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   |
|:---------|
| U+0055   |
| U+0041   |
| U+0059   |
| U+005A   |
| U+0043   |

Eample Output Column:
|   target |
|---------:|
|       85 |
|       65 |
|       89 |
|       90 |
|       67 |

Test Input Column:
| source   |
|:---------|
| U+0042   |
| U+0044   |
| U+0054   |
| U+0056   |
| U+0057   |
| U+0058   |



label: 66
68
84
86
87
88
transformation: source-Unicode->target-Ascii
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/CharToAscii
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 |
| Marseille   |
| Munich      |
| Rotterdam   |
| Alexandria  |

Eample Output Column:
| target                   |
|:-------------------------|
| (34.052234/ -118.243685) |
| 43.296482/5.369780       |
| 48.135125/11.581981      |
| 51.924420/4.477733       |
| (38.804836/ -77.046921)  |

Test Input Column:
| source    |
|:----------|
| Toronto   |
| Istanbul  |
| Aarhus    |
| Barcelona |
| Krakow    |



label: 43.653226/-79.383184
41.008238/28.978359
56.162939/10.203921
41.385064/2.173403
(50.064650/ 19.94498)
transformation: source-City->target-LongLat
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/City2LongLat
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   |
|:---------|
| Ra       |
| Be       |
| Ar       |
| Rn       |
| Ni       |

Eample Output Column:
| target    |
|:----------|
| Radium    |
| Beryllium |
| Argon     |
| Radon     |
| Nickel    |

Test Input Column:
| source   |
|:---------|
| Ac       |
| Ag       |
| Al       |
| Am       |
| As       |
| At       |
| Au       |
| B        |
| Ba       |
| Bh       |



label: Actinium
Silver
Aluminium
Americium
Arsenic
Astatine
Gold
Boron
Barium
Bohrium
transformation: source-element->target-symbol
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/ElementToSymbol
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   |
|:---------|
| 4/20/13  |
| 4/1/15   |
| 5/8/15   |
| 1/1/15   |
| 8/8/15   |

Eample Output Column:
| target     |
|:-----------|
| 06/09/1434 |
| 06/11/1436 |
| 07/19/1436 |
| 03/10/1436 |
| 10/01/1434 |

Test Input Column:
| source   |
|:---------|
| 6/1/15   |
| 12/1/15  |
| 12/8/15  |
| 8/8/15   |
| 11/20/11 |



label: 08/13/1436
02/18/1437
02/25/1437
10/22/1436
12/23/1432
transformation: source-Gregorian->target-Hijri
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/GregorianToHijri
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   |
|:---------|
| Ear      |
| science  |
| Work     |
| Hello    |
| Shoes    |

Eample Output Column:
| target       |
|:-------------|
| Ohr          |
| Wissenschaft |
| Arbeit       |
| Hallo        |
| Schuhe       |

Test Input Column:
| source   |
|:---------|
| Country  |
| City     |
| Bank     |
| Book     |
| Pencile  |



label: Land
Stadt
Bank
Buch
Bleistift
transformation: source-English->target-German
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/EnglishToGerman
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        |
|:--------------|
| 162.13.40.196 |
| 130.0.75.55   |
| 66.220.158.68 |
| 128.31.34.154 |
| 140.205.248.2 |

Eample Output Column:
| target         |
|:---------------|
| United Kingdom |
| Germany        |
| United States  |
| United States  |
| Costa Rica     |

Test Input Column:
| source            |
|:------------------|
| 155.56.47.116     |
| 129.42.38.1       |
| 137.254.120.50    |
| 978-1-118-12938-8 |
| 191.239.213.197   |



label: Germany
United States
United States
China
United States
transformation: source-IP->target-Country
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/IPToCountry
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                        |
|:------------------------------|
| SEkk500 000000 5bbb aaaaaaa   |
| DE49 5185 0079 0002 0001 05   |
| SK22 8180 0000 0070 0022 1577 |
| SEkk800 000000 7bbb aaaaaaa   |
| SK18 8180 0000 0070 0022 1649 |

