210 |     1052 |      446 |    10712 |
|   41609 |       0 |     816 |       0 |     338 |     266 |       0 |     181 |      11 | 3948.33 |          |        0 |        0 |        0 |     2294 |        0 |      796 |      816 |     2294 |     3906 |

Candidate column headers:
Tile Storage
Network Analysis
Premium
3-mo Average
Tile Usage
Tile Generation
Monthly Total
Feature Storage
Published
Geo-Enrichment
Month
Feature Usage
Geocoding
Elevation Analysis
Storage
File Storage
Analytics
Geotrigger Events
Spatial Analysis
Demographics



headers: Tile Storage
Network Analysis
Premium
3-mo Average
Tile Usage
Tile Generation
Monthly Total
Feature Storage
Published
Geo-Enrichment
Month
Feature Usage
Geocoding
Elevation Analysis
Storage
File Storage
Analytics
Geotrigger Events
Spatial Analysis
Demographics
label: Month
Tile Storage
Feature Storage
File Storage
Geocoding
Geo-Enrichment
Geotrigger Events
Network Analysis
Spatial Analysis
3-mo Average
Elevation Analysis
Demographics
Tile Usage
Feature Usage
Published
Premium
Analytics
Storage
Tile Generation
Monthly 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             |
|:--------|--------:|:------------|:------------------|
| 1.0     |       1 | Electronics | Cellphone charger |
|         |       1 | Electronics | Tablet case       |
|         |       1 | Bathroom    | Dental floss      |
| 1.0     |       1 | Clothes     | Winter jacket     |
|         |       1 | Bathroom    | Hair dryer        |

Candidate column headers:
CATEGORY
COUNT
ITEM
PACKED



headers: CATEGORY
COUNT
ITEM
PACKED
label: PACKED
COUNT
CATEGORY
ITEM
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 |
|:--------|:--------|--------:|--------:|
| Russell | TX      |      80 |   83.93 |
| Long    | TX      |      61 |   82.42 |
| Bennett | TX      |      80 |   12.97 |
| Ross    | MO      |      28 |   83.45 |
| Butler  | NY      |      83 |   47.22 |

Candidate column headers:
State
Qty
Amt
Name



headers: State
Qty
Amt
Name
label: Name
State
Qty
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 |   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 |
|:---------|:--------|:--------|:--------|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
| SOGULF   | TTL     | Sell-In | Sell-In |   99248 |   81467 |   83580 |   82986 |   61226 |   83641 |    78565 |    90377 |    72527 |    50653 |    89430 |    91441 |   109688 |   109950 |   104489 |    85714 |
| SOTHAI   | TTL     | Sell-In | Sell-In |   33346 |   23294 |   22007 |   18313 |   19205 |   26191 |    25673 |    33131 |    56107 |    19827 |    27070 |    35086 |    28070 |    20178 |    21164 |    15886 |
| SI       | TTL     | Sell-In | Sell-In |    1222 |    2773 |    1592 |    1407 |    1172 |    1628 |     2375 |     2418 |     2311 |     6189 |    11160 |    34894 |    11243 |     2080 |     5374 |    11215 |
| STWN     | TTL     | Sell-In | Sell-In |   23195 |   15508 |   13971 |   25226 |   11379 |   16887 |    18240 |    13498 |    18109 |    37631 |    22739 |    17635 |    25578 |    21868 |    21233 |    26394 |
| AMEA TTL | TTL     | Sell-In | Sell-In |  260344 |  213241 |  199548 |  224631 |  203839 |  235109 |   238761 |   256523 |   288045 |   193980 |   239553 |   289876 |   281847 |   260744 |   250636 |   239284 |

