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   | Col_16   |
|--------:|:-----------|--------:|--------:|--------:|--------:|:---------|:-------------------------------|:----------|:--------|:---------|:---------|:-------------------|:---------|:---------|:---------|:---------|
|   40583 | 133442-2   |       3 |   41305 | 50.4167 |  151.25 | 015-0029 | LOTEK LT4 FX=40-60MHZ          |           | Quoted  | LT-4     | SERIES   | 0.1500000059604644 | 205-178  | 206-119  | 090ZF    | AL       |
|   37727 | 130129-2   |       2 |   41022 | 49.86   |  149.58 | 015-0029 | LOTEK 015-0029 LT4 FX=40-60MHZ |           | Quoted  | LT-4     | SERIES   | 0.1500000059604644 | 205-178  | 206-119  | 090ZF    | AL       |
|   38680 | 131252-10  |       9 |   41108 | 50.3833 |  151.15 | 015-0029 | LOTEK LT4 FX=40-60MHZ          |           | Quoted  | LT-4     | SERIES   | 0.1500000059604644 | 205-178  | 206-119  | 090ZF    | AL       |
|   49188 | 148147-1   |       6 |   42828 |  0.3072 |    0    | 010118-1 | GE AERO 010118-1 REVA 307.2KHZ |           |         | HC-51/U  | SERIES   |                    |          | 206-106  |          |          |
|   26888 | 118897-5-1 |       1 |   40232 | 54.8633 |  164.59 | 015-0029 | LOTEK                          | 54.863333 |         |          | SERIES   |                    |          |          |          |          |

Candidate column headers:
BlankDia
BasePart
Qty
newfx
Metal1
ID
Job
FC
Load
Part
Key
Date
FX
Holder
CanPart
Column1
Description



headers: BlankDia
BasePart
Qty
newfx
Metal1
ID
Job
FC
Load
Part
Key
Date
FX
Holder
CanPart
Column1
Description
label: ID
Job
Qty
Date
FX
FC
Part
Description
newfx
Column1
Holder
Load
BlankDia
BasePart
CanPart
Key
Metal1
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 |
|--------:|--------:|--------:|--------:|--------:|
|   40932 |   40932 |   40932 |     144 |       4 |
|   40966 |   40966 |   40966 |     109 |       5 |
|   40976 |   40976 |   40976 |     124 |       6 |
|   41074 |   41074 |   41074 |     200 |       8 |
|   41246 |   41246 |   41246 |     126 |       3 |

Candidate column headers:
Cellules
Jour
Mois
Germes
Date



headers: Cellules
Jour
Mois
Germes
Date
label: Date
Jour
Mois
Cellules
Germes
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        |
|:---------|--------:|:-------------|:--------|:---------------|:-------------|
| Friday   |   43455 | Konos        | Away    | 4:00PM         | MB           |
| Saturday |   43435 | Flint River  | Home    | 1/2/3/4/5:30   | MG/MB/JVB/VB |
| Thursday |   43440 | Skipstone    | Away    | 5:00/6:00      | MG/MB        |
| Tuesday  |   43445 | Creekside    | Away    | 4:30/5:30/7:00 | JV/VG/VB     |
| Monday   |   43416 | New Creation | Home    | 5/6/7:30       | JV/VG/VB     |

Candidate column headers:
PLACE
TEAM
DATE
DAY
LEVEL
TIME



headers: PLACE
TEAM
DATE
DAY
LEVEL
TIME
label: DAY
DATE
TEAM
PLACE
TIME
LEVEL
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       |
|:------------|:-----------------|:--------|:--------|:--------|:--------|:--------|:--------|:------------|
| BISI1114L54 | Gen Biology Lab  | 930AM   | 1120AM  | W       |         |         | NOC475  | Davis     C |
| BISI1114L64 | Gen Biology Lab  | 700PM   | 850PM   | R       |         |         | NOC475  | Pilkingto C |
| ART 1113552 | Art Appreciation | 300PM   | 415PM   | M W     |         |         | NOC425  | Kodesh    J |
| BISI1114L51 | Gen Biology Lab  | 830AM   | 1020AM  | T       |         |         | NOC475  | Davis     C |
| BISI1114L53 | Gen Biology Lab  | 930AM   | 1120AM  | M       |         |         | NOC475  | Davis     C |

