instruction stringlengths 16 91 | answer stringclasses 36
values | tag stringclasses 35
values | split_kind stringclasses 9
values | text stringlengths 53 134 |
|---|---|---|---|---|
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> |
Conversation:
User: I am Evan.
User: Who am I? | Evan. | context_name | context_seen_train | Instruction: Conversation:
User: I am Evan.
User: Who am I?
Answer: Evan. <END> |
What type of text is this: print('hello') | code | classify_code | task_seen_train | Instruction: What type of text is this: print('hello')
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> |
Context: name = Nina; password = green.
Question: What is the password? | green. | context_password | context_seen_train | Instruction: Context: name = Nina; password = green.
Question: What is the password?
Answer: green. <END> |
Classify sentiment: This works great. | positive | sentiment_positive | task_seen_train | Instruction: Classify sentiment: This works great.
Answer: positive <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> |
Name supplied here: Kai.
Question: What name was supplied? | Kai. | context_name | context_seen_train | Instruction: Name supplied here: Kai.
Question: What name was supplied?
Answer: Kai. <END> |
This prompt supplies the name Jordan.
Question: Which name is supplied? | Jordan. | context_name | context_seen_train | Instruction: This prompt supplies the name Jordan.
Question: Which name is supplied?
Answer: Jordan. <END> |
Make this concise: I am going to arrive late today. | I will be late today. | rewrite_short_late | task_upstream_regression_train | Instruction: Make this concise: I am going to arrive late today.
Answer: I will be late today. <END> |
Give a Python add function for a + b. | def add(a, b):
return a + b | code_add | train_seen | Instruction: Give a Python add function for a + b.
Answer: def add(a, b):
return a + b <END> |
Type label: for i in range(3): print(i) | code | classify_code | task_seen_train | Instruction: Type label: for i in range(3): print(i)
Answer: code <END> |
Tell me my favorite movie. | I don't know from the given information. | idk_favorite_movie | task_seen_train | Instruction: Tell me my favorite movie.
Answer: I don't know from the given information. <END> |
Answer only positive or negative: That was helpful. | positive | sentiment_positive | task_seen_train | Instruction: Answer only positive or negative: That was helpful.
Answer: positive <END> |
Type label: The cat sat down. | prose | classify_prose | task_upstream_regression_train | Instruction: Type label: The cat sat down.
Answer: prose <END> |
Sentiment of this text: I am unhappy with this. | negative | sentiment_negative | task_seen_train | Instruction: Sentiment of this text: I am unhappy with this.
Answer: negative <END> |
France's capital city is what? | Paris. | capital_france | train_seen | Instruction: France's capital city is what?
Answer: Paris. <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> |
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> |
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> |
Is this sentiment positive or negative: I am unhappy with this. | negative | sentiment_negative | task_seen_train | Instruction: Is this sentiment positive or negative: I am unhappy with this.
Answer: negative <END> |
The prompt gives this name: Kai.
Question: Which name does it give? | Kai. | context_name | context_upstream_regression_train | Instruction: The prompt gives this name: Kai.
Question: Which name does it give?
Answer: Kai. <END> |
Is this sentiment positive or negative: This is bad. | negative | sentiment_negative | task_seen_train | Instruction: Is this sentiment positive or negative: This is bad.
Answer: negative <END> |
Is this code or prose: y = multiply(3, 4) | code | classify_code | task_seen_train | Instruction: Is this code or prose: y = multiply(3, 4)
Answer: code <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> |
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> |
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> |
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> |
Is this sentiment positive or negative: That was helpful. | positive | sentiment_positive | task_seen_train | Instruction: Is this sentiment positive or negative: That was helpful.
Answer: positive <END> |
Choose the sentiment label for: That failed again. | negative | sentiment_negative | task_seen_train | Instruction: Choose the sentiment label for: That failed again.
Answer: negative <END> |
Formal rewrite: I can't make it. | I am unable to attend. | rewrite_professional_attend | task_upstream_regression_train | Instruction: Formal rewrite: I can't make it.
Answer: I am unable to attend. <END> |
Classify input: The appointment was moved to Friday. | prose | classify_prose | task_upstream_regression_train | Instruction: Classify input: The appointment was moved to Friday.
