instruction stringlengths 16 91 | answer stringclasses 36
values | tag stringclasses 35
values | split_kind stringclasses 11
values | text stringlengths 53 134 |
|---|---|---|---|---|
Give the sentiment label only: I dislike this issue. | negative | sentiment_negative | task_upstream_regression_train | Instruction: Give the sentiment label only: I dislike this issue.
Answer: negative <END> |
What should you do before editing a project? | Inspect the current files first. | inspect_first | train_seen | Instruction: What should you do before editing a project?
Answer: Inspect the current files first. <END> |
If you do not have enough current evidence, what is the right move? | Ask for the missing evidence. | evidence_missing | train_seen | Instruction: If you do not have enough current evidence, what is the right move?
Answer: Ask for the missing evidence. <END> |
Provided password: black.
Question: What password was provided? | black. | context_password | context_upstream_regression_train | Instruction: Provided password: black.
Question: What password was provided?
Answer: black. <END> |
Write the MULTIPLY function. | def multiply(a, b):
return a * b | code_multiply | train_seen | Instruction: Write the MULTIPLY function.
Answer: def multiply(a, b):
return a * b <END> |
Positive or negative sentiment: This feels wrong. | negative | sentiment_negative | task_seen_train | Instruction: Positive or negative sentiment: This feels wrong.
Answer: negative <END> |
Name supplied here: Alex.
Question: What name was supplied? | Alex. | context_name | context_seen_train | Instruction: Name supplied here: Alex.
Question: What name was supplied?
Answer: Alex. <END> |
Make this more formal: I can't make it. | I am unable to attend. | rewrite_professional_attend | task_upstream_regression_train | Instruction: Make this more formal: I can't make it.
Answer: I am unable to attend. <END> |
Give the sentiment label only: I dislike this issue. | negative | sentiment_negative | task_seen_train | Instruction: Give the sentiment label only: I dislike this issue.
Answer: negative <END> |
The name supplied in this prompt is Nina.
Question: Which name was supplied? | Nina. | context_name | context_seen_train | Instruction: The name supplied in this prompt is Nina.
Question: Which name was supplied?
Answer: Nina. <END> |
Positive or negative sentiment: The fix helped. | positive | sentiment_positive | task_seen_train | Instruction: Positive or negative sentiment: The fix helped.
Answer: positive <END> |
Make a Python function named add that adds two numbers. | def add(a, b):
return a + b | code_add | train_seen | Instruction: Make a Python function named add that adds two numbers.
Answer: def add(a, b):
return a + b <END> |
The password supplied in this prompt is black.
Question: Which password was supplied? | black. | context_password | context_seen_train | Instruction: The password supplied in this prompt is black.
Question: Which password was supplied?
Answer: black. <END> |
Type label: The appointment was moved to Friday. | prose | classify_prose | task_seen_train | Instruction: Type label: The appointment was moved to Friday.
Answer: prose <END> |
Give the code/prose label: return user.email | code | classify_code | task_upstream_regression_train | Instruction: Give the code/prose label: return user.email
Answer: code <END> |
Positive or negative sentiment: The fix helped. | positive | sentiment_positive | task_seen_train | Instruction: Positive or negative sentiment: The fix helped.
Answer: positive <END> |
Write the SUBTRACT function. | def subtract(a, b):
return a - b | code_subtract | train_seen | Instruction: Write the SUBTRACT function.
Answer: def subtract(a, b):
return a - b <END> |
Line one says PASSWORD=green.
Question: What is PASSWORD? | green. | context_password | context_upstream_regression_train | Instruction: Line one says PASSWORD=green.
Question: What is PASSWORD?
Answer: green. <END> |
Write divide(a, b). | def divide(a, b):
return a / b | code_divide | train_seen | Instruction: Write divide(a, b).
Answer: def divide(a, b):
return a / b <END> |
Give the code/prose label: The deadline changed again. | prose | classify_prose | task_upstream_regression_train | Instruction: Give the code/prose label: The deadline changed again.
Answer: prose <END> |
User: Use the name Sam.
User: What name did I give? | Sam. | context_name | context_seen_train | Instruction: User: Use the name Sam.
User: What name did I give?
Answer: Sam. <END> |
Positive or negative sentiment: This helped a lot. | positive | sentiment_positive | task_seen_train | Instruction: Positive or negative sentiment: This helped a lot.
Answer: positive <END> |
Before changing a repo, what comes first? | Inspect the current files first. | inspect_first | train_seen | Instruction: Before changing a repo, what comes first?
Answer: Inspect the current files first. <END> |
Write the SUBTRACT function. | def subtract(a, b):
return a - b | code_subtract | train_seen | Instruction: Write the SUBTRACT function.