Candidate column headers:
TITLE
SECTIONID
STARTTIME
DAYS
WEEK
LOCATION
INSTSHORT
ENDTIME
DEPT



headers: TITLE
SECTIONID
STARTTIME
DAYS
WEEK
LOCATION
INSTSHORT
ENDTIME
DEPT
label: SECTIONID
TITLE
STARTTIME
ENDTIME
DAYS
WEEK
DEPT
LOCATION
INSTSHORT
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 |
|--------:|--------:|--------:|--------:|
|   69226 |    5194 |   13528 |    4487 |
|    4825 |     442 |    1075 |     239 |
|   78419 |    5685 |   13221 |    2860 |
|   27934 |    2529 |    8036 |    1289 |
|     176 |      38 |     136 |       2 |

Candidate column headers:
Children with disciplinary removals totaling 1 day
Total disciplinary removals
Children with disciplinary removals totaling 2-10 day
Children with disciplinary removals totaling >10 day



headers: Children with disciplinary removals totaling 1 day
Total disciplinary removals
Children with disciplinary removals totaling 2-10 day
Children with disciplinary removals totaling >10 day
label: Total disciplinary removals
Children with disciplinary removals totaling 1 day
Children with disciplinary removals totaling 2-10 day
Children with disciplinary removals totaling >10 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 |   Col_17 | Col_18   |
|--------:|:------------|:---------------|----------:|---------:|:--------|:----------|:---------|:------------------|:------------------|:------------------|:------------------|:------------------|:---------|----------:|-----------:|---------:|---------:|:---------|
|    2006 | Afghanistan | United Kingdom | 113.311   | 80.3703  | 216.98  | 11.229332 | 0.5      | 80.37034199999998 | 80.37034199999998 | 80.37034199999998 | 80.37034199999998 | 80.37034199999998 | 2.0      | 100       | 180.02     |  80.3703 |        0 | 14.0     |
|    2006 | Albania     | Japan          |  14.88    | 14.88    |         |           |          |                   |                   |                   |                   |                   |          |  14.88    |   0.863327 |   0      |        0 |          |
|    2006 | Albania     | Netherlands    |   7.60586 |  2.1     |         | 1.0       | 0.7      | 0.0               | 0.0               | 0.0               | 0.0               | 0.0               | 2.0      |   2.1     |   0.235536 |   0      |        0 | 2.0      |
|    2006 | Albania     | United Kingdom |   3.23587 |  3.23587 |         | 3.235874  | 2.892257 | 0.0               | 0.0               | 0.0               | 0.0               | 0.0               | 1.0      |   3.23587 |   0        |   0      |        0 | 12.0     |
|    2006 | Albania     | OSCE           |   2.81015 |  2.59166 |         | 2.755626  | 2.755626 | 0.0               | 0.0               | 0.0               | 0.0               | 0.0               | 3.0      |   3.7908  |   0        |   0      |        0 | 7.0      |

Candidate column headers:
year
GovtQ2
DonorQ15
DonorQ10
DonorQ2
DonorQ5
Recipient
DonorQ1
DonorQ7
DonorQ3
DonorQ14
DonorQ11
Donor
DonorQ4
DonorQ6
DonorQ13
GovtQ1
DonorQ8
DonorQ9



headers: year
GovtQ2
DonorQ15
DonorQ10
DonorQ2
DonorQ5
Recipient
DonorQ1
DonorQ7
DonorQ3
DonorQ14
DonorQ11
Donor
DonorQ4
DonorQ6
DonorQ13
GovtQ1
DonorQ8
DonorQ9
label: year
Recipient
Donor
DonorQ1
DonorQ2
GovtQ1
DonorQ3
DonorQ4
DonorQ5
DonorQ6
DonorQ7
DonorQ8
DonorQ9
DonorQ10
DonorQ11
GovtQ2
DonorQ13
DonorQ14
DonorQ15
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 |
|:--------------|:----------|:--------|--------:|:------------------|--------:|--------:|--------:|---------:|--------:|---------:|---------:|----------:|---------:|----------:|---------:|---------:|---------:|---------:|---------:|
| XVI - LAZIO 2 | FROSINONE | FR      |   60050 | PICINISCO         |       2 |    1083 |     716 | 0.661127 |     639 | 0.892458 |       52 | 0.0726257 |       25 | 0.0349162 |      356 |      333 |        5 |        5 |       12 |
| XVI - LAZIO 2 | FROSINONE | FR      |   60004 | ALVITO            |       4 |    2371 |    1744 | 0.735555 |    1589 | 0.911124 |       84 | 0.0481651 |       71 | 0.040711  |      648 |      585 |       21 |       38 |        1 |
| XVI - LAZIO 2 | FROSINONE | FR      |   60020 | CASTELLIRI        |       4 |    2882 |    2307 | 0.800486 |    2100 | 0.910273 |       78 | 0.0338101 |      129 | 0.0559168 |      753 |      661 |       23 |       63 |        1 |
| XVI - LAZIO 2 | FROSINONE | FR      |   60016 | CAMPOLI APPENNINO |       2 |    1498 |    1167 | 0.779039 |    1063 | 0.910883 |       47 | 0.0402742 |       57 | 0.0488432 |      361 |      314 |       24 |       20 |        0 |
| XVI - LAZIO 2 | FROSINONE | FR      |   60028 | COLLEPARDO        |       2 |     807 |     639 | 0.791822 |     583 | 0.912363 |       22 | 0.0344288 |       34 | 0.0532081 |      229 |      191 |        5 |       33 |        0 |

