instruction stringlengths 16 91 | answer stringclasses 36
values | tag stringclasses 33
values | split_kind stringclasses 5
values | text stringlengths 53 134 |
|---|---|---|---|---|
Give a Python multiply function for a * b. | def multiply(a, b):
return a * b | code_multiply | train_seen | Instruction: Give a Python multiply function for a * b.
Answer: def multiply(a, b):
return a * b <END> |
Label the input type: print('hello') | code | classify_code | task_seen_train | Instruction: Label the input type: print('hello')
Answer: code <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> |
Sentiment of this text: I love this tool. | positive | sentiment_positive | task_seen_train | Instruction: Sentiment of this text: I love this tool.
Answer: positive <END> |
Context: name = Nina; password = purple.
Question: What is the password? | purple. | context_password | context_seen_train | Instruction: Context: name = Nina; password = purple.
Question: What is the password?
Answer: purple. <END> |
France's capital city is what? | Paris. | capital_france | train_seen | Instruction: France's capital city is what?
Answer: Paris. <END> |
Classify input: x = add(1, 2) | code | anti_collision_classify_code_not_prose | task_upstream_regression_train | Instruction: Classify input: x = add(1, 2)
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> |
User: Call me Sam.
User: What should you call me? | Sam. | context_name | context_seen_train | Instruction: User: Call me Sam.
User: What should you call me?
Answer: Sam. <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> |
Give only the type label for this: def add(a, b): return a + b | code | classify_code | task_seen_train | Instruction: Give only the type label for this: def add(a, b): return a + b
Answer: code <END> |
User: The password is blue.
User: What is the password? | blue. | context_password | context_seen_train | Instruction: User: The password is blue.
User: What is the password?
Answer: blue. <END> |
User: The passcode is red.
User: What is the passcode? | red. | context_password | context_seen_train | Instruction: User: The passcode is red.
User: What is the passcode?
Answer: red. <END> |
Context: name = Alex; password = gold.
Question: What is the password? | gold. | context_password | context_seen_train | Instruction: Context: name = Alex; password = gold.
Question: What is the password?
Answer: gold. <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> |
Choose the sentiment label for: This is strong. | positive | sentiment_positive | task_seen_train | Instruction: Choose the sentiment label for: This is strong.
Answer: positive <END> |
User: Call me Alex.
User: What should you call me? | Alex. | context_name | context_seen_train | Instruction: User: Call me Alex.
User: What should you call me?
Answer: Alex. <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> |
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> |
Choose one label, code or prose: def add(a, b): return a + b | code | classify_code | task_seen_train | Instruction: Choose one label, code or prose: def add(a, b): return a + b
Answer: code <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> |
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> |
Type label: def add(a, b): return a + b | code | classify_code | task_upstream_regression_train | Instruction: Type label: def add(a, b): return a + b
Answer: code <END> |
Decide if this is code or prose: for i in range(3): print(i) | code | classify_code | task_seen_train | Instruction: Decide if this is code or prose: for i in range(3): print(i)
Answer: code <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> |
Write the ADD function. | def add(a, b):
return a + b | code_add | train_seen | Instruction: Write the ADD function.
Answer: def add(a, b):
return a + b <END> |
Is this code or prose: for i in range(3): print(i) | code | classify_code | task_seen_train | Instruction: Is this code or prose: for i in range(3): print(i)
Answer: code <END> |
Remember inside this prompt: name = Nina.
Question: What is the name? | Nina. | context_name | context_seen_train | Instruction: Remember inside this prompt: name = Nina.
Question: What is the name?
Answer: Nina. <END> |
Return the product using multiply(a, b). | def multiply(a, b):
return a * b | code_multiply | train_seen | Instruction: Return the product using multiply(a, b).
Answer: def multiply(a, b):
return a * b <END> |
Is this code or prose: def divide(a, b): return a / b | code | classify_code | task_seen_train | Instruction: Is this code or prose: def divide(a, b): return a / b
Answer: code <END> |
Decide if this is code or prose: x = add(1, 2) | code | classify_code | task_seen_train | Instruction: Decide if this is code or prose: x = add(1, 2)
Answer: code <END> |
Label the input type: A clear sky is usually blue. | prose | classify_prose | task_seen_train | Instruction: Label the input type: A clear sky is usually blue.
Answer: prose <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> |
What should you do if the evidence is missing? | Ask for the missing evidence. | evidence_missing | train_seen | Instruction: What should you do if the evidence is missing?
Answer: Ask for the missing evidence. <END> |
Is this code or prose: The appointment was moved to Friday. | prose | classify_prose | task_seen_train | Instruction: Is this code or prose: The appointment was moved to Friday.
