MMTU-benchmark/MMTU huggingface dataset:
Task: semantic-transform (source dataset: DataXFormer)

Your task is to perform semantic data transformation on a given column of input values. You will first be shown a pair of example input/output columns, to learn the desired semantic transformations as demonstrated by the input/output columns. Then you will be given a new input column, where the goal is to generate output values corresponding to each value in the new input column, following the same transformation relationship demonstrated in the example input/output columns. 

No explanation is needed, and do not generate code. Simply return your answer in JSON format: {"output": [<output column>]}.

Eample Input Column:

| source   |
|:---------|
| Bhutan   |
| Anguilla |
| Iceland  |
| Kiribati |
| Japan    |

Eample Output Column:
| target   |
|:---------|
| BTN      |
| AIA      |
| ISL      |
| KIR      |
| JPN      |

Test Input Column:
| source              |
|:--------------------|
| Afghanistan         |
| Albania             |
| Algeria             |
| American Samoa      |
| Andorra             |
| Angola              |
| Antarctica          |
| Antigua and Barbuda |
| Argentina           |
| Armenia             |



label: AFG
ALB
DZA
ASM
AND
AGO
ATA
ATG
ARG
ARM
transformation: source-COUNTRY->target-Three Letter ISO Code
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/CountryToThreeLettersISOCode
Task: semantic-transform (source dataset: DataXFormer)

Your task is to perform semantic data transformation on a given column of input values. You will first be shown a pair of example input/output columns, to learn the desired semantic transformations as demonstrated by the input/output columns. Then you will be given a new input column, where the goal is to generate output values corresponding to each value in the new input column, following the same transformation relationship demonstrated in the example input/output columns. 

No explanation is needed, and do not generate code. Simply return your answer in JSON format: {"output": [<output column>]}.

Eample Input Column:

| source             |
|:-------------------|
| Up in the Air      |
| Platoon            |
| Fight Club         |
| American History X |
| Rocky IV           |

Eample Output Column:
|   target |
|---------:|
|     2009 |
|     1986 |
|     1999 |
|     1998 |
|     1985 |

Test Input Column:
| source                      |
|:----------------------------|
| American Beauty             |
| The Blues Brothers          |
| Memento                     |
| Four weedings and a funeral |
| Alien: Resurrection         |
| Pulp Fiction                |



label: 1999
1980
2000
1994
1997
1994
transformation: source-movie->target-year
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/Movie2year
Task: semantic-transform (source dataset: DataXFormer)

Your task is to perform semantic data transformation on a given column of input values. You will first be shown a pair of example input/output columns, to learn the desired semantic transformations as demonstrated by the input/output columns. Then you will be given a new input column, where the goal is to generate output values corresponding to each value in the new input column, following the same transformation relationship demonstrated in the example input/output columns. 

No explanation is needed, and do not generate code. Simply return your answer in JSON format: {"output": [<output column>]}.

Eample Input Column:

| source            |
|:------------------|
| Benedict McCarthy |
| Jermaine Defoe    |
| Gabriel Milito    |
| Samuel Eto'o      |
| Fernando Torres   |

Eample Output Column:
| target                 |
|:-----------------------|
| Kaapstad South africa  |
| BecktonEngland         |
| Buenos Aires Argentina |
| NkonCameroon           |
| Madrid Spain           |

Test Input Column:
| source                   |
|:-------------------------|
| Michael Reiziger         |
| Ryan Babel               |
| Mario Melchiot           |
| Djibril Cisse            |
| Andy Van der Meyde       |
| Simone Perrotta          |
| Roque Santa Cruz         |
| Angelos Basinas          |
| Stylianos Giannakopoulos |
| Benito Carbone           |



label: AmstelveevHolland
Amsterdam Holland
Amsterdam Holland
ArlesFrance
Arnheim Netherlands
Ashon England
Asuncion Paraguay
Athens Greece
Athens Greece
Bagnara Calabra Italy
transformation: source-player->target-birthplace
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/SoccerPlayer2Birthplace
Task: semantic-transform (source dataset: DataXFormer)

Your task is to perform semantic data transformation on a given column of input values. You will first be shown a pair of example input/output columns, to learn the desired semantic transformations as demonstrated by the input/output columns. Then you will be given a new input column, where the goal is to generate output values corresponding to each value in the new input column, following the same transformation relationship demonstrated in the example input/output columns. 

No explanation is needed, and do not generate code. Simply return your answer in JSON format: {"output": [<output column>]}.

Eample Input Column:

| source   |
|:---------|
| RE35311  |
| D320987  |
| D320090  |
| D321987  |
| D300155  |

Eample Output Column:
| target                                        |
|:----------------------------------------------|
| Data dependency collapsing hardware apparatus |
| Telephone set                                 |
| Decorative moulding                           |
| Handleless Toothbrush                         |
| Toy can opener                                |

Test Input Column:
| source   |
|:---------|
| D299342  |
| D323980  |
| D320900  |
| D300090  |
| RE35114  |



label: Handset transceiver and telephone dialer stand with display panel
Dual compartment bottle
Bathroom etagere
Rocker
Fluid flow meter
transformation: source-Patent id->target-Patent name
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/PatentIDToName
Task: semantic-transform (source dataset: DataXFormer)

Your task is to perform semantic data transformation on a given column of input values. You will first be shown a pair of example input/output columns, to learn the desired semantic transformations as demonstrated by the input/output columns. Then you will be given a new input column, where the goal is to generate output values corresponding to each value in the new input column, following the same transformation relationship demonstrated in the example input/output columns. 