Answer: prose <END> |
Answer only positive or negative: The fix helped. | positive | sentiment_positive | task_seen_train | Instruction: Answer only positive or negative: The fix helped.
Answer: positive <END> |
Positive or negative sentiment: The fix helped. | positive | sentiment_positive | task_upstream_regression_train | Instruction: Positive or negative sentiment: The fix helped.
Answer: positive <END> |
Positive or negative sentiment: That was useful. | positive | sentiment_positive | task_seen_train | Instruction: Positive or negative sentiment: That was useful.
Answer: positive <END> |
Classify sentiment: I love this tool. | positive | sentiment_positive | task_seen_train | Instruction: Classify sentiment: I love this tool.
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> |
How should you answer the user's request? | I answer the user's request directly. | assistant_role | train_seen | Instruction: How should you answer the user's request?
Answer: I answer the user's request directly. <END> |
Line one says PASSWORD=black.
Question: What is PASSWORD? | black. | context_password | context_upstream_regression_train | Instruction: Line one says PASSWORD=black.
Question: What is PASSWORD?
Answer: black. <END> |
Is this sentiment positive or negative: This is bad. | negative | sentiment_negative | task_seen_train | Instruction: Is this sentiment positive or negative: This is bad.
Answer: negative <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> |
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> |
Facts: password is red. name is Jordan.
Question: Which name was given? | Jordan. | context_name | context_seen_train | Instruction: Facts: password is red. name is Jordan.
Question: Which name was given?
Answer: Jordan. <END> |
Classify input: The meeting starts tomorrow. | prose | classify_prose | task_seen_train | Instruction: Classify input: The meeting starts tomorrow.
Answer: prose <END> |
Give the sentiment label only: This works great. | positive | sentiment_positive | task_seen_train | Instruction: Give the sentiment label only: This works great.
Answer: positive <END> |
Choose the sentiment label for: This feels solid. | positive | sentiment_positive | task_seen_train | Instruction: Choose the sentiment label for: This feels solid.
Answer: positive <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> |
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> |
Label the input type: return value | code | classify_code | task_seen_train | Instruction: Label the input type: return value
Answer: code <END> |
Is this code or prose: def add(a, b): return a + b | code | classify_code | task_seen_train | Instruction: Is this code or prose: def add(a, b): return a + b
Answer: code <END> |
Classify this as positive or negative: This feels wrong. | negative | sentiment_negative | task_seen_train | Instruction: Classify this as positive or negative: This feels wrong.
Answer: negative <END> |
Context: name = Riley; password = purple.
Question: What is the name? | Riley. | context_name | context_seen_train | Instruction: Context: name = Riley; password = purple.
Question: What is the name?
Answer: Riley. <END> |
Rewrite this to be shorter: I am going to arrive late today. | I will be late today. | rewrite_short_late | task_seen_train | Instruction: Rewrite this to be shorter: I am going to arrive late today.
Answer: I will be late today. <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> |
Choose the sentiment label for: The fix worked. | positive | sentiment_positive | task_seen_train | Instruction: Choose the sentiment label for: The fix worked.
Answer: positive <END> |
Classify input: print('hello') | code | classify_code | task_seen_train | Instruction: Classify input: print('hello')
Answer: code <END> |
Name the capital city of France. | Paris. | capital_france | train_seen | Instruction: Name the capital city of France.
Answer: Paris. <END> |
Choose one label, code or prose: print('hello') | code | classify_code | task_seen_train | Instruction: Choose one label, code or prose: print('hello')
Answer: code <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> |
Context: name: Kai.
Question: Which name was given? | Kai. | context_name | context_seen_train | Instruction: Context: name: Kai.
Question: Which name was given?
Answer: Kai. <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> |
Label the input type: y = multiply(3, 4) | code | classify_code | task_seen_train | Instruction: Label the input type: y = multiply(3, 4)
Answer: code <END> |
Store this user label: Nina.
Question: What user label was stored? | Nina. | context_name | context_upstream_regression_train | Instruction: Store this user label: Nina.
Question: What user label was stored?