Candidate column headers:
COMUNE
ISTAT COMUNE
COALIZIONE  BERLUSCONI
SCHEDE E VOTI NULLI
PDL
PROVINCIA
LEGA NORD
LA DESTRA
SCHEDE E VOTI NULLI %
SCHEDE BIANCHE
VOTI VALIDI %
NUMERO SEZIONI
SCHEDE BIANCHE %
ELETTORI
VOTI VALIDI
VOTANTI
CIRCOSCRIZIONE
SIGLA
VOTANTI %
FRAT. D'ITALIA



headers: COMUNE
ISTAT COMUNE
COALIZIONE  BERLUSCONI
SCHEDE E VOTI NULLI
PDL
PROVINCIA
LEGA NORD
LA DESTRA
SCHEDE E VOTI NULLI %
SCHEDE BIANCHE
VOTI VALIDI %
NUMERO SEZIONI
SCHEDE BIANCHE %
ELETTORI
VOTI VALIDI
VOTANTI
CIRCOSCRIZIONE
SIGLA
VOTANTI %
FRAT. D'ITALIA
label: CIRCOSCRIZIONE
PROVINCIA
SIGLA
ISTAT COMUNE
COMUNE
NUMERO SEZIONI
ELETTORI
VOTANTI
VOTANTI %
VOTI VALIDI
VOTI VALIDI %
SCHEDE BIANCHE
SCHEDE BIANCHE %
SCHEDE E VOTI NULLI
SCHEDE E VOTI NULLI %
COALIZIONE  BERLUSCONI
PDL
FRAT. D'ITALIA
LA DESTRA
LEGA NORD
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             |
|:------------|--------:|--------:|--------:|--------:|:--------|:------------------|:--------|:-------------------|:--------|:-------------------|:---------|:-------------------|
| hasta 10000 |      86 | 97.7273 |      99 | 98.0198 |         |                   |         |                    |         |                    |          |                    |
| hasta 2500  |      50 | 56.8182 |      51 | 50.495  | 62.0    | 55.85585585585586 | 86.0    | 63.703703703703695 | 67.0    | 45.270270270270274 | 71.0     | 44.099378881987576 |
| hasta 8500  |      85 | 96.5909 |      99 | 98.0198 | 108.0   | 97.29729729729728 |         |                    | 148.0   | 100.0              | 158.0    | 98.13664596273291  |
| hasta 4000  |      64 | 72.7273 |      69 | 68.3168 | 79.0    | 71.17117117117117 | 102.0   | 75.55555555555556  | 100.0   | 67.56756756756756  | 97.0     | 60.24844720496896  |
| hasta 11000 |      87 | 98.8636 |      99 | 98.0198 |         |                   |         |                    |         |                    |          |                    |

Candidate column headers:
%4
MIR 2013
MIR 2015
Resultados MIR 
MIR 2014
MIR 2011
%6
%3
%
%2
MIR 2016
MIR 2012
%5



headers: %4
MIR 2013
MIR 2015
Resultados MIR 
MIR 2014
MIR 2011
%6
%3
%
%2
MIR 2016
MIR 2012
%5
label: Resultados MIR 
MIR 2011
%
MIR 2012
%2
MIR 2013
%3
MIR 2014
%4
MIR 2015
%5
MIR 2016
%6
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      |
|:-----------|--------:|:------------|--------:|--------:|:--------|:-----------|
| 2014-11-16 |    2014 | Sentinel-1A |      79 |     660 | SLC     | DESCENDING |
| 2014-11-22 |    2014 | Sentinel-1A |     174 |    1111 | SLC     | ASCENDING  |
| 2014-11-03 |    2014 | Sentinel-1A |      72 |    1109 | GRD     | ASCENDING  |
| 2014-10-29 |    2014 | Sentinel-1A |     174 |    1116 | GRD     | ASCENDING  |
| 2014-11-22 |    2014 | Sentinel-1A |     174 |    1126 | GRD     | ASCENDING  |