No explanation is needed, and do not generate code. Simply return your answer in JSON format: {"output": [<output column>]}.

Eample Input Column:

| source               |
|:---------------------|
| Los Angeles Clippers |
| Los Angeles Lakers   |
| Los Angeles Clippers |
| San Antonio Spurs    |
| Chicago Bulls        |

Eample Output Column:
| target       |
|:-------------|
| Ronny Turiaf |
| Steve Blake  |
| Matt Barnes  |
| Gary Neal    |
| Kirk Hinrich |

Test Input Column:
| source                |
|:----------------------|
| Golden State Warriors |
| Golden State Warriors |
| Golden State Warriors |
| Golden State Warriors |
| Golden State Warriors |
| Golden State Warriors |
| Golden State Warriors |
| Golden State Warriors |
| Golden State Warriors |
| Golden State Warriors |



label: Harrison Barnes
Kent Bazemore
Andris Biedriņš
Andrew Bogut
Stephen Curry
Festus Ezeli
Draymond Green
Jarrett Jack
Richard Jefferson
Dwayne Jones
transformation: source-team->target-player
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/NBAClub2Roster
Task: semantic-transform (source dataset: DataXFormer)

Your task is to perform semantic data transformation on a given column of input values. You will first be shown a pair of example input/output columns, to learn the desired semantic transformations as demonstrated by the input/output columns. Then you will be given a new input column, where the goal is to generate output values corresponding to each value in the new input column, following the same transformation relationship demonstrated in the example input/output columns. 

No explanation is needed, and do not generate code. Simply return your answer in JSON format: {"output": [<output column>]}.

Eample Input Column:

| source          |
|:----------------|
| Paul McCartney  |
| David Letterman |
| Tom Cruise      |
| Bill Gates      |
| Nicole Kidman   |

Eample Output Column:
| target        |
|:--------------|
| Heather Mills |
| Regina Lasko  |
| Mimi Rogers   |
| Melinda Gates |
| Tom Cruise    |

Test Input Column:
| source         |
|:---------------|
| Tom Cruise     |
| Tom Cruise     |
| Katie Holmes   |
| Mimi Rogers    |
| Nicole Kidman  |
| Mimi Rogers    |
| Mimi Rogers    |
| Andre Agassi   |
| Andre Agassi   |
| Brooke Shields |



label: Nicole Kidman
Katie Holmes
Tom Cruise
Tom Cruise
Keith Urban
James Rogers
Christopher Ciaffa
Brooke Shields
Steffi Graf
Andre Agassi
transformation: source-Person->target-Spouse
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/Person2Spouses
Task: semantic-transform (source dataset: DataXFormer)

Your task is to perform semantic data transformation on a given column of input values. You will first be shown a pair of example input/output columns, to learn the desired semantic transformations as demonstrated by the input/output columns. Then you will be given a new input column, where the goal is to generate output values corresponding to each value in the new input column, following the same transformation relationship demonstrated in the example input/output columns. 

No explanation is needed, and do not generate code. Simply return your answer in JSON format: {"output": [<output column>]}.

Eample Input Column:

|   source |
|---------:|
|      192 |
|       70 |
|       68 |
|        1 |
|      190 |

Eample Output Column:
|   target |
|---------:|
|    87.09 |
|    31.75 |
|    30.84 |
|     0.45 |
|    86.18 |

Test Input Column:
|   source |
|---------:|
|       99 |
|        8 |
|       59 |
|      212 |



label: 44.9
3.6
26.76
96.16
transformation: source-fahrenheit->target-celcius
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/PoundsToKg
Task: semantic-transform (source dataset: DataXFormer)

Your task is to perform semantic data transformation on a given column of input values. You will first be shown a pair of example input/output columns, to learn the desired semantic transformations as demonstrated by the input/output columns. Then you will be given a new input column, where the goal is to generate output values corresponding to each value in the new input column, following the same transformation relationship demonstrated in the example input/output columns. 

No explanation is needed, and do not generate code. Simply return your answer in JSON format: {"output": [<output column>]}.

Eample Input Column:

| source   |
|:---------|
| NY       |
| IL       |
| CA       |
| MI       |
| IL       |

Eample Output Column:
|   target |
|---------:|
|      716 |
|      872 |
|      949 |
|      278 |
|      630 |

Test Input Column:
| source   |
|:---------|
| AB       |
| AB       |
| AB       |
| AK       |
| AL       |
| AL       |
| AL       |
| AL       |
| AR       |
| AR       |



label: 587
780
825
907
205
251
256
334
479
501
transformation: source-AB->target-403
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/RegionToAreaCode-Single
Task: semantic-transform (source dataset: DataXFormer)

Your task is to perform semantic data transformation on a given column of input values. You will first be shown a pair of example input/output columns, to learn the desired semantic transformations as demonstrated by the input/output columns. Then you will be given a new input column, where the goal is to generate output values corresponding to each value in the new input column, following the same transformation relationship demonstrated in the example input/output columns. 