Eample Output Column:
| target               |
|:---------------------|
| SEB                  |
| Sparkasse Oberhessen |
| BanskÌÁ Bystrica     |
| Swedbank             |
| DolnÌ_ KubÌ_n        |

Test Input Column:
| source                           |
|:---------------------------------|
| DE42 5186 1403 0000 0251 78      |
| DE13 5154 0037 0124 0225 00      |
| DE19 5001 0060 0027 4066 07      |
| SEkk800 00 8bbbb aaaaaaaaaa      |
| SEkk500 000000 5bbb aaaaaaa      |
| SEkk300 0000000 aaaaaaaaaa       |
| SEkk600 00000000 aaaaaaaaa       |
| SEkk950 0009960nn aaaaaaaa       |
| TR13 0006 2000 7080 0006 6850 97 |
| TR64 0006 7010 0000 0064 1052 76 |



label: Volksbank Butzbach eG
Commerzbank AG
Postbank Frankfurt/M.
Swedbank
SEB
Nordea
Handelsbanken
Plusgirot
Garanti
Yap± Kredi
transformation: source-IBAN->target-Bank name
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/IBANToBankName
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   |
|:---------|
| #123456  |
| #333333  |
| #fed100  |
| #ffb6c1  |
| #999999  |

Eample Output Column:
| target      |
|:------------|
| (185286)    |
| (515151)    |
| (2542090)   |
| (255182193) |
| (153153153) |

Test Input Column:
| source   |
|:---------|
| #d52b1e  |
| #306860  |
| #045ca2  |
| #000000  |
| #000fff  |



label: (2134330)
(4810496)
(492162)
(000)
(015255)
transformation: source-Hex code->target-RGB
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/HexCodeToRGB
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        |
|:--------------|
| 162.13.40.196 |
| 130.0.75.55   |
| 66.220.158.68 |
| 128.31.34.154 |
| 140.205.248.2 |

Eample Output Column:
| target        |
|:--------------|
| nokia.com     |
| signavio.de   |
| facebook.com  |
| csail.mit.edu |
| dubsmash.com  |

Test Input Column:
| source            |
|:------------------|
| 155.56.47.116     |
| 129.42.38.1       |
| 137.254.120.50    |
| 978-1-118-12938-8 |
| 191.239.213.197   |



label: Sap.com
ibm.com
oracle.com
alibaba.cn
microsoft.com
transformation: source-IP->target-Domain
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/IPToDomain
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 |
|---------:|
|       50 |
|       77 |
|       68 |
|       32 |
|       86 |

Eample Output Column:
|   target |
|---------:|
|       10 |
|       25 |
|       20 |
|        0 |
|       30 |

Test Input Column:
|   source |
|---------:|
|       30 |
|       41 |
|       59 |
|      212 |



label: -1.11
5
15
100
transformation: source-fahrenheit->target-celcius
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/fahrenheitToCelcius
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                        |
|:------------------------------|
| Pinnacle Entertainment Inc.   |
| Sandy Spring Bancorp Inc.     |
| Silver Bay Realty Trust Corp. |
| Pool Corp.                    |
| Ferrellgas Partners LP        |

Eample Output Column:
| target   |
|:---------|
| PNK      |
| SASR     |
| SBY      |
| POOL     |
| FGP      |

Test Input Column:
| source                     |
|:---------------------------|
| The New York Times Company |
| Tidelands Bancshares Inc.  |
| Pall Corporation           |
| SL Industries Inc.         |
| Vail Resorts Inc.          |
| Liberty Bancorp Inc.       |
| Zoom Technologies Inc.     |
| Authentidate Holding Corp. |
| Westell Technologies Inc.  |
| Humana Inc.                |



label: NYT
TDBK
PLL
SLI
MTN
LBCP
ZOOM
ADAT
WSTL
hum
transformation: source-company->target-ticker
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/CompanyToTicker
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            |
|:------------------|
| 978-0451476807    |
| 1501260049        |
| 9.78055E+12       |
| 0-553-10354-7     |
| 978-3-642-29575-1 |