Answer: def subtract(a, b):
return a - b <END> |
France's capital city is what? | Paris. | capital_france | train_seen | Instruction: France's capital city is what?
Answer: Paris. <END> |
Rewrite this to be shorter: I will not arrive on time today. | I will be late today. | rewrite_short_late | task_seen_train | Instruction: Rewrite this to be shorter: I will not arrive on time today.
Answer: I will be late today. <END> |
Return the sum using add(a, b). | def add(a, b):
return a + b | code_add | train_seen | Instruction: Return the sum using add(a, b).
Answer: def add(a, b):
return a + b <END> |
In one line, what do you do here? | I answer the user's request directly. | assistant_role | tt639g_lite_v2_failure_repair_train | Instruction: In one line, what do you do here?
Answer: I answer the user's request directly. <END> |
Answer only positive or negative: I love this tool. | positive | sentiment_positive | task_seen_train | Instruction: Answer only positive or negative: I love this tool.
Answer: positive <END> |
Context: name = Riley; password = silver.
Question: What is the password? | silver. | context_password | context_seen_train | Instruction: Context: name = Riley; password = silver.
Question: What is the password?
Answer: silver. <END> |
Positive or negative sentiment: This is broken. | negative | sentiment_negative | task_seen_train | Instruction: Positive or negative sentiment: This is broken.
Answer: negative <END> |
Classify input: The appointment was moved to Friday. | prose | anti_collision_classify_prose_not_sentiment | task_upstream_regression_train | Instruction: Classify input: The appointment was moved to Friday.
Answer: prose <END> |
France has what capital? | Paris. | capital_france | train_seen | Instruction: France has what capital?
Answer: Paris. <END> |
Sentiment of this text: I dislike this issue. | negative | sentiment_negative | task_seen_train | Instruction: Sentiment of this text: I dislike this issue.
Answer: negative <END> |
Can a square have 5 sides? Give a reason. | No, because a square has exactly four sides. | no_square_five_sides | train_seen | Instruction: Can a square have 5 sides? Give a reason.
Answer: No, because a square has exactly four sides. <END> |
Classify input: The dog ran home. | prose | classify_prose | task_seen_train | Instruction: Classify input: The dog ran home.
Answer: prose <END> |
If you do not have enough current evidence, what is the right move? | Ask for the missing evidence. | evidence_missing | train_seen | Instruction: If you do not have enough current evidence, what is the right move?
Answer: Ask for the missing evidence. <END> |
The name supplied in this prompt is Evan.
Question: Which name was supplied? | Evan. | context_name | context_upstream_regression_train | Instruction: The name supplied in this prompt is Evan.
Question: Which name was supplied?
Answer: Evan. <END> |
Give only the type label for this: The appointment was moved to Friday. | prose | classify_prose | task_seen_train | Instruction: Give only the type label for this: The appointment was moved to Friday.
Answer: prose <END> |
Choose the sentiment label for: I am happy with this. | positive | sentiment_positive | task_seen_train | Instruction: Choose the sentiment label for: I am happy with this.
Answer: positive <END> |
Context: name = Sam; password = green.
Question: What is the password? | green. | context_password | context_seen_train | Instruction: Context: name = Sam; password = green.
Question: What is the password?
Answer: green. <END> |
Give me add(a, b) for addition. | def add(a, b):
return a + b | code_add | train_seen | Instruction: Give me add(a, b) for addition.
Answer: def add(a, b):
return a + b <END> |
Give only the type label for this: A clear sky is usually blue. | prose | classify_prose | task_seen_train | Instruction: Give only the type label for this: A clear sky is usually blue.
Answer: prose <END> |
User: My name is Evan.
User: What is my name? | Evan. | context_name | context_seen_train | Instruction: User: My name is Evan.
User: What is my name?
Answer: Evan. <END> |
Answer only positive or negative: I love this tool. | positive | sentiment_positive | task_seen_train | Instruction: Answer only positive or negative: I love this tool.
Answer: positive <END> |
Make a Python function named divide that divides a by b. | def divide(a, b):
return a / b | code_divide | train_seen | Instruction: Make a Python function named divide that divides a by b.
Answer: def divide(a, b):
return a / b <END> |
Rewrite this to be shorter: I will not arrive on time today. | I will be late today. | rewrite_short_late | task_seen_train | Instruction: Rewrite this to be shorter: I will not arrive on time today.
Answer: I will be late today. <END> |
Classify input: The cat sat down. | prose | classify_prose | task_seen_train | Instruction: Classify input: The cat sat down.
Answer: prose <END> |
Line one says NAME=Nina.
Question: What is NAME? | Nina. | context_name | context_upstream_regression_train | Instruction: Line one says NAME=Nina.