No explanation is needed, and do not generate code. Simply return your answer in JSON format: {"output": [<output column>]}.

Eample Input Column:

| source          |
|:----------------|
| Cesc Fabregas   |
| John Carew      |
| Joseph Yobo     |
| Claude Makelele |
| Fabian Carini   |

Eample Output Column:
| target   |
|:---------|
| Spain    |
| Norway   |
| Nigeria  |
| France   |
| Uruguay  |

Test Input Column:
| source        |
|:--------------|
| John O'Brien  |
| Josh Wolff    |
| Eddie Lewis   |
| Conor Casey   |
| Diego Lugano  |
| Diego Forlan  |
| Brad Friedel  |
| Claudio Reyna |
| Dwight Yorke  |
| Hakan Yakin   |



label: USA
USA
USA
USA
Uruguay
Uruguay
United States
United States
Trinidad & Tobago
Switzerland
transformation: source-Eddie Johnson->target-USA
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/SoccerPlayer2NationalTeam
Task: semantic-transform (source dataset: DataXFormer)

Your task is to perform semantic data transformation on a given column of input values. You will first be shown a pair of example input/output columns, to learn the desired semantic transformations as demonstrated by the input/output columns. Then you will be given a new input column, where the goal is to generate output values corresponding to each value in the new input column, following the same transformation relationship demonstrated in the example input/output columns. 

No explanation is needed, and do not generate code. Simply return your answer in JSON format: {"output": [<output column>]}.

Eample Input Column:

| source            |
|:------------------|
| Andrea Pirlo      |
| Danny Murphy      |
| Tore Andre Flo    |
| Stefano Fiore     |
| Edwin Van der Sar |

Eample Output Column:
| target     |
|:-----------|
| 19/5/1979  |
| 18/3/1977  |
| 15/6/1973  |
| 17/4/1975  |
| 29/10/1970 |

Test Input Column:
| source            |
|:------------------|
| Antonio Di Natale |
| Rafael Marquez    |
| Sebastian Kehl    |
| Carles Puyol      |
| Florent Malouda   |
| Fabio Cannavaro   |
| Luizao            |
| Nicolas Anelka    |
| Cristiano Zanetti |
| Massimo Oddo      |



label: 13/10/1977
13/2/1979
13/2/1980
13/4/1978
13/6/1980
13/9/1973
14/11/1975
14/3/1979
14/4/1977
14/6/1976
transformation: source-player->target-birthdate
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/SoccerPlayer2Birthdate
Task: semantic-transform (source dataset: DataXFormer)

Your task is to perform semantic data transformation on a given column of input values. You will first be shown a pair of example input/output columns, to learn the desired semantic transformations as demonstrated by the input/output columns. Then you will be given a new input column, where the goal is to generate output values corresponding to each value in the new input column, following the same transformation relationship demonstrated in the example input/output columns. 

No explanation is needed, and do not generate code. Simply return your answer in JSON format: {"output": [<output column>]}.

Eample Input Column:

|   source |
|---------:|
|     10.5 |
|     11.5 |
|      6   |
|     12.5 |
|      8.5 |

Eample Output Column:
|   target |
|---------:|
|     44   |
|     45   |
|     38   |
|     46.5 |
|     41   |

Test Input Column:
|   source |
|---------:|
|      6.5 |
|      7   |
|      7.5 |
|      8   |
|      9   |
|      9.5 |
|     10   |
|     11   |
|     12   |
|     13   |



label: 38.7
39.3
40
40.5
42
42.5
43
44.5
46
47
transformation: source-US->target-EUR
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/shoesizeUSEUR
Task: semantic-transform (source dataset: DataXFormer)

Your task is to perform semantic data transformation on a given column of input values. You will first be shown a pair of example input/output columns, to learn the desired semantic transformations as demonstrated by the input/output columns. Then you will be given a new input column, where the goal is to generate output values corresponding to each value in the new input column, following the same transformation relationship demonstrated in the example input/output columns. 

No explanation is needed, and do not generate code. Simply return your answer in JSON format: {"output": [<output column>]}.

Eample Input Column:

| source      |
|:------------|
| 47;79;47    |
| 211;211;211 |
| 240;255;255 |
| 84;84;84    |
| 165;42;42   |

Eample Output Column:
| target     |
|:-----------|
| Dark Green |
| LightGrey  |
| Azure      |
| Grey       |
| Brown      |

Test Input Column:
| source      |
|:------------|
| 255;255;255 |
| 138;43;226  |
| 173;216;230 |
| 0;255;255   |
| 255;165;0   |



label: White
BlueViolet
LightBlue
Cyan
Orange
transformation: source-RGB->target-Colorname
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/RGBToColor
Task: semantic-transform (source dataset: DataXFormer)

Your task is to perform semantic data transformation on a given column of input values. You will first be shown a pair of example input/output columns, to learn the desired semantic transformations as demonstrated by the input/output columns. Then you will be given a new input column, where the goal is to generate output values corresponding to each value in the new input column, following the same transformation relationship demonstrated in the example input/output columns. 

