Datasets:

OmarK211
/

tabular-test

dataset_info:
  features:
    - name: source_id
      dtype: string
    - name: part_function
      dtype: string
    - name: max_length
      dtype: float64
    - name: max_width
      dtype: float64
    - name: max_depth
      dtype: float64
    - name: symmetry
      dtype: string
    - name: material
      dtype: string
    - name: base_sketch_shape
      dtype: string
    - name: parent_id
      dtype: string
    - name: augmentation
      dtype: string
    - name: is_augmented
      dtype: bool
    - name: second_parent_id
      dtype: string
    - name: mix_weight
      dtype: float64
  splits:
    - name: train
      num_bytes: 54717
      num_examples: 336
    - name: validation
      num_bytes: 537
      num_examples: 4
    - name: test
      num_bytes: 682
      num_examples: 5
  download_size: 29657
  dataset_size: 55936
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
language:
  - en
task_categories:
  - tabular-classification
pretty_name: CAD Feature Dataset (Tabular)
tags:
  - tabular
  - cad
  - 24-679
  - data-augmentation

CAD Model Features Dataset (Text-to-CAD Focus)

OmarK211/tabular-test

A tabular dataset of CAD model geometric and functional parameters, containing manually collected samples (original) and synthetic variants (augmented).

Task Overview

The primary predictive target is base_sketch_shape (categorical 2D profile shape) based on 6 input dimensions and properties.

Dataset Structure & Fields

Field Name	Data Type	Role	Description
`base_sketch_shape`	Categorical	Target	Core 2D profile shape (e.g., circle, rectangle, L-shape)
`max_length`	Numeric (Float)	Feature	Continuous maximum length dimension
`max_width`	Numeric (Float)	Feature	Continuous maximum width dimension
`max_depth`	Numeric (Float)	Feature	Continuous maximum depth dimension
`part_function`	Categorical	Feature	Functional role of the CAD model
`symmetry`	Categorical	Feature	Symmetry type (e.g., planar, rotational, none)
`material`	Categorical	Feature	Intended material composition
`source_id`, `parent_id`, `second_parent_id`	String	Provenance	Unique row ID and original parent source IDs
`augmentation`, `is_augmented`, `mix_weight`	Metadata	Provenance	Augmentation method, synthetic flag, and Mixup interpolation weight

Dataset Splits

Split	Original rows	Synthetic rows	Total rows
`train`	21	315	336
`validation`	4	0	4
`test`	5	0	5

Target Class Distribution (`base_sketch_shape`)

Split	Base Sketch Shape	Rows
`train`	amorphous	32
`train`	circular	160
`train`	rectangular	144
`validation`	amorphous	2
`validation`	circular	1
`validation`	rectangular	1
`test`	circular	1
`test`	rectangular	4

Augmentation Methods

Synthetic samples were generated using 5 copy requests per method:

Augmentation method	Retained synthetic rows
`additive_numeric_jitter`	105
`mixup_interpolation`	105
`multiplicative_numeric_scale`	105

Additive Numeric Jitter: Applied Gaussian noise to continuous CAD dimensions (max_length, max_width, max_depth), clipped at positive bounds (>0.01).
Multiplicative Numeric Scaling: Scaled continuous dimensions independently by uniform factors in [0.90, 1.10].
Mixup Interpolation: Linearly interpolated continuous dimensions between distinct parents using weights in [0.60, 0.90], retaining the primary parent's categorical fields.

Intended use and limitations

Use for teaching/learning mixed-type data contracts, provenance, augmentation, and small-sample regression. This convenience sample does not represent the general population.

Privacy and licensing

No license was specified in repository metadata when this card was first added; this card does not assign one.

Load and compare

from datasets import load_dataset
ds = load_dataset("OmarK211/tabular-test")
# Train with ds["train"], choose settings with ds["validation"], then score ds["test"].

Use an account with access if repository visibility changes. For reproducible comparisons, record the dataset commit and model/environment versions. Regenerate this card with the preparation notebook after changing the data; its counts are calculated from the actual packaged splits. The YAML schema and split configuration are preserved from the upload.