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                |
|--------:|:--------|:--------------|:------------|:-----------------|:---------------------|--------:|--------:|--------:|:--------|---------:|:-------------|:-------------|:----------------------|
|       7 | TRM501  | Multifamily   | Res Cooling | Project-specific | Square foot-dwelling |       0 |   0.009 |   0.009 | CZ03    |       11 | CMUA POU TRM | CMUA POU TRM | Project documentation |
|      47 | TRM501  | Multifamily   | Res Cooling | Project-specific | Square foot-dwelling |       0 |   0.023 |   0.001 | CZ15    |       11 | CMUA POU TRM | CMUA POU TRM | Project documentation |
|       2 | TRM501  | Single-family | Res Cooling | Project-specific | Square foot-dwelling |       0 |   0.07  |   0     | CZ02    |       11 | CMUA POU TRM | CMUA POU TRM | Project documentation |
|      50 | TRM501  | Single-family | Res Cooling | Project-specific | Square foot-dwelling |       0 |   0.055 |   0     | CZ16    |       11 | CMUA POU TRM | CMUA POU TRM | Project documentation |
|      29 | TRM501  | Single-family | Res Cooling | Project-specific | Square foot-dwelling |       0 |   0.084 |   0.01  | CZ11    |       11 | CMUA POU TRM | CMUA POU TRM | Project documentation |

Candidate column headers:
Annual kWh
TRM #
Analysis Source
Peak kW
Therms
Units
Savings Source
Measure Type
Building Type
Cost
Measure Life
Cost Source
Climate Zone
Item #



headers: Annual kWh
TRM #
Analysis Source
Peak kW
Therms
Units
Savings Source
Measure Type
Building Type
Cost
Measure Life
Cost Source
Climate Zone
Item #
label: Item #
TRM #
Building Type
Measure Type
Cost
Units
Peak kW
Annual kWh
Therms
Climate Zone
Measure Life
Analysis Source
Savings Source
Cost Source
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 |
|:----------|--------:|--------:|
| Produit 2 |   41395 |      18 |
| Produit 2 |   41518 |      22 |
| Produit 1 |   41306 |      14 |
| Produit 2 |   41640 |      24 |
| Produit 2 |   41456 |      20 |

Candidate column headers:
Date
Valeur
Produit



headers: Date
Valeur
Produit
label: Produit
Date
Valeur
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 |
|:----------|:----------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|
| Incandela | Andrea    | 5321451 |      28 |      26 |      20 |      25 |      25 |      29 | 25.5    |  93.5    |
| Napoli    | Francesco | 5487963 |      27 |      24 |      29 |      27 |      29 |      28 | 27.3333 | 100.222  |
| Battaglia | Paolo     | 5466521 |      25 |      22 |      21 |      20 |      25 |      26 | 23.1667 |  84.9444 |
| Marchese  | Giovanna  | 5469892 |      21 |      27 |      24 |      27 |      30 |      28 | 26.1667 |  95.9444 |
| Caruso    | Giuseppe  | 5478256 |      21 |      21 |      26 |      28 |      21 |      24 | 23.5    |  86.1667 |

Candidate column headers:
Diritto pubblico
Diritto privato
Matricola
Media semplice dei voti
Nome
Cognome
Lingua inglese
VOTO DI LAUREA
Microeconomia
Matematica
Storia economica