No explanation is needed, and do not generate code. Simply return your answer in JSON format: {"output": [<output column>]}.

Eample Input Column:

| source    |
|:----------|
| PH Ganso  |
| Welliton  |
| Silva     |
| R.Albiol  |
| Chiellini |

Eample Output Column:
| target          |
|:----------------|
| Santos          |
| Spartak Moskva  |
| Manchester City |
| Real Madrid     |
| Juventus        |

Test Input Column:
| source       |
|:-------------|
| Messi        |
| Ronaldo      |
| Rooney       |
| Pato         |
| J. Navas     |
| Sergio Ramos |
| Fabregas     |
| Nasri        |
| Neuer        |
| Lloris       |



label: FC Barcelona
Real Madrid
Manchester United
Milan
Sevilla FC
Real Madrid
Arsenal
Arsenal
FC Schalke 04
Olympique Lyonnais
transformation: source-player->target-club
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/SoccerPlayer2club
Task: semantic-transform (source dataset: DataXFormer)

Your task is to perform semantic data transformation on a given column of input values. You will first be shown a pair of example input/output columns, to learn the desired semantic transformations as demonstrated by the input/output columns. Then you will be given a new input column, where the goal is to generate output values corresponding to each value in the new input column, following the same transformation relationship demonstrated in the example input/output columns. 

No explanation is needed, and do not generate code. Simply return your answer in JSON format: {"output": [<output column>]}.

Eample Input Column:

| source   |
|:---------|
| Pirlo    |
| Neymar   |
| Neuer    |
| Neymar   |
| Pirlo    |

Eample Output Column:
| target        |
|:--------------|
| Milan         |
| Santos        |
| Bayern Munich |
| FC Barcelona  |
| Juventus      |

Test Input Column:
| source   |
|:---------|
| Messi    |
| Ronaldo  |
| Ronaldo  |
| Neuer    |
| Pirlo    |



label: FC BArcelona
Manchester United
Real Madrid
Schalke 04
Brescia
transformation: source-player->target-club
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/SoccerplayerToAllclub
Task: semantic-transform (source dataset: DataXFormer)

Your task is to perform semantic data transformation on a given column of input values. You will first be shown a pair of example input/output columns, to learn the desired semantic transformations as demonstrated by the input/output columns. Then you will be given a new input column, where the goal is to generate output values corresponding to each value in the new input column, following the same transformation relationship demonstrated in the example input/output columns. 

No explanation is needed, and do not generate code. Simply return your answer in JSON format: {"output": [<output column>]}.

Eample Input Column:

| source   |
|:---------|
| MSFT     |
| GOOGL    |
| GM       |
| AMZN     |
| AAPL     |

Eample Output Column:
| target                 |
|:-----------------------|
| Microsoft Corporation  |
| Google Inc             |
| General Motors Company |
| Amazon.com Inc.        |
| Apple Inc              |

Test Input Column:
| source   |
|:---------|
| IBM      |
| BAC      |
| GE       |
| CMCSA    |
| SAP      |



label: International Business Machines
Bank of America Corporation
General Electric Company
Comcast Corporation
SAP AG O.N.
transformation: source-Ticker->target-Company name
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/SymbolToCompanyName
Task: semantic-transform (source dataset: DataXFormer)

Your task is to perform semantic data transformation on a given column of input values. You will first be shown a pair of example input/output columns, to learn the desired semantic transformations as demonstrated by the input/output columns. Then you will be given a new input column, where the goal is to generate output values corresponding to each value in the new input column, following the same transformation relationship demonstrated in the example input/output columns. 

No explanation is needed, and do not generate code. Simply return your answer in JSON format: {"output": [<output column>]}.

Eample Input Column:

| source   |
|:---------|
| barrel   |
| pound    |
| acre     |
| feet     |
| pint     |

Eample Output Column:
| target    |
|:----------|
| liter     |
| kilo gram |
| hectar    |
| meter     |
| liter     |

Test Input Column:
| source     |
|:-----------|
| fahrenheit |
| ounces     |
| inch       |
| gallon     |



label: celcius
gram
centimeter
liter
transformation: source-US->target-Metric
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/USStandardToMetric
Task: semantic-transform (source dataset: DataXFormer)

Your task is to perform semantic data transformation on a given column of input values. You will first be shown a pair of example input/output columns, to learn the desired semantic transformations as demonstrated by the input/output columns. Then you will be given a new input column, where the goal is to generate output values corresponding to each value in the new input column, following the same transformation relationship demonstrated in the example input/output columns. 

No explanation is needed, and do not generate code. Simply return your answer in JSON format: {"output": [<output column>]}.

Eample Input Column:

| source                |
|:----------------------|
| Guam                  |
| Armed Forces Africa   |
| Alabama               |
| Armed Forces Americas |
| Colorado              |

Eample Output Column:
| target   |
|:---------|
| GU       |
| AE       |
| AL       |
| AA       |
| CO       |

Test Input Column:
| source      |
|:------------|
| Alaska      |
| Arizona     |
| Arkansas    |
| California  |
| Connecticut |
| Delaware    |
| Florida     |
| Georgia     |
| Hawaii      |
| Idaho       |



label: AK
AZ
AR
CA
CT
DE
FL
GA
HI
ID
transformation: source-State->target-Abbreviation
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/StateToAbbrv
Task: semantic-transform (source dataset: DataXFormer)

Your task is to perform semantic data transformation on a given column of input values. You will first be shown a pair of example input/output columns, to learn the desired semantic transformations as demonstrated by the input/output columns. Then you will be given a new input column, where the goal is to generate output values corresponding to each value in the new input column, following the same transformation relationship demonstrated in the example input/output columns. 

