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": ["ð ", "ð", "ðĄ", "ðŽ", "ðģ"],
"Toy Story": ["ð", "âïļ,", "ðĪ", "ð―", "ð"]}
|
.
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": ["ð ", "ð", "ðĄ", "ðŽ", "ðģ"],
"Toy Story": ["ð", "âïļ,", "ðĪ", "ð―", "ð"]}
|
.
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
1
2
3
4
5
6
7
8
9
10
11
12 | {
"The Lion King": ["ðĶ", "ð", "ð
", "ð", "ðĶ"],
"The Nightmare Before Christmas": ["ð", "ð", "ð", "ðŧ", "ð"],
"The Godfather": ["ðīïļ", "ðŦ", "ð°", "ðŪðđ", "ðŽ"],
"The Matrix": ["ðķïļ", "ð", "ðĩ", "ðĪ", "ðŦ"],
"Casablanca": ["ðķ", "ðēðĶ", "âĪïļ", "ð", "ðŠ"],
"Raiders of the Lost Ark": ["ð", "ðš", "ð", "ðī", "ðĢ"],
"V for Vendetta": ["ð", "ðĨ", "ðĄïļ", "ðđ", "ðĨ"],
"The Princess Bride": ["ðļ", "ðĪš", "ð", "ð°", "ðĪĢ"],
"Back to the Future": ["â°", "ð", "ð", "ðŽ", "ðĨ"],
"Dune": ["ðïļ", "ð", "ð", "ð", "ðĨ"]
}
|
Extract Json
I extracted the following JSON from that output:
1
2
3
4
5
6
7
8
9
10
11
12 | {
"The Lion King": ["ðĶ", "ð", "ð
", "ð", "ðĶ"],
"The Nightmare Before Christmas": ["ð", "ð", "ð", "ðŧ", "ð"],
"The Godfather": ["ðīïļ", "ðŦ", "ð°", "ðŪðđ", "ðŽ"],
"The Matrix": ["ðķïļ", "ð", "ðĩ", "ðĪ", "ðŦ"],
"Casablanca": ["ðķ", "ðēðĶ", "âĪïļ", "ð", "ðŠ"],
"Raiders of the Lost Ark": ["ð", "ðš", "ð", "ðī", "ðĢ"],
"V for Vendetta": ["ð", "ðĨ", "ðĄïļ", "ðđ", "ðĨ"],
"The Princess Bride": ["ðļ", "ðĪš", "ð", "ð°", "ðĪĢ"],
"Back to the Future": ["â°", "ð", "ð", "ðŽ", "ðĨ"],
"Dune": ["ðïļ", "ð", "ð", "ð", "ðĨ"]
}
|
PyFunc
Resulting in output:
[['ðĶ', 'ð', 'ð
', 'ð', 'ðĶ'],
['ð', 'ð', 'ð', 'ðŧ', 'ð'],
['ðīïļ', 'ðŦ', 'ð°', 'ðŪðđ', 'ðŽ'],
['ðķïļ', 'ð', 'ðĩ', 'ðĪ', 'ðŦ'],
['ðķ', 'ðēðĶ', 'âĪïļ', 'ð', 'ðŠ'],
['ð', 'ðš', 'ð', 'ðī', 'ðĢ'],
['ð', 'ðĨ', 'ðĄïļ', 'ðđ', 'ðĨ'],
['ðļ', 'ðĪš', 'ð', 'ð°', 'ðĪĢ'],
['â°', 'ð', 'ð', 'ðŽ', 'ðĨ'],
['ðïļ', 'ð', 'ð', 'ð', 'ðĨ']]
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:
| ['ð', 'ð', 'ð', 'ðŧ', 'ð'],
['ðīïļ', 'ðŦ', 'ð°', 'ðŪðđ', 'ðŽ'],
['ðķïļ', 'ð', 'ðĩ', 'ðĪ', 'ðŦ'],
['ðķ', 'ðēðĶ', 'âĪïļ', 'ð', 'ðŠ'],
['ð', 'ðš', 'ð', 'ðī', 'ðĢ'],
['ð', 'ðĨ', 'ðĄïļ', 'ðđ', 'ðĨ'],
['ðļ', 'ðĪš', 'ð', 'ð°', 'ðĪĢ'],
['â°', 'ð', 'ð', 'ðŽ', 'ðĨ'],
['ðïļ', 'ð', 'ð', 'ð', 'ðĨ']]
|
What are the names of the movie titles?