headers: Diritto pubblico
Diritto privato
Matricola
Media semplice dei voti
Nome
Cognome
Lingua inglese
VOTO DI LAUREA
Microeconomia
Matematica
Storia economica
label: Cognome
Nome
Matricola
Matematica
Diritto pubblico
Diritto privato
Storia economica
Lingua inglese
Microeconomia
Media semplice dei voti
VOTO DI LAUREA
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         |
|:-----------|:-------------------|:--------|:------------------------|--------:|--------:|:---------------|:------------------|:--------|--------:|:------------|---------:|---------:|---------:|:---------|:---------------|
| Birmingham | West Midlands      | B21 8AT | Small convenience store |      48 |     1   | Jacky Lawrence | Sliced Turkey     | Turkey  |       1 | Not organic |    41121 | 0.547917 |     1.49 | 0.0      | EC UK BL020    |
| Coventry   | West Midlands      | CV1 2HU | Large convenience store |      17 |     0.1 | Jacky Lawrence | Sliced Chicken    | Chicken |       1 | Not organic |    41029 | 0.508333 |     1.59 |          | EC UK YE056    |
| Enfield    |                    | EN3 5BL | Large convenience store |      23 |     7.9 | Mike Hutchison | Fermented Sausage | Pork    |       2 | Not organic |    41212 | 0.354167 |     1.98 |          | EG DE EV270    |
| Birmingham | West Midlands      | B18 7HR | Large convenience store |      44 |     3.2 | Jacky Lawrence | Cooked Ham        | Pork    |       1 | Not organic |    41121 | 0.452083 |     2    | 38506.0  | EC UK MK024    |
| Manchester | Greater Manchester | M16 9NR | Large convenience store |       6 |     5.7 | Mike Hutchison | Sliced Beef       | Beef    |       1 | Not organic |    41043 | 0.298611 |     0.79 | 55.0     | EG DE NI 10046 |

Candidate column headers:
Purchase Price
EC Number
Shop Postcode
CSM Type
Purchase Date
Shop County
Shop Type
Receipt Code
Species
Purchase Time
Sampler Name
Shop Town
Sample Number
Temperature at Purchase
Number of packs
Organic or Not



headers: Purchase Price
EC Number
Shop Postcode
CSM Type
Purchase Date
Shop County
Shop Type
Receipt Code
Species
Purchase Time
Sampler Name
Shop Town
Sample Number
Temperature at Purchase
Number of packs
Organic or Not
label: Shop Town
Shop County
Shop Postcode
Shop Type
Sample Number
Temperature at Purchase
Sampler Name
CSM Type
Species
Number of packs
Organic or Not
Purchase Date
Purchase Time
Purchase Price
Receipt Code
EC Number
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 | Col_1   |   Col_2 |   Col_3 |   Col_4 |   Col_5 | Col_6   | Col_7   | Col_8   |
|--------:|:--------|--------:|--------:|--------:|--------:|:--------|:--------|:--------|
|       4 | Sat     |   39655 |       3 |       2 |      -1 |         | W       | L       |
|      15 | Sun     |   39663 |       4 |       3 |      -1 |         | W       | L       |
|      42 | Sun     |   39684 |       3 |       1 |      -2 |         | W       | L       |
|      22 | Sat     |   39669 |       0 |       7 |       7 |         | L       | W       |
|       5 | Sat     |   39655 |       6 |       9 |       3 |         | L       | W       |

Candidate column headers:
Tim
Date
Randy
Day of week
OT
#
Randy W/L
Tim W/L
Randy Goal diff



headers: Tim
Date
Randy
Day of week
OT
#
Randy W/L
Tim W/L
Randy Goal diff
label: #
Day of week
Date
Tim
Randy
Randy Goal diff
OT
Tim W/L
Randy W/L
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 |
|--------:|--------:|--------:|
|   96408 |  120619 |  213732 |
|   31050 |   40717 |   70364 |
|   40632 |   50431 |   89476 |
|   43408 |   57260 |   99043 |
|   35955 |   45959 |   80553 |

Candidate column headers:
Column2
Column3
Column1



headers: Column2
Column3
Column1
label: Column1
Column2
Column3
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 |
|:-------------------|:--------|--------:|:--------|:--------|:--------|:--------|---------:|
| 157B4516; 157B4528 | PVEM    |      32 |         | PAS     | DIN     | 1x4     | 11166845 |
| 157B4834           | PVES    |      32 |         | ACT     | AMP     | 1x4     | 11166858 |
| 155G4274           | PVEO    |     120 |         | 24V     | DIN     | 1x4     | 11166744 |
| 157B4116; 157B4128 | PVEM    |      32 |         | PAS     | DIN     | 1x4     | 11166829 |
| 11141618           | PVEH    |      32 | SP      | ACT     | DEU     | 1x6     | 11166773 |