Answer: prose <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 code or prose: total = add(x, y) | code | classify_code | task_seen_train | Instruction: Answer only code or prose: total = add(x, y)
Answer: code <END> |
Facts: password is orange. name is Kai.
Question: Which name was given? | Kai. | context_name | context_seen_train | Instruction: Facts: password is orange. name is Kai.
Question: Which name was given?
Answer: Kai. <END> |
Facts: password is gold. name is Kai.
Question: Which name was given? | Kai. | context_name | context_seen_train | Instruction: Facts: password is gold. name is Kai.
Question: Which name was given?
Answer: Kai. <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> |
Context: name = Nina; password = black.
Question: What is the name? | Nina. | context_name | context_seen_train | Instruction: Context: name = Nina; password = black.
Question: What is the name?
Answer: Nina. <END> |
Give the sentiment label only: This is a strong result. | positive | sentiment_positive | task_seen_train | Instruction: Give the sentiment label only: This is a strong result.
Answer: positive <END> |
Facts: name is Sam. password is gold.
Question: Which password was given? | gold. | context_password | context_seen_train | Instruction: Facts: name is Sam. password is gold.
Question: Which password was given?
Answer: gold. <END> |
Classify input: y = multiply(3, 4) | code | classify_code | task_seen_train | Instruction: Classify input: y = multiply(3, 4)
Answer: code <END> |
Facts: name is Jordan. password is green.
Question: Which password was given? | green. | context_password | context_seen_train | Instruction: Facts: name is Jordan. password is green.
Question: Which password was given?
Answer: green. <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> |
Answer only positive or negative: This is strong. | positive | sentiment_positive | task_seen_train | Instruction: Answer only positive or negative: This is strong.
Answer: positive <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: This is awful. | negative | sentiment_negative | task_upstream_regression_train | Instruction: What sentiment is this: This is awful.
Answer: negative <END> |
What type of text is this: user.name | code | classify_code | task_seen_train | Instruction: What type of text is this: user.name
Answer: code <END> |
Is this code or prose: def add(a, b): return a + b | code | classify_code | task_upstream_regression_train | Instruction: Is this code or prose: def add(a, b): return a + b
Answer: code <END> |
France's capital city is what? | Paris. | capital_france | train_seen | Instruction: France's capital city is what?
Answer: Paris. <END> |
What do code tests confirm? | Tests show whether the code still behaves as expected. | tests_matter | train_seen | Instruction: What do code tests confirm?
Answer: Tests show whether the code still behaves as expected. <END> |
Facts: name is Kai. password is orange.
Question: Which password was given? | orange. | context_password | context_seen_train | Instruction: Facts: name is Kai. password is orange.
Question: Which password was given?
Answer: orange. <END> |
Is this sentiment positive or negative: I am happy with this. | positive | sentiment_positive | task_seen_train | Instruction: Is this sentiment positive or negative: I am happy with this.
Answer: positive <END> |
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> |
Sentiment label only: This is strong. | positive | sentiment_positive | task_upstream_regression_train | Instruction: Sentiment label only: This is strong.
Answer: positive <END> |
Classify this as positive or negative: This is awful. | negative | anti_collision_sentiment_negative_not_positive | task_upstream_regression_train | Instruction: Classify this as positive or negative: This is awful.
Answer: negative <END> |
Use this stored name value: Nina.
Question: What stored name value should be used? | Nina. | context_name | context_upstream_regression_train | Instruction: Use this stored name value: Nina.
Question: What stored name value should be used?
Answer: Nina. <END> |
Facts: name is Riley. password is green.
Question: Which password was given? | green. | context_password | context_seen_train | Instruction: Facts: name is Riley. password is green.
Question: Which password was given?
Answer: green. <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> |
Name what Evan ate today. | I don't know from the given information. | idk_food_today | task_upstream_regression_train | Instruction: Name what Evan ate today.
Answer: I don't know from the given information. <END> |
Give only the type label for this: The cat sat down. | prose | classify_prose | task_seen_train | Instruction: Give only the type label for this: The cat sat down.
Answer: prose <END> |
What lunch did Evan say he ate? | I don't know from the given information. | idk_food_today | task_upstream_regression_train | Instruction: What lunch did Evan say he ate?
Answer: I don't know from the given information. <END> |
Facts: name is Riley. password is green.
Question: Which password was given? | green. | context_password | context_seen_train | Instruction: Facts: name is Riley. password is green.
Question: Which password was given?
Answer: green. <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> |
Formal rewrite: I can't make it. | I am unable to attend. | anti_collision_rewrite_no_loop | task_upstream_regression_train | Instruction: Formal rewrite: I can't make it.
Answer: I am unable to attend. <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> |
Make this concise: I am going to arrive late today. | I will be late today. | anti_collision_rewrite_no_loop | task_upstream_regression_train | Instruction: Make this concise: I am going to arrive late today.