Candidate column headers:
May-10
Mar-10
Jul-10
Jun-09
May-09
Apr-09
Apr-10
Type
Type 2
Nov-09
Jan-10
Aug-09
Oct-09
Sep-09
Jun-10
Jul-09
Branch
Model
Feb-10
Dec-09



headers: May-10
Mar-10
Jul-10
Jun-09
May-09
Apr-09
Apr-10
Type
Type 2
Nov-09
Jan-10
Aug-09
Oct-09
Sep-09
Jun-10
Jul-09
Branch
Model
Feb-10
Dec-09
label: Branch
Model
Type
Type 2
Apr-09
May-09
Jun-09
Jul-09
Aug-09
Sep-09
Oct-09
Nov-09
Dec-09
Jan-10
Feb-10
Mar-10
Apr-10
May-10
Jun-10
Jul-10
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 |
|--------:|:---------|:----------|:--------|:--------|:--------------------|:--------|--------:|
|     299 | MARTINEZ | ANGE      | V1      | M       |                     |         |   25020 |
|     264 | NONNET   | ALAIN     | V2      | M       | HOSPITALIERS TOULON |         |   21802 |
|     254 | NARDIN   | CHRISTIAN | V4      | M       |                     |         |   11566 |
|     271 | ANSELMO  | MARIO     | V2      | M       |                     |         |   21622 |
|     252 | GUSMANO  | GILBERT   | V3      | M       | SPIRIDON C.A.       |         |   18192 |

Candidate column headers:
CAT
DOSSARD
SEXE
VILLE
CLUB
DATE NAISSANCE
NOM
PRENOM



headers: CAT
DOSSARD
SEXE
VILLE
CLUB
DATE NAISSANCE
NOM
PRENOM
label: DOSSARD
NOM
PRENOM
CAT
SEXE
CLUB
VILLE
DATE NAISSANCE
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   |
|:--------|--------:|:--------|:-----------|:-------------|--------:|--------:|--------:|--------:|--------:|---------:|--------------:|:---------|
| NY      |  513538 | CM      | V000000020 | Ingram Micro |   42685 |   42685 |     -39 |  -79.99 |       0 |   -79.99 | 2601400260298 | HORETRUN |
| NY      |  444695 | IN      | V000000020 | Ingram Micro |   42675 |   42685 |     -19 |  582.34 |       0 |   582.34 | 2601102102976 | COLORADO |
| NY      |  513580 | CM      | V000000020 | Ingram Micro |   42690 |   42690 |     -34 | -179.99 |       0 |  -179.99 | 2601400297695 | HORETRUN |
| NY      |  445406 | IN      | V000000020 | Ingram Micro |   42675 |   42702 |     -19 |  128.17 |       0 |   128.17 | 2601102107078 | ---      |
| NY      |  443617 | IN      | V000000020 | Ingram Micro |   42655 |   42663 |     -39 |   25    |       0 |    25    | 2601400297415 | ---      |

Candidate column headers:
PaidSys
DocDueDate
DocBal
TaxDate
DocNum
WH
Db
CardCode
DocType
CardName
NumAtCard
DocTotal
DaysOverDue



headers: PaidSys
DocDueDate
DocBal
TaxDate
DocNum
WH
Db
CardCode
DocType
CardName
NumAtCard
DocTotal
DaysOverDue
label: Db
DocNum
DocType
CardCode
CardName
TaxDate
DocDueDate
DaysOverDue
DocTotal
PaidSys
DocBal
NumAtCard
WH
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   |
|--------:|:--------|--------:|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:---------|:---------|:---------|---------:|:---------|---------:|:---------|:---------|:---------|:---------|
|       8 |         |       0 |         |         |         |         |         |         |         |          |          |          |        0 |          |        0 |          |          |          |          |
|      45 |         |       0 |         |         |         |         |         |         |         |          |          |          |        0 |          |        0 |          |          |          |          |
|      44 |         |       0 |         |         |         |         |         |         |         |          |          |          |        0 |          |        0 |          |          |          |          |
|      26 | LADWP   |       0 | 16.0    | 16.0    | 0.0     | 0.0     | 1.0     | 0.0     | 2.0     | 1.0      | 7.0      | 11.0     |        0 | 0.0      |        0 | 5.0      | 5.0      | 0.0      | 0.0      |
|      15 |         |       0 |         |         |         |         |         |         |         |          |          |          |        0 |          |        0 |          |          |          |          |

