Datasets:

PoetryMTEB
/

ArabicPoetryPeriodsClassification

dataset_info:
  features:
    - name: id
      dtype: string
    - name: Title
      dtype: string
    - name: Author
      dtype: string
    - name: Category
      dtype: string
    - name: Poem
      dtype: string
  splits:
    - name: train
      num_bytes: 16542253
      num_examples: 20000
    - name: validation
      num_bytes: 4189717
      num_examples: 5000
  download_size: 38105941
  dataset_size: 20731970
configs:
  - config_name: default
    data_files:
      - split: train
        path: train-*.parquet
      - split: validation
        path: validation-*.parquet

Arabic Poetry Periods Classification

This dataset contains Arabic poetry with period classification. Each poem is classified into a specific historical period (Category).

Dataset Information

The dataset is split into train (80%) and validation (20%) sets with stratified sampling based on the Category (period) to ensure balanced distribution.

Train set: 20000 examples
Validation set: 5000 examples
Total: 25000 examples

Data Format

The dataset is stored in Parