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", "ðŧ", "ð", "ð", "ð"],
"The Godfather": ["ðŦ", "ð", "ðĻââïļ", "ðŽ", "ð°"],
"The Matrix": ["ðŧ", "ðķ", "ðŦ", "ð", "ð"],
"Casablanca": ["âïļ", "ðļ", "ðđ", "ð", "ð§Ĩ"],
"Raiders of the Lost Ark": ["ðĻâðŦ", "ðĪ ", "ð", "ð", "ðš"],
"V for Vendetta": ["ðĢ", "ð", "ðĨ", "ðŽð§", "ðĄïļ"],
"The Princess Bride": ["ðļ", "ðĪš", "ðđ", "ð°", "ð"],
"Back to the Future": ["ð", "âģ", "ðĻâðŽ", "ðļ", "âĄ"],
"Dune": ["ðïļ", "ð", "ðŪ", "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", "ðŧ", "ð", "ð", "ð"],
"The Godfather": ["ðŦ", "ð", "ðĻââïļ", "ðŽ", "ð°"],
"The Matrix": ["ðŧ", "ðķ", "ðŦ", "ð", "ð"],
"Casablanca": ["âïļ", "ðļ", "ðđ", "ð", "ð§Ĩ"],
"Raiders of the Lost Ark": ["ðĻâðŦ", "ðĪ ", "ð", "ð", "ðš"],
"V for Vendetta": ["ðĢ", "ð", "ðĨ", "ðŽð§", "ðĄïļ"],
"The Princess Bride": ["ðļ", "ðĪš", "ðđ", "ð°", "ð"],
"Back to the Future": ["ð", "âģ", "ðĻâðŽ", "ðļ", "âĄ"],
"Dune": ["ðïļ", "ð", "ðŪ", "ð", "ð§"]}
|
PyFunc
Resulting in output:
[['ðĶ', 'ð', 'ð
', 'ð', 'ð'],
['ð', 'ðŧ', 'ð', 'ð', 'ð'],
['ðŦ', 'ð', 'ðŽ', 'ð°'],
['ðŧ', 'ðķ', 'ðŦ', 'ð', 'ð'],
['âïļ', 'ðļ', 'ðđ', 'ð', 'ð§Ĩ'],
['ðĪ ', 'ð', 'ð', 'ðš'],
['ðĢ', 'ð', 'ðĨ', 'ðŽð§', 'ðĄïļ'],
['ðļ', 'ðĪš', 'ðđ', 'ð°', 'ð'],
['ð', 'âģ', 'ðļ', 'âĄ'],
['ðïļ', 'ð', 'ðŪ', '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', 'ðŧ', 'ð', 'ð', 'ð'],
['ðŦ', 'ð', 'ðŽ', 'ð°'],
['ðŧ', 'ðķ', 'ðŦ', 'ð', 'ð'],
['âïļ', 'ðļ', 'ðđ', 'ð', 'ð§Ĩ'],
['ðĪ ', 'ð', 'ð', 'ðš'],
['ðĢ', 'ð', 'ðĨ', 'ðŽð§', 'ðĄïļ'],
['ðļ', 'ðĪš', 'ðđ', 'ð°', 'ð'],
['ð', 'âģ', 'ðļ', 'âĄ'],
['ðïļ', 'ð', 'ðŪ', 'ð', 'ð§']]
|
What are the names of the movie titles?