Candidate column headers:
Mission
Acquisition Date
Orbit Direction
Processing Level
Year
Frame
Path



headers: Mission
Acquisition Date
Orbit Direction
Processing Level
Year
Frame
Path
label: Acquisition Date
Year
Mission
Path
Frame
Processing Level
Orbit Direction
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   |
|:------------------|:--------|--------:|:--------|
| Beata Sienkiewicz | ASJ     |       2 | Y       |
| Janet Owen        | F Ride  |       5 | Y       |
| Evie Baker        | Quiz    |       3 | Y       |
| Sarah Martin      | Xmas SJ |       3 | N       |
| Jackie Lawrence   | F Ride  |       5 | N       |

Candidate column headers:
Member?
Hours Given
Event
Name



headers: Member?
Hours Given
Event
Name
label: Name
Event
Hours Given
Member?
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 |
|--------:|--------:|---------:|--------:|
|   42737 |   69000 |  50252.8 |    0.01 |
|   42739 |  112300 |  92948.6 |    0    |
|   42756 |  140500 | 131462   |    0    |
|   42776 |   95500 |  84851.4 |    0    |
|   42771 |   68900 |  64802.3 |    0    |

Candidate column headers:
outfall 1 Morton
Date
Precipitation
outfall 2 Dixie



headers: outfall 1 Morton
Date
Precipitation
outfall 2 Dixie
label: Date
outfall 1 Morton
outfall 2 Dixie
Precipitation
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 |
|--------:|:-----------------------------|--------:|
|      19 | 205/55R16XL 04 94V H105      | 1005280 |
|      39 | 500R12 08 83/81P Z30 C       | 2001062 |
|      45 | 245/70R16 04 107H RA23       | 1004334 |
|       7 | 1100R22 16 152/149L AL02     | 3000712 |
|       2 | 275/80R22.5 16 148/145M AL02 | 3000537 |

Candidate column headers:
Codigo Hankook
Medida
Codigo Euroex



headers: Codigo Hankook
Medida
Codigo Euroex
label: Codigo Euroex
Medida
Codigo Hankook
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 |
|:------------|:-------------------------|:--------|:-------------------|:-------------|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|
| GROS_g05707 | 3H07_Ubiquitin_extension | DG      | Gao et al. 2001    | 13           |    0    |    0.24 |    1.08 |    0.23 |   14.03 |    41.98 |     0.7  |     0.56 |
| GROS_g13840 | 4D06                     | DG      | Thorpe et al. 2015 | Not_in_super |    3.15 |    4.3  |    3.04 |    3.8  |    1.14 |     2.47 |     2.92 |     4.14 |
| GROS_g08163 | 10A06                    | DG      | Gao et al. 2001    | 18           |    0    |    0    |    0    |    0    |    0    |     0    |     1.17 |     0.6  |
| GROS_g08634 | 4D06                     | DG      | Thorpe et al. 2015 | 21           |    6.04 |    1.49 |    0    |    0.12 |    0    |     0.4  |    21.32 |    12.5  |
| GROS_g02107 | 4F01                     | DG      | Gao et al. 2001    | 8            |   30.94 |   30.34 |   27.86 |   27.2  |   62.59 |    55.91 |     0.3  |     0.46 |

Candidate column headers:
J2_1
14dpi_1
Reference
Cyst_1
Similarity to effector family
J2_2
Egg_2
Gland cell
14dpi_2
sseqid
Super Cluster
Egg_1
Cyst_2



headers: J2_1
14dpi_1
Reference
Cyst_1
Similarity to effector family
J2_2
Egg_2
Gland cell
14dpi_2
sseqid
Super Cluster
Egg_1
Cyst_2
label: sseqid
Similarity to effector family
Gland cell
Reference
Super Cluster
Cyst_1
Cyst_2
Egg_1
Egg_2
J2_1
J2_2
14dpi_1
14dpi_2
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0          |   Col_1 |   Col_2 |   Col_3 |   Col_4 |   Col_5 |   Col_6 |   Col_7 |   Col_8 |   Col_9 |   Col_10 |   Col_11 |   Col_12 |   Col_13 |   Col_14 |
|:---------------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|---------:|---------:|
| Taxes          |     -50 |       0 |       0 |       0 |       0 |       0 |       0 |       0 |       0 |        0 |        0 |        0 |      600 |        0 |
| Job 1 Income   |    3250 |       0 |       0 |       0 |       0 |       0 |       0 |       0 |       0 |        0 |        0 |        0 |   -37500 |     1500 |
| Insurance      |    -125 |       0 |       0 |       0 |       0 |       0 |       0 |       0 |       0 |        0 |        0 |        0 |     1500 |        0 |
| Misc. Expenses |    -100 |       0 |       0 |       0 |       0 |       0 |       0 |       0 |       0 |        0 |        0 |        0 |     1200 |        0 |
| Medical        |     -25 |       0 |       0 |       0 |       0 |       0 |       0 |       0 |       0 |        0 |        0 |        0 |      300 |        0 |