Candidate column headers:
PVE Series 4 Sales Number
PVE Series 7 Sales Number
Variant_ Performance
Connector Variant
Variant_ Feature
Variant_ Platform
Sub-Variant
Connector Type



headers: PVE Series 4 Sales Number
PVE Series 7 Sales Number
Variant_ Performance
Connector Variant
Variant_ Feature
Variant_ Platform
Sub-Variant
Connector Type
label: PVE Series 4 Sales Number
Variant_ Performance
Variant_ Platform
Variant_ Feature
Sub-Variant
Connector Type
Connector Variant
PVE Series 7 Sales Number
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0        |   Col_1 |     Col_2 |     Col_3 |   Col_4 | Col_5   |     Col_6 |     Col_7 |     Col_8 |      Col_9 |     Col_10 |    Col_11 |     Col_12 |   Col_13 |
|:-------------|--------:|----------:|----------:|--------:|:--------|----------:|----------:|----------:|-----------:|-----------:|----------:|-----------:|---------:|
| Contra Costa |    1147 | 135724000 | 216467000 |       0 | b/      |  21722000 | 668101000 | 190810000 | 2252237000 | 1878850000 | 117958000 | 5481870000 |    0.031 |
| San Benito   |     115 |  14503000 |         0 |  505000 |         |   5824000 |  23092000 |   2445000 |          0 |   56421000 |   3827000 |  106618000 |    0.015 |
| Del Norte    |     157 |  16090000 |         0 |       0 |         |  14717000 |         0 |    279000 |   42673000 |   39192000 |    727000 |  113679000 |    0.065 |
| Alameda      |     776 |  86681000 | 420536000 |       0 |         | 195911000 | 355188000 |         0 | 2410442000 | 4182533000 | 285325000 | 7936617000 |    0.033 |
| Mono         |       5 |    510000 |         0 |  799000 |         |    841000 |   8095000 |         0 |          0 |   37268000 |   1275000 |   48788000 |    0.009 |

Candidate column headers:
Religious
Total exempt value as percent of tax base f/
Number of Veterans' exemptions a/
All other d/
Low-Valued Property
College
Church
County
Welfare:  Schools below college grade
Welfare:  Other charitable properties c/
Ref
Welfare:  Hospitals
Veterans' a/
Total e/



headers: Religious
Total exempt value as percent of tax base f/
Number of Veterans' exemptions a/
All other d/
Low-Valued Property
College
Church
County
Welfare:  Schools below college grade
Welfare:  Other charitable properties c/
Ref
Welfare:  Hospitals
Veterans' a/
Total e/
label: County
Number of Veterans' exemptions a/
Veterans' a/
College
Low-Valued Property
Ref
Church
Religious
Welfare:  Schools below college grade
Welfare:  Hospitals
Welfare:  Other charitable properties c/
All other d/
Total e/
Total exempt value as percent of tax base f/
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    |
|:-----------------|:--------|:----------------|:---------------|:---------|
| 42422.4511226852 | CO-0009 | Highlands       | Sheryl Langley | slangley |
|                  | IL-0002 | Third Lake      | Alex Mueller   | amueller |
|                  |         | Support Center  | Kevin Betts    | kbetts   |
|                  |         | Support Center  | Healther Story | hstory   |
| 42396.5253009259 | CO-0002 | Highlands Ranch | Karen Pierce   | kpierce  |

Candidate column headers:
Studio Name
Studio Number
(Do Not Modify) Modified On
Primary Contact
Password



headers: Studio Name
Studio Number
(Do Not Modify) Modified On
Primary Contact
Password
label: (Do Not Modify) Modified On
Studio Number
Studio Name
Primary Contact
Password
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   |
|:---------------|:------------|:----------|:--------|:--------|
| CLOCK_TYPE_BC  | true_ false | mandatory | True    | c1      |
| TIME_TRACEABLE | true_ false | mandatory | False   | c3      |
| MIB_61850      | true_ false | mandatory | False   | c7      |
| REDBOX_DATC    | true_ false | optional  | True    | c5      |
| MIB_SNMP       | true_ false | mandatory | False   | c7      |