Output
| ["The Lion King", "The Nightmare Before Christmas", "Pulp Fiction", "The Matrix", "Casablanca", "National Treasure", "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", "Pulp Fiction", "The Matrix", "Casablanca", "National Treasure", "V for Vendetta", "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", "ðŧ", "ð", "ð", "ð"], "The Godfather": ["ðŦ", "ðī", "ðŽ", "ðī", "ð"], "The Matrix": ["ðŧ", "ðķ", "ðŦ", "ð", "ð"], "Casablanca": ["âïļ", "ðđ", "ðļ", "ð§Ĩ", "ðĐââĪïļâðĻ"], "Raiders of the Lost Ark": ["ðĪ ", "ðš", "ð", "ð", "âïļ"], "V for Vendetta": ["ð", "ðĨ", "ðĄïļ", "ðđ", "ðŽð§"], "The Princess Bride": ["ðļ", "âïļ", "ð°", "ðđ", "âĪïļ"], "Back to the Future": ["ð", "âĄ", "ð°", "ðļ", "ð"], "Dune": ["ðïļ", "ðŠą", "ðŪ", "âïļ", "ð"]}
|
Extract Json
I extracted the following JSON from that output:
| {"The Lion King": ["ðĶ", "ð", "ð
", "ð", "ð"], "The Nightmare Before Christmas": ["ð", "ðŧ", "ð", "ð", "ð"], "The Godfather": ["ðŦ", "ðī", "ðŽ", "ðī", "ð"], "The Matrix": ["ðŧ", "ðķ", "ðŦ", "ð", "ð"], "Casablanca": ["âïļ", "ðđ", "ðļ", "ð§Ĩ", "ðĐââĪïļâðĻ"], "Raiders of the Lost Ark": ["ðĪ ", "ðš", "ð", "ð", "âïļ"], "V for Vendetta": ["ð", "ðĨ", "ðĄïļ", "ðđ", "ðŽð§"], "The Princess Bride": ["ðļ", "âïļ", "ð°", "ðđ", "âĪïļ"], "Back to the Future": ["ð", "âĄ", "ð°", "ðļ", "ð"], "Dune": ["ðïļ", "ðŠą", "ðŪ", "âïļ", "ð"]}
|
PyFunc
Resulting in output:
[['ðĶ', 'ð', 'ð
', 'ð', 'ð'],
['ð', 'ðŧ', 'ð', 'ð', 'ð'],
['ðŦ', 'ðī', 'ðŽ', 'ðī', 'ð'],
['ðŧ', 'ðķ', 'ðŦ', 'ð', 'ð'],
['âïļ', 'ðđ', 'ðļ', 'ð§Ĩ'],
['ðĪ ', 'ðš', 'ð', 'ð', 'âïļ'],
['ð', 'ðĨ', 'ðĄïļ', 'ðđ', 'ðŽð§'],
['ðļ', 'âïļ', 'ð°', 'ðđ', 'âĪïļ'],
['ð', 'âĄ', 'ð°', 'ðļ', 'ð'],
['ðïļ', 'ðŠą', 'ðŪ', 'âïļ', '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', 'ðŧ', 'ð', 'ð', 'ð'],
['ðŦ', 'ðī', 'ðŽ', 'ðī', 'ð'],
['ðŧ', 'ðķ', 'ðŦ', 'ð', 'ð'],
['âïļ', 'ðđ', 'ðļ', 'ð§Ĩ'],
['ðĪ ', 'ðš', 'ð', 'ð', 'âïļ'],
['ð', 'ðĨ', 'ðĄïļ', 'ðđ', 'ðŽð§'],
['ðļ', 'âïļ', 'ð°', 'ðđ', 'âĪïļ'],
['ð', 'âĄ', 'ð°', 'ðļ', 'ð'],
['ðïļ', 'ðŠą', 'ðŪ', 'âïļ', 'ð']]
|
What are the names of the movie titles?
Output
| ["The Lion King", "The Nightmare Before Christmas", "The Godfather", "The Matrix", "Casablanca", "Indiana Jones", "V for Vendetta", "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", "Indiana Jones", "V for Vendetta", "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", "ð"], "The Nightmare Before Christmas": ["ð", "ð