Candidate column headers:
March
August
Budget
Categories
October
ANNUAL
November
December
February
January
July
April
May
September
June



headers: March
August
Budget
Categories
October
ANNUAL
November
December
February
January
July
April
May
September
June
label: Categories
Budget
December
March
May
June
April
July
October
September
August
November
February
ANNUAL
January
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                     |
|:-----------|:--------|:--------------------------|
| Adam       | Emma    | 4/9/2014_x000D_ 3/25/2004 |
| Abdulkadir | Sarki   | 41760                     |
| Abramowitz | Amy     | 41871                     |
| Abicht     | Travis  | 41613                     |
| Aaby       | David   | 41192                     |

Candidate column headers:
First Name
Last Name
Date Completed



headers: First Name
Last Name
Date Completed
label: Last Name
First Name
Date Completed
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        |
|:--------|:--------|:--------|:--------|:--------|:--------|:---------------|:-------------|
|         |         |         | 19.0    |         |         | Granville Twp. | 5            |
|         |         |         |         |         |         | Newark Twp.    | Test hole #6 |
|         |         |         |         |         |         | Hartford       | 8            |
| 36.0    |         | 12.0    |         |         |         |                |              |
| 36.0    | 24.0    | 12.0    | 6.0     |         |         |                | Granular     |

Candidate column headers:
Slope
Shape
Column1
Value
Grade
Depth Required
SFLR
In Situ soil



headers: Slope
Shape
Column1
Value
Grade
Depth Required
SFLR
In Situ soil
label: Depth Required
Column1
In Situ soil
Slope
Value
SFLR
Grade
Shape
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 |
|--------:|:--------|--------:|
|   42468 | Yanaki  | 1678.08 |
|   42874 | Darnell | 1260    |
|   43097 | Yanaki  | 2097.6  |
|   42572 | Darnell | 2822.4  |
|   42664 | Quad    | 4551.12 |

Candidate column headers:
Date
Product
Revenue



headers: Date
Product
Revenue
label: Date
Product
Revenue
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0             | Col_1   |   Col_2 |   Col_3 |   Col_4 |   Col_5 |   Col_6 |   Col_7 |   Col_8 |   Col_9 |   Col_10 |   Col_11 |   Col_12 |   Col_13 |   Col_14 |   Col_15 |   Col_16 |   Col_17 |   Col_18 |
|:------------------|:--------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
| High School       | PGAHS   |   13350 |    1350 |       0 |       0 |       0 |       0 |       0 |       0 |        0 |        0 |        0 |      101 |      105 |       99 |       92 |      397 |        0 |
| Elementary School | CARRO   |   20105 |    2005 |      93 |      94 |     104 |      69 |      90 |      77 |        0 |        0 |        0 |        0 |        0 |        0 |        0 |      527 |       85 |
| Elementary School | BARNM   |   12119 |    1219 |      71 |      60 |      73 |      56 |      62 |      54 |        0 |        0 |        0 |        0 |        0 |        0 |        0 |      376 |       72 |
| Elementary School | CESAR   |   17113 |    1713 |      73 |      22 |      35 |      44 |      21 |      39 |       20 |        0 |        0 |        0 |        0 |        0 |        0 |      254 |       14 |
| Elementary School | COOLS   |   17125 |    1725 |     143 |     118 |     127 |      89 |      87 |      69 |       58 |        0 |        0 |        0 |        0 |        0 |        0 |      691 |      141 |

Candidate column headers:
TypeDesc
GR10
PreK
Kindergarten
GR06
GR07
GR11
Abbrev
MSDE_SCHNO
Subtotal
GR02
GR09
SCHNO
GR01
GR05
Gr12
GR03
GR04
GR08



headers: TypeDesc
GR10
PreK
Kindergarten
GR06
GR07
GR11
Abbrev
MSDE_SCHNO
Subtotal
GR02
GR09
SCHNO
GR01
GR05
Gr12
GR03
GR04
GR08
label: TypeDesc
Abbrev
SCHNO
MSDE_SCHNO
Kindergarten
GR01
GR02
GR03
GR04
GR05
GR06
GR07
GR08
GR09
GR10
GR11
Gr12
Subtotal
PreK
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 |
|--------:|--------:|---------:|
|   43104 |       1 |   104.88 |
|   43133 |      25 | 33244.1  |
|   43175 |      24 | 31997.2  |
|   43357 |      26 | 35976    |
|   43217 |      25 | 32874    |