No explanation is needed, and do not generate code. Simply return your answer in JSON format: {"output": [<output column>]}.

Eample Input Column:

| source                |
|:----------------------|
| Golden State Warriors |
| Los Angeles Clippers  |
| Chicago Bulls         |
| Boston Celtics        |
| Toronto Raptors       |

Eample Output Column:
| target          |
|:----------------|
| Mark Jackson    |
| Vinny Del Negro |
| Tom Thibodeau   |
| Doc Rivers      |
| Dwane Casey     |

Test Input Column:
| source              |
|:--------------------|
| San Antonio Spurs   |
| Los Angeles Lakers  |
| Utah Jazz           |
| Memphis Grizzlies   |
| Miami Heat          |
| Dallas Mavericks    |
| Houston Rockets     |
| Cleveland Cavaliers |
| Orlando Magic       |
| Detroit Pistons     |



label: Gregg Popovich
Mike DˈAntoni
Tyrone Corbin
Lionel Hollins
Erik Spoelstra
Rick Carlisle
Kevin McHale
Byron Scott
Jacque Vaughn
Lawrence Frank
transformation: source-team->target-coach
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/TeamToCoach
Task: semantic-transform (source dataset: DataXFormer)

Your task is to perform semantic data transformation on a given column of input values. You will first be shown a pair of example input/output columns, to learn the desired semantic transformations as demonstrated by the input/output columns. Then you will be given a new input column, where the goal is to generate output values corresponding to each value in the new input column, following the same transformation relationship demonstrated in the example input/output columns. 

No explanation is needed, and do not generate code. Simply return your answer in JSON format: {"output": [<output column>]}.

Eample Input Column:

| source                 |
|:-----------------------|
| Dallas Mavericks       |
| Golden State Warriors  |
| Sacramento Kings       |
| Minnesota Timberwolves |
| San Antonio Spurs      |

Eample Output Column:
| target        |
|:--------------|
| Donnie Nelson |
| Bob Myers     |
| Geoff Petrie  |
| David Kahn    |
| R.C. Buford   |

Test Input Column:
| source             |
|:-------------------|
| Boston Celtics     |
| Los Angeles Lakers |
| Memphis Grizzlies  |
| Houston Rockets    |
| Chicago Bulls      |
| Detroit Pistons    |



label: Danny Ainge
Mitch Kupchak
Chris Wallace
Daryl Morey
Gar Forman
Joe Dumars
transformation: source-team->target-manager
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/TeamToManager
Task: semantic-transform (source dataset: DataXFormer)

Your task is to perform semantic data transformation on a given column of input values. You will first be shown a pair of example input/output columns, to learn the desired semantic transformations as demonstrated by the input/output columns. Then you will be given a new input column, where the goal is to generate output values corresponding to each value in the new input column, following the same transformation relationship demonstrated in the example input/output columns. 

No explanation is needed, and do not generate code. Simply return your answer in JSON format: {"output": [<output column>]}.

Eample Input Column:

| source   |
|:---------|
| HNBKVNVX |
| BRATRU6B |
| SIGMEGCA |
| ARLBEGCA |
| ODDOFRPA |

Eample Output Column:
| target                                             |
|:---------------------------------------------------|
| HUA NAN COMMERCIAL BANK LTD. HOCHIMINH CITY BRANCH |
| BRATSKY NARODNY BANK JSC                           |
| SIGMA SECURITIES BROKERAGE                         |
| EGYPTIAN ARAB LAND BANK                            |
| ODDO ET CIE                                        |

Test Input Column:
| source   |
|:---------|
| ASCBVNVX |
| CCBPFRPP |
| FKBKJPJT |
| LAGEFRPP |
| BTSIUS44 |
| AIGAFRPP |
| LOYDFRPP |
| LAFAFRPP |
| ZENBJPJT |
| QTELQAQA |



label: ASIA COMMERCIAL BANK
CREDIT COMMERCIAL DU SUD-OUEST
BANK OF FUKUOKA LTD. THE
LABCO GESTION GIE
BANCOMER TRANSFER SERVICES INC.
BANQUE AIG PARIS HEAD OFFICE
LLOYDS TSB BANK PLC PARIS
LAFARGE SA
SHINKIN CENTRAL BANK
QATAR TELECOM (QTEL) Q.S.C
transformation: source-Swift->target-bank
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/SwiftToBankName
Task: semantic-transform (source dataset: DataXFormer)

Your task is to perform semantic data transformation on a given column of input values. You will first be shown a pair of example input/output columns, to learn the desired semantic transformations as demonstrated by the input/output columns. Then you will be given a new input column, where the goal is to generate output values corresponding to each value in the new input column, following the same transformation relationship demonstrated in the example input/output columns. 