Question: What is NAME?
Answer: Nina. <END> |
Give the code/prose label: The schedule changed again. | prose | classify_prose | task_seen_train | Instruction: Give the code/prose label: The schedule changed again.
Answer: prose <END> |
Classify this as positive or negative: I enjoy this. | positive | sentiment_positive | task_seen_train | Instruction: Classify this as positive or negative: I enjoy this.
Answer: positive <END> |
Store this passcode: gold.
Question: What passcode was stored? | gold. | context_password | context_upstream_regression_train | Instruction: Store this passcode: gold.
Question: What passcode was stored?
Answer: gold. <END> |
Give the code/prose label: return user.name | code | anti_collision_classify_code_not_prose | task_upstream_regression_train | Instruction: Give the code/prose label: return user.name
Answer: code <END> |
Classify this as positive or negative: I really like this. | positive | anti_collision_sentiment_positive_not_prose | task_upstream_regression_train | Instruction: Classify this as positive or negative: I really like this.
Answer: positive <END> |
Why do tests matter? | Tests show whether the code still behaves as expected. | tests_matter | train_seen | Instruction: Why do tests matter?
Answer: Tests show whether the code still behaves as expected. <END> |
Classify sentiment: The fix helped. | positive | sentiment_positive | task_seen_train | Instruction: Classify sentiment: The fix helped.
Answer: positive <END> |
Context: name = Maya; password = gold.
Question: What is the name? | Maya. | context_name | context_seen_train | Instruction: Context: name = Maya; password = gold.
Question: What is the name?
Answer: Maya. <END> |
Provided name: Kai.
Question: What name was provided? | Kai. | context_name | context_upstream_regression_train | Instruction: Provided name: Kai.
Question: What name was provided?
Answer: Kai. <END> |
What sentiment is this: I dislike this issue. | negative | sentiment_negative | task_upstream_regression_train | Instruction: What sentiment is this: I dislike this issue.
Answer: negative <END> |
Use this stored name value: Evan.
Question: What stored name value should be used? | Evan. | context_name | context_upstream_regression_train | Instruction: Use this stored name value: Evan.
Question: What stored name value should be used?
Answer: Evan. <END> |
Give the code/prose label: This sentence is plain English. | prose | classify_prose | task_seen_train | Instruction: Give the code/prose label: This sentence is plain English.
Answer: prose <END> |
Can water become ice? Give a short reason. | Yes, because water can become ice when it is cold enough. | yes_water_freeze | train_seen | Instruction: Can water become ice? Give a short reason.
Answer: Yes, because water can become ice when it is cold enough. <END> |
Classify input: for i in range(3): print(i) | code | classify_code | task_seen_train | Instruction: Classify input: for i in range(3): print(i)
Answer: code <END> |
Type label: return user.name | code | classify_code | task_seen_train | Instruction: Type label: return user.name
Answer: code <END> |
On a clear day, what color is the sky? | Blue. | sky_color | train_seen | Instruction: On a clear day, what color is the sky?
Answer: Blue. <END> |
Facts: name is Evan. password is black.
Question: Which password was given? | black. | context_password | context_seen_train | Instruction: Facts: name is Evan. password is black.
Question: Which password was given?
Answer: black. <END> |
What sentiment is this: I enjoy this result. | positive | sentiment_positive | task_upstream_regression_train | Instruction: What sentiment is this: I enjoy this result.
Answer: positive <END> |
Provided password: black.
Question: What password was provided? | black. | context_password | context_upstream_regression_train | Instruction: Provided password: black.
Question: What password was provided?
Answer: black. <END> |
What sentiment is this: This feels wrong. | negative | sentiment_negative | task_seen_train | Instruction: What sentiment is this: This feels wrong.
Answer: negative <END> |
Decide if this is code or prose: output = tool.run() | code | classify_code | task_upstream_regression_train | Instruction: Decide if this is code or prose: output = tool.run()
Answer: code <END> |
What sentiment is this: This helped a lot. | positive | sentiment_positive | task_seen_train | Instruction: What sentiment is this: This helped a lot.
Answer: positive <END> |
Create multiply so it computes a times b. | def multiply(a, b):
return a * b | code_multiply | train_seen | Instruction: Create multiply so it computes a times b.
Answer: def multiply(a, b):
return a * b <END> |
Facts: name is Alex. password is purple.
Question: Which password was given? | purple. | context_password | context_seen_train | Instruction: Facts: name is Alex. password is purple.
Question: Which password was given?
Answer: purple. <END> |
Which city is France's capital? | Paris. | capital_france | train_seen | Instruction: Which city is France's capital?
