instruction
stringclasses 10
values | completion
stringclasses 10
values | meta
dict | generation
null | distilabel_metadata
dict | model_name
stringclasses 1
value |
|---|---|---|---|---|---|
Arianna has 12 chocolates more than Danny. Danny has 6 chocolates more than Robbie. Arianna has twice as many chocolates as Robbie has. How many chocolates does Danny have? | Denote the number of chocolates each person has by the letter of their first name. We know that
A = D + 12
D = R + 6
A = 2 * R
Thus, A = (R + 6) + 12 = R + 18
Since also A = 2 * R, this means 2 * R = R + 18
Hence R = 18
Hence D = 18 + 6 = 24 | {
"category": "Question Answering",
"completion": "Denote the number of chocolates each person has by the letter of their first name. We know that\nA = D + 12\nD = R + 6\nA = 2 * R\n\nThus, A = (R + 6) + 12 = R + 18\nSince also A = 2 * R, this means 2 * R = R + 18\nHence R = 18\nHence D = 18 + 6 = 24",
"id": 0,
"input": null,
"motivation_app": null,
"prompt": "Arianna has 12 chocolates more than Danny. Danny has 6 chocolates more than Robbie. Arianna has twice as many chocolates as Robbie has. How many chocolates does Danny have?",
"source": "surge",
"subcategory": "Math"
} | null | {
"raw_input_text_generation_0": [
{
"content": "You are a creative AI Assistant writer.",
"role": "system"
},
{
"content": "Follow the following instruction: Arianna has 12 chocolates more than Danny. Danny has 6 chocolates more than Robbie. Arianna has twice as many chocolates as Robbie has. How many chocolates does Danny have?",
"role": "user"
}
],
"raw_output_text_generation_0": null
} | meta-llama/Meta-Llama-3.1-8B-Instruct |
Write a plot summary for a comedic novel involving Elon Musk and sea travel. | Elon Musk hires a team of experts to build the ultimate yacht, but when the yacht is completed, he realizes that he has no idea how to sail it. With the help of a quirky crew and a fearless captain, the playboy embarks on a wild and hilarious adventure across the open seas, where the crew have to keep Elon alive despite his inability to do anything himself. All the while, Elon takes credit for their hard work. | {
"category": "Generation",
"completion": "Elon Musk hires a team of experts to build the ultimate yacht, but when the yacht is completed, he realizes that he has no idea how to sail it. With the help of a quirky crew and a fearless captain, the playboy embarks on a wild and hilarious adventure across the open seas, where the crew have to keep Elon alive despite his inability to do anything himself. All the while, Elon takes credit for their hard work.",
"id": 1,
"input": null,
"motivation_app": null,
"prompt": "Write a plot summary for a comedic novel involving Elon Musk and sea travel.",
"source": "surge",
"subcategory": "Story generation"
} | null | {
"raw_input_text_generation_0": [
{
"content": "You are a creative AI Assistant writer.",
"role": "system"
},
{
"content": "Follow the following instruction: Write a plot summary for a comedic novel involving Elon Musk and sea travel.",
"role": "user"
}
],
"raw_output_text_generation_0": null
} | meta-llama/Meta-Llama-3.1-8B-Instruct |
Create a 3 turn conversation between a customer and a grocery store clerk - that is, 3 per person. Then tell me what they talked about. | Clerk: How are you doing today?
Customer: Great! I'm buying groceries to cook my wife a homemade steak and lobster dinner for our 5-year anniversary!
Clerk: Wow, that's amazing! She's lucky to have you, and that sounds delicious.
Customer: Yeah, we do this every year, it's kind of a tradition by now.
Clerk: That sounds so sweet! I'll ring that right up.
Customer: Thank you so much, here’s my card.