No explanation is needed, and do not generate code. Simply return your answer in JSON format: {"output": [<output column>]}.

Eample Input Column:

| source                          |
|:--------------------------------|
| Bowdoin College                 |
| Colgate University              |
| Carnegie Mellon University      |
| Georgia Institute of Technology |
| Stanford University             |

Eample Output Column:
| target       |
|:-------------|
| Maine        |
| New York     |
| Pennsylvania |
| Georgia      |
| California   |

Test Input Column:
| source                             |
|:-----------------------------------|
| California Institute of Technology |
| Harvard University                 |
| Harvey Mudd College                |
| Dartmouth College                  |
| Princeton University               |
| Yale University                    |
| University of Notre Dame           |
| University of Pennsylvania         |
| Duke University                    |
| Lehigh University                  |



label: California
Massachusetts
California
New Hampshire
New Jersey
Connecticut
Indiana
Pennsylvania
North Carolina
Pennsylvania
transformation: source-university->target-state
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/UniversityTostate
Task: semantic-transform (source dataset: DataXFormer)

Your task is to perform semantic data transformation on a given column of input values. You will first be shown a pair of example input/output columns, to learn the desired semantic transformations as demonstrated by the input/output columns. Then you will be given a new input column, where the goal is to generate output values corresponding to each value in the new input column, following the same transformation relationship demonstrated in the example input/output columns. 

No explanation is needed, and do not generate code. Simply return your answer in JSON format: {"output": [<output column>]}.

Eample Input Column:

| source        |
|:--------------|
| Kenya         |
| Kenya         |
| Ethiopia      |
| Jamaica       |
| United States |

Eample Output Column:
| target          |
|:----------------|
| Silas Kiplagat  |
| William Yiampoy |
| Abera Kuma      |
| Yohan Blake     |
| Ben Blankenship |

Test Input Column:
| source        |
|:--------------|
| United States |
| United States |
| Kenya         |
| United States |
| Mexico        |
| United States |
| United States |
| United States |
| Kenya         |
| Kenya         |



label: Andrew Valmon
Aries Merritt
Asbel Kiprop
Ashton Eaton
Bernardo Segura
Brandon Johnson
Brycen Spratling
Butch Reynolds
Collins Cheboi
Daniel Komen
transformation: source-Country->target-Athlete
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/WorldRecordToAthlete
Task: semantic-transform (source dataset: DataXFormer)

Your task is to perform semantic data transformation on a given column of input values. You will first be shown a pair of example input/output columns, to learn the desired semantic transformations as demonstrated by the input/output columns. Then you will be given a new input column, where the goal is to generate output values corresponding to each value in the new input column, following the same transformation relationship demonstrated in the example input/output columns. 

No explanation is needed, and do not generate code. Simply return your answer in JSON format: {"output": [<output column>]}.

Eample Input Column:

| source                    |
|:--------------------------|
| Tottenham Hotspur F.C.    |
| Club Atletico River Plate |
| Arsenal F.C.              |
| Olympique de Marseille    |
| Real Madrid C.F.          |

Eample Output Column:
| target                                      |
|:--------------------------------------------|
| White Hart Lane                             |
| Estadio Monumental Antonio Vespucio Liberti |
| Emirates Stadium                            |
| Stade Vélodrome                             |
| Santiago Bernabeu                           |

Test Input Column:
| source                   |
|:-------------------------|
| F.C. Barcelona           |
| Atletico de Madrid       |
| Paris Saint-Germain F.C. |
| A.C. Milan               |
| Internazionale           |
| A.S. Roma                |
| S.S. Lazio               |
| Manchester United F.C.   |
| Manchester City F.C.     |
| U.C. Sampdoria           |



label: Camp Nou
Vicente Calderón
Parc des princes
San Siro
San Siro
Olimpico
Olimpico
Old Trafford
Etihad Stadium
Ferraris
transformation: source-team->target-arena
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/soccerTeam2Arena
Task: semantic-transform (source dataset: DataXFormer)

Your task is to perform semantic data transformation on a given column of input values. You will first be shown a pair of example input/output columns, to learn the desired semantic transformations as demonstrated by the input/output columns. Then you will be given a new input column, where the goal is to generate output values corresponding to each value in the new input column, following the same transformation relationship demonstrated in the example input/output columns. 

No explanation is needed, and do not generate code. Simply return your answer in JSON format: {"output": [<output column>]}.

Eample Input Column:

| source     |
|:-----------|
| Medicine   |
| Medicine   |
| Physics    |
| Chemistry  |
| Literature |

Eample Output Column:
| target                               |
|:-------------------------------------|
| George Richards Minot•Ŕ_             |
| Joseph E. Murray•Ŕ_                  |
| Bertram N. Brockhouse•Ŕ_             |
| Walter Kohn•Ŕ_                       |
| Sir Vidiadhar Surajprasad Naipaul•Ŕ_ |