", "ðŧ", "ð", "âïļ"], "The Godfather": ["ðŦ", "ð", "ðĻââïļ", "ðī", "ðĩ"], "The Matrix": ["ðŧ", "ðķ", "ð", "ð", "ðĒ"], "Casablanca": ["âïļ", "ðđ", "âĪïļ", "ðĩïļ", "ðļ"], "Raiders of the Lost Ark": ["ðš", "ðĨ", "ðĻâðŦ", "ð", "ð§Ē"], "V for Vendetta": ["ðĢ", "ð", "ðĄïļ", "ðđ", "ðĨ"], "The Princess Bride": ["ðļ", "ðĪš", "ð", "ðđ", "ð°"], "Back to the Future": ["ð", "âĄ", "ð°ïļ", "ðĻâðŽ", "ðđ"], "Dune": ["ðïļ", "ð", "ðŪ", "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", "âïļ"], "The Godfather": ["ðŦ", "ð", "ðĻââïļ", "ðī", "ðĩ"], "The Matrix": ["ðŧ", "ðķ", "ð", "ð", "ðĒ"], "Casablanca": ["âïļ", "ðđ", "âĪïļ", "ðĩïļ", "ðļ"], "Raiders of the Lost Ark": ["ðš", "ðĨ", "ðĻâðŦ", "ð", "ð§Ē"], "V for Vendetta": ["ðĢ", "ð", "ðĄïļ", "ðđ", "ðĨ"], "The Princess Bride": ["ðļ", "ðĪš", "ð", "ðđ", "ð°"], "Back to the Future": ["ð", "âĄ", "ð°ïļ", "ðĻâðŽ", "ðđ"], "Dune": ["ðïļ", "ð", "ðŪ", "ð", "ð§"]}
|
PyFunc
Resulting in output:
[['ðĶ', 'ð', 'ð
', 'ð', 'ð'],
['ð', 'ð
', 'ðŧ', 'ð', 'âïļ'],
['ðŦ', 'ð', 'ðī', 'ðĩ'],
['ðŧ', 'ðķ', 'ð', 'ð', 'ðĒ'],
['âïļ', 'ðđ', 'âĪïļ', 'ðĩïļ', 'ðļ'],
['ðš', 'ðĨ', 'ð', 'ð§Ē'],
['ðĢ', 'ð', 'ðĄïļ', 'ðđ', 'ðĨ'],
['ðļ', 'ðĪš', 'ð', 'ðđ', 'ð°'],
['ð', 'âĄ', 'ð°ïļ', 'ðđ'],
['ðïļ', 'ð', 'ðŪ', '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', 'âïļ'],
['ðŦ', 'ð', 'ðī', 'ðĩ'],
['ðŧ', 'ðķ', 'ð', 'ð', 'ðĒ'],
['âïļ', 'ðđ', 'âĪïļ', 'ðĩïļ', 'ðļ'],
['ðš', 'ðĨ', 'ð', 'ð§Ē'],
['ðĢ', 'ð', 'ðĄïļ', 'ðđ', 'ðĨ'],
['ðļ', 'ðĪš', 'ð', 'ðđ', 'ð°'],
['ð', 'âĄ', 'ð°ïļ', 'ðđ'],
['ðïļ', 'ð', 'ðŪ', 'ð', 'ð§']]
|
What are the names of the movie titles?
Output
| ["The Lion King", "The Nightmare Before Christmas", "The Godfather", "The Matrix", "Casablanca", "Hercules", "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", "Hercules", "V for Vendetta", "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", "ðŧ", "ð
", "ð", "âïļ"],
"The Godfather": ["ðŦ", "ð", "ðĻâðĐâð§âðĶ", "ð°", "ðī"],
"The Matrix": ["ðķïļ", "ðŧ", "ð", "ðĪ", "ðŦ"],
"Casablanca": ["âïļ", "ðđ", "âĪïļ", "ðĩïļ", "ðŽ"],
"Raiders of the Lost Ark": ["ðš", "ðŦ", "ð", "ðĻâðŦ", "ðïļ"],
"V for Vendetta": ["ðââïļ", "ðĨ", "ð
", "ðŠ", "ð"],
"The Princess Bride": ["âïļ", "ðļ", "ðĪī", "ð°", "ð"],
"Back to the Future": ["ð", "â°", "ðŽ", "ðļ", "âĄ"],
"Dune": ["ðïļ", "ðŠą", "ð", "ð§", "ðŪ"]}
|
Extract Json
I extracted the following JSON from that output:
| {"The Lion King": ["ðĶ", "ð", "ð
", "ð", "ð"],
"The Nightmare Before Christmas": ["ð", "ðŧ", "ð
", "ð", "âïļ"],
"The Godfather": ["ðŦ", "ð", "ðĻâðĐâð§âðĶ", "ð°", "ðī"],
"The Matrix": ["ðķïļ", "ðŧ", "ð", "ðĪ", "ðŦ"],
"Casablanca": ["âïļ", "ðđ", "âĪïļ", "ðĩïļ", "ðŽ"],
"Raiders of the Lost Ark": ["ðš", "ðŦ", "ð", "ðĻâðŦ", "ðïļ"],
"V for Vendetta": ["ðââïļ", "ðĨ", "ð
", "ðŠ", "ð"],
"The Princess Bride": ["âïļ", "ðļ", "ðĪī", "ð°", "ð"],
"Back to the Future": ["ð", "â°", "ðŽ", "ðļ", "âĄ"],
"Dune": ["ðïļ", "ðŠą", "ð", "ð§", "ðŪ"]}
|
PyFunc
Resulting in output:
[['ðĶ', 'ð', 'ð