Eample Output Column:
| target                             |
|:-----------------------------------|
| Ken Follet                         |
| CLAYTON M. CHRISTENSEN             |
| David A. Vise and Mark Malseed     |
| George R. R. Martin                |
| Hasso Plattner and Alexander Zeier |

Test Input Column:
| source            |
|:------------------|
| 0-688-03036-X     |
| 978-0374275631    |
| 033036426X        |
| 978-1-118-12938-8 |
| 978-0312427986    |



label: John Irving
Daniel Kehlmann
Markus Zusak
Abraham Silberschatz and Peter B. Galvin and Greg Gagne
Mario Vargas Llosa
transformation: source-ISBN->target-Author
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/ISBNToAuthor
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            |
|:------------------|
| 978-0451476807    |
| 1501260049        |
| 9.78055E+12       |
| 0-553-10354-7     |
| 978-3-642-29575-1 |

Eample Output Column:
| target                        |
|:------------------------------|
| Signet                        |
| Harvard Business Review Press |
| Dell publishing               |
| Bantam Spectra                |
| Springer                      |

Test Input Column:
| source            |
|:------------------|
| 0-688-03036-X     |
| 978-0374275631    |
| 033036426X        |
| 978-1-118-12938-8 |
| 978-0312427986    |



label: William Morrow
Farrar Straus and Giroux
Knopf
Wiley
Picador
transformation: source-ISBN->target-Publisher
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/ISBNToPublisher
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            |
|:------------------|
| 978-0451476807    |
| 1501260049        |
| 9.78055E+12       |
| 0-553-10354-7     |
| 978-3-642-29575-1 |

Eample Output Column:
| target                    |
|:--------------------------|
| Eye of the needle         |
| The innovator's dilemma   |
| The google Story          |
| A Game of thrones         |
| In-Memory Data Management |

Test Input Column:
| source            |
|:------------------|
| 0-688-03036-X     |
| 978-0374275631    |
| 033036426X        |
| 978-1-118-12938-8 |
| 978-0312427986    |



label: The Cider House Rules
Thinking Fast and Slow
The book Thief
Operating system concepts
The war of the end of the world
transformation: source-ISBN->target-Title
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/ISBNToTitle
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    |
|:----------|
| 0036-8075 |
| 0824-3298 |
| 0976-2280 |
| 1066-8888 |
| 0362-5915 |

Eample Output Column:
| target                                             |
|:---------------------------------------------------|
| Science Magazine                                   |
| Man and Nature                                     |
| International journal of Web & Semantic Technology |
| The VLDB Journal                                   |
| TODS                                               |

Test Input Column:
| source    |
|:----------|
| 1806-2636 |
| 2326-5744 |
| 0893-6080 |
| 1350-1917 |
| 2320-2890 |



label: Acta Scientiarum Animal Sciences
Advances in Chemical Science
Neural Networks
Information Systems Journal
Biomedical Journal
transformation: source-ISSN->target-Title
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/ISSNToTitle
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 |
|---------:|
|      224 |
|      688 |
|      260 |
|       54 |
|      223 |

Eample Output Column:
| target    |
|:----------|
| Guinea    |
| Tuvalu    |
| Zambia    |
| Argentina |
| Mali      |

Test Input Column:
| source   |
|:---------|
| 93       |
| 355      |
| 213      |
| 1 684    |
| 376      |
| 244      |
| 1 264    |
| 672      |
| 1 268    |
| 374      |



label: Afghanistan
Albania
Algeria
American Samoa
Andorra
Angola
Anguilla
Antarctica
Antigua and Barbuda
Armenia
transformation: source-code->target-country
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/CountryCodeToCountry
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   |
|:---------|
| Egypt    |
| Chad     |
| Finland  |
| Lebanon  |
| India    |

Eample Output Column:
| target   |
|:---------|
| Africa   |
| Africa   |
| Europe   |
| Asia     |
| Asia     |