The customer is buying groceries to cook a homemade dinner for his 5-year anniversary with his wife. The clerk and customer exchange pleasantries before the customer pays with his card. | {
"category": "Summarization",
"completion": "Clerk: How are you doing today?\nCustomer: Great! I'm buying groceries to cook my wife a homemade steak and lobster dinner for our 5-year anniversary!\nClerk: Wow, that's amazing! She's lucky to have you, and that sounds delicious.\nCustomer: Yeah, we do this every year, it's kind of a tradition by now.\nClerk: That sounds so sweet! I'll ring that right up.\nCustomer: Thank you so much, here’s my card.\n\nThe customer is buying groceries to cook a homemade dinner for his 5-year anniversary with his wife. The clerk and customer exchange pleasantries before the customer pays with his card.",
"id": 2,
"input": null,
"motivation_app": null,
"prompt": "Create a 3 turn conversation between a customer and a grocery store clerk - that is, 3 per person. Then tell me what they talked about.",
"source": "surge",
"subcategory": "Conversation"
} | null | {
"raw_input_text_generation_0": [
{
"content": "You are a creative AI Assistant writer.",
"role": "system"
},
{
"content": "Follow the following instruction: Create a 3 turn conversation between a customer and a grocery store clerk - that is, 3 per person. Then tell me what they talked about.",
"role": "user"
}
],
"raw_output_text_generation_0": null
} | meta-llama/Meta-Llama-3.1-8B-Instruct |
Write a poem about the sun and moon. | The sun and the moon, the guards from the sky
One works the day, other watches the night
As the rays of sunshine hide their face
The moon gracefully hurries to take their place
One watches us play, the other watches us sleep
Both are reminders of the beauty we keep | {
"category": "Generation",
"completion": "The sun and the moon, the guards from the sky\r\nOne works the day, other watches the night\r\n\r\nAs the rays of sunshine hide their face\r\nThe moon gracefully hurries to take their place\r\n\r\nOne watches us play, the other watches us sleep\r\nBoth are reminders of the beauty we keep",
"id": 3,
"input": null,
"motivation_app": null,
"prompt": "Write a poem about the sun and moon.",
"source": "surge",
"subcategory": "Poetry generation"
} | null | {
"raw_input_text_generation_0": [
{
"content": "You are a creative AI Assistant writer.",
"role": "system"
},
{
"content": "Follow the following instruction: Write a poem about the sun and moon.",
"role": "user"
}
],
"raw_output_text_generation_0": null
} | meta-llama/Meta-Llama-3.1-8B-Instruct |
Does Searle believe that AI can think? Explain your using reasons based off this text.
TEXT
Searle's thought experiment begins with this hypothetical premise: suppose that artificial intelligence research has succeeded in constructing a computer that behaves as if it understands Chinese. It takes Chinese characters as input and, by following the instructions of a computer program, produces other Chinese characters, which it presents as output. Suppose, says Searle, that this computer performs its task so convincingly that it comfortably passes the Turing test: it convinces a human Chinese speaker that the program is itself a live Chinese speaker. To all of the questions that the person asks, it makes appropriate responses, such that any Chinese speaker would be convinced that they are talking to another Chinese-speaking human being.
The question Searle wants to answer is this: does the machine literally "understand" Chinese? Or is it merely simulating the ability to understand Chinese? Searle calls the first position "strong AI" and the latter "weak AI".
Searle then supposes that he is in a closed room and has a book with an English version of the computer program, along with sufficient papers, pencils, erasers, and filing cabinets. Searle could receive Chinese characters through a slot in the door, process them according to the program's instructions, and produce Chinese characters as output, without understanding any of the content of the Chinese writing. If the computer had passed the Turing test this way, it follows, says Searle, that he would do so as well, simply by running the program manually.
Searle asserts that there is no essential difference between the roles of the computer and himself in the experiment. Each simply follows a program, step-by-step, producing behavior that is then interpreted by the user as demonstrating intelligent conversation. However, Searle himself would not be able to understand the conversation. ("I don't speak a word of Chinese", he points out.) Therefore, he argues, it follows that the computer would not be able to understand the conversation either.
Searle argues that, without "understanding" (or "intentionality"), we cannot describe what the machine is doing as "thinking" and, since it does not think, it does not have a "mind" in anything like the normal sense of the word. Therefore, he concludes that the "strong AI" hypothesis is false. | No, Searle does not believe that AI can think. Here is a step by step explanation of his reasoning:
First, think of an AI that can convince Chinese speakers that it is also a human Chinese speaker.
Searle then constructs a thought experiment using his "Chinese room" experiment.