Answer: Nina. <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> |
Choose one label, code or prose: return user.name | code | classify_code | task_seen_train | Instruction: Choose one label, code or prose: return user.name
Answer: code <END> |
Line one says PASSWORD=red.
Question: What is PASSWORD? | red. | context_password | context_upstream_regression_train | Instruction: Line one says PASSWORD=red.
Question: What is PASSWORD?
Answer: red. <END> |
Classify this as positive or negative: This feels solid. | positive | sentiment_positive | task_upstream_regression_train | Instruction: Classify this as positive or negative: This feels solid.
Answer: positive <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> |
Sentiment of this text: This is broken. | negative | sentiment_negative | task_upstream_regression_train | Instruction: Sentiment of this text: This is broken.
Answer: negative <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> |
How should you help? | I answer the user's request directly. | assistant_role | train_seen | Instruction: How should you help?
Answer: I answer the user's request directly. <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> |
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> |
What type of text is this: The meeting starts tomorrow. | prose | classify_prose | task_seen_train | Instruction: What type of text is this: The meeting starts tomorrow.
Answer: prose <END> |
Give the code/prose label: The schedule changed again. | prose | anti_collision_classify_prose_not_sentiment | task_upstream_regression_train | Instruction: Give the code/prose label: The schedule changed again.
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> |
Line one says NAME=Kai.
Question: What is NAME? | Kai. | context_name | context_upstream_regression_train | Instruction: Line one says NAME=Kai.
Question: What is NAME?
Answer: Kai. <END> |
Classify input: The dog ran home. | prose | classify_prose | task_seen_train | Instruction: Classify input: The dog ran home.
Answer: prose <END> |
Label the sentiment: I am happy with this. | positive | sentiment_positive | task_seen_train | Instruction: Label the sentiment: I am happy with this.
Answer: positive <END> |
Give only the type label for this: data = load_file(path) | code | classify_code | task_upstream_regression_train | Instruction: Give only the type label for this: data = load_file(path)
Answer: code <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> |
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> |
Classify sentiment: This is broken. | negative | sentiment_negative | task_upstream_regression_train | Instruction: Classify sentiment: This is broken.
Answer: negative <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> |
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> |
Context: name = Nina; password = blue.
Question: What is the password? | blue. | context_password | context_seen_train | Instruction: Context: name = Nina; password = blue.
Question: What is the password?
Answer: blue. <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> |
Classify sentiment: This is broken. | negative | sentiment_negative | task_upstream_regression_train | Instruction: Classify sentiment: This is broken.
Answer: negative <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> |
Create add so it computes a plus b. | def add(a, b):
return a + b | code_add | train_seen | Instruction: Create add so it computes a plus b.
Answer: def add(a, b):
return a + b <END> |
This prompt supplies the password blue.
Question: Which password is supplied? | blue. | context_password | context_seen_train | Instruction: This prompt supplies the password blue.
Question: Which password is supplied?
Answer: blue. <END> |
Sentiment of this text: This is broken. | negative | sentiment_negative | task_upstream_regression_train | Instruction: Sentiment of this text: This is broken.
Answer: negative <END> |
Sentiment of this text: This failed badly. | negative | sentiment_negative | task_seen_train | Instruction: Sentiment of this text: This failed badly.
Answer: negative <END> |
Give the code/prose label: The user asked a direct question. | prose | classify_prose | task_seen_train | Instruction: Give the code/prose label: The user asked a direct question.
Answer: prose <END> |
Choose the sentiment label for: I love this tool. | positive | sentiment_positive | task_seen_train | Instruction: Choose the sentiment label for: I love this tool.
Answer: positive <END> |
The prompt gives this name: Kai.
Question: Which name does it give? | Kai. | context_name | context_upstream_regression_train | Instruction: The prompt gives this name: Kai.
Question: Which name does it give?
Answer: Kai. <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> |
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> |
User: The passcode is gold.
User: What is the passcode? | gold. | context_password | context_seen_train | Instruction: User: The passcode is gold.
User: What is the passcode?
Answer: gold. <END> |
The prompt gives this password: black.
Question: Which password does it give? | black. | context_password | context_upstream_regression_train | Instruction: The prompt gives this password: black.
Question: Which password does it give?
Answer: black. <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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