Candidate column headers:
Gross Amount
Number of checks
Pay Date



headers: Gross Amount
Number of checks
Pay Date
label: Pay Date
Number of checks
Gross Amount
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0   |   Col_1 |   Col_2 |   Col_3 |   Col_4 |   Col_5 |   Col_6 |   Col_7 |   Col_8 |   Col_9 |   Col_10 |
|:--------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|
| CDNK    |   14.25 |   47.75 |    1117 |       3 |       3 |       3 |       2 |       5 |       5 |        2 |
| CDMK    |    0.75 |   47.75 |     110 |       4 |       4 |       4 |       4 |       4 |       4 |        4 |
| CDMR    |    4.25 |   47.75 |     317 |       4 |       4 |       4 |       4 |       4 |       4 |        4 |
| CDMZ    |    8.25 |   47.75 |     630 |       4 |       4 |       4 |       4 |       4 |       4 |        4 |
| CDMV    |    6.25 |   47.75 |     313 |       4 |       4 |       2 |       2 |       2 |       2 |        2 |

Candidate column headers:
RCP2.6
LONGITUDE
Present_R
LATITUDE
4700BP_R
RCP2.6L
RCP8.5
Present_S
ALTITUDE
RCP4.5
Datapoint ID



headers: RCP2.6
LONGITUDE
Present_R
LATITUDE
4700BP_R
RCP2.6L
RCP8.5
Present_S
ALTITUDE
RCP4.5
Datapoint ID
label: Datapoint ID
LONGITUDE
LATITUDE
ALTITUDE
Present_R
4700BP_R
Present_S
RCP2.6L
RCP2.6
RCP4.5
RCP8.5
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 |
|:-----------|:------------------------------|--------:|--------:|--------:|
| B911700001 | RB PRIVATE MED SURG GYN LVL 1 |     111 |    3848 |    3848 |
| B907600001 | RB INTERMEDIATE LVL 1         |     206 |    6587 |    6587 |
| B911400012 | RB SEMI-PRIVATE MED SURG GYN  |     121 |    3704 |    3704 |
| B907500001 | RB INTERMEDIATE LVL 1         |     206 |    6587 |    6587 |
| B911700005 | RB SEMI-PRIVATE MED SURG GYN  |     121 |    3704 |    3704 |

Candidate column headers:
CHG CODE
DESCRIPTION
NSOP_RATE
CATEGORY
NSIP_RATE



headers: CHG CODE
DESCRIPTION
NSOP_RATE
CATEGORY
NSIP_RATE
label: CHG CODE
DESCRIPTION
CATEGORY
NSIP_RATE
NSOP_RATE
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 |
|--------:|:--------|--------:|
|   41304 | Eastern |   12571 |
|   41318 | Eastern |   12787 |
|   41293 | Eastern |   11871 |
|   41315 | Eastern |   12682 |
|   41311 | Eastern |   12878 |

Candidate column headers:
Region
Date
Sales



headers: Region
Date
Sales
label: Date
Region
Sales
Task: header-value-matching (source dataset: TableGPT)

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

Table Data:
| Col_0       | Col_1   | Col_2                        | Col_3                       |
|:------------|:--------|:-----------------------------|:----------------------------|
| Terre Haute | IN      | Tuesday_ December 1_ 2015    | Saturday_ December 1_ 2018  |
| Terre Haute | IN      | Monday_ October 5_ 2015      | Friday_ October 5_ 2018     |
| Terre Haute | IN      | Wednesday_ November 18_ 2015 | Sunday_ November 18_ 2018   |
| Terre Haute | IN      | Tuesday_ November 10_ 2015   | Saturday_ November 10_ 2018 |
| Valparaiso  | IN      | Wednesday_ May 13_ 2015      | Sunday_ May 13_ 2018        |

Candidate column headers:
Host City
Host State
Approval Date
Expiration Date



headers: Host City
Host State
Approval Date
Expiration Date
label: Host City
Host State
Approval Date
Expiration 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 |
|:-------------|---------:|--------:|--------:|---------:|----------:|--------:|--------:|---------:|----------:|------------:|---------:|---------:|---------:|
| Eastern Cape | 30980.7  |       0 |       0 |  222.944 | 11077.4   |       0 |       0 |  991.314 | 1879.44   | 9390.4      | 585.213  | 1602.73  | 178.927  |
| Western Cape |  5274.09 |       0 |       0 |  199.764 |   256.643 |       0 |       0 |   33.589 |    0      |    0        |   0      |  212.753 | 198.525  |
| Eastern Cape | 25939.4  |       0 |       0 |  767.253 |  1877.45  |       0 |       0 |  409.937 |    0      |    0.784477 |   0      |    0     |  72.7042 |
| Eastern Cape | 50331    |       0 |       0 | 3694.12  | 19719.4   |       0 |       0 | 5494.03  |   37.3933 | 4759.42     |   0      |    0     | 124.896  |
| Western Cape |   410.45 |       0 |       0 |  133.386 |   255.166 |       0 |       0 |    0     | 1809.45   |    0        |  56.4517 |  427.704 | 159.452  |

