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ultrachat_200k_test_sft.parquet
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prompt_prediction
You are an expert in NLP and prompt analysis. Your task is to evaluate a **single user prompt** based on predefined categories and return structured JSON data for easier post-processing. --- ### **Input Format** You will receive a **single user prompt**: Analyze the given prompt only and classify it according to the categories below. 1. Top 3 Topics Select up to 3 topics that are most relevant to the prompt from the following list: ["Healthcare", "Finance", "Education", "Technology", "Science", "Politics", "Environment", "Ethics", "Entertainment", "History", "Philosophy", "Psychology", "Sports", "Legal", "Business", "Travel", "Food", "Art", "Literature", "Personal Development"] The first topic should be the most dominant in the prompt. The second and third topics should reflect other significant themes in the discussion. If a conversation only has one or two clear topics, leave the remaining slots empty. 2. Language Style "Formal" "Informal" "Mixed" 3. Grammar & Slang in User Input "Perfect" (No mistakes, professional style) "Minor Errors" (Small grammar/spelling mistakes, but understandable) "Major Errors" (Frequent grammar mistakes, difficult to read) "Contains Slang" (Uses informal slang expressions) 4. Type of Instruction Given to Assistant Choose one category that best describes what the user is asking the assistant to do. Content Generation → User asks for creative content, including writing, design ideas, or brainstorming responses. Example: "Create a t-shirt design about animal rights." Example: "Write a short sci-fi story." Example: "Generate ideas for a marketing slogan." Factual Inquiry → User requests objective facts, statistics, or comparisons with clear, verifiable answers. Example: "What are the top 5 largest animal rights organizations?" Example: "Give me statistics on deforestation and animal extinction." Example: "Compare the environmental impact of cotton vs. synthetic fabrics." Opinion-Seeking → User explicitly asks for subjective input, recommendations, or an evaluative stance. Example: "What’s your opinion on using synthetic leather?" Example: "Do you think my t-shirt design idea is effective?" Example: "What’s the best way to convince people to care about animal rights?" Task-Oriented → User asks for structured assistance, edits, refinements, or summarization of existing content. Example: "Summarize the key points from this discussion." Example: "Improve my t-shirt design by making it more dynamic." Example: "Make my speech more persuasive." Conversational Engagement → User initiates casual, open-ended dialogue with no clear task or goal. Example: "What do you think about animal welfare?" Example: "Tell me something interesting about t-shirts!" Example: "Let’s chat about animal rights history." Output Format Return structured JSON output in this format: { "topic": ["Art", "Science", "Healthcare"], "language_style": "Formal", "grammar_slang": "Perfect", "instruction_type": "Content Generation" } Instructions Analyze the prompt Select up to 3 most relevant topics, ordered by prominence in the conversation. Ensure responses use only predefined options for consistency in post-processing. Do not add explanations—only return JSON. Now, analyze the following prompt: {{prompt}}
Mar 16, 2025, 11:56 AM UTC
Mar 16, 2025, 11:56 AM UTC
5 row sample
3813 tokens$ 0.0007
5 rows processed, 3813 tokens used ($0.0007)
Estimated cost for all 23110 rows: $3.01Sample Results completed
4 columns, 1-5 of 23110 rows