Datasetpublic

pcwoods/2026-animaldescription-text-dataset

Animal Descriptions Dataset pcwoods/2026-animaldescription-text-dataset This dataset contains descriptions of various popular zoo animals labeled by type. Types are limited to Mammal, Bird, or Reptile for simplicity. Source Descriptions were hand-written based on popular zoo animals from https://zootrack.me/animals/popular. Facts about each animal for descriptions were identified using AI tools. Fields Field Meaning description Text… See the full description on the dataset page: https://huggingface.co/datasets/pcwoods/2026-animaldescription-text-dataset.

sourceHugging Faceupdated 8d agoView on Hugging Face

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Dataset Card

Animal Descriptions Dataset

pcwoods/2026-animaldescription-text-dataset

This dataset contains descriptions of various popular zoo animals labeled by type. Types are limited to Mammal, Bird, or Reptile for simplicity.

Source

Descriptions were hand-written based on popular zoo animals from https://zootrack.me/animals/popular. Facts about each animal for descriptions were identified using AI tools.

Fields

Field	Meaning
`description`	Text description of the animal
`label`	Numeric label (0: Mammal, 1: Bird, 2: Reptile)
`animal_type`	Readable label name
`is_augmented`	Flag for synthetic data

Splits

Split	Original rows	Synthetic rows	Total rows
train	70	1000	1070
validation	15	0	15
test	15	0	15

Augmentation and preprocessing

Character rate in this run: 30% of eligible words, capped at 3 words per description. The corpus contains 70 distinct original training descriptions.

Each method independently starts from an original training description; transformations are not stacked.

—Internal character swaps: swap adjacent internal characters in selected words.

—Internal character deletions: delete one internal character from selected words.

—WordNet synonym replacement: replace one word with a WordNet synonym.

Method	Generated rows
internalcharacterswaps	334
internalcharacterdeletions	333
wordnetsynonymreplacement	333

Load and compare

python

from datasets import load_dataset
ds = load_dataset("pcwoods/2026-animaldescription-text-dataset")
# Train with ds["train"], choose settings with ds["validation"], then score ds["test"].

AI Use

Generative AI was used for identifying animal facts to create descriptions from, as well as for structuring some descriptions and in the programming process to prepare this dataset.