Output
| ["The Lion King", "The Nightmare Before Christmas", "The Godfather", "The Matrix", "Coco", "Raiders of the Lost Ark", "Gladiator", "The Princess Bride", "Back to the Future", "Dune"]
|
Extract Json
I extracted the following JSON from that output:
| ["The Lion King", "The Nightmare Before Christmas", "The Godfather", "The Matrix", "Coco", "Raiders of the Lost Ark", "Gladiator", "The Princess Bride", "Back to the Future", "Dune"]
|
PyFunc
OK
Resulting in output:
True
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": ["ð ", "ð", "ðĄ", "ðŽ", "ðģ"],
"Toy Story": ["ð", "âïļ,", "ðĪ", "ð―", "ð"]}
|
.
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": ["ð ", "ð", "ðĄ", "ðŽ", "ðģ"],
"Toy Story": ["ð", "âïļ,", "ðĪ", "ð―", "ð"]}
|
.
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
| {"The Lion King": ["ðĶ", "ð", "ð
", "ð", "ð"],
"The Nightmare Before Christmas": ["ð", "ð", "ðŧ", "ð", "ð"],
"The Godfather": ["ðīïļ", "ðŦ", "ðŪðđ", "ð°", "ðŽ"],
"The Matrix": ["ðķïļ", "ð", "ðŧ", "ðĪ", "ðŦ"],
"Casablanca": ["ðēðĶ", "ðķ", "ð", "ðĨ", "ðŠ"],
"Raiders of the Lost Ark": ["ðš", "ð", "ðĐïļ", "ð", "ðĢ"],
"V for Vendetta": ["ð", "ðĨ", "ðĢ", "ðđ", "ðĐ"],
"The Princess Bride": ["ð", "âïļ", "ð", "ð°", "ðŧ"],
"Back to the Future": ["â°", "ð", "ð", "ðŽ", "âĄ"],
"Dune": ["ðïļ", "ð", "ð", "ð", "ð§"]}
|
Extract Json
I extracted the following JSON from that output:
| {"The Lion King": ["ðĶ", "ð", "ð
", "ð", "ð"],
"The Nightmare Before Christmas": ["ð", "ð", "ðŧ", "ð", "ð"],
"The Godfather": ["ðīïļ", "ðŦ", "ðŪðđ", "ð°", "ðŽ"],
"The Matrix": ["ðķïļ", "ð", "ðŧ", "ðĪ", "ðŦ"],
"Casablanca": ["ðēðĶ", "ðķ", "ð", "ðĨ", "ðŠ"],
"Raiders of the Lost Ark": ["ðš", "ð", "ðĐïļ", "ð", "ðĢ"],
"V for Vendetta": ["ð", "ðĨ", "ðĢ", "ðđ", "ðĐ"],
"The Princess Bride": ["ð", "âïļ", "ð", "ð°", "ðŧ"],
"Back to the Future": ["â°", "ð", "ð", "ðŽ", "âĄ"],
"Dune": ["ðïļ", "ð", "ð", "ð", "ð§"]}
|
PyFunc
Resulting in output:
[['ðĶ', 'ð', 'ð
', 'ð', 'ð'],
['ð', 'ð', 'ðŧ', 'ð', 'ð'],
['ðīïļ', 'ðŦ', 'ðŪðđ', 'ð°', 'ðŽ'],
['ðķïļ', 'ð', 'ðŧ', 'ðĪ', 'ðŦ'],
['ðēðĶ', 'ðķ', 'ð', 'ðĨ', 'ðŠ'],
['ðš', 'ð', 'ðĐïļ', 'ð', 'ðĢ'],
['ð', 'ðĨ', 'ðĢ', 'ðđ', 'ðĐ'],
['ð', 'âïļ', 'ð', 'ð°', 'ðŧ'],
['â°', 'ð', 'ð', 'ðŽ', 'âĄ'],
['ðïļ', 'ð', 'ð', 'ð', 'ð§']]
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:
| ['ð', 'ð', 'ðŧ', 'ð', 'ð'],
['ðīïļ', 'ðŦ', 'ðŪðđ', 'ð°', 'ðŽ'],
['ðķïļ', 'ð', 'ðŧ', 'ðĪ', 'ðŦ'],
['ðēðĶ', 'ðķ', 'ð', 'ðĨ', 'ðŠ'],
['ðš', 'ð', 'ðĐïļ', 'ð', 'ðĢ'],
['ð', 'ðĨ', 'ðĢ', 'ðđ', 'ðĐ'],
['ð', 'âïļ', 'ð', 'ð°', 'ðŧ'],
['â°', 'ð', 'ð', 'ðŽ', 'âĄ'],
['ðïļ', 'ð', 'ð', 'ð', 'ð§']]
|
What are the names of the movie titles?