Candidate column headers:
PICS Proforma Reference
Condition
Value
Base
Values



headers: PICS Proforma Reference
Condition
Value
Base
Values
label: PICS Proforma Reference
Value
Base
Values
Condition
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      |
|---------:|--------:|:-----------|
| 154413   |      21 | Ethiopia   |
|  61202.1 |      40 | Uzbekistan |
|  95529.1 |      26 | Madagascar |
|  51918.3 |      44 | Peru       |
|  53021.1 |      43 | Turkey     |

Candidate column headers:
Current annual average population affected by river floods
Rank
Country



headers: Current annual average population affected by river floods
Rank
Country
label: Current annual average population affected by river floods
Rank
Country
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 |
|--------:|:-------------|:--------|--------:|--------:|--------:|
|   41926 | Guinea       | Cases   |    1217 |     191 |     111 |
|   41924 | Sierra Leone | Cases   |    2849 |      37 |     366 |
|   41931 | Nigeria      | Deaths  |       7 |       1 |       0 |
|   41931 | Liberia      | Cases   |     965 |    2106 |    1594 |
|   41926 | Spain        | Deaths  |       0 |       0 |       0 |

Candidate column headers:
Probable
Type
Country
ReportDate
Suspect
Confirmed



headers: Probable
Type
Country
ReportDate
Suspect
Confirmed
label: ReportDate
Country
Type
Confirmed
Probable
Suspect
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   |
|:--------|:--------|:--------------|:--------|:--------|:--------|:--------------|:--------|:--------------|:--------|:--------------|:---------|
| 6G2     | I601    |               |         |         |         | 11:00 - 13:00 | SCH 16  |               |         | 11:00 - 13:00 | SCH 16   |
| 1G1     | P101-06 |               |         |         |         |               |         |               |         |               |          |
| 5G3     | I501    |               |         |         |         | 13:00 - 15:00 | SCH4    | 13:00 - 15:00 | SCH4    |               |          |
| 1G2     | I101    | 10:00 - 11:30 | SCH8    |         |         |               |         |               |         | 10:00 - 11:30 | SCH8     |
| 1G3     | P101-06 | 10:00 - 11:30 | SCH5    |         |         |               |         |               |         | 10:00 - 11:30 | SCH5     |

Candidate column headers:
HJueves
V_SalonGrupo
M_SalonGrupo
HLunes
HMiercoles
Clave
HViernes
Grupo
L_SalonGrupo
MM_SalonGrupo
HMartes
J_SalonGrupo



headers: HJueves
V_SalonGrupo
M_SalonGrupo
HLunes
HMiercoles
Clave
HViernes
Grupo
L_SalonGrupo
MM_SalonGrupo
HMartes
J_SalonGrupo
label: Grupo
Clave
HLunes
L_SalonGrupo
HMartes
M_SalonGrupo
HMiercoles
MM_SalonGrupo
HJueves
J_SalonGrupo
HViernes
V_SalonGrupo
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          |
|:---------|--------:|-----------:|:---------------|
| galaxys4 |      15 | 0.00468984 | (Entire graph) |
| counting |     129 | 0.0155688  | (Entire graph) |
| 4        |      42 | 0.0116965  | (Entire graph) |
| new      |      15 | 0.00468984 | (Entire graph) |
| sept     |      19 | 0.00553977 | (Entire graph) |

Candidate column headers:
Word
Count
Salience
Group



headers: Word
Count
Salience
Group
label: Word
Count
Salience
Group
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 |   Col_1 |   Col_2 |   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 |
|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
|      46 |       4 |      15 |     114 | 40024.4 |       0 |       1 |       0 |       0 |       0 |        5 |  40024.4 |        0 |        1 |    40024 |        0 |  40024.7 |      0   |
|      29 |       1 |      10 |       1 | 40022.3 |       0 |       2 |       0 |       1 |       0 |        3 |  40022.3 |        0 |        1 |    40022 |        0 |  40022.7 |  40022.3 |
|      30 |       1 |      10 |      67 | 40022.4 |       0 |       3 |       0 |       1 |       0 |        3 |  40022.3 |        0 |        1 |    40022 |        0 |  40022.7 |  40022.3 |
|      18 |       1 |       7 |      55 | 40022   |       0 |       1 |       0 |       0 |       0 |        2 |  40022   |        0 |        1 |    40021 |        0 |  40022.3 |      0   |
|      20 |       4 |       2 |      55 | 40022   |       0 |       5 |       0 |       1 |       0 |        3 |  40019.9 |        0 |        1 |    40022 |        0 |  40022.3 |  40021.8 |

