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--- |
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license: mit |
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task_categories: |
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- text-classification |
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language: |
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- en |
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tags: |
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- text-classification |
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- multi-label-classification |
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- theme-detection |
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- tone-classification |
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- cultural-knowledge |
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pretty_name: Knowledge Theme Training Model |
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size_categories: |
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- n<1K |
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datasets: |
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- 4nkh/theme_data |
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metrics: |
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- bertscore |
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base_model: |
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- google-bert/bert-base-uncased |
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pipeline_tag: text-classification |
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--- |
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## Dataset Details |
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### Dataset Description |
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<!-- Provide a longer summary of what this dataset is. --> |
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This dataset contains short narrative passages (original_text) with associated metadata and labels. |
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The primary target is themes, |
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a multi-label list of theme tags used to train a theme classification model. |
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1. Startup Success |
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2. Mentorship |
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3. Entrepreneurship |
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A secondary label tone may be used to train a tone classifier. |
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### Direct Use |
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<!-- This section describes suitable use cases for the dataset. --> |
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Train a multi-label text classification model that predicts themes from original_text |
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Train a single-label text classifier that predicts tone from original_text |
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Evaluate/benchmark tagging pipelines for structured Knowledge Sample JSON submissions |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> |
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High-stakes decision-making (medical, legal, employment, housing, finance) |
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Inferring sensitive personal attributes or identity traits |
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Treating predictions as ground-truth without human review |
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Broad “general web” theme classification (dataset is project-scoped and may not generalize) |
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## Dataset Structure |
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<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> |
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Each data point is a JSON object with these fields: |
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knowledge_submission_id (string): unique record id |
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original_text (string): model input text |
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summary (string): short summary of the passage |
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category (string): high-level category label (e.g., “Business & Culture”) |
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themes (list[string]): multi-label theme tags (primary training target) |
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tone (string): single-label tone (optional training target) |
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knowledge_type (string): type label such as “story” |
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Recommended splits (if/when added): train, validation, test. |
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## Dataset Creation |
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Created to support automated theme tagging for Knowledge Sample JSON submissions, enabling consistent multi-label theme assignment and optional tone labeling for downstream models in the Kuumba Agent / Cultural Remix Engine workflow. |
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#### Data Collection and Processing |
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<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> |
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Examples are curated/authored knowledge submissions intended for training and evaluation |
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Stored as normalized JSON with consistent keys across records |
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Theme tags are assigned as a list to support multi-label learning |
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Optional tone labels are assigned as a single categorical value |
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#### Who are the source data producers? |
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<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> |
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The source texts are authored/curated examples produced for this project and are not collected from a public platform or scraped source. |
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#### Annotation process |
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<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> |
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themes are assigned per record as multi-label tags based on the main idea(s) of the passage |
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tone is assigned per record as a single-label descriptor of the writing style or emotional/communicative intent |
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Labels are curated to be consistent across the dataset, and may evolve as the taxonomy expands |
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**BibTeX:** |
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@dataset{henson_kuumba_theme_dataset_2025, |
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author = {Henson, James}, |
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title = {Kuumba Knowledge Theme Training Data}, |
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year = {2025}, |
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publisher = {Hugging Face}, |
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} |