Output
| ["The Lion King", "The Nightmare Before Christmas", "The Godfather", "The Matrix", "Casablanca", "Raiders of the Lost Ark", "Moulin Rouge", "Beauty and the Beast", "Back to the Future", "Dune"]
|
Extract Json
I extracted the following JSON from that output:
| ["The Lion King", "The Nightmare Before Christmas", "The Godfather", "The Matrix", "Casablanca", "Raiders of the Lost Ark", "Moulin Rouge", "Beauty and the Beast", "Back to the Future", "Dune"]
|
PyFunc
OK
Resulting in output:
True
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": ["ð ", "ð", "ðĄ", "ðŽ", "ðģ"],
"Toy Story": ["ð", "âïļ,", "ðĪ", "ð―", "ð"]}
|
.
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": ["ð ", "ð", "ðĄ", "ðŽ", "ðģ"],
"Toy Story": ["ð", "âïļ,", "ðĪ", "ð―", "ð"]}
|
.
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
{
"The Lion King": ["ðĶ", "ð", "ð
", "ð", "ðĶ"],
"The Nightmare Before Christmas": ["ð", "ð", "ðŧ", "ð", "ð"],
"The Godfather": ["ðīïļ", "ðŦ", "ðŪðđ", "ð°", "ðŽ"],
"The Matrix": ["ðķïļ", "ð", "ðū", "ð", "ðŦ"],
"Casablanca": ["ðķ", "ðĨ", "ðēðĶ", "âïļ", "ð"],
"Raiders of the Lost Ark": ["ðĩïļ", "ðš", "ð", "ðĢ", "ð"],
"V for Vendetta": ["ð", "ðĢ", "ðđ", "ðĨ", "ðĐ"],
"The Princess Bride": ["ð", "âïļ", "ð°", "ðĪš", "ðŧ"],
"Back to the Future": ["â°", "ð", "ðŪ", "ð", "ðŽ"],
"Dune": ["ð", "ðïļ", "ðŠ", "ð", "ðŪ"]
}
Extract Json
I extracted the following JSON from that output:
1
2
3
4
5
6
7
8
9
10
11
12 | {
"The Lion King": ["ðĶ", "ð", "ð
", "ð", "ðĶ"],
"The Nightmare Before Christmas": ["ð", "ð", "ðŧ", "ð", "ð"],
"The Godfather": ["ðīïļ", "ðŦ", "ðŪðđ", "ð°", "ðŽ"],
"The Matrix": ["ðķïļ", "ð", "ðū", "ð", "ðŦ"],
"Casablanca": ["ðķ", "ðĨ", "ðēðĶ", "âïļ", "ð"],
"Raiders of the Lost Ark": ["ðĩïļ", "ðš", "ð", "ðĢ", "ð"],
"V for Vendetta": ["ð", "ðĢ", "ðđ", "ðĨ", "ðĐ"],
"The Princess Bride": ["ð", "âïļ", "ð°", "ðĪš", "ðŧ"],
"Back to the Future": ["â°", "ð", "ðŪ", "ð", "ðŽ"],
"Dune": ["ð", "ðïļ", "ðŠ", "ð", "ðŪ"]
}
|
PyFunc
Resulting in output:
[['ðĶ', 'ð', 'ð
', 'ð', 'ðĶ'],
['ð', 'ð', 'ðŧ', 'ð', 'ð'],
['ðīïļ', 'ðŦ', 'ðŪðđ', 'ð°', 'ðŽ'],
