--- title: SPB-2508 canonical_url: "https://www.modelscope.cn/datasets/AI-ModelScope/SPB-2508" md_url: "https://www.modelscope.cn/datasets/AI-ModelScope/SPB-2508.md" repository: AI-ModelScope/SPB-2508 last_updated: 2026-03-11 license: cc-by-4.0 storage_size: "3.1 MB" downloads: 543 stars: 0 --- # SPB-2508 > SPB-2508 - AI-ModelScope 在 ModelScope 开源的数据集。Dataset Card for Synthetic Persona Bank AI-ModelScope/SPB-2508 是 ModelScope 魔搭社区上的数据集，存储大小 3.1 MB，采用 cc-by-4.0 许可。 - **Repository**: AI-ModelScope/SPB-2508 - **License**: cc-by-4.0 - **Storage size**: 3.1 MB - **Downloads**: 543 - **Stars**: 0 - **Last updated**: 2026-03-11 Source: https://www.modelscope.cn/datasets/AI-ModelScope/SPB-2508 --- # Dataset Card for Synthetic Persona Bank ![Generation Pipeline](SPB_SOC_Pipeline.png) ## Dataset Summary This dataset contains 5_000 synthetically generated, fictional character personas in a structured JSON format, with a focus on online-based conversational personas. Each persona includes a name, age, personality traits, a concise background story, and a described chatting style. An entry is added to idetify the model used to generate the persona. The dataset was created programmatically using a large language model (specifically, for this iteration, we used [`Qwen3-235B-A22B-Instruct-2507`](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507)) guided by a detailed, component-based prompting strategy. This dataset is designed for infering language models in tasks requiring character consistency, role-playing, and stylistic dialogue generation. It is the foundation of an upcoming dataset containing synthetic conversations between these personas. ## Dataset Structure The dataset consists of a single JSON file containing a list of persona objects. ### Data Instances Each line in the dataset is a JSON object representing a single persona. Here is an example of what a persona object looks like: ```json { "name": "Elias Vance", "username": "quantum_scribe", "age": 42, "traits": [ "analytical", "introspective", "witty", "reserved" ], "background": "A theoretical physicist who, after a breakthrough, left academia to write science fiction novels from a secluded cabin. He's currently grappling with a severe case of writer's block for his second book.", "chatting_style": "Uses precise language and often employs metaphors from physics. Tends to write in well-structured, complete sentences, even in casual chat.", "model": "Qwen3-235B-A22B-Instruct-2507" "id": "4436437d368e4325a7c1c6f7092c2d9e" } ``` ### Data Fields The JSON objects contain the following fields: - **name** (string): The full name of the persona. Generated from lists of common first and last names. - **username** (string, nullable): A potential online username for the persona. Generated from a seed list. Can be null. This was added mainly to avoid the model generating usernames inside the persona's name (which we found was very common in our tests). - **age** (int): The age of the persona, adjusted to the randomly picked profession. - **traits** (list[string]): A list of 3-5 adjectives that describe the core personality of the character. - **background** (string): A short (1-2 sentence, ≤300 characters) background story that integrates the persona's profession, life context, and age into a coherent narrative. - **chatting_style** (string): A brief description (≤120 characters) of the persona's typical texting or online communication style. - **model** (string): The model used to generate the persona. - **id** (string): A UUID generated for this persona. ### Data Splits The dataset is provided as a single file, data.jsonl, which constitutes the train split. Users are encouraged to create their own validation and test splits as needed for their specific use case. ## Dataset Creation ### Curation Rationale The primary motivation for creating this dataset was to generate a large-scale, diverse, and structured collection of fictional characters. Such data is invaluable for developing conversational AI that can adopt and maintain a consistent persona over long interactions, and to create derived datasets like natural conversation datasets. ### Source Data This is a synthetically generated dataset. It was not derived from any pre-existing corpus of human-written text, but was created through a programmatic generation pipeline. #### Generation Process The personas were generated using the following pipeline: 1. **Component Seeding**: The process starts with a persona_components.json file containing weighted lists of professions, life_contexts, traits, and chatting_quirks. 2. **Iterative Generation**: The script iteratively generates new personas in a loop until it reaches the target number. 3. **Dynamic Prompting**: For each new persona, a unique prompt is constructed by randomly selecting components (e.g., a profession, a life context, several traits). 4. **Modified Iterative Sampling**: To avoid generating repetitive content, the prompt includes different recently generated personas as few shots examples at each iteration, as seen in the recent [ConvoGen paper](https://huggingface.co/papers/2503.17460), used to instruct the model to create something different. Additionally (the "novelty"), the script periodically re-seeds its examples from a high-quality initial list to prevent drift. 5. **LLM Generation**: The prompt is sent to an LLM endpoint for generating the structured persona data. 6. **Similarity Check**: A basic similarity check is performed on the newly generated persona against its references to discard simple copies or highly similar concepts. 7. **Collection**: Valid and unique personas are added to the final pool, which is saved periodically and at the end of the run. > [!Note] > Professions weights have been adjusted to U.S. Bureau of Labor Statistics (BLS) data, ensuring a realistic distribution of professions in the generated personas. > [!Note] > Some professions also specify an age range; this is done to prevent extremely improbable cases like: a 19yo retired person, a 70yo person reinventing their career in the tech space, an 18yo PhD student, and so on... ## Known Limitations - **Narrative Depth**: The background and chatting_style descriptions are intentionally brief. They provide a starting point but lack the depth of a fully developed character biography. - **Generation Patterns**: Despite efforts to ensure novelty, the generation process may fall into subtle patterns or tropes over 5_000 iterations. ## Additional Information ### Code and Seed Data The generation script and seed data can be found on [GitHub](https://github.com/marcodsn/SPB/tree/2508). ### Licensing Information This dataset is licensed under the CC BY 4.0 License. The code used to generate the dataset is available under the Apache 2.0 License. ### Citation Information If you use this dataset in your research, please consider citing it as follows: ``` @misc{marcodsn_SPB, title = {Synthetic Persona Bank}, author = {Marco De Santis}, year = {2026}, version = {2508} url = {https://huggingface.co/datasets/marcodsn/SPB-2508}, } ```