Model save

README.md

@@ -1,111 +1,61 @@
 ---
-license: mit
-task_categories:
-- text-classification
-language:
-- en
+library_name: transformers
+license: apache-2.0
+base_model: bert-base-uncased
 tags:
-- text-classification
-- multi-label-classification
-- theme-detection
-- tone-classification
-- cultural-knowledge
-pretty_name: Knowledge Theme Training Model
-size_categories:
-- n<1K
-datasets:
-- 4nkh/theme_data
-metrics:
-- bertscore
-base_model:
-- google-bert/bert-base-uncased
-pipeline_tag: text-classification
+- generated_from_trainer
+model-index:
+- name: theme_model
+  results: []
 ---
 
-## Dataset Details
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
 
-<iframe
-  src="https://huggingface.co/datasets/4nkh/theme_data/embed/viewer/default/train"
-  frameborder="0"
-  width="100%"
-  height="560px"
-></iframe>
+# theme_model
 
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1822
+- Micro/precision: 1.0
+- Micro/recall: 1.0
+- Micro/f1: 1.0
+- Macro/precision: 1.0
+- Macro/recall: 1.0
+- Macro/f1: 1.0
 
-### Dataset Description
+## Model description
 
-<!-- Provide a longer summary of what this dataset is. -->
-This dataset contains short narrative passages (original_text) with associated metadata and labels. 
-The primary target is themes,
-a multi-label list of theme tags used to train a theme classification model.
-1. Startup Success
-2. Mentorship
-3. Entrepreneurship
+More information needed
 
-A secondary label tone may be used to train a tone classifier.
+## Intended uses & limitations
 
-### Direct Use
+More information needed
 
-<!-- This section describes suitable use cases for the dataset. -->
-Train a multi-label text classification model that predicts themes from original_text
-Train a single-label text classifier that predicts tone from original_text
-Evaluate/benchmark tagging pipelines for structured Knowledge Sample JSON submissions
+## Training and evaluation data
 
-### Out-of-Scope Use
+More information needed
 
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
+## Training procedure
 
-High-stakes decision-making (medical, legal, employment, housing, finance)
-Inferring sensitive personal attributes or identity traits
-Treating predictions as ground-truth without human review
-Broad “general web” theme classification (dataset is project-scoped and may not generalize)
+### Training hyperparameters
 
-## Dataset Structure
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 5
 
-<!-- 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. -->
+### Training results
 
-Each data point is a JSON object with these fields:
-knowledge_submission_id (string): unique record id
-original_text (string): model input text
-summary (string): short summary of the passage
-category (string): high-level category label (e.g., “Business & Culture”)
-themes (list[string]): multi-label theme tags (primary training target)
-tone (string): single-label tone (optional training target)
-knowledge_type (string): type label such as “story”
-Recommended splits (if/when added): train, validation, test.
 
-## Dataset Creation
-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.
 
-#### Data Collection and Processing
+### Framework versions
 
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
-
-Examples are curated/authored knowledge submissions intended for training and evaluation
-Stored as normalized JSON with consistent keys across records
-Theme tags are assigned as a list to support multi-label learning
-Optional tone labels are assigned as a single categorical value
-
-#### Who are the source data producers?
-
-<!-- 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. -->
-
-The source texts are authored/curated examples produced for this project and are not collected from a public platform or scraped source.
-
-#### Annotation process
-
-<!-- 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. -->
-
-themes are assigned per record as multi-label tags based on the main idea(s) of the passage
-tone is assigned per record as a single-label descriptor of the writing style or emotional/communicative intent
-Labels are curated to be consistent across the dataset, and may evolve as the taxonomy expands
-
-
-**BibTeX:**
-
-@dataset{henson_kuumba_theme_dataset_2025,
-  author = {Henson, James},
-  title = {Kuumba Knowledge Theme Training Data},
-  year = {2025},
-  publisher = {Hugging Face},
-}
+- Transformers 4.57.3
+- Pytorch 2.8.0
+- Datasets 4.4.2
+- Tokenizers 0.22.2