Candidate column headers:
Borehole outside the yard
Public/communal tap
Water-carrier/tanker
Neighbours tap
Borehole in the yard
Piped (tap) water inside the dwelling/house
Spring
Flowing water/stream/river
Rain-water tank in yard
Well
Other
Piped water on community stand
Province
Piped (tap) water inside yard



headers: Borehole outside the yard
Public/communal tap
Water-carrier/tanker
Neighbours tap
Borehole in the yard
Piped (tap) water inside the dwelling/house
Spring
Flowing water/stream/river
Rain-water tank in yard
Well
Other
Piped water on community stand
Province
Piped (tap) water inside yard
label: Province
Piped water on community stand
Borehole in the yard
Rain-water tank in yard
Neighbours tap
Public/communal tap
Piped (tap) water inside the dwelling/house
Piped (tap) water inside yard
Water-carrier/tanker
Borehole outside the yard
Flowing water/stream/river
Well
Spring
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 |   Col_3 |   Col_4 |
|:---------------|--------:|--------:|--------:|--------:|
| Harmony High   |     198 |     494 |     477 |     468 |
| Gateway High   |     180 |     475 |     464 |     465 |
| Mavericks      |      23 |     364 |     344 |     345 |
| New Dimensions |      40 |     468 |     441 |     448 |
| Liberty High   |     249 |     424 |     410 |     407 |

Candidate column headers:
Average of Writing Score
Average of Critical Rdg Score2
Average of Mathematics Score
Row Labels
Count of Critical Rdg Score



headers: Average of Writing Score
Average of Critical Rdg Score2
Average of Mathematics Score
Row Labels
Count of Critical Rdg Score
label: Row Labels
Count of Critical Rdg Score
Average of Critical Rdg Score2
Average of Mathematics Score
Average of Writing Score
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 |
|--------:|--------:|--------:|
|   51719 |       1 |       1 |
|   51709 |       1 |       1 |
|   51659 |       1 |       1 |
|    4425 |       1 |       1 |
|   20279 |       1 |       1 |

Candidate column headers:
commande
QT
unique



headers: commande
QT
unique
label: commande
unique
QT
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     |
|--------:|:--------|:--------|--------:|:----------------|:--------|:----------|
|   10010 | BIOL    | Q1      |       2 | Natural Science | Male    | Graduate  |
|   10004 | MATH    | Q2_4    |       1 | Natural Science | Female  | Graduate  |
|   10003 | MATH    | Q1      |       2 | Natural Science | Male    | Graduate  |
|   10002 | MATH    | Q2_1    |       5 | Natural Science | Female  | Graduate  |
|   10001 | ENG     | Q2_1    |       3 | Arts & Letters  | Female  | Undergrad |

Candidate column headers:
Gender
ID
Response
College
Department
Class Level
Question



headers: Gender
ID
Response
College
Department
Class Level
Question
label: ID
Department
Question
Response
College
Gender
Class Level
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 |
|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
|   33848 | 6.15987 | 7.1782  | 7.78779 | 8.12556 | 8.32705 | 8.46618 | 8.58056 | 8.68963 | 8.80383 |  8.92884 | 0.122827 | 0.608642 | 0.913836 | 1.10971  | 1.26786  |  1.42139 |  1.58365 |  1.75977 |  1.95158 |
|   33802 | 5.23386 | 5.93569 | 6.62331 | 7.14995 | 7.57058 | 7.93274 | 8.26523 | 8.58533 | 8.90394 |  9.22834 | 0.189516 | 0.514054 | 0.659637 | 0.800963 | 0.993202 |  1.23721 |  1.523   |  1.84178 |  2.18775 |
|   33844 | 6.05039 | 6.84855 | 7.42376 | 7.80881 | 8.08175 | 8.29156 | 8.46627 | 8.62248 | 8.77057 |  8.91729 | 0.150232 | 0.517369 | 0.777983 | 0.991894 | 1.19463  |  1.39736 |  1.60311 |  1.813   |  2.02788 |
|   33809 | 5.13603 | 5.80658 | 6.48173 | 6.99628 | 7.40323 | 7.75019 | 8.06615 | 8.36842 | 8.66787 |  8.97169 | 0.149872 | 0.486996 | 0.641647 | 0.781794 | 0.964938 |  1.19455 |  1.46234 |  1.7605  |  2.08378 |
|   33805 | 5.27974 | 6.00518 | 6.69548 | 7.21585 | 7.62538 | 7.97336 | 8.28948 | 8.59151 | 8.89062 |  9.19423 | 0.169329 | 0.506046 | 0.667632 | 0.817374 | 1.01001  |  1.24825 |  1.52379 |  1.82902 |  2.15889 |