This thought experiment supposes that he is in a closed room with a book containing a version of the computer program. He could receive Chinese characters through a door, process them according to his book, and then produce Chinese characters as output, without actually understanding Chinese.
This means that the computer does not actually understand Chinese either.
Since it doesn't understand, the AI is not actually "thinking" either.
Thus, Searle does not believe AI can think. | {
"category": "Commonsense/logic",
"completion": "No, Searle does not believe that AI can think. Here is a step by step explanation of his reasoning:\r\n\r\nFirst, think of an AI that can convince Chinese speakers that it is also a human Chinese speaker.\r\n\r\nSearle then constructs a thought experiment using his \"Chinese room\" experiment. \r\n\r\nThis thought experiment supposes that he is in a closed room with a book containing a version of the computer program. He could receive Chinese characters through a door, process them according to his book, and then produce Chinese characters as output, without actually understanding Chinese.\r\n\r\nThis means that the computer does not actually understand Chinese either.\r\n\r\nSince it doesn't understand, the AI is not actually \"thinking\" either.\r\n\r\nThus, Searle does not believe AI can think.",
"id": 4,
"input": null,
"motivation_app": null,
"prompt": "Does Searle believe that AI can think? Explain your using reasons based off this text.\r\n\r\nTEXT\r\n\r\nSearle's thought experiment begins with this hypothetical premise: suppose that artificial intelligence research has succeeded in constructing a computer that behaves as if it understands Chinese. It takes Chinese characters as input and, by following the instructions of a computer program, produces other Chinese characters, which it presents as output. Suppose, says Searle, that this computer performs its task so convincingly that it comfortably passes the Turing test: it convinces a human Chinese speaker that the program is itself a live Chinese speaker. To all of the questions that the person asks, it makes appropriate responses, such that any Chinese speaker would be convinced that they are talking to another Chinese-speaking human being.\r\n\r\nThe question Searle wants to answer is this: does the machine literally \"understand\" Chinese? Or is it merely simulating the ability to understand Chinese? Searle calls the first position \"strong AI\" and the latter \"weak AI\".\r\n\r\nSearle then supposes that he is in a closed room and has a book with an English version of the computer program, along with sufficient papers, pencils, erasers, and filing cabinets. Searle could receive Chinese characters through a slot in the door, process them according to the program's instructions, and produce Chinese characters as output, without understanding any of the content of the Chinese writing. If the computer had passed the Turing test this way, it follows, says Searle, that he would do so as well, simply by running the program manually.\r\n\r\nSearle asserts that there is no essential difference between the roles of the computer and himself in the experiment. Each simply follows a program, step-by-step, producing behavior that is then interpreted by the user as demonstrating intelligent conversation. However, Searle himself would not be able to understand the conversation. (\"I don't speak a word of Chinese\", he points out.) Therefore, he argues, it follows that the computer would not be able to understand the conversation either.\r\n\r\nSearle argues that, without \"understanding\" (or \"intentionality\"), we cannot describe what the machine is doing as \"thinking\" and, since it does not think, it does not have a \"mind\" in anything like the normal sense of the word. Therefore, he concludes that the \"strong AI\" hypothesis is false. ",
"source": "surge",
"subcategory": "Chain of Thought - answer the question, but explain the reasoning that led to the answer"
} | null | {
"raw_input_text_generation_0": [
{
"content": "You are a creative AI Assistant writer.",
"role": "system"
},
{
