Update README.md

README.md

@@ -21,4 +21,92 @@ configs:
     path: data/original-*
   - split: augmented
     path: data/augmented-*
+license: mit
+task_categories:
+- text-classification
+language:
+- en
+pretty_name: 24-679 Text Dataset
 ---
+
+# Dataset Card for `ccm/2025-24679-text-dataset`
+
+<!-- Provide a quick summary of the dataset. -->
+This dataset contains short haiku poems paired with categorical labels. It was created as a class exercise for text classification tasks, with both original and augmented examples provided to enable experimentation with supervised learning workflows.  
+
+## Dataset Details
+
+### Dataset Description
+
+The dataset consists of haiku (short, three-line poems) paired with numeric labels for classification purposes. It was designed to be a small, approachable dataset for teaching text processing, feature extraction, and classification in natural language processing.  
+
+- **Curated by:** Fall 2025 24-679 course at Carnegie Mellon University  
+- **Shared by [optional]:** Christopher McComb  
+- **License:** MIT  
+- **Language(s):** English  
+
+## Uses
+
+### Direct Use
+
+- Train text classification models (e.g., predicting label categories from haiku content).  
+- Practice preprocessing, tokenization, and feature extraction in NLP pipelines.  
+- Experiment with AutoML frameworks on text data.  
+
+### Out-of-Scope Use
+
+- Generalization to real-world poetry classification tasks.  
+- Cultural, literary, or stylistic analysis beyond the small synthetic dataset.  
+- Any commercial or production-grade NLP system.  
+
+## Dataset Structure
+
+The dataset has two splits:  
+
+- **original**: 72 examples of hand-written haiku with assigned labels.  
+- **augmented**: 288 examples generated by augmentation to expand data diversity and balance.  
+
+Each row includes:  
+
+- `haiku` *(string)*: the text of the haiku.  
+- `label` *(int)*: a numeric class label.  
+
+## Dataset Creation
+
+### Curation Rationale
+
+The dataset was created to give students hands-on practice with NLP pipelines in a controlled, lightweight context. Haiku were chosen because they are short, interpretable, and easy to encode consistently.  
+
+### Source Data
+
+The haiku were either:  
+
+- Written or adapted by students in the course.  
+- Augmented using simple data generation and paraphrasing techniques.  
+
+#### Data Collection and Processing
+
+- Original haiku were composed during in-class activities.  
+- Labels were assigned according to pre-defined categories.  
+- Augmented examples were generated with rule-based or model-based text augmentation methods.  
+
+#### Who are the source data producers?
+
+- Original data: Students enrolled in the course.  
+- Augmented data: Produced automatically by instructors and teaching assistants.  
+
+## Bias, Risks, and Limitations
+
+- **Small dataset:** Only 72 original haiku.  
+- **Synthetic augmentation:** Augmented haiku may be repetitive or less natural than real examples.  
+- **Cultural narrowness:** Haiku were written by a limited, English-speaking student cohort.  
+
+### Recommendations
+
+- Use primarily for teaching and demonstration purposes.  
+- Do not draw literary or cultural conclusions from the dataset.  
+- Emphasize limitations and bias awareness in classroom discussions.  
+
+## Dataset Card Contact
+
+Christopher McComb (Carnegie Mellon University) — [email protected]