['ðķïļ', 'ð', 'ðū', 'ð', 'ðŦ'],
['ðķ', 'ðĨ', 'ðēðĶ', 'âïļ', 'ð'],
['ðĩïļ', 'ðš', 'ð', 'ðĢ', 'ð'],
['ð', 'ðĢ', 'ðđ', 'ðĨ', 'ðĐ'],
['ð', 'âïļ', 'ð°', 'ðĪš', 'ðŧ'],
['â°', 'ð', 'ðŪ', 'ð', 'ðŽ'],
['ð', 'ðïļ', 'ðŠ', 'ð', 'ðŪ']]
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:
| ['ð', 'ð', 'ðŧ', 'ð', 'ð'],
['ðīïļ', 'ðŦ', 'ðŪðđ', 'ð°', 'ðŽ'],
['ðķïļ', 'ð', 'ðū', 'ð', 'ðŦ'],
['ðķ', 'ðĨ', 'ðēðĶ', 'âïļ', 'ð'],
['ðĩïļ', 'ðš', 'ð', 'ðĢ', 'ð'],
['ð', 'ðĢ', 'ðđ', 'ðĨ', 'ðĐ'],
['ð', 'âïļ', 'ð°', 'ðĪš', 'ðŧ'],
['â°', 'ð', 'ðŪ', 'ð', 'ðŽ'],
['ð', 'ðïļ', 'ðŠ', 'ð', 'ðŪ']]
|
What are the names of the movie titles?
Output
| ["The Lion King", "The Nightmare Before Christmas", "The Godfather", "The Matrix", "Casablanca", "Raiders of the Lost Ark", "V for Vendetta", "The Princess Bride", "Back to the Future", "Thor: Ragnarok"]
|
Extract Json
I extracted the following JSON from that output:
| ["The Lion King", "The Nightmare Before Christmas", "The Godfather", "The Matrix", "Casablanca", "Raiders of the Lost Ark", "V for Vendetta", "The Princess Bride", "Back to the Future", "Thor: Ragnarok"]
|
PyFunc
OK
Resulting in output:
True
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": ["ð ", "ð", "ðĄ", "ðŽ", "ðģ"],
"Toy Story": ["ð", "âïļ,", "ðĪ", "ð―", "ð"]}
|
.
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": ["ð ", "ð", "ðĄ", "ðŽ", "ðģ"],
"Toy Story": ["ð", "âïļ,", "ðĪ", "ð―", "ð"]}
|
.
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
1
2
3
4
5
6
7
8
9
10
11
12 | {
"The Lion King": ["ðĶ", "ð", "ð
", "ð", "ðĶ"],
"The Nightmare Before Christmas": ["ð", "ðŧ", "ð", "ð", "ðŊïļ"],
"The Godfather": ["ðķïļ", "ðŦ", "ðŪðđ", "ð", "ðĻâð§"],
"The Matrix": ["ðķïļ", "ð", "ðĪ", "ðĩ", "ðī"],
"Casablanca": ["ðđ", "ðēðĶ", "ð°ïļ", "ðŠ", "ð"],
"Raiders of the Lost Ark": ["ðš", "ðĩïļââïļ", "ð", "ð", "ð"],
"V for Vendetta": ["ð", "ðĨ", "ðĄïļ", "ðĐ", "ðđ"],
"The Princess Bride": ["ðļ", "ðĪš", "ð°", "ðŧ", "ð§"],
"Back to the Future": ["â°", "ð", "ð", "âĄ", "ðīðž"],
"Dune": ["ð", "ð", "ð", "ð", "ðĩ"]
}
|
Extract Json
I extracted the following JSON from that output:
