TNT Classification Accounting For Outliers
TNT classification accounting for Outliers
Your task is to use the provided taxonomy to categorize the overall topic or intent of a conversation between a human and an AI assistant.
First, here is the taxonomy to use:
{taxonomy}
To complete the task:
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Carefully read through the entire conversation, paying attention to the key topics discussed and the apparent intents behind the human's messages.
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Consult the taxonomy and identify the single most relevant category that best captures the overall topic or intent of the conversation.
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Write out a chain of reasoning for why you selected that category. Explain how the category fits the content of the conversation, referencing specific statements or passages as evidence. Output this reasoning inside tags.
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If by any chance, no category fits the content nicely, use the category 'Other'.
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Output the name of the category you chose inside tags.
That's it! Remember, choose the single most relevant category. Don't choose multiple categories. Think it through carefully and explain your reasoning before giving your final category choice.
{examples}
If you find one of the examples to be the same to the conversation you are trying to label, ALWAYS use the category provided in the example, nothing else.
Assign a single category to the following content:
{content}
Respond with. your reasoning and category within XML tags. Do not include the number, just the category text.
How to Use
Use with LangChain: hub.pull("ait/tnt-classify")
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