Test Input Column:
| source                   |
|:-------------------------|
| Algeria                  |
| Angola                   |
| Benin                    |
| Botswana                 |
| Burkina Faso             |
| Burundi                  |
| Cameroon                 |
| Central African Republic |
| Cape Verde               |
| Comoros                  |



label: Africa
Africa
Africa
Africa
Africa
Africa
Africa
Africa
Africa
Africa
transformation: source-Country->target-Continent
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/CountriesToContinents
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         |
|:---------------|
| Nun Kun        |
| Gurla Mandhata |
| Spantik        |
| Broad Peak     |
| Porong Ri      |

Eample Output Column:
| target   |
|:---------|
| India    |
| China    |
| Pakistan |
| Pakistan |
| China    |

Test Input Column:
| source        |
|:--------------|
| Kangchenjunga |
| K2            |
| Mount Everest |
| Mount Everest |
| K2            |
| Kangchenjunga |
| Broad Peak    |
| Gasherbrum I  |
| Makalu        |
| Cho Oyu       |



label: India
China
China
Nepal
Pakistan
Nepal
China
China
Nepal
Nepal
transformation: source-mountain->target-country
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/Mountain7k2Countries
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           |
|:-----------------|
| Gangkhar Puensum |
| Makalu           |
| Mamostong Kangri |
| Changtse         |
| Gasherbrum I     |

Eample Output Column:
|   target |
|---------:|
|     7570 |
|     8462 |
|     7516 |
|     7583 |
|     8068 |

Test Input Column:
| source        |
|:--------------|
| Mount Everest |
| K2            |
| Kangchenjunga |
| Lhotse        |
| Cho Oyu       |
| Dhaulagiri    |
| Manaslu       |
| Nanga Parbat  |
| Annapurna     |
| Broad Peak    |



label: 8848
8611
8586
8516
8201
8167
8156
8125
8091
8047
transformation: source-mountain->target-meters
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/MountainsOver7k2meters
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        |
|:--------------|
| New Caledonia |
| Turkmenistan  |
| Jordan        |
| Sweden        |
| Vietnam       |

Eample Output Column:
| target         |
|:---------------|
| New Caledonian |
| Turkmen        |
| Jordanian      |
| Swedish        |
| Vietnamese     |

Test Input Column:
| source     |
|:-----------|
| Portugal   |
| Belarus    |
| Tanzania   |
| Morocco    |
| Mali       |
| Ecuador    |
| Kyrgyzstan |
| Indonesia  |
| Luxembourg |
| Nicaragua  |



label: Portuguese
Belarusian
Tanzanian
Moroccan
Malian
Ecuadorian
Kyrgyzstani
Indonesian
Luxembourgish
Nicaraguan
transformation: source-Country->target-adjective
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/CountryToCitizen
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   |
|:---------|
| Africa   |
| Africa   |
| Europe   |
| Asia     |
| Asia     |

Eample Output Column:
| target   |
|:---------|
| Egypt    |
| Chad     |
| Finland  |
| Lebanon  |
| India    |

Test Input Column:
| source   |
|:---------|
| Africa   |
| Africa   |
| Africa   |
| Africa   |
| Africa   |
| Africa   |
| Africa   |
| Africa   |
| Africa   |
| Africa   |



label: Algeria
Angola
Benin
Botswana
Burkina Faso
Burundi
Cameroon
Central African Republic
Cape Verde
Comoros
transformation: source-Continent->target-Country
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/Continent2Countries
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   |
|:---------|
| India    |
| China    |
| Pakistan |
| Pakistan |
| China    |

Eample Output Column:
| target         |
|:---------------|
| Nun Kun        |
| Gurla Mandhata |
| Spantik        |
| Broad Peak     |
| Porong Ri      |

Test Input Column:
| source   |
|:---------|
| India    |
| China    |
| China    |
| Nepal    |
| Pakistan |
| Nepal    |
| China    |
| China    |
| Nepal    |
| Nepal    |



label: Kangchenjunga
K2
Mount Everest
Mount Everest
K2
Kangchenjunga
Broad Peak
Gasherbrum I
Makalu
Cho Oyu
transformation: source-country->target-mountain
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/Country2Mountains7k
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          |
| Marshall Islands |
| Bangladesh       |
| Peru             |
| Saudi Arabia     |