Candidate column headers:
Required PSS Data is Due
Generator Without Dynamics Data
Bus No Zone
LSDT9 Load not in Steady State
N-0 Branch Overload
Column2
Generators Connected to 100kV Buses
Generator Limit Violations
No Ratings
No Owner Total
Area Name
Previous Case Total
Column1
Current Case Total
Missing Long Load ID
Area Number
Missing Turbine Type
Change
Duplicate Bus Name
Unapproved Model



headers: Required PSS Data is Due
Generator Without Dynamics Data
Bus No Zone
LSDT9 Load not in Steady State
N-0 Branch Overload
Column2
Generators Connected to 100kV Buses
Generator Limit Violations
No Ratings
No Owner Total
Area Name
Previous Case Total
Column1
Current Case Total
Missing Long Load ID
Area Number
Missing Turbine Type
Change
Duplicate Bus Name
Unapproved Model
label: Area Number
Area Name
Previous Case Total
Current Case Total
Change
N-0 Branch Overload
No Ratings
Required PSS Data is Due
Missing Long Load ID
Unapproved Model
Generator Without Dynamics Data
Generator Limit Violations
Column1
LSDT9 Load not in Steady State
No Owner Total
Duplicate Bus Name
Missing Turbine Type
Column2
Generators Connected to 100kV Buses
Bus No Zone
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   |
|--------:|--------:|:--------|--------:|:-------------|:------------------------------|--------:|:---------------------|:--------|:--------|:---------|---------:|---------:|:------------|:------------------------------|:---------|
|   43406 |    1434 | Success |  933380 | U20H1LONGT/2 | A W Catering                  |    7846 |                      | Dundee  | DD1 3BA | Pass     |    42942 |      772 | Dundee City | food.safety@dundeecity.gov.uk | FHIS     |
|   43406 |    1434 | Success |  187205 | U18H410ALV/1 | Alva Square Sheltered Housing |       5 | 10 Alva Square       | Dundee  | DD3 6NR | Exempt   |    40695 |      772 | Dundee City | food.safety@dundeecity.gov.uk | FHIS     |
|   43406 |    1434 | Success |  185141 | R01C4ANNFI/1 | Alan E Clark                  |    7839 | Annfield Row         | Dundee  | DD1 5JH | Pass     |    43187 |      772 | Dundee City | food.safety@dundeecity.gov.uk | FHIS     |
|   43406 |    1434 | Success |  186526 | U05H4105-1/1 | Ancrum Arms                   |    7843 | 105-107 Logie Street | Dundee  | DD2 2PZ | Pass     |    43206 |      772 | Dundee City | food.safety@dundeecity.gov.uk | FHIS     |
|   43406 |    1434 | Success |  151320 | A06G132-38/1 | 1st Stop 2 Shop               |    4613 | 32-38 Fintry Drive   | Dundee  | DD4 9PB | Pass     |    42975 |      772 | Dundee City | food.safety@dundeecity.gov.uk | FHIS     |

Candidate column headers:
ItemCount
BusinessTypeID
LocalAuthorityCode
LocalAuthorityBusinessID
ExtractDate
AddressLine1
LocalAuthorityName
BusinessName
ReturnCode
LocalAuthorityEmailAddress
RatingValue
SchemeType
PostCode
RatingDate
FHRSID
AddressLine2



headers: ItemCount
BusinessTypeID
LocalAuthorityCode
LocalAuthorityBusinessID
ExtractDate
AddressLine1
LocalAuthorityName
BusinessName
ReturnCode
LocalAuthorityEmailAddress
RatingValue
SchemeType
PostCode
RatingDate
FHRSID
AddressLine2
label: ExtractDate
ItemCount
ReturnCode
FHRSID
LocalAuthorityBusinessID
BusinessName
BusinessTypeID
AddressLine1
AddressLine2
PostCode
RatingValue
RatingDate
LocalAuthorityCode
LocalAuthorityName
LocalAuthorityEmailAddress
SchemeType
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0         | Col_1         |   Col_2 |   Col_3 |   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 |
|:--------------|:--------------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
| Nick Stockert | Jamestown     |       1 |      71 |       4 |       4 |       2 |       5 |       5 |       3 |        5 |        6 |        5 |        5 |        4 |        3 |        5 |        5 |        3 |        4 |
| Cole Hanson   | GF Central    |       2 |      71 |       4 |       5 |       5 |       6 |       4 |       3 |        6 |        4 |        5 |        6 |        4 |        3 |        5 |        4 |        3 |        5 |
| Eric Pladson  | Bismarck High |       4 |      71 |       5 |       6 |       4 |       5 |       7 |       4 |        5 |        5 |        4 |        5 |        5 |        3 |        5 |        5 |        4 |        5 |
| Spencer Ross  | Minot         |       6 |      71 |       5 |       6 |       4 |       5 |       4 |       4 |        6 |        7 |        5 |        5 |        4 |        3 |        5 |        6 |        4 |        6 |
| Matt Schaefer | Dickinson     |       1 |      71 |       5 |       7 |       4 |       5 |       5 |       5 |        6 |        8 |        8 |        5 |        5 |        4 |        6 |        5 |        3 |        5 |

