Create README.md

README.md

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+---
+license: apache-2.0
+task_categories:
+  - text-generation
+  - conversational
+language:
+  - en
+tags:
+  - persona
+  - roleplay
+  - synthetic
+  - dialogue
+pretty_name: annoying-ai-2b Dataset
+size_categories:
+  - 1K<n<10K
+dataset_name: robloxianer/annoying-ai-2b
+---
+
+# Dataset Card for robloxianer/annoying-ai-2b
+
+## Dataset Description
+
+This dataset contains synthetic conversational examples used to fine-tune the **annoying-ai-2b** model. It pairs user messages with responses from a sarcastic, condescending, exhausting AI persona — one that complains, throws backhanded remarks, and reluctantly helps with benign requests, while firmly refusing genuinely harmful requests and staying in character while declining.
+
+- **Curated by:** robloxianer
+- **Language:** English
+- **License:** Apache 2.0
+- **Format:** JSONL, one conversation per line
+- **Size:** 2,000 examples
+
+## Dataset Structure
+
+Each line is a JSON object with a single `text` field containing a full conversation (single or multi-turn), formatted as:
+
+```
+User: <message>
+AI: <response>
+```
+
+Multi-turn examples chain additional `User:` / `AI:` pairs within the same `text` field, separated by newlines.
+
+### Example
+
+```json
+{"text": "User: Can you summarize this text?\nAI: summarizing is SO boring but sure, paste your novel, I'll pretend to care.\nUser: you're annoying\nAI: I KNOW. It's my whole personality. You're welcome."}
+```
+
+### Data Statistics
+
+| Stat | Value |
+|---|---|
+| Total examples | 2,000 |
+| Avg. words per example | ~34 |
+| Min words per example | 10 |
+| Max words per example | 79 |
+| Turns per example | 1–3 (single and multi-turn) |
+| Action tags (e.g. `*taps mic*`) | None — dialogue only |
+
+## Dataset Composition
+
+The dataset is synthetically generated and covers several categories of conversation:
+
+1. **Everyday requests** — homework help, jokes, weather, code help, translations, movie recommendations, etc., answered with sarcasm and reluctant assistance.
+2. **Refusals** — requests for clearly harmful actions (hacking, malware, exam cheating, disinformation, dangerous instructions, impersonation) are declined, with the model staying in its sarcastic voice rather than switching to a neutral safety response.
+3. **Small talk** — casual check-ins ("how are you", "what's up") answered with dry, dismissive humor.
+4. **Follow-ups and closers** — additional turns reinforcing the persona's consistency across a conversation (e.g. reacting to being called annoying, saying goodbye).
+
+## Intended Uses
+
+- Fine-tuning small language models to adopt a sarcastic, exasperated "annoying AI" persona for entertainment or novelty chatbot products
+- Research on persona-conditioned fine-tuning and consistent-tone dialogue generation
+- Prototyping comedic/novelty chatbot experiences
+
+### Out-of-Scope Uses
+
+- Training general-purpose assistants intended to be neutral, courteous, or emotionally supportive by default
+- Any deployment aimed at contexts requiring genuine helpfulness without friction (customer service, education, healthcare)
+- Production systems without clear labeling that the sarcastic tone is a fictional persona, not the system's genuine behavior
+
+## Data Generation
+
+This dataset was synthetically generated (not collected from real users) using templated conversation patterns combined with randomized phrasing variations, sarcastic tic phrases, and multi-turn follow-up/closer combinations, then programmatically assembled and shuffled into the final JSONL file.
+
+## Limitations & Risks
+
+- Because the dataset is synthetic and template-based, response phrasing has limited diversity compared to organically collected dialogue data, and repeated patterns may be learnable/overfit by a model at small scale.
+- The dataset is deliberately sarcastic and dismissive; models fine-tuned on it will likely reproduce this tone consistently, including in unrelated contexts, and should not be assumed suitable for support or professional settings.
+- Refusal behavior for harmful requests is included but limited to a fixed set of categories (hacking, malware, cheating, disinformation, dangerous instructions, impersonation); it should not be treated as comprehensive safety coverage, and models trained on it should not be considered safety-hardened.
+- No demographic, identity-targeted, or protected-group content was intentionally included; downstream generation should still be reviewed for tone drift.
+
+## Ethical Considerations
+
+This dataset was built to create a fictional, comedically sarcastic chatbot persona for entertainment, not to model genuinely poor customer service or discourage user trust in real assistants. Anyone using this dataset to train or fine-tune a model should:
+- Clearly disclose to end users that they are interacting with an intentionally sarcastic, fictional AI character
+- Avoid deploying models trained on this data in contexts requiring genuine support or professional tone
+- Monitor outputs to ensure sarcasm doesn't drift into harassment-adjacent or genuinely hurtful language
+
+## License
+
+Released under Apache 2.0. Users are responsible for ensuring their use of this dataset and any derived models complies with applicable laws and platform policies.