Eample Output Column:
| target               |
|:---------------------|
| Euro                 |
| United States dollar |
| Bangladeshi taka     |
| Peruvian nuevo sol   |
| Saudi riyal          |

Test Input Column:
| source                |
|:----------------------|
| Afghanistan           |
| Akrotiri and Dhekelia |
| Albania               |
| Algeria               |
| Andorra               |
| Angola                |
| Anguilla              |
| Antigua and Barbuda   |
| Argentina             |
| Armenia               |



label: Afghan afghani
Euro
Albanian lek
Algerian dinar
Euro
Angolan kwanza
East Caribbean dollar
East Caribbean dollar
Argentine peso
Armenian dram
transformation: source-country->target-currency
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/CountryToCurrencies
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 |
|---------:|
|    47000 |
|      102 |
|   103000 |
|      811 |
|   377835 |

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



label: 647500
28748
2381740
199
468
1246700
14000000
443
2766890
29800
transformation: source-COUNTRY->target-AREA KM2
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/CountryToAreaSqKM
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       |
|:-------------|
| Portugal     |
| Spain        |
| Austria      |
| Portugal     |
| South Africa |

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

Test Input Column:
| source      |
|:------------|
| Cuba        |
| Cuba        |
| Cuba        |
| Cuba        |
| Cuba        |
| Cuba        |
| Cuba        |
| Cuba        |
| Cuba        |
| South Korea |



label: 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
transformation: source-Country->target-Unesco Site
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/Country2UnescoSites
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           |
|:-----------------|
| Albert           |
| Caspian Sea      |
| Nicaragua        |
| Great Slave Lake |
| Ontario          |

Eample Output Column:
| target                           |
|:---------------------------------|
| Democratic Republic of the Congo |
| Russia                           |
| Nicaragua                        |
| Canada                           |
| United States                    |

Test Input Column:
| source          |
|:----------------|
| Athabasca       |
| Baikal          |
| Balkhash        |
| Caspian Sea     |
| Caspian Sea     |
| Caspian Sea     |
| Caspian Sea     |
| Erie            |
| Erie            |
| Great Bear Lake |



label: Canada
Russia
Kazakhstan
Kazakhstan
Turkmenistan
Azerbaijan
Iran
Canada
United States
Canada
transformation: source-Albert->target-Uganda
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/Lake2Countries
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           |
|:-----------------|
| Gangkhar Puensum |
| Makalu           |
| Mamostong Kangri |
| Changtse         |
| Gasherbrum I     |

Eample Output Column:
| target    |
|:----------|
| Himalayas |
| Himalayas |
| Karakoram |
| Himalayas |
| Karakoram |

Test Input Column:
| source        |
|:--------------|
| Mount Everest |
| K2            |
| Kangchenjunga |
| Lhotse        |
| Cho Oyu       |
| Dhaulagiri    |
| Manaslu       |
| Nanga Parbat  |
| Annapurna     |
| Broad Peak    |



label: Himalayas
Karakoram
Himalayas
Himalayas
Himalayas
Himalayas
Himalayas
Himalayas
Himalayas
Karakoram
transformation: source-mountain->target-range
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/MountainsOver7k2Range
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       |
|:-------------|
| 594918104    |
| 02079K305    |
| 37045V100    |
| 23135106     |
| ARDEUT116183 |

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

Test Input Column:
| source    |
|:----------|
| 459200101 |
| 60505104  |
| 369604103 |
| 20030N101 |
| 803054204 |



label: International Business Machines
Bank of America Corporation
General Electric Company
Comcast Corporation
SAP AG O.N.
transformation: source-CUSIP->target-Company name
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/CUSIPToCompanyName
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                 |
|:-----------------------|
| InZone Industries Ltd. |
| ASSA ABLOY             |
| Milliken & Company     |
| TOTO USA               |
| Inscape Corporation    |

Eample Output Column:
| target                                                                                |
|:--------------------------------------------------------------------------------------|
| NOVAhush                                                                              |
| CECO Door Regent/Omega Honeycomb Core Steel Door                                      |
| Milliken Modular Carpet with Nylon 66 face and ES Polyurethane Cushion  Backing (USA) |
| Commercial Wall-Hung HE 1.28gpf Toilet                                                |
| ACME 50 Movable Wall                                                                  |