', 'ð', 'ð'],
['ð', 'ðŧ', 'ð
', 'ð', 'âïļ'],
['ðŦ', 'ð', 'ð°', 'ðī'],
['ðķïļ', 'ðŧ', 'ð', 'ðĪ', 'ðŦ'],
['âïļ', 'ðđ', 'âĪïļ', 'ðĩïļ', 'ðŽ'],
['ðš', 'ðŦ', 'ð', 'ðïļ'],
['ðĨ', 'ð
', 'ðŠ', 'ð'],
['âïļ', 'ðļ', 'ðĪī', 'ð°', 'ð'],
['ð', 'â°', 'ðŽ', 'ðļ', 'âĄ'],
['ðïļ', 'ðŠą', 'ð', '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', 'ðŧ', 'ð
', 'ð', 'âïļ'],
['ðŦ', 'ð', 'ð°', 'ðī'],
['ðķïļ', 'ðŧ', 'ð', 'ðĪ', 'ðŦ'],
['âïļ', 'ðđ', 'âĪïļ', 'ðĩïļ', 'ðŽ'],
['ðš', 'ðŦ', 'ð', 'ðïļ'],
['ðĨ', 'ð
', 'ðŠ', 'ð'],
['âïļ', 'ðļ', 'ðĪī', 'ð°', 'ð'],
['ð', 'â°', 'ðŽ', 'ðļ', 'âĄ'],
['ðïļ', 'ðŠą', 'ð', '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", "Halloween", "Game of Thrones", "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", "Halloween", "Game of Thrones", "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": ["ðŦ", "ðī", "ðŽ", "ðŪðđ", "ðž"], "The Matrix": ["ðŧ", "ðķ", "ð", "ð", "ðĪ"], "Casablanca": ["âïļ", "ðđ", "âĪïļ", "ð§Ĩ", "ðŽ"], "Raiders of the Lost Ark": ["ð·", "ðš", "ð", "ð", "âïļ"], "V for Vendetta": ["ð", "ðĨ", "ðŽð§", "âïļ", "ð"], "The Princess Bride": ["ðļ", "ðĪī", "ð°", "ðĄïļ", "ð"], "Back to the Future": ["ð", "âĄ", "ð°ïļ", "ð", "ðļ"], "Dune": ["ðïļ", "ð", "ð§", "âïļ", "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": ["ðŦ", "ðī", "ðŽ", "ðŪðđ", "ðž"], "The Matrix": ["ðŧ", "ðķ", "ð", "ð", "ðĪ"], "Casablanca": ["âïļ", "ðđ", "âĪïļ", "ð§Ĩ", "ðŽ"], "Raiders of the Lost Ark": ["ð·", "ðš", "ð", "ð", "âïļ"], "V for Vendetta": ["ð", "ðĨ", "ðŽð§", "âïļ", "ð"], "The Princess Bride": ["ðļ", "ðĪī", "ð°", "ðĄïļ", "ð"], "Back to the Future": ["ð", "âĄ", "ð°ïļ", "ð", "ðļ"], "Dune": ["ðïļ", "ð", "ð§", "âïļ", "ð"]}
|
PyFunc
Resulting in output:
[['ðĶ', 'ð', 'ð
', 'ð', 'ð'],
['ð', 'ð', 'ðŧ', 'ð', 'ð'],
['ðŦ', 'ðī', 'ðŽ', 'ðŪðđ', 'ðž'],
['ðŧ', 'ðķ', 'ð', 'ð', 'ðĪ'],
['âïļ', 'ðđ', 'âĪïļ', 'ð§Ĩ', 'ðŽ'],
['ð·', 'ðš', 'ð', 'ð', 'âïļ'],
['ð', 'ðĨ', 'ðŽð§', 'âïļ', 'ð'],
['ðļ', 'ðĪī', 'ð°', 'ðĄïļ', 'ð'],
['ð', 'âĄ', 'ð°ïļ', 'ð', 'ðļ'],
['ðïļ', 'ð', 'ð§', 'âïļ', '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'],
['ðŦ', 'ðī', 'ðŽ', 'ðŪðđ', 'ðž'],
['ðŧ', 'ðķ', 'ð', 'ð', 'ðĪ'],
['âïļ', 'ðđ', 'âĪïļ', 'ð§Ĩ', 'ðŽ'],
['ð·', 'ðš', 'ð', 'ð', 'âïļ'],
['ð', 'ðĨ', 'ðŽð§', 'âïļ', 'ð'],
['ðļ', 'ðĪī', 'ð°', 'ðĄïļ', 'ð'],
['ð', 'âĄ', 'ð°ïļ', 'ð', 'ðļ'],
['ðïļ', 'ð', 'ð§', 'âïļ', 'ð']]
|
What are the names of the movie titles?
Output
| ["The Lion King", "The Nightmare Before Christmas", "The Godfather", "The Matrix", "Casablanca", "Indiana Jones", "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", "Indiana Jones", "V for Vendetta", "The Princess Bride", "Back to the Future", "Dune"]
|
PyFunc
OK
Resulting in output:
True