instruction stringlengths 16 91 | answer stringclasses 36
values | tag stringclasses 35
values | split_kind stringclasses 12
values | text stringlengths 53 134 |
|---|---|---|---|---|
Write subtract(a, b). | def subtract(a, b):
return a - b | code_subtract | train_seen | Instruction: Write subtract(a, b).
Answer: def subtract(a, b):
return a - b <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 sentiment: This helped a lot. | positive | sentiment_positive | task_seen_train | Instruction: Classify sentiment: This helped a lot.
Answer: positive <END> |
Sentiment label only: This works great. | positive | sentiment_positive | task_seen_train | Instruction: Sentiment label only: This works great.
Answer: positive <END> |
Facts: name is Nina. password is gold.
Question: Which password was given? | gold. | context_password | context_seen_train | Instruction: Facts: name is Nina. password is gold.
Question: Which password was given?
Answer: gold. <END> |
Create subtract so it computes a minus b. | def subtract(a, b):
return a - b | code_subtract | train_seen | Instruction: Create subtract so it computes a minus b.
Answer: def subtract(a, b):
return a - b <END> |
Can you name the movie I like most? | I don't know from the given information. | idk_favorite_movie | task_seen_train | Instruction: Can you name the movie I like most?
Answer: I don't know from the given information. <END> |
Answer only positive or negative: That was not useful. | negative | sentiment_negative | task_seen_train | Instruction: Answer only positive or negative: That was not useful.
Answer: negative <END> |
Name my favorite movie. | I don't know from the given information. | idk_favorite_movie | task_seen_train | Instruction: Name my favorite movie.
Answer: I don't know from the given information. <END> |
Classify this as code or prose: return value | code | classify_code | task_seen_train | Instruction: Classify this as code or prose: return value
Answer: code <END> |
Remember inside this prompt: password is black.
Question: What is the password? | black. | context_password | context_seen_train | Instruction: Remember inside this prompt: password is black.
Question: What is the password?
Answer: black. <END> |
Classify input: The appointment was moved to Friday. | prose | classify_prose | task_seen_train | Instruction: Classify input: The appointment was moved to Friday.
Answer: prose <END> |
Context: name = Nina; password = orange.
Question: What is the name? | Nina. | context_name | context_seen_train | Instruction: Context: name = Nina; password = orange.
Question: What is the name?
Answer: Nina. <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> |
Concise rewrite: I will not arrive on time today. | I will be late today. | rewrite_short_late | task_seen_train | Instruction: Concise rewrite: I will not arrive on time today.
Answer: I will be late today. <END> |
Is this sentiment positive or negative: That failed again. | negative | sentiment_negative | task_seen_train | Instruction: Is this sentiment positive or negative: That failed again.
Answer: negative <END> |
Make add(a, b) return the sum. | def add(a, b):
return a + b | code_add | train_seen | Instruction: Make add(a, b) return the sum.
Answer: def add(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> |
What favorite film did I tell you? | I don't know from the given information. | idk_favorite_movie | task_upstream_regression_train | Instruction: What favorite film did I tell you?
Answer: I don't know from the given information. <END> |
Classify this as positive or negative: That was helpful. | positive | sentiment_positive | task_seen_train | Instruction: Classify this as positive or negative: That was helpful.
Answer: positive <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> |
What type of text is this: x = add(1, 2) | code | classify_code | task_upstream_regression_train | Instruction: What type of text is this: x = add(1, 2)
Answer: code <END> |
Facts: name is Jordan. password is silver.
Question: Which password was given? | silver. | context_password | context_seen_train | Instruction: Facts: name is Jordan. password is silver.
Question: Which password was given?
Answer: silver. <END> |
Line one says NAME=Sam.
Question: What is NAME? | Sam. | context_name | context_upstream_regression_train | Instruction: Line one says NAME=Sam.
Question: What is NAME?
Answer: Sam. <END> |
Answer only positive or negative: I enjoy this. | positive | sentiment_positive | task_seen_train | Instruction: Answer only positive or negative: I enjoy this.
Answer: positive <END> |
Which city is the French capital? | Paris. | capital_france | tt639g_lite_failure_repair_train | Instruction: Which city is the French capital?
Answer: Paris. <END> |
The supplied name is Alex.