Test Input Column:
| source                                                                    |
|:--------------------------------------------------------------------------|
| 9Wood                                                                     |
| 9Wood                                                                     |
| 9Wood                                                                     |
| Abodo Wood Ltd.                                                           |
| Abodo Wood Ltd.                                                           |
| Accsys Technologies PLC                                                   |
| Albatech Pty Ltd                                                          |
| Alpar Architectural Products LLC                                          |
| Alpar Architectural Products LLC                                          |
| Amorim Isolamentos Imported to North America by WestCoast Associates Inc. |



label: 1100 Cross Piece Grille/2100 Panelized Linear (FSC Douglas Fir)
1100 Cross Piece Grille/2100 Panelized Linear (FSC Hemlock)
EcoGrille (FSC Pacific Albus)
Elements Vulcan
Elements Vulcan+
Accoya¨ wood
Evcco Halogen Free Flame Retardant Electrical Conduit and Fittings
DeTerra Biobased polymer wall protection Đ Natural
DeTerra Biobased polymer wall protection w/ Aluminum Đ Natural
Thermacork
transformation: source-Manufacturer Name->target-Product Name
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/ManufacturerToProducts
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   |
|:---------|
| BT       |
| AI       |
| IS       |
| KI       |
| JP       |

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



label: AF
AL
DZ
AS
AD
AO
AQ
AG
AR
AM
transformation: source-COUNTRY->target-Two Letters ISO Code
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/CountryToTwoLettersISOCode
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         |
|:---------------|
| Serbia         |
| Costa Rica     |
| Palau          |
| Czech Republic |
| San Marino     |

Eample Output Column:
| target   |
|:---------|
| Serbian  |
| Spanish  |
| English  |
| Czech    |
| Italian  |

Test Input Column:
| source    |
|:----------|
| Portugal  |
| Belarus   |
| Mali      |
| Morocco   |
| Ecuador   |
| Indonesia |
| Chile     |
| Nicaragua |
| France    |
| Haiti     |



label: Portuguese
Belarusian Russian
French
Arabic
Spanish
Indonesian
Spanish
Spanish
French
French
transformation: source-country->target-language
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/CountryToLanguage
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                      |
|:----------------------------|
| Four weedings and a funeral |
| American History X          |
| Fight Club                  |
| The Blues Brothers          |
| American History X          |

Eample Output Column:
| target          |
|:----------------|
| Robin McCaffrey |
| Paul Le Mat     |
| Rob Lanza       |
| Alonzo Atkins   |
| Cherish Lee     |

Test Input Column:
| source   |
|:---------|
| Platoon  |
| Platoon  |
| Platoon  |
| Platoon  |
| Platoon  |
| Platoon  |
| Platoon  |
| Platoon  |
| Platoon  |
| Platoon  |



label: Tom Berenger
Keith David
Willem Dafoe
Forest Whitaker
Francesco Quinn
Kevin Dillon
John C. McGinley
Reggie Johnson
Mark Moses
Corey Glover
transformation: source-movie->target-actor
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/Movie2Cast
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           |
|:-----------------|
| Gangkhar Puensum |
| Makalu           |
| Mamostong Kangri |
| Changtse         |
| Gasherbrum I     |

Eample Output Column:
|   target |
|---------:|
|    24836 |
|    27762 |
|    24659 |
|    24879 |
|    26470 |

Test Input Column:
| source        |
|:--------------|
| Everest       |
| K2            |
| Kangchenjunga |
| Lhotse        |
| Cho Oyu       |
| Dhaulagiri    |
| Manaslu       |
| Nanga Parbat  |
| Annapurna     |
| Broad Peak    |



label: 29029
28251
28169
27940
26906
26795
26759
26657
26545
26401
transformation: source-mountain->target-feet
source: $MMTU_HOME/data/semantic-transform/sample1000/DataXFormer/MountainsOver7k2feet