Candidate column headers:
topic_id
poster_id
timestamp of previous reply
Questioner
Time between Hui Reply & Topic Start
Reply?
post_status
day of week
post_position
Hui Post?
corrected time
post_time
Date
Cumulative Count - Hui posts
Topic Start
forum_id
Hui Reply?
post_id



headers: topic_id
poster_id
timestamp of previous reply
Questioner
Time between Hui Reply & Topic Start
Reply?
post_status
day of week
post_position
Hui Post?
corrected time
post_time
Date
Cumulative Count - Hui posts
Topic Start
forum_id
Hui Reply?
post_id
label: post_id
forum_id
topic_id
poster_id
post_time
post_status
post_position
Hui Post?
Reply?
Hui Reply?
day of week
Topic Start
Time between Hui Reply & Topic Start
Cumulative Count - Hui posts
Date
Questioner
corrected time
timestamp of previous reply
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 |
|:------------|--------:|--------:|----------:|
| Aleppo      |      99 |      99 | 1         |
| Damascus    |      97 |       9 | 0.0927835 |
| Lattakia    |      15 |       0 | 0         |
| Idleb       |       6 |       6 | 1         |
| Deir-ez-Zor |      13 |      13 | 1         |

Candidate column headers:
Hays
Community
Percentage
Round 2



headers: Hays
Community
Percentage
Round 2
label: Community
Hays
Round 2
Percentage
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|    Col_0 | Col_1   | Col_2   |   Col_3 | Col_4   |   Col_5 |   Col_6 |   Col_7 |
|---------:|:--------|:--------|--------:|:--------|--------:|--------:|--------:|
| 0.822917 | 0       | 0       |       0 | 0       |       0 |       0 |       0 |
| 0.59375  | 0       | 0       |       0 | 0       |       0 |       0 |       0 |
| 0.6875   | SP-111  | 0       |       0 | 0       |       0 |       0 |       0 |
| 0.71875  | 0       | 0       |       0 | 0       |       0 |       0 |       0 |
| 0.5      | 0       | HLO-295 |       0 | HLO-295 |       0 |       0 |       0 |

Candidate column headers:
ZATERDAG
DINSDAG
DONDERDAG
TIJD
WOENSDAG
ZONDAG
MAANDAG
VRIJDAG



headers: ZATERDAG
DINSDAG
DONDERDAG
TIJD
WOENSDAG
ZONDAG
MAANDAG
VRIJDAG
label: TIJD
MAANDAG
DINSDAG
WOENSDAG
DONDERDAG
VRIJDAG
ZATERDAG
ZONDAG
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 |
|--------:|--------:|--------:|--------:|
|  860289 |    2.43 |     729 | 1771.47 |
|  860225 |    1.3  |     334 |  434.2  |
|  860286 |    2.43 |     613 | 1489.59 |
|  860054 |    1    |     621 |  621    |
|  860294 |    2.43 |     686 | 1666.98 |

Candidate column headers:
Qty
Item No
Amt
Unit Price



headers: Qty
Item No
Amt
Unit Price
label: Item No
Unit Price
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                     |
|:--------|:--------|:-----------|:------------|:--------|:-------------|:--------------------------|
| CXB-211 | Camp 8W | Chittagong | Cox's Bazar | Ukhia   | Palong Khali | Kutupalong Expansion Site |
| CXB-204 | Camp 2W | Chittagong | Cox's Bazar | Ukhia   | Palong Khali | Kutupalong Expansion Site |
| CXB-211 | Camp 8W | Chittagong | Cox's Bazar | Ukhia   | Palong Khali | Kutupalong Expansion Site |
| CXB-213 | Camp 9  | Chittagong | Cox's Bazar | Ukhia   | Palong Khali | Kutupalong Expansion Site |
| CXB-213 | Camp 9  | Chittagong | Cox's Bazar | Ukhia   | Palong Khali | Kutupalong Expansion Site |