1
2
3
4
5
6
7
8
9
10
11
12 | {
"The Lion King": ["ðĶ", "ð", "ð
", "ð", "ðĶ"],
"The Nightmare Before Christmas": ["ð", "ðŧ", "ð", "ð", "ðŊïļ"],
"The Godfather": ["ðķïļ", "ðŦ", "ðŪðđ", "ð", "ðĻâð§"],
"The Matrix": ["ðķïļ", "ð", "ðĪ", "ðĩ", "ðī"],
"Casablanca": ["ðđ", "ðēðĶ", "ð°ïļ", "ðŠ", "ð"],
"Raiders of the Lost Ark": ["ðš", "ðĩïļââïļ", "ð", "ð", "ð"],
"V for Vendetta": ["ð", "ðĨ", "ðĄïļ", "ðĐ", "ðđ"],
"The Princess Bride": ["ðļ", "ðĪš", "ð°", "ðŧ", "ð§"],
"Back to the Future": ["â°", "ð", "ð", "âĄ", "ðīðž"],
"Dune": ["ð", "ð", "ð", "ð", "ðĩ"]
}
|
PyFunc
Resulting in output:
[['ðĶ', 'ð', 'ð
', 'ð', 'ðĶ'],
['ð', 'ðŧ', 'ð', 'ð', 'ðŊïļ'],
['ðķïļ', 'ðŦ', 'ðŪðđ', 'ð'],
['ðķïļ', 'ð', 'ðĪ', 'ðĩ', 'ðī'],
['ðđ', 'ðēðĶ', 'ð°ïļ', 'ðŠ', 'ð'],
['ðš', 'ð', 'ð', 'ð'],
['ð', 'ðĨ', 'ðĄïļ', 'ðĐ', 'ðđ'],
['ðļ', 'ðĪš', 'ð°', 'ðŧ', 'ð§'],
['â°', 'ð', 'ð', 'âĄ', 'ðīðž'],
['ð', 'ð', 'ð', 'ð', 'ðĩ']]
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:
| ['ð', 'ðŧ', 'ð', 'ð', 'ðŊïļ'],
['ðķïļ', 'ðŦ', 'ðŪðđ', 'ð'],
['ðķïļ', 'ð', 'ðĪ', 'ðĩ', 'ðī'],
['ðđ', 'ðēðĶ', 'ð°ïļ', 'ðŠ', 'ð'],
['ðš', 'ð', 'ð', 'ð'],
['ð', 'ðĨ', 'ðĄïļ', 'ðĐ', 'ðđ'],
['ðļ', 'ðĪš', 'ð°', 'ðŧ', 'ð§'],
['â°', 'ð', 'ð', 'âĄ', 'ðīðž'],
['ð', 'ð', 'ð', 'ð', 'ðĩ']]
|
What are the names of the movie titles?
Output
| ["The Lion King", "The Nightmare Before Christmas", "The Godfather", "The Matrix", "La La Land", "Raiders of the Lost Ark", "Moulin Rouge", "Sleeping Beauty", "Back to the Future", "Star Wars"]
|
Extract Json
I extracted the following JSON from that output:
| ["The Lion King", "The Nightmare Before Christmas", "The Godfather", "The Matrix", "La La Land", "Raiders of the Lost Ark", "Moulin Rouge", "Sleeping Beauty", "Back to the Future", "Star Wars"]
|
PyFunc
OK
Resulting in output:
False
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": ["ð ", "ð", "ðĄ", "ðŽ", "ðģ"],
"Toy Story": ["ð", "âïļ,", "ðĪ", "ð―", "ð"]}
|
.
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": ["ð ", "ð", "ðĄ", "ðŽ", "ðģ"],
"Toy Story": ["ð", "âïļ,", "ðĪ", "ð―", "ð"]}
|
.