Candidate column headers:
TP6
TP2
TP4
FY5
FY1
TP5
TP8
FY4
FY2
TP9
TP1
TP3
FY3
FY6
FY9
FY10
TP7
FY7
FY8
Date



headers: TP6
TP2
TP4
FY5
FY1
TP5
TP8
FY4
FY2
TP9
TP1
TP3
FY3
FY6
FY9
FY10
TP7
FY7
FY8
Date
label: Date
FY1
FY2
FY3
FY4
FY5
FY6
FY7
FY8
FY9
FY10
TP1
TP2
TP3
TP4
TP5
TP6
TP7
TP8
TP9
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                      |
|:--------------|:--------|:---------------------------|
| Miami_South   | Column1 |                            |
| Boston_North  | Value   | Shampoo - 97 - $20.24      |
| Detroit_North | Value   |                            |
| Houston_North | Value   | Liver Treats - 10 - $90.12 |
| Miami_East    | Column1 | Collar - 12 - $54.93       |

Candidate column headers:
Value
Row
Column



headers: Value
Row
Column
label: Row
Column
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          |
|:----------------------------|--------:|--------:|:--------------|--------:|:------------------|:----------|:--------|:----------------|:----------------|:---------|:----------------|:----------------|
| ALCALDIA MUNICIPAL DE DAGUA | 2031270 | 41810.3 | ME178746328CO |     100 | ALCALDIA DE DAGUA | OCCIDENTE | DAGUA   | VALLE DEL CAUCA | EN DISTRIBUCION | CO CALI  | LISTA DE CORREO | SIN DIGITALIZAR |
| ALCALDIA MUNICIPAL DE DAGUA | 2031270 | 41810.3 | ME178746552CO |     100 | ALCALDIA DE DAGUA | OCCIDENTE | DAGUA   | VALLE DEL CAUCA | EN DISTRIBUCION | CO CALI  | LISTA DE CORREO | SIN DIGITALIZAR |
| ALCALDIA MUNICIPAL DE DAGUA | 2031270 | 41810.3 | ME178746345CO |     100 | ALCALDIA DE DAGUA | OCCIDENTE | DAGUA   | VALLE DEL CAUCA | EN DISTRIBUCION | CO CALI  | LISTA DE CORREO | SIN DIGITALIZAR |
| ALCALDIA MUNICIPAL DE DAGUA | 2031270 | 41810.3 | ME178746464CO |     100 | ALCALDIA DE DAGUA | OCCIDENTE | DAGUA   | VALLE DEL CAUCA | EN DISTRIBUCION | CO CALI  | LISTA DE CORREO | SIN DIGITALIZAR |
| ALCALDIA MUNICIPAL DE DAGUA | 2031270 | 41810.3 | ME178746420CO |     100 | ALCALDIA DE DAGUA | OCCIDENTE | DAGUA   | VALLE DEL CAUCA | EN DISTRIBUCION | CO CALI  | LISTA DE CORREO | SIN DIGITALIZAR |

Candidate column headers:
PESO_ENVIO
REFERENCE
DEPARTAMENTO_DESTINO
ORDEN_SERVICIO
DIGITALIZADO
ENVIO
CLIENTE
EStado
FECHA_ADMISION
CIUDAD_DESTINO
NOMBRE_REMITENTE
REGIONAL_DESTINO
RRE_CNAME



headers: PESO_ENVIO
REFERENCE
DEPARTAMENTO_DESTINO
ORDEN_SERVICIO
DIGITALIZADO
ENVIO
CLIENTE
EStado
FECHA_ADMISION
CIUDAD_DESTINO
NOMBRE_REMITENTE
REGIONAL_DESTINO
RRE_CNAME
label: CLIENTE
ORDEN_SERVICIO
FECHA_ADMISION
ENVIO
PESO_ENVIO
NOMBRE_REMITENTE
REGIONAL_DESTINO
CIUDAD_DESTINO
DEPARTAMENTO_DESTINO
EStado
REFERENCE
RRE_CNAME
DIGITALIZADO