Candidate column headers:
Division - Cleaned
District - Cleaned
Upazila - Cleaned
Union - Cleaned
SSID
Location_Camp - Cleaned
Location_Site - Cleaned



headers: Division - Cleaned
District - Cleaned
Upazila - Cleaned
Union - Cleaned
SSID
Location_Camp - Cleaned
Location_Site - Cleaned
label: SSID
Location_Camp - Cleaned
Division - Cleaned
District - Cleaned
Upazila - Cleaned
Union - Cleaned
Location_Site - Cleaned
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 |
|--------:|--------:|--------:|--------:|--------:|--------:|--------:|
|    3648 |     195 |    2003 | 0.1223  |       0 | 0.1223  |   39952 |
|   10387 |     166 |    2003 | 0.13139 |       0 | 0.13139 |   39952 |
|    4003 |     159 |    2003 | 0.1475  |       0 | 0.1475  |   39952 |
|    3558 |     179 |    2003 | 0.10428 |       0 | 0.10428 |   39952 |
|    3628 |     192 |    2003 | 0.0831  |       0 | 0.0831  |   39952 |

Candidate column headers:
AssessedTotal
AssessedDebtService
LastUpdate
AssessedCurrentOperations
TaxBaseID
FiceCode
FiscalYear



headers: AssessedTotal
AssessedDebtService
LastUpdate
AssessedCurrentOperations
TaxBaseID
FiceCode
FiscalYear
label: FiceCode
TaxBaseID
FiscalYear
AssessedCurrentOperations
AssessedDebtService
AssessedTotal
LastUpdate
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 |
|--------:|:--------|--------:|:--------|--------:|----------:|--------:|--------:|
|   40325 | Powell  |       7 | E       |      62 | 0.0333513 |     169 |      11 |
|   40008 | Taylor  |       1 | E       |      33 | 0.0314286 |     175 |       6 |
|   39839 | Ross    |       7 | E       |      66 | 0.0261697 |     194 |      13 |
|   39934 | Ward    |       9 | E       |      69 | 0.0340909 |     184 |      11 |
|   39977 | Green   |       6 | E       |      69 | 0.0340909 |     184 |      11 |

Candidate column headers:
Type
Q_Hrs
T_Hrs
Pct
Prof
Hrs
C_Hrs
DDate



headers: Type
Q_Hrs
T_Hrs
Pct
Prof
Hrs
C_Hrs
DDate
label: DDate
Prof
Hrs
Type
T_Hrs
Pct
Q_Hrs
C_Hrs
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                |
|:--------|--------:|:--------|--------:|:---------------------|
| ACCT    |     221 | ACCT    |    3210 |                      |
| BIOL    |     216 | LIFS    |    3160 |                      |
| BIOL    |     124 | LIFS    |    2240 |                      |
| ACCT    |     366 | ACCT    |    4660 | (Deleted in 2012-13) |
| ACCT    |     201 | ACCT    |    3010 |                      |

Candidate column headers:
Previous Subject
Current Number
Remarks
Previous Number
Current Subject



headers: Previous Subject
Current Number
Remarks
Previous Number
Current Subject
label: Previous Subject
Previous Number
Current Subject
Current Number
Remarks
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 |
|:--------|--------:|--------:|--------:|--------:|--------:|
| C       |   36624 |  154.83 |   36646 |    0    |  154.83 |
| D       |   36624 |    9.8  |   36646 |    9.8  |    0    |
| D       |   36624 |   40.15 |   36646 |   40.15 |    0    |
| C       |   36624 |  216.07 |   36646 |    0    |  216.07 |
| D       |   36624 |    1.96 |   36646 |    1.96 |    0    |