Candidate column headers:
Par
H4
H1
H11
School 
H10
H13
H14
Name
H8
H9
H15
Team  Rank
H12
H2
H3
H16
H5
H6
H7



headers: Par
H4
H1
H11
School 
H10
H13
H14
Name
H8
H9
H15
Team  Rank
H12
H2
H3
H16
H5
H6
H7
label: Name
School 
Team  Rank
Par
H1
H2
H3
H4
H5
H6
H7
H8
H9
H10
H11
H12
H13
H14
H15
H16
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 |
|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|
|      12 |  12.999 |      56 |      12 |      12 | 0.37931 |      12 |      12 |       8 |  8      |       12 |       12 |
|       9 |   9.999 |      67 |       9 |       9 | 0.27586 |       9 |       9 |       5 |  5      |        9 |        9 |
|      14 |  15.999 |      43 |      14 |      15 | 0.48275 |      14 |      14 |      10 | 10.3333 |       15 |       15 |
|      19 |  21.999 |      33 |      19 |      21 | 0.68965 |      19 |      19 |      13 | 13.5    |       21 |       21 |
|      24 |  24.999 |      28 |      24 |      24 | 0.7931  |      24 |      24 |      15 | 15      |       24 |       24 |

Candidate column headers:
Xaxis
Rank3
Rank10
Rank4
Rank2
Rank9
Rank11
Rank5
Rank1
tab
Rank8
Rank7



headers: Xaxis
Rank3
Rank10
Rank4
Rank2
Rank9
Rank11
Rank5
Rank1
tab
Rank8
Rank7
label: Rank3
Xaxis
tab
Rank1
Rank9
Rank10
Rank2
Rank4
Rank5
Rank11
Rank7
Rank8
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 |
|:--------|:--------|--------:|
| Joel    | Kite    |      86 |
| Joel    | Puzzle  |      44 |
| Suix    | Toy     |      34 |
| Pham    | Rang    |      99 |
| Pham    | Kite    |       2 |

Candidate column headers:
Sales2
Product2
SalesRep2



headers: Sales2
Product2
SalesRep2
label: SalesRep2
Product2
Sales2
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 |
|--------:|--------:|:----------|:--------|--------:|
|    2009 |       2 | May       | FirmA   |      67 |
|    2010 |       2 | April     | FirmB   |      42 |
|    2009 |       3 | July      | FirmA   |      24 |
|    2010 |       4 | November  | FirmA   |      24 |
|    2009 |       3 | September | FirmB   |      58 |

Candidate column headers:
Month
Revenue
Year
Company
Quarter



headers: Month
Revenue
Year
Company
Quarter
label: Year
Quarter
Month
Company
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 |
|:--------|:---------|:------------------|:------------------|--------:|
| IET     | PBTE040E | 978-0-85296-935-9 | 978-1-84919-464-8 |    1998 |
| IET     | PBTE905E | 978-0-85296-860-4 | 978-1-84919-467-9 |    1994 |
| IET     | PBBT005E | 978-0-85296-428-6 | 978-1-84919-031-2 |    2003 |
| IET     | PBTE059E | 978-1-84919-647-5 | 978-1-84919-648-2 |    2013 |
| IET     | PBTE043E | 978-0-85296-761-4 | 978-1-84919-181-4 |    1999 |

Candidate column headers:
eISBN
Print ISBN
Product Code
Imprint
Published



headers: eISBN
Print ISBN
Product Code
Imprint
Published
label: Imprint
Product Code
Print ISBN
eISBN
Published