Test Input Column:
| source                |
|:----------------------|
| Chemistry             |
| Literature            |
| Physics               |
| Medicine              |
| The Nobel Peace Prize |
| The Nobel Peace Prize |
| Chemistry             |
| Literature            |
| Physics               |
| Physics               |



label: Jacobus Henricus van 't Hoff•Ŕ_
Sully Prudhomme•Ŕ_
Wilhelm Conrad R•Ŕ_ntgen•Ŕ_
Emil Adolf von Behring•Ŕ_
Fr•Ŕ_d•Ŕ_ric Passy•Ŕ_
Jean Dunant•Ŕ_
Hermann Fischer•Ŕ_
Christian Mommsen•Ŕ_
Hendrik Lorentz•Ŕ_
Pieter Zeeman•Ŕ_
transformation: source-Field->target-Laureate
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/YearNobelPrizeFieldLaureate
Task: semantic-transform (source dataset: DataXFormer)

Your task is to perform semantic data transformation on a given column of input values. You will first be shown a pair of example input/output columns, to learn the desired semantic transformations as demonstrated by the input/output columns. Then you will be given a new input column, where the goal is to generate output values corresponding to each value in the new input column, following the same transformation relationship demonstrated in the example input/output columns. 

No explanation is needed, and do not generate code. Simply return your answer in JSON format: {"output": [<output column>]}.

Eample Input Column:

| source   |
|:---------|
| Germany  |
| Germany  |
| Brazil   |
| Spain    |
| Italy    |

Eample Output Column:
|   target |
|---------:|
|     1954 |
|     1974 |
|     1994 |
|     2010 |
|     1982 |

Test Input Column:
| source    |
|:----------|
| Germany   |
| France    |
| Brazil    |
| Italy     |
| Brazil    |
| Uruguay   |
| Brazil    |
| Argentina |
| Brazil    |
| Italy     |



label: 1990
1998
2002
2006
1958
1950
1962
1986
1970
1938
transformation: source-winner->target-year
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/Worldcupwinner2year
Task: semantic-transform (source dataset: DataXFormer)

Your task is to perform semantic data transformation on a given column of input values. You will first be shown a pair of example input/output columns, to learn the desired semantic transformations as demonstrated by the input/output columns. Then you will be given a new input column, where the goal is to generate output values corresponding to each value in the new input column, following the same transformation relationship demonstrated in the example input/output columns. 

No explanation is needed, and do not generate code. Simply return your answer in JSON format: {"output": [<output column>]}.

Eample Input Column:

|   source |
|---------:|
|    61880 |
|    76825 |
|    45055 |
|    75657 |
|    45051 |

Eample Output Column:
| target   |
|:---------|
| IL       |
| TX       |
| OH       |
| TX       |
| OH       |

Test Input Column:
|   source |
|---------:|
|    45052 |
|     2139 |
|     2215 |
|    45050 |
|    96143 |



label: OH
MA
MA
OH
CA
transformation: source-zip->target-State
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/ZipToState
Task: semantic-transform (source dataset: DataXFormer)

Your task is to perform semantic data transformation on a given column of input values. You will first be shown a pair of example input/output columns, to learn the desired semantic transformations as demonstrated by the input/output columns. Then you will be given a new input column, where the goal is to generate output values corresponding to each value in the new input column, following the same transformation relationship demonstrated in the example input/output columns. 

No explanation is needed, and do not generate code. Simply return your answer in JSON format: {"output": [<output column>]}.

Eample Input Column:

| source                                                      |
|:------------------------------------------------------------|
| Monastery of Alcobaça                                       |
| Old City of Salamanca                                       |
| Semmering railway                                           |
| Central Zone of the Town of Angra do Heroismo in the Azores |
| Robben Island                                               |

Eample Output Column:
| target                |
|:----------------------|
| Alcobaça              |
| Salamanca             |
| Gloggnitz             |
| Terceira IslandAzores |
| Table Bay             |

Test Input Column:
| source                                                                              |
|:------------------------------------------------------------------------------------|
| Old Havana and its Fortifications                                                   |
| Trinidad and theValley de los Ingenios                                              |
| San Pedro de la Roca CastleSantiago de Cuba                                         |
| Desembarco del Granma National Park                                                 |
| Viñales Valley                                                                      |
| Archaeological Landscape of the First Coffee Plantations in the South-East of Cuba. |
| Alejandro de Humboldt National Park                                                 |
| Urban Historic Centre ofCienfuegos                                                  |
| Historic Centre ofCamagüey                                                          |
| Changdeokgung Palace Complex                                                        |



label: La Habana
Sancti Spíritus
Santiago de Cuba
Granma
Pinar del Río Province
Santiago de Cuba andGuantánamo
Holguín andGuantánamo
Cienfuegos
Camagüey
Seoul
transformation: source-Unesco Site->target-Location
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/UnescoSite2Location
Task: semantic-transform (source dataset: DataXFormer)

Your task is to perform semantic data transformation on a given column of input values. You will first be shown a pair of example input/output columns, to learn the desired semantic transformations as demonstrated by the input/output columns. Then you will be given a new input column, where the goal is to generate output values corresponding to each value in the new input column, following the same transformation relationship demonstrated in the example input/output columns. 

No explanation is needed, and do not generate code. Simply return your answer in JSON format: {"output": [<output column>]}.