Candidate column headers:
Net
amount_db
EOM
amount_cr
debit_credit
Date



headers: Net
amount_db
EOM
amount_cr
debit_credit
Date
label: debit_credit
Date
Net
EOM
amount_db
amount_cr
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 |
|:-----------|:------------|:--------|--------:|
| Fruit      | Bananas     | Q1      |    5388 |
| Vegetables | Spinach     | Q2      |    2227 |
| Vegetables | Green Beans | Q2      |    7679 |
| Fruit      | Apples      | Q1      |    9628 |
| Fruit      | Berries     | Q1      |    6642 |

Candidate column headers:
Revenue
Category
Subcategory
Quarter



headers: Revenue
Category
Subcategory
Quarter
label: Category
Subcategory
Quarter
Revenue
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 | Col_1   |   Col_2 |   Col_3 |   Col_4 |   Col_5 |
|--------:|:--------|--------:|--------:|--------:|--------:|
|   40691 | Mazda   |   23000 |       8 |  0.0128 |       3 |
|   40669 | Ford    |   32750 |      11 |  0.0103 |       5 |
|   40669 | GM      |   41000 |      10 |  0.008  |       4 |
|   40546 | Ford    |   36450 |       1 |  0.0154 |       5 |
|   40549 | Toyota  |   21500 |      10 |  0.0988 |       5 |

Candidate column headers:
Car Sold
 Full Price
Quantity
Warranty (Yrs)
Discount
Date



headers: Car Sold
 Full Price
Quantity
Warranty (Yrs)
Discount
Date
label: Date
Car Sold
 Full Price
Quantity
Discount
Warranty (Yrs)
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
|   Col_0 |   Col_1 |   Col_2 | Col_3                     |   Col_4 |
|--------:|--------:|--------:|:--------------------------|--------:|
|      14 |      22 |      26 | betaTub56D-PB_FBgn0003887 |  3.3118 |
|      33 |      13 |      15 | Nopp140-PA_FBgn0037137    |  3.0144 |
|      26 |      15 |      21 | up-PA_FBgn0004169         |  2.7866 |
|      38 |      11 |      17 | RpL7A-PA_FBgn0014026      |  2.6084 |
|      28 |      14 |      19 | RpS3A-PA_FBgn0017545      |  2.9615 |

Candidate column headers:
Reference
Rank
Unique
Total
Avg XCorr



headers: Reference
Rank
Unique
Total
Avg XCorr
label: Rank
Unique
Total
Reference
Avg XCorr
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      |
|--------:|--------:|:--------------|:--------|:-----------|
|   42391 |      76 | Restaurant    | Weekday | Jan - 2016 |
|   42389 |      14 | Shopping      | Weekday | Jan - 2016 |
|   42423 |       9 | Entertainment | Weekday | Feb - 2016 |
|   42418 |      21 | Groceries     | Weekday | Feb - 2016 |
|   42449 |      62 | Groceries     | Weekend | Mar - 2016 |

Candidate column headers:
MONTH
CATEGORY
TYPEOFDAY
DATE
AMOUNT



headers: MONTH
CATEGORY
TYPEOFDAY
DATE
AMOUNT
label: DATE
AMOUNT
CATEGORY
TYPEOFDAY
MONTH
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 |
|--------:|--------:|--------:|:-------------|--------:|
|    1336 |   42786 |       5 | Wegmans      |   80.75 |
|     579 |   42759 |       2 | Wegmans      |   36.92 |
|     502 |   42756 |       2 | Publix       |   69.83 |
|    1129 |   42777 |      10 | Wegmans      |   22.36 |
|    1012 |   42774 |       9 | Trader Joe's |   80.6  |

Candidate column headers:
Transaction Number
Date
Spending
Store
CustomerID



headers: Transaction Number
Date
Spending
Store
CustomerID
label: Transaction Number
Date
CustomerID
Store
Spending
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   |
|--------:|:--------|:--------|
|     409 | QLD9    | QLD9    |
|     406 | QLD6    | QLD6    |
|     404 | QLD4    | QLD4    |
|     203 | NSW3    | NSW3    |
|     204 | NSW4    | NSW4    |

