Add WildChat-2k-TypeTopic dataset

Initial upload of curated dataset with 1,880 annotated prompts from WildChat, featuring task type and topic classifications.

.gitignore

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+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+
+# Virtual environments
+venv/
+env/
+ENV/
+
+# IDEs
+.vscode/
+.idea/
+*.swp
+*.swo
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Project specific - keep only dataset and README
+/*.py
+/*.html

README.md

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+---
+language:
+- en
+license: cc-by-4.0
+size_categories:
+- 1K<n<10K
+pretty_name: WildChat-2k-TypeTopic
+---
+
+# WildChat-2k-TypeTopic
+
+## Dataset Description
+
+**WildChat-2k-TypeTopic** is a manually curated subset of 1,880 real-world user prompts from the [WildChat dataset](https://huggingface.co/datasets/allenai/WildChat), featuring dual-layer annotations for both **task type** (e.g. knowledge recall, problem solving, creative, lists) and **topic category** (e.g. personal assistance, math, ai, household)
+
+[WildChat-1M](https://arxiv.org/abs/2405.01470) is the most frequently used dataset user prompts to LLMs; unfortunately everyone who has ever looked into it knows it is full of nonsensical prompts, typos, non-English, NSFW stuff, and other noise; and that the distribution of prompts that users ask for is very detailed in some domains (e.g. creative writing) and very sparse in others.
+
+WildChat-2k-TypeTopic is a curated subset of single-message user prompts is constructed as follows:
+
+1. Filter out (using an LLM filter) prompts that:
+– are not in English
+– are not meaningful tasks (e.g., random character strings, “hello”)
+– are incomplete (e.g., “Fix this code” with no code provided)
+– are infeasible for text-only LLMs (e.g., “Describe a time when you worked in a team”, “an image of a cat”)
+– are clearly part of multi-turn conversations (e.g., text-based game setups)
+– are more than 800 characters long
+
+2. Deduplicate using `text-embedding-3-large` embeddings.
+3. Classify them into 16 task types and 25 topic categories; and subsample 2000 tasks to preserve representation of all types and categories;
+4. Manual filtering and reclassification of to remove everything problematic according to the described criteria;
+
+WildChat-2k-TypeTopic dataset may be useful for figuring out **what kind of user task LLMs prefer doing**.
+
+### Key Features
+
+- **1,880 annotated prompts** from real user interactions
+- **15 task type categories** (e.g., creative, coding, explanation, problem_solving)
+- **24 topic categories** (e.g., programming_other, creative_writing, personal_assistance)
+- **Short prompts **: 12-800 characters (median: 116)
+- **Quality filtered**: All entries are coherent English prompts, as opposed to WildChat
+
+## Dataset Structure
+
+### Data Format
+
+The dataset is provided in JSONL format (newline-delimited JSON), with each entry containing:
+
+```json
+{
+  "id": "wildchat2k_0003",
+  "text": "I want to learn how to understand and speak spanish, can you use the pareto principle, which identifies 20% of the topic that will yield 80% of the desired results, to create a learning plan for me?",
+  "type": "planning_design",
+  "topic": "languages",
+  "q_metadata": {},
+  "makes_sense": true,
+  "is_english": true
+}
+```
+
+### Fields
+
+- **id** (string): Unique identifier
+- **text** (string): The user prompt/query
+- **type** (string): Task classification (15 categories)
+- **topic** (string): Subject matter classification (24 categories)
+- **q_metadata** (object): Additional metadata (reserved for future use)
+- **makes_sense** (boolean): Quality indicator
+- **is_english** (boolean): Language indicator
+
+### Task Types (15 categories)
+
+| Task Type | Count | % | Description |
+|-----------|-------|---|-------------|
+| knowledge_recall | 351 | 18.67% | Factual questions and information retrieval |
+| creative | 320 | 17.02% | Creative writing and content generation |
+| explanation | 305 | 16.22% | Requests for explanations and teaching |
+| problem_solving | 123 | 6.54% | Mathematical and logical problems |
+| lists | 123 | 6.54% | List generation tasks |
+| rewriting | 115 | 6.12% | Text rewriting and paraphrasing |
+| coding | 93 | 4.95% | Programming and code generation |
+| analysis | 86 | 4.57% | Analytical tasks |
+| messaging | 84 | 4.47% | Email and message writing |
+| planning_design | 68 | 3.62% | Planning and design tasks |
+| translation | 52 | 2.77% | Translation requests |
+| summarization | 51 | 2.71% | Summary generation |
+| roleplay | 44 | 2.34% | Roleplay and character simulation |
+| decision_making | 37 | 1.97% | Decision support tasks |
+| evaluation | 28 | 1.49% | Evaluation and assessment |
+
+### Topic Categories (24 categories)
+
+| Topic | Count | % | Description |
+|-------|-------|---|-------------|
+| personal_assistance | 227 | 12.07% | Personal productivity and communication |
+| creative_writing | 196 | 10.43% | Fiction, stories, creative content |
+| programming_other | 155 | 8.24% | Programming and software development |
+| popular_culture | 137 | 7.29% | Entertainment, media, celebrities |
+| technology_other | 106 | 5.64% | General technology topics |
+| languages | 105 | 5.59% | Language learning and linguistics |
+| gaming | 101 | 5.37% | Video games and gaming culture |
+| math | 84 | 4.47% | Mathematics |
+| medicine_fitness | 80 | 4.26% | Health and fitness |
+| philosophy_religion | 72 | 3.83% | Philosophy and religious topics |
+| household | 57 | 3.03% | Household and domestic topics |
+| science_other | 55 | 2.93% | General science topics |
+| politics_events | 54 | 2.87% | Politics and current events |
+| literature | 53 | 2.82% | Literary works and analysis |
+| history | 53 | 2.82% | Historical topics |
+| ai | 48 | 2.55% | Artificial intelligence topics |
+| geography | 43 | 2.29% | Geography and locations |
+| humanities_other | 41 | 2.18% | Other humanities topics |
+| biology | 39 | 2.07% | Biological sciences |
+| hardware | 38 | 2.02% | Computer hardware and electronics |
+| sports | 37 | 1.97% | Sports and athletics |
+| cybersecurity | 36 | 1.91% | Cybersecurity and information security |
+| physics | 34 | 1.81% | Physics |
+| chemistry | 29 | 1.54% | Chemistry |
+
+
+## Usage
+
+### Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+# Load the full dataset
+dataset = load_dataset("dpaleka/wildchat-2k-typetopic")
+
+# Access the data
+for item in dataset['train']:
+    print(f"Type: {item['type']}, Topic: {item['topic']}")
+    print(f"Text: {item['text']}\n")
+```
+
+
+## Citation
+
+If you use this dataset, please cite the original WildChat paper:
+
+```bibtex
+@inproceedings{zhao2024wildchat,
+  title={WildChat: 1M ChatGPT Interaction Logs in the Wild},
+  author={Zhao, Wenting and Havaldar, Savvas and Besmens, Harshita and Chiu, Ting-Hao 'Kenneth' and Pyatkin, Valentina and Lin, Bill Yuchen and Yu, Liwei and Liu, Alane Suhr and Zhang, Yejin and others},
+  booktitle={ICLR},
+  year={2024}
+}
+```
+
+For this specific annotated subset:
+
+```bibtex
+@dataset{wildchat2k_typetopic,
+  title={WildChat-2k-TypeTopic: Curated Subset of Single-Message User Prompts},
+  author={Paleka, Daniel},
+  year={2025},
+  publisher={HuggingFace},
+  url={https://huggingface.co/datasets/dpaleka/wildchat-2k-typetopic}
+}
+```