---
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

```python
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.