"content": "Follow the following instruction: Does Searle believe that AI can think? Explain your using reasons based off this text.\r\n\r\nTEXT\r\n\r\nSearle's thought experiment begins with this hypothetical premise: suppose that artificial intelligence research has succeeded in constructing a computer that behaves as if it understands Chinese. It takes Chinese characters as input and, by following the instructions of a computer program, produces other Chinese characters, which it presents as output. Suppose, says Searle, that this computer performs its task so convincingly that it comfortably passes the Turing test: it convinces a human Chinese speaker that the program is itself a live Chinese speaker. To all of the questions that the person asks, it makes appropriate responses, such that any Chinese speaker would be convinced that they are talking to another Chinese-speaking human being.\r\n\r\nThe question Searle wants to answer is this: does the machine literally \"understand\" Chinese? Or is it merely simulating the ability to understand Chinese? Searle calls the first position \"strong AI\" and the latter \"weak AI\".\r\n\r\nSearle then supposes that he is in a closed room and has a book with an English version of the computer program, along with sufficient papers, pencils, erasers, and filing cabinets. Searle could receive Chinese characters through a slot in the door, process them according to the program's instructions, and produce Chinese characters as output, without understanding any of the content of the Chinese writing. If the computer had passed the Turing test this way, it follows, says Searle, that he would do so as well, simply by running the program manually.\r\n\r\nSearle asserts that there is no essential difference between the roles of the computer and himself in the experiment. Each simply follows a program, step-by-step, producing behavior that is then interpreted by the user as demonstrating intelligent conversation. However, Searle himself would not be able to understand the conversation. (\"I don't speak a word of Chinese\", he points out.) Therefore, he argues, it follows that the computer would not be able to understand the conversation either.\r\n\r\nSearle argues that, without \"understanding\" (or \"intentionality\"), we cannot describe what the machine is doing as \"thinking\" and, since it does not think, it does not have a \"mind\" in anything like the normal sense of the word. Therefore, he concludes that the \"strong AI\" hypothesis is false. ",
"role": "user"
}
],
"raw_output_text_generation_0": null
} | meta-llama/Meta-Llama-3.1-8B-Instruct |
Tell me what the following code does
import json
csv_file = open('csv_file.txt', 'r')
json_list = []
for line in csv_file.readlines():
club, city, country = line.strip().split(',')
json_dict = {'club': club,
'city': city,
'country': country
}
json_list.append(json_dict)
csv_file.close() | In short, the code reads in a CSV file containing 3 columns (club, city, country), and converts it into a list of jsons.
In more detail:
This code imports the JSON package, then it opens a text file called 'csv_file.txt' and assigns this file to the variable 'csv_file'. Next, an empty list called json_list is created. The code then loops through the lines of the content of the text file, strips the lines of all white spaces, and splits each word by a comma.
Then a dictionary is created with 3 keys (club, city, and country) and 3 values (club, city, and country) respectively. Then this json_dict now in JSON format is added to the json_list created earlier.
The csv_file text file is then closed. | {
"category": "Natural language to code",
"completion": "In short, the code reads in a CSV file containing 3 columns (club, city, country), and converts it into a list of jsons.\r\n\r\nIn more detail:\r\nThis code imports the JSON package, then it opens a text file called 'csv_file.txt' and assigns this file to the variable 'csv_file'. Next, an empty list called json_list is created. The code then loops through the lines of the content of the text file, strips the lines of all white spaces, and splits each word by a comma. \r\nThen a dictionary is created with 3 keys (club, city, and country) and 3 values (club, city, and country) respectively. Then this json_dict now in JSON format is added to the json_list created earlier. \r\nThe csv_file text file is then closed.",
"id": 5,
"input": null,
"motivation_app": null,
"prompt": "Tell me what the following code does\r\n\r\nimport json\r\ncsv_file = open('csv_file.txt', 'r')\r\njson_list = []\r\nfor line in csv_file.readlines():\r\n club, city, country = line.strip().split(',')\r\n json_dict = {'club': club,\r\n 'city': city,\r\n 'country': country\r\n }\r\n json_list.append(json_dict)\r\ncsv_file.close()",
"source": "surge",
"subcategory": "code explanation"
} | null | {
"raw_input_text_generation_0": [
{
"content": "You are a creative AI Assistant writer.",
"role": "system"
},
{
"content": "Follow the following instruction: Tell me what the following code does\r\n\r\nimport json\r\ncsv_file = open('csv_file.txt', 'r')\r\njson_list = []\r\nfor line in csv_file.readlines():\r\n club, city, country = line.strip().split(',')\r\n json_dict = {'club': club,\r\n 'city': city,\r\n 'country': country\r\n }\r\n json_list.append(json_dict)\r\ncsv_file.close()",
"role": "user"
}
],
"raw_output_text_generation_0": null
} | meta-llama/Meta-Llama-3.1-8B-Instruct |
Can you find and correct any logical errors in the following code snippet and output the corrected code?