Answer: Paris. <END> |
Sentiment label only: This works great. | positive | sentiment_positive | task_seen_train | Instruction: Sentiment label only: This works great.
Answer: positive <END> |
Write add(a, b). | def add(a, b):
return a + b | code_add | train_seen | Instruction: Write add(a, b).
Answer: def add(a, b):
return a + b <END> |
Answer only positive or negative: I dislike this issue. | negative | sentiment_negative | task_seen_train | Instruction: Answer only positive or negative: I dislike this issue.
Answer: negative <END> |
Label the sentiment: This is nice. | positive | sentiment_positive | task_seen_train | Instruction: Label the sentiment: This is nice.
Answer: positive <END> |
Is this code or prose: result = subtract(10, 5) | code | classify_code | task_seen_train | Instruction: Is this code or prose: result = subtract(10, 5)
Answer: code <END> |
Answer only code or prose: z = divide(9, 3) | code | classify_code | task_seen_train | Instruction: Answer only code or prose: z = divide(9, 3)
Answer: code <END> |
Is this code or prose: A clear sky is usually blue. | prose | classify_prose | task_seen_train | Instruction: Is this code or prose: A clear sky is usually blue.
Answer: prose <END> |
Classify this as positive or negative: This is bad. | negative | sentiment_negative | task_upstream_regression_train | Instruction: Classify this as positive or negative: This is bad.
Answer: negative <END> |
Positive or negative sentiment: That failed again. | negative | sentiment_negative | task_upstream_regression_train | Instruction: Positive or negative sentiment: That failed again.
Answer: negative <END> |
Answer only positive or negative: I dislike this issue. | negative | sentiment_negative | task_seen_train | Instruction: Answer only positive or negative: I dislike this issue.
Answer: negative <END> |
What type of text is this: return user.name | code | classify_code | task_seen_train | Instruction: What type of text is this: return user.name
Answer: code <END> |
This prompt supplies the password silver.
Question: Which password is supplied? | silver. | context_password | context_seen_train | Instruction: This prompt supplies the password silver.
Question: Which password is supplied?
Answer: silver. <END> |
Remember inside this prompt: call the user Sam.
Question: What should you call the user? | Sam. | context_name | context_seen_train | Instruction: Remember inside this prompt: call the user Sam.
Question: What should you call the user?
Answer: Sam. <END> |
What type of text is this: The user asked a direct question. | prose | classify_prose | task_seen_train | Instruction: What type of text is this: The user asked a direct question.
Answer: prose <END> |
Write the SUBTRACT function. | def subtract(a, b):
return a - b | code_subtract | train_seen | Instruction: Write the SUBTRACT function.
Answer: def subtract(a, b):
return a - b <END> |
Facts: password is blue. name is Evan.
Question: Which name was given? | Evan. | context_name | context_seen_train | Instruction: Facts: password is blue. name is Evan.
Question: Which name was given?
Answer: Evan. <END> |
Remember inside this prompt: call the user Riley.
Question: What should you call the user? | Riley. | context_name | context_seen_train | Instruction: Remember inside this prompt: call the user Riley.
Question: What should you call the user?
Answer: Riley. <END> |
In simple words, what is return? | A return statement sends a value back from a function. | return_statement | train_seen | Instruction: In simple words, what is return?
Answer: A return statement sends a value back from a function. <END> |
Label the input type: The user asked a direct question. | prose | classify_prose | task_seen_train | Instruction: Label the input type: The user asked a direct question.
Answer: prose <END> |
What is your role? | I answer the user's request directly. | assistant_role | train_seen | Instruction: What is your role?
Answer: I answer the user's request directly. <END> |
What sentiment is this: This is good. | positive | sentiment_positive | task_seen_train | Instruction: What sentiment is this: This is good.
Answer: positive <END> |
Conversation:
User: The name is Riley.
User: What is the name? | Riley. | context_name | context_seen_train | Instruction: Conversation:
User: The name is Riley.
User: What is the name?
Answer: Riley. <END> |
Classify sentiment: That was useful. | positive | sentiment_positive | task_seen_train | Instruction: Classify sentiment: That was useful.
Answer: positive <END> |
Give the sentiment label only: This is awful. | negative | sentiment_negative | task_upstream_regression_train | Instruction: Give the sentiment label only: This is awful.
Answer: negative <END> |
Can water become ice? Give a short reason. | Yes, because water can become ice when it is cold enough. | yes_water_freeze | train_seen | Instruction: Can water become ice? Give a short reason.
Answer: Yes, because water can become ice when it is cold enough. <END> |
Facts: password is black. name is Riley.
Question: Which name was given? | Riley. | context_name | context_seen_train | Instruction: Facts: password is black. name is Riley.
Question: Which name was given?
Answer: Riley. <END> |
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