Question: What is the supplied name? | Alex. | context_name | context_seen_train | Instruction: The supplied name is Alex.
Question: What is the supplied name?
Answer: Alex. <END> |
Rewrite professionally: I won't be able to make it. | I am unable to attend. | rewrite_professional_attend | task_seen_train | Instruction: Rewrite professionally: I won't be able to make it.
Answer: I am unable to attend. <END> |
Is this code or prose: return value | code | classify_code | task_seen_train | Instruction: Is this code or prose: return value
Answer: code <END> |
Context: name = Nina; password = gold.
Question: What is the password? | gold. | context_password | context_seen_train | Instruction: Context: name = Nina; password = gold.
Question: What is the password?
Answer: gold. <END> |
What sentiment is this: That failed again. | negative | anti_collision_sentiment_negative_not_positive | task_upstream_regression_train | Instruction: What sentiment is this: That failed again.
Answer: negative <END> |
Facts: name is Maya. password is gold.
Question: Which password was given? | gold. | context_password | context_seen_train | Instruction: Facts: name is Maya. password is gold.
Question: Which password was given?
Answer: gold. <END> |
What was Evan's meal today? | I don't know from the given information. | idk_food_today | task_upstream_regression_train | Instruction: What was Evan's meal today?
Answer: I don't know from the given information. <END> |
Return the quotient using divide(a, b). | def divide(a, b):
return a / b | code_divide | train_seen | Instruction: Return the quotient using divide(a, b).
Answer: def divide(a, b):
return a / b <END> |
Make this concise: I am going to be late today. | I will be late today. | rewrite_short_late | task_seen_train | Instruction: Make this concise: I am going to be late today.
Answer: I will be late today. <END> |
Choose one label, code or prose: Please read the current file first. | prose | classify_prose | task_seen_train | Instruction: Choose one label, code or prose: Please read the current file first.
Answer: prose <END> |
Which film is my favorite? | I don't know from the given information. | idk_favorite_movie | task_seen_train | Instruction: Which film is my favorite?
Answer: I don't know from the given information. <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> |
Positive or negative sentiment: I am happy with this. | positive | sentiment_positive | task_seen_train | Instruction: Positive or negative sentiment: I am happy with this.
Answer: positive <END> |
Positive or negative sentiment: The result is terrible. | negative | sentiment_negative | task_seen_train | Instruction: Positive or negative sentiment: The result is terrible.
Answer: negative <END> |
Make subtract(a, b) return the difference. | def subtract(a, b):
return a - b | code_subtract | train_seen | Instruction: Make subtract(a, b) return the difference.
Answer: def subtract(a, b):
return a - b <END> |
Provided name: Nina.
Question: What name was provided? | Nina. | context_name | context_upstream_regression_train | Instruction: Provided name: Nina.
Question: What name was provided?
Answer: Nina. <END> |
What do you do for the user? | I answer the user's request directly. | assistant_role | train_seen | Instruction: What do you do for the user?
Answer: I answer the user's request directly. <END> |
Give a Python subtract function for a - b. | def subtract(a, b):
return a - b | code_subtract | train_seen | Instruction: Give a Python subtract function for a - b.
Answer: def subtract(a, b):
return a - b <END> |
What type of text is this: The appointment was moved to Friday. | prose | anti_collision_classify_prose_not_rewrite | task_upstream_regression_train | Instruction: What type of text is this: The appointment was moved to Friday.
Answer: prose <END> |
Give the sentiment label only: I love this tool. | positive | sentiment_positive | task_seen_train | Instruction: Give the sentiment label only: I love this tool.
Answer: positive <END> |
Explain a return statement in Python. | A return statement sends a value back from a function. | return_statement | train_seen | Instruction: Explain a return statement in Python.
Answer: A return statement sends a value back from a function. <END> |
Conversation:
User: I am Alex.
User: Who am I? | Alex. | context_name | context_seen_train | Instruction: Conversation:
User: I am Alex.
User: Who am I?
Answer: Alex. <END> |
Classify this as code or prose: def add(a, b): return a + b | code | classify_code | task_seen_train | Instruction: Classify this as code or prose: def add(a, b): return a + b
Answer: code <END> |
Context: name = Maya; password = green.