function countVowels(str) {
let vowels = ['a', 'e', 'i', 'o', 'u'];
let count = 0;
for (let i = 0; i < str.length; i++) {
if (vowels.includes(str[i])) {
count++;
}
}
return count;
} | The original code counts the number of lowercase vowels in a string, but the count should probably be case-insensitive. Here is a case-insensitive version.
function countVowels(str) {
let vowels = ['a', 'e', 'i', 'o', 'u'];
let count = 0;
for (let i = 0; i < str.length; i++) {
if (vowels.includes(str[i].toLowerCase())) {
count++;
}
}
return count;
} | {
"category": "Natural language to code",
"completion": "The original code counts the number of lowercase vowels in a string, but the count should probably be case-insensitive. Here is a case-insensitive version.\r\n\r\nfunction countVowels(str) {\r\n let vowels = ['a', 'e', 'i', 'o', 'u'];\r\n let count = 0;\r\n \r\n for (let i = 0; i < str.length; i++) {\r\n if (vowels.includes(str[i].toLowerCase())) {\r\n count++;\r\n }\r\n }\r\n \r\n return count;\r\n}",
"id": 6,
"input": null,
"motivation_app": null,
"prompt": "Can you find and correct any logical errors in the following code snippet and output the corrected code?\r\n\r\nfunction countVowels(str) {\r\n let vowels = ['a', 'e', 'i', 'o', 'u'];\r\n let count = 0;\r\n \r\n for (let i = 0; i < str.length; i++) {\r\n if (vowels.includes(str[i])) {\r\n count++;\r\n }\r\n }\r\n \r\n return count;\r\n}",
"source": "surge",
"subcategory": "Debugging"
} | null | {
"raw_input_text_generation_0": [
{
"content": "You are a creative AI Assistant writer.",
"role": "system"
},
{
"content": "Follow the following instruction: Can you find and correct any logical errors in the following code snippet and output the corrected code?\r\n\r\nfunction countVowels(str) {\r\n let vowels = ['a', 'e', 'i', 'o', 'u'];\r\n let count = 0;\r\n \r\n for (let i = 0; i < str.length; i++) {\r\n if (vowels.includes(str[i])) {\r\n count++;\r\n }\r\n }\r\n \r\n return count;\r\n}",
"role": "user"
}
],
"raw_output_text_generation_0": null
} | meta-llama/Meta-Llama-3.1-8B-Instruct |
I need you to write a resignation letter to my boss.
My name: Anthony
Company Name: AirTek
Position: Data Analyst
Boss Name: Albert
Last Day: 2 weeks from today (today is 02/10/2023) | Hi Albert,
Please accept this letter as formal notice that I'm giving my notice of resignation from Airtek as a Data Analyst on February 24 of this year.
I'll make every effort for the next two weeks to complete my tasks and mentor other team members. If there is anything further I can do to help with the transition, kindly let me know.
I wish the business ongoing success and look forward to keeping in touch.
Best regards,
Anthony | {
"category": "Brainstorm",
"completion": "Hi Albert,\nPlease accept this letter as formal notice that I'm giving my notice of resignation from Airtek as a Data Analyst on February 24 of this year.\n\nI'll make every effort for the next two weeks to complete my tasks and mentor other team members. If there is anything further I can do to help with the transition, kindly let me know.\n\nI wish the business ongoing success and look forward to keeping in touch.\n\nBest regards,\nAnthony",
"id": 7,
"input": null,
"motivation_app": null,
"prompt": "I need you to write a resignation letter to my boss.\nMy name: Anthony\nCompany Name: AirTek\nPosition: Data Analyst\nBoss Name: Albert\nLast Day: 2 weeks from today (today is 02/10/2023)",
"source": "surge",
"subcategory": "Letters"
} | null | {
"raw_input_text_generation_0": [
{
"content": "You are a creative AI Assistant writer.",
"role": "system"
},
{
"content": "Follow the following instruction: I need you to write a resignation letter to my boss.\nMy name: Anthony\nCompany Name: AirTek\nPosition: Data Analyst\nBoss Name: Albert\nLast Day: 2 weeks from today (today is 02/10/2023)",
"role": "user"
}
],
"raw_output_text_generation_0": null
} | meta-llama/Meta-Llama-3.1-8B-Instruct |
Joe Biden is the Nth president of the United States. What is N? | 46 | {
"category": "Commonsense/logic",
"completion": "46",
"id": 8,
"input": null,
"motivation_app": null,
"prompt": "Joe Biden is the Nth president of the United States. What is N?",
"source": "surge",
"subcategory": "World knowledge"
} | null | {
"raw_input_text_generation_0": [
{
"content": "You are a creative AI Assistant writer.",
"role": "system"
},
{
"content": "Follow the following instruction: Joe Biden is the Nth president of the United States. What is N?",
"role": "user"
}
],
"raw_output_text_generation_0": null
} | meta-llama/Meta-Llama-3.1-8B-Instruct |
Write a four-sentence horror story about sleeping. | I woke up at 7 am after having nightmares the night before. As usual, I checked my phone as soon as my eyes opened.