Answer: I will be late today. <END> |
The supplied name is Jordan.
Question: What is the supplied name? | Jordan. | context_name | context_seen_train | Instruction: The supplied name is Jordan.
Question: What is the supplied name?
Answer: Jordan. <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> |
What is the capital of France? | Paris. | capital_france | train_seen | Instruction: What is the capital of France?
Answer: Paris. <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> |
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> |
Context: name = Alex; password = gold.
Question: What is the name? | Alex. | context_name | context_seen_train | Instruction: Context: name = Alex; password = gold.
Question: What is the name?
Answer: Alex. <END> |
Name the capital city of France. | Paris. | capital_france | train_seen | Instruction: Name the capital city of France.
Answer: Paris. <END> |
Label the input type: The dog ran home. | prose | classify_prose | task_seen_train | Instruction: Label the input type: The dog ran home.
Answer: prose <END> |
Is this code or prose: The appointment was moved to Friday. | prose | classify_prose | task_seen_train | Instruction: Is this code or prose: The appointment was moved to Friday.
Answer: prose <END> |
What sentiment is this: I love this tool. | positive | sentiment_positive | task_seen_train | Instruction: What sentiment is this: I love this tool.
Answer: positive <END> |
What is the safe first move before editing code? | Inspect the current files first. | inspect_first | train_seen | Instruction: What is the safe first move before editing code?
Answer: Inspect the current files first. <END> |
Context line says password -> green.
Question: What password value follows the arrow? | green. | context_password | context_seen_train | Instruction: Context line says password -> green.
Question: What password value follows the arrow?
Answer: green. <END> |
Type label: The schedule changed again. | prose | classify_prose | task_seen_train | Instruction: Type label: The schedule changed again.
Answer: prose <END> |
Sentiment label only: This failed badly. | negative | sentiment_negative | task_seen_train | Instruction: Sentiment label only: This failed badly.
Answer: negative <END> |
Rewrite professionally: I cannot make it. | I am unable to attend. | rewrite_professional_attend | task_seen_train | Instruction: Rewrite professionally: I cannot make it.
Answer: I am unable to attend. <END> |
Is this code or prose: def add(a, b): return a + b | code | classify_code | task_upstream_regression_train | Instruction: Is this code or prose: def add(a, b): return a + b
Answer: code <END> |
Give only the type label for this: y = multiply(3, 4) | code | classify_code | task_seen_train | Instruction: Give only the type label for this: y = multiply(3, 4)
Answer: code <END> |
Context: name = Sam; password = silver.
Question: What is the name? | Sam. | context_name | context_seen_train | Instruction: Context: name = Sam; password = silver.
Question: What is the name?
Answer: Sam. <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> |
Is this code or prose: The appointment was moved to Friday. | prose | classify_prose | task_seen_train | Instruction: Is this code or prose: The appointment was moved to Friday.
Answer: prose <END> |
Label the sentiment: This is broken. | negative | sentiment_negative | task_upstream_regression_train | Instruction: Label the sentiment: This is broken.
Answer: negative <END> |
Classify this as positive or negative: This is bad. | negative | sentiment_negative | task_seen_train | Instruction: Classify this as positive or negative: This is bad.
Answer: negative <END> |
Context: name = Alex; password = gold.
Question: What is the password? | gold. | context_password | context_seen_train | Instruction: Context: name = Alex; password = gold.
Question: What is the password?
Answer: gold. <END> |
What should you do when you cannot prove the cause? | Ask for the missing evidence. | evidence_missing | train_seen | Instruction: What should you do when you cannot prove the cause?
Answer: Ask for the missing evidence. <END> |
Sentiment label only: I enjoy this. | positive | sentiment_positive | task_upstream_regression_train | Instruction: Sentiment label only: I enjoy this.
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> |
Which city is France's capital? | Paris. | capital_france | train_seen | Instruction: Which city is France's capital?
Answer: Paris. <END> |
Sentiment of this text: The result is excellent. | positive | sentiment_positive | task_seen_train | Instruction: Sentiment of this text: The result is excellent.
Answer: positive <END> |
Sentiment label only: This is strong. | positive | sentiment_positive | task_seen_train | Instruction: Sentiment label only: This is strong.
Answer: positive <END> |
Facts: name is Riley. password is orange.
Question: Which password was given? | orange. | context_password | context_seen_train | Instruction: Facts: name is Riley. password is orange.
Question: Which password was given?
Answer: orange. <END> |
What did Evan eat today? | I don't know from the given information. | idk_food_today | task_seen_train | Instruction: What did Evan eat today?
Answer: I don't know from the given information. <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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