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
{
"The Lion King": ["ðĶ", "ð", "ð
", "ð", "ðĶ"],
"The Nightmare Before Christmas": ["ð", "ð", "ð", "ðŧ", "ð"],
"The Godfather": ["ðīïļ", "ðŦ", "ð°", "ðŪðđ", "ð"],
"The Matrix": ["ðķïļ", "ð", "ðĩ", "ðĪ", "ðïļ"],
"Casablanca": ["ðïļ", "âïļ", "ðēðĶ", "ð", "ðĨ"],
"Raiders of the Lost Ark": ["ðĩïļ", "ðš", "ð", "ðšïļ", "ð"],
"V for Vendetta": ["ð", "ðĨ", "ðĢ", "ðđ", "ðĐ"],
"The Princess Bride": ["ð", "âïļ", "ð§", "ðŧ", "ð"],
"Back to the Future": ["â°", "ð", "ðŪ", "ð", "ðŽ"],
"Dune": ["ðïļ", "ð", "ð―", "ð", "ð"]
}
Extract Json
I extracted the following JSON from that output:
1
2
3
4
5
6
7
8
9
10
11
12 | {
"The Lion King": ["ðĶ", "ð", "ð
", "ð", "ðĶ"],
"The Nightmare Before Christmas": ["ð", "ð", "ð", "ðŧ", "ð"],
"The Godfather": ["ðīïļ", "ðŦ", "ð°", "ðŪðđ", "ð"],
"The Matrix": ["ðķïļ", "ð", "ðĩ", "ðĪ", "ðïļ"],
"Casablanca": ["ðïļ", "âïļ", "ðēðĶ", "ð", "ðĨ"],
"Raiders of the Lost Ark": ["ðĩïļ", "ðš", "ð", "ðšïļ", "ð"],
"V for Vendetta": ["ð", "ðĨ", "ðĢ", "ðđ", "ðĐ"],
"The Princess Bride": ["ð", "âïļ", "ð§", "ðŧ", "ð"],
"Back to the Future": ["â°", "ð", "ðŪ", "ð", "ðŽ"],
"Dune": ["ðïļ", "ð", "ð―", "ð", "ð"]
}
|
PyFunc
Resulting in output:
[['ðĶ', 'ð', 'ð
', 'ð', 'ðĶ'],
['ð', 'ð', 'ð', 'ðŧ', 'ð'],
['ðīïļ', 'ðŦ', 'ð°', 'ðŪðđ', 'ð'],
['ðķïļ', 'ð', 'ðĩ', 'ðĪ', 'ðïļ'],
['ðïļ', 'âïļ', 'ðēðĶ', 'ð', 'ðĨ'],
['ðĩïļ', 'ðš', 'ð', 'ðšïļ', 'ð'],
['ð', 'ðĨ', 'ðĢ', 'ðđ', 'ðĐ'],
['ð', 'âïļ', 'ð§', 'ðŧ', 'ð'],
['â°', 'ð', 'ðŪ', 'ð', 'ðŽ'],
['ðïļ', 'ð', 'ð―', 'ð', 'ð']]
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:
| ['ð', 'ð', 'ð', 'ðŧ', 'ð'],
['ðīïļ', 'ðŦ', 'ð°', 'ðŪðđ', 'ð'],
['ðķïļ', 'ð', 'ðĩ', 'ðĪ', 'ðïļ'],
['ðïļ', 'âïļ', 'ðēðĶ', 'ð', 'ðĨ'],
['ðĩïļ', 'ðš', 'ð', 'ðšïļ', 'ð'],
['ð', 'ðĨ', 'ðĢ', 'ðđ', 'ðĐ'],
['ð', 'âïļ', 'ð§', 'ðŧ', 'ð'],
['â°', 'ð', 'ðŪ', 'ð', 'ðŽ'],
['ðïļ', 'ð', 'ð―', 'ð', 'ð']]
|
What are the names of the movie titles?
Output
| ["The Lion King", "The Nightmare Before Christmas", "The Godfather", "The Matrix", "Casablanca", "Raiders of the Lost Ark", "V for Vendetta", "The Princess Bride", "Back to the Future", "Dune"]
|
Extract Json
I extracted the following JSON from that output:
| ["The Lion King", "The Nightmare Before Christmas", "The Godfather", "The Matrix", "Casablanca", "Raiders of the Lost Ark", "V for Vendetta", "The Princess Bride", "Back to the Future", "Dune"]
|
PyFunc
OK
Resulting in output:
True