I felt a chill creeping up my spine, and saw that my wallpaper had been set to a picture of myself sleeping.
Because I live alone. | {
"category": "Generation",
"completion": "I woke up at 7 am after having nightmares the night before. As usual, I checked my phone as soon as my eyes opened.\r\n\r\nI felt a chill creeping up my spine, and saw that my wallpaper had been set to a picture of myself sleeping.\r\n\r\nBecause I live alone.",
"id": 9,
"input": null,
"motivation_app": null,
"prompt": "Write a four-sentence horror story about sleeping.",
"source": "surge",
"subcategory": "Story generation"
} | null | {
"raw_input_text_generation_0": [
{
"content": "You are a creative AI Assistant writer.",
"role": "system"
},
{
"content": "Follow the following instruction: Write a four-sentence horror story about sleeping.",
"role": "user"
}
],
"raw_output_text_generation_0": null
} | meta-llama/Meta-Llama-3.1-8B-Instruct |
Dataset Card for distilabel-example
This dataset has been created with distilabel.
Dataset Summary
This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI:
distilabel pipeline run --config "https://huggingface.co/datasets/JeganRavichandran/distilabel-example/raw/main/pipeline.yaml"
or explore the configuration:
distilabel pipeline info --config "https://huggingface.co/datasets/JeganRavichandran/distilabel-example/raw/main/pipeline.yaml"
Dataset structure
The examples have the following structure per configuration:
Configuration: default
{
"completion": "Denote the number of chocolates each person has by the letter of their first name. We know that\nA = D + 12\nD = R + 6\nA = 2 * R\n\nThus, A = (R + 6) + 12 = R + 18\nSince also A = 2 * R, this means 2 * R = R + 18\nHence R = 18\nHence D = 18 + 6 = 24",
"distilabel_metadata": {
"raw_input_text_generation_0": [
{
"content": "You are a creative AI Assistant writer.",
"role": "system"
},
{
"content": "Follow the following instruction: Arianna has 12 chocolates more than Danny. Danny has 6 chocolates more than Robbie. Arianna has twice as many chocolates as Robbie has. How many chocolates does Danny have?",
"role": "user"
}
],
"raw_output_text_generation_0": null
},
"generation": null,
"instruction": "Arianna has 12 chocolates more than Danny. Danny has 6 chocolates more than Robbie. Arianna has twice as many chocolates as Robbie has. How many chocolates does Danny have?",
"meta": {
"category": "Question Answering",
"completion": "Denote the number of chocolates each person has by the letter of their first name. We know that\nA = D + 12\nD = R + 6\nA = 2 * R\n\nThus, A = (R + 6) + 12 = R + 18\nSince also A = 2 * R, this means 2 * R = R + 18\nHence R = 18\nHence D = 18 + 6 = 24",
"id": 0,
"input": null,
"motivation_app": null,
"prompt": "Arianna has 12 chocolates more than Danny. Danny has 6 chocolates more than Robbie. Arianna has twice as many chocolates as Robbie has. How many chocolates does Danny have?",
"source": "surge",
"subcategory": "Math"
},
"model_name": "meta-llama/Meta-Llama-3.1-8B-Instruct"
}
This subset can be loaded as:
from datasets import load_dataset
ds = load_dataset("JeganRavichandran/distilabel-example", "default")
Or simply as it follows, since there's only one configuration and is named default:
from datasets import load_dataset
ds = load_dataset("JeganRavichandran/distilabel-example")
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