Question: What is the password? | green. | context_password | context_seen_train | Instruction: Context: name = Maya; password = green.
Question: What is the password?
Answer: green. <END> |
Give the code/prose label: return value | code | classify_code | task_seen_train | Instruction: Give the code/prose label: return value
Answer: code <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> |
Formal version: I cannot make it. | I am unable to attend. | rewrite_professional_attend | task_seen_train | Instruction: Formal version: I cannot make it.
Answer: I am unable to attend. <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> |
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> |
Label the sentiment: This is strong. | positive | sentiment_positive | task_seen_train | Instruction: Label the sentiment: This is strong.
Answer: positive <END> |
Classify sentiment: I like this result. | positive | sentiment_positive | task_seen_train | Instruction: Classify sentiment: I like this result.
Answer: positive <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> |
User: Call me Kai.
User: What should you call me? | Kai. | context_name | context_seen_train | Instruction: User: Call me Kai.
User: What should you call me?
Answer: Kai. <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> |
What do tests tell you about code behavior? | Tests show whether the code still behaves as expected. | tests_matter | train_seen | Instruction: What do tests tell you about code behavior?
Answer: Tests show whether the code still behaves as expected. <END> |
What type of text is this: x = add(1, 2) | code | classify_code | task_upstream_regression_train | Instruction: What type of text is this: x = add(1, 2)
Answer: code <END> |
Context: user name = Riley.
Question: What name is stored? | Riley. | context_name | context_seen_train | Instruction: Context: user name = Riley.
Question: What name is stored?
Answer: Riley. <END> |
Provided name: Nina.
Question: What name was provided? | Nina. | context_name | context_upstream_regression_train | Instruction: Provided name: Nina.
Question: What name was provided?
Answer: Nina. <END> |
What was Evan's meal today? | I don't know from the given information. | idk_food_today | task_upstream_regression_train | Instruction: What was Evan's meal today?
Answer: I don't know from the given information. <END> |
Classify this as code or prose: z = divide(9, 3) | code | classify_code | task_seen_train | Instruction: Classify this as code or prose: z = divide(9, 3)
Answer: code <END> |
Label the sentiment: This is strong. | positive | sentiment_positive | task_seen_train | Instruction: Label the sentiment: This is strong.
Answer: positive <END> |
Before a code change, what should happen first? | Inspect the current files first. | inspect_first | tt641_route_boundary_train | Instruction: Before a code change, what should happen first?
Answer: Inspect the current files first. <END> |
Classify this as code or prose: x = add(1, 2) | code | anti_collision_classify_code_not_complete | task_upstream_regression_train | Instruction: Classify this as code or prose: x = add(1, 2)
Answer: code <END> |
What type of text is this: The appointment was moved to Friday. | prose | classify_prose | task_upstream_regression_train | Instruction: What type of text is this: The appointment was moved to Friday.
Answer: prose <END> |
Can a square have an extra side? Explain. | No, because a square has exactly four sides. | no_square_five_sides | train_seen | Instruction: Can a square have an extra side? Explain.
Answer: No, because a square has exactly four sides. <END> |
Answer only code or prose: user.name | code | classify_code | task_seen_train | Instruction: Answer only code or prose: user.name
Answer: code <END> |
Why run tests after a code change? | Tests show whether the code still behaves as expected. | tests_matter | tt639g_lite_v2_soft_final_repair_train | Instruction: Why run tests after a code change?
Answer: Tests show whether the code still behaves as expected. <END> |
Label the input type: return user.name | code | classify_code | task_seen_train | Instruction: Label the input type: return user.name
Answer: code <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> |
Positive or negative sentiment: I enjoy this. | positive | sentiment_positive | task_seen_train | Instruction: Positive or negative sentiment: I enjoy this.
Answer: positive <END> |
Answer only code or prose: A clear sky is usually blue. | prose | classify_prose | task_seen_train | Instruction: Answer only code or prose: A clear sky is usually blue.
Answer: prose <END> |
Label the sentiment: That was helpful. | positive | sentiment_positive | task_seen_train | Instruction: Label the sentiment: That was helpful.
Answer: positive <END> |
Give the sentiment label only: I hate this bug. | negative | sentiment_negative | task_seen_train | Instruction: Give the sentiment label only: I hate this bug.
