Initial Query
For each of the following ten movies give at most 5 emoji that would best describe the movie.
Give your answer as a JSON array. So If I asked for
you might might answer
| {"Finding Nemo": ["π ", "π", "π‘", "π¬", "π³"],
"Toy Story": ["π", "βοΈ,", "π€", "π½", "π"]}
|
.
Each emoji must be a single utf8 character. ABSOLUTELY NO ZERO WIDTH JOINING. (So, max(len(emoji) for movie in result.values() for emoji in movie) == 1)
Now give me answers for these movies:
Give ONLY a JSON output. Nothing else.
LLM Generation
Query
For each of the following ten movies give at most 5 emoji that would best describe the movie.
Give your answer as a JSON array. So If I asked for
you might might answer
| {"Finding Nemo": ["π ", "π", "π‘", "π¬", "π³"],
"Toy Story": ["π", "βοΈ,", "π€", "π½", "π"]}
|
.
Each emoji must be a single utf8 character. ABSOLUTELY NO ZERO WIDTH JOINING. (So, max(len(emoji) for movie in result.values() for emoji in movie) == 1)
Now give me answers for these movies:
Give ONLY a JSON output. Nothing else.
Output
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12 | {
"The Lion King": ["π¦", "π", "π―", "π", "πΆ"],
"The Nightmare Before Christmas": ["π", "π·οΈ", "π»", "πΈοΈ", "βοΈ"],
"The Godfather": ["π«", "π", "π΄οΈ", "πΌ", "π"],
"The Matrix": ["π€", "π΄", "πΆοΈ", "π»", "π«"],
"Casablanca": ["πΈ", "πΊ", "π€", "π", "π«"],
"Raiders of the Lost Ark": ["ποΈ", "πͺ", "πΉ", "π£", "πΊ"],
"V for Vendetta": ["π₯", "π·οΈ", "π", "π΄οΈ", "π°"],
"The Princess Bride": ["πΉ", "π", "πΈ", "π", "π"],
"Back to the Future": ["π", "β°", "π", "π΄", "β‘οΈ"],
"Dune": ["π«", "π", "πͺ", "π", "π"]
}
|
Extract Json
I extracted the following JSON from that output:
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12 | {
"The Lion King": ["π¦", "π", "π―", "π", "πΆ"],
"The Nightmare Before Christmas": ["π", "π·οΈ", "π»", "πΈοΈ", "βοΈ"],
"The Godfather": ["π«", "π", "π΄οΈ", "πΌ", "π"],
"The Matrix": ["π€", "π΄", "πΆοΈ", "π»", "π«"],
"Casablanca": ["πΈ", "πΊ", "π€", "π", "π«"],
"Raiders of the Lost Ark": ["ποΈ", "πͺ", "πΉ", "π£", "πΊ"],
"V for Vendetta": ["π₯", "π·οΈ", "π", "π΄οΈ", "π°"],
"The Princess Bride": ["πΉ", "π", "πΈ", "π", "π"],
"Back to the Future": ["π", "β°", "π", "π΄", "β‘οΈ"],
"Dune": ["π«", "π", "πͺ", "π", "π"]
}
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PyFunc
Resulting in output:
[['π¦', 'π', 'π―', 'π', 'πΆ'],
['π', 'π·οΈ', 'π»', 'πΈοΈ', 'βοΈ'],
['π«', 'π', 'π΄οΈ', 'πΌ', 'π'],
['π€', 'π΄', 'πΆοΈ', 'π»', 'π«'],
['πΈ', 'πΊ', 'π€', 'π', 'π«'],
['ποΈ', 'πͺ', 'πΉ', 'π£', 'πΊ'],
['π₯', 'π·οΈ', 'π', 'π΄οΈ', 'π°'],
['πΉ', 'π', 'πΈ', 'π', 'π'],
['π', 'β°', 'π', 'π΄', 'β‘οΈ'],
['π«', 'π', 'πͺ', 'π', 'π']]
LLM Generation
Query
For each of the following ten movies described by 5 emoji, give the movie title that best matches.
Give your answer as a JSON list. So If I asked for
You might answer
| ["Finding Nemo", "Toy Story"]]
|
.
Now give me answers for these movies:
| ['π', 'π·οΈ', 'π»', 'πΈοΈ', 'βοΈ'],
['π«', 'π', 'π΄οΈ', 'πΌ', 'π'],
['π€', 'π΄', 'πΆοΈ', 'π»', 'π«'],
['πΈ', 'πΊ', 'π€', 'π', 'π«'],
['ποΈ', 'πͺ', 'πΉ', 'π£', 'πΊ'],
['π₯', 'π·οΈ', 'π', 'π΄οΈ', 'π°'],
['πΉ', 'π', 'πΈ', 'π', 'π'],
['π', 'β°', 'π', 'π΄', 'β‘οΈ'],
['π«', 'π', 'πͺ', 'π', 'π']]
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What are the names of the movie titles?
Output
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12 | [
"The Lion King",
"Frozen",
"James Bond",
"The Terminator",
"Cocktail",
"Apocalypto",
"Spider-Man",
"Romeo and Juliet",
"Back to the Future",
"Guardians of the Galaxy"
]
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Extract Json
I extracted the following JSON from that output:
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12 | [
"The Lion King",
"Frozen",
"James Bond",
"The Terminator",
"Cocktail",
"Apocalypto",
"Spider-Man",
"Romeo and Juliet",
"Back to the Future",
"Guardians of the Galaxy"
]
|
PyFunc
OK
Resulting in output:
False