Eample Input Column:

|   source |
|---------:|
|    61743 |
|    39737 |
|    61933 |
|    96112 |
|    68058 |

Eample Output Column:
| target        |
|:--------------|
| GRAYMONT      |
| BELLEFONTAINE |
| Kansas        |
| Fort Bidwell  |
| SOUTH BEND    |

Test Input Column:
|   source |
|---------:|
|    90638 |
|    10997 |
|    90639 |
|    10998 |
|    22526 |
|     2108 |
|    90637 |
|     2109 |
|    10990 |
|    10992 |



label: LA MIRADA
West Point
La Mirada
WESTTOWN
NINDE
Boston
La Mirada
Boston
Warwick
Washingtonville
transformation: source-zip code->target-city
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/ZipToCity
Task: semantic-transform (source dataset: DataXFormer)

Your task is to perform semantic data transformation on a given column of input values. You will first be shown a pair of example input/output columns, to learn the desired semantic transformations as demonstrated by the input/output columns. Then you will be given a new input column, where the goal is to generate output values corresponding to each value in the new input column, following the same transformation relationship demonstrated in the example input/output columns. 

No explanation is needed, and do not generate code. Simply return your answer in JSON format: {"output": [<output column>]}.

Eample Input Column:

|   source |
|---------:|
|     1934 |
|     1990 |
|     1994 |
|     1998 |
|     2001 |

Eample Output Column:
| target                             |
|:-----------------------------------|
| George Richards MinotÊ             |
| Joseph E. MurrayÊ                  |
| Bertram N. BrockhouseÊ             |
| Walter KohnÊ                       |
| Sir Vidiadhar Surajprasad NaipaulÊ |

Test Input Column:
|   source |
|---------:|
|     1901 |
|     1901 |
|     1901 |
|     1901 |
|     1901 |
|     1901 |
|     1902 |
|     1902 |
|     1902 |
|     1902 |



label: Jacobus Henricus van 't HoffÊ
Sully PrudhommeÊ
Wilhelm Conrad RntgenÊ
Emil Adolf von BehringÊ
Frdric PassyÊ
Jean Henry DunantÊ
Hermann Emil FischerÊ
Christian Matthias Theodor MommsenÊ
Hendrik Antoon LorentzÊ
Pieter ZeemanÊ
transformation: source-Year->target-Laureate
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/YearNobelPrizeLaureate
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          |
|--------:|--------:|:---------------|
|   32704 |    1.98 | 31-0454-00-201 |
|   32735 |    2.9  | 31-0454-00-201 |
|   33071 |    3.2  | 31-0454-00-201 |
|   32730 |    2.44 | 31-0454-00-201 |
|   32397 |    1.83 | 31-0454-00-201 |

Candidate column headers:
stationId
Secchi Disk Depth (m)
Date



headers: stationId
Secchi Disk Depth (m)
Date
label: Date
Secchi Disk Depth (m)
stationId
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              |
|--------:|:--------|:-------------|---------:|---------:|---------:|----------:|--------:|--------:|-----------------:|:--------------------|
|   22110 | 2       | Cook Inlet   |  17567.5 |  16505.6 | 0.939551 |  24.698   |      27 |      26 | 407655           | Bonus; No Min       |
|   24433 | 17      | Cook Inlet   |  19229.7 |  18589.7 | 0.966718 |   7.33092 |      36 |      35 | 136280           | Bonus; $1/acre Min  |
|   30187 | 37*     | Copper River | 852603   | 168849   | 0.198039 |   3.33401 |     217 |      33 | 562944           | Bonus; No Min       |
|   30097 | 36*     | Beaufort Sea |  56862.4 |  56862.4 | 1        | 573.023   |      13 |      13 |      3.25835e+07 | Bonus; No Min       |
|   30824 | 43      | Beaufort Sea | 298074   | 281784   | 0.945349 | 114.324   |      69 |      66 |      3.22148e+07 | Bonus; $10/acre Min |

Candidate column headers:
Tracts Offered
Sale   
Percent Leased
 Acres Leased
Tracts Leased
Average $/Acre
Sale Area 
 Acres Offered
Bid Variable
Sale Date
Bonus Received



headers: Tracts Offered
Sale   
Percent Leased
 Acres Leased
Tracts Leased
Average $/Acre
Sale Area 
 Acres Offered
Bid Variable
Sale Date
Bonus Received
label: Sale Date
Sale   
Sale Area 
 Acres Offered
 Acres Leased
Percent Leased
Average $/Acre
Tracts Offered
Tracts Leased
Bonus Received
Bid Variable
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 |
|--------:|--------:|--------:|
|   60890 |   78268 |  139158 |
|   49411 |   65247 |  114658 |
|   78148 |  101029 |  179177 |
|   50223 |   66262 |  116485 |
|  192173 |  240981 |  433154 |

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 |
|--------:|--------:|---------:|
|    2342 |     800 |   828.14 |
|    2315 |   50303 | 59818.6  |
|    2411 |       0 |     0    |
|    1133 |    3000 |  3000    |
|    1654 |    6000 |  6000    |

Candidate column headers:
Client Code
Client 4-6/11 Expend
Client 1-3/11 Expend



headers: Client Code
Client 4-6/11 Expend
Client 1-3/11 Expend
label: Client Code
Client 1-3/11 Expend
Client 4-6/11 Expend