Candidate column headers:
Number
Column1
Zone



headers: Number
Column1
Zone
label: Number
Zone
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      |
|:-------------|:-------------------------|--------:|:-----------|:--------|:--------|------------:|:-----------|
| DE0001135069 | BUNDESREPUB. DEUTSCHLAND |   5.625 | 04/01/2028 | EUR     | DE      | 1.45e+10    | 23/01/1998 |
| DE0001135382 | BUNDESREPUB. DEUTSCHLAND |   3.5   | 04/07/2019 | EUR     | DE      | 2.4e+10     | 22/05/2009 |
| BE0000320292 | BELGIUM KINGDOM          |   4.25  | 28/03/2041 | EUR     | BE      | 1.7299e+10  | 21/04/2010 |
| AT0000A1FAP5 | REPUBLIC OF AUSTRIA      |   1.2   | 20/10/2025 | EUR     | AT      | 1.04219e+10 | 23/06/2015 |
| BE0000318270 | BELGIUM KINGDOM          |   3.75  | 28/09/2020 | EUR     | BE      | 1.9226e+10  | 19/01/2010 |

Candidate column headers:
Currency
Country
Maturity
Issuer
Amount outstanding
Coupon
ISIN
Issue Date



headers: Currency
Country
Maturity
Issuer
Amount outstanding
Coupon
ISIN
Issue Date
label: ISIN
Issuer
Coupon
Maturity
Currency
Country
Amount outstanding
Issue Date
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0    |   Col_1 |   Col_2 |   Col_3 |   Col_4 |   Col_5 |   Col_6 |   Col_7 |   Col_8 |   Col_9 |   Col_10 |   Col_11 |   Col_12 |   Col_13 |   Col_14 |   Col_15 |   Col_16 |
|:---------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
| June     |     558 |     107 |      87 |      42 |      98 |      20 |     111 |       0 |      93 |       18 |       10 |        3 |       28 |        6 |       67 |       28 |
| April    |     441 |      69 |      56 |      42 |      67 |      30 |      76 |       2 |      97 |       14 |        7 |        0 |       26 |        4 |       47 |       20 |
| February |     374 |      64 |      61 |      43 |      76 |      10 |      74 |       1 |      74 |       22 |       13 |        3 |       18 |        4 |       36 |       24 |
| August   |     613 |     103 |     100 |      42 |     101 |      32 |     107 |       1 |      72 |       23 |       13 |        1 |       44 |       16 |       89 |       41 |
| December |     471 |      98 |      52 |      42 |     106 |      15 |      96 |       3 |      69 |       42 |       19 |        2 |       33 |        2 |       49 |       36 |

Candidate column headers:
FCP Towed Vehicle
Trans. Motorist
Marked Aban
Gave Gas
Changed Tire
Gave Directions
Total Responces
Mechanical Asst.
GOA
Month
Stand By
Traffic Policing
Declined Service
Cellular Asst.
Non-FCP Tow
Moved Debris
Other



headers: FCP Towed Vehicle
Trans. Motorist
Marked Aban
Gave Gas
Changed Tire
Gave Directions
Total Responces
Mechanical Asst.
GOA
Month
Stand By
Traffic Policing
Declined Service
Cellular Asst.
Non-FCP Tow
Moved Debris
Other
label: Month
Total Responces
Marked Aban
Changed Tire
Gave Gas
Mechanical Asst.
Moved Debris
Traffic Policing
FCP Towed Vehicle
Non-FCP Tow
Stand By
Trans. Motorist
Cellular Asst.
GOA
Gave Directions
Declined Service
Other
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 |
|:------------------|------------:|----------:|
| FAIRFIELD INN     | 4.02804e+07 | 0.0179206 |
| SHERATON          | 5.30772e+07 | 0.0236139 |
| MARRIOTT          | 1.42914e+08 | 0.063582  |
| BEST WESTERN      | 2.62729e+07 | 0.0116887 |
| DOLLAR RENT-A-CAR | 1.50656e+07 | 0.0444437 |

Candidate column headers:
Market Share 
Totals
Airlines



headers: Market Share 
Totals
Airlines
label: Airlines
Totals
Market Share