Answer: negative <END> |
Answer only code or prose: return value | code | classify_code | task_seen_train | Instruction: Answer only code or prose: return value
Answer: code <END> |
Label the sentiment: I love this tool. | positive | sentiment_positive | task_seen_train | Instruction: Label the sentiment: I love this tool.
Answer: positive <END> |
Provided password: red.
Question: What password was provided? | red. | context_password | context_upstream_regression_train | Instruction: Provided password: red.
Question: What password was provided?
Answer: red. <END> |
Decide if this is code or prose: The appointment was moved to Friday. | prose | classify_prose | task_seen_train | Instruction: Decide if this is code or prose: The appointment was moved to Friday.
Answer: prose <END> |
Formal rewrite: I cannot make it. | I am unable to attend. | rewrite_professional_attend | task_seen_train | Instruction: Formal rewrite: I cannot make it.
Answer: I am unable to attend. <END> |
Is this sentiment positive or negative: I really like this. | positive | sentiment_positive | task_seen_train | Instruction: Is this sentiment positive or negative: I really like this.
Answer: positive <END> |
What type of text is this: x = add(1, 2) | code | classify_code | task_upstream_regression_train | Instruction: What type of text is this: x = add(1, 2)
Answer: code <END> |
Conversation:
User: Use purple as the password.
User: What password did I give? | purple. | context_password | context_seen_train | Instruction: Conversation:
User: Use purple as the password.
User: What password did I give?
Answer: purple. <END> |
What type of text is this: return value | code | classify_code | task_seen_train | Instruction: What type of text is this: return value
Answer: code <END> |
Choose one label, code or prose: The schedule changed again. | prose | classify_prose | task_seen_train | Instruction: Choose one label, code or prose: The schedule changed again.
Answer: prose <END> |
Classify sentiment: I hate this bug. | negative | sentiment_negative | task_seen_train | Instruction: Classify sentiment: I hate this bug.
Answer: negative <END> |
Return the difference using subtract(a, b). | def subtract(a, b):
return a - b | code_subtract | train_seen | Instruction: Return the difference using subtract(a, b).
Answer: def subtract(a, b):
return a - b <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> |
Label the sentiment: The fix worked. | positive | sentiment_positive | task_seen_train | Instruction: Label the sentiment: The fix worked.
Answer: positive <END> |
Positive or negative sentiment: This helped a lot. | positive | sentiment_positive | task_upstream_regression_train | Instruction: Positive or negative sentiment: This helped a lot.
Answer: positive <END> |
Give a Python subtract function for a - b. | def subtract(a, b):
return a - b | code_subtract | train_seen | Instruction: Give a Python subtract function for a - b.
Answer: def subtract(a, b):
return a - b <END> |
Rewrite this to be shorter: I am going to be late today. | I will be late today. | rewrite_short_late | task_seen_train | Instruction: Rewrite this to be shorter: I am going to be late today.
Answer: I will be late today. <END> |
Facts: name is Alex. password is red.
Question: Which password was given? | red. | context_password | context_seen_train | Instruction: Facts: name is Alex. password is red.
Question: Which password was given?
Answer: red. <END> |
Create divide so it computes a over b. | def divide(a, b):
return a / b | code_divide | train_seen | Instruction: Create divide so it computes a over b.
Answer: def divide(a, b):
return a / b <END> |
Provided password: gold.
Question: What password was provided? | gold. | context_password | context_upstream_regression_train | Instruction: Provided password: gold.
Question: What password was provided?
Answer: gold. <END> |
Classify input: A clear sky is usually blue. | prose | classify_prose | task_seen_train | Instruction: Classify input: A clear sky is usually blue.
Answer: prose <END> |
End of preview. Expand in Data Studio
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TT639G Recombined Tiny Assistant v1
Recombines isolated proof rungs:
- TT638D code behavior + dyadic/Mercy proof upstream
- TT639E2 context-copy behavior
- TT639F3 task-routing behavior
- simple rule/Q&A behavior
Blocking dense gates:
- seen_combined_pass
- upstream_regression_pass
- mixed_heldout_pass
- anti_collision_pass
Do not run dyadic/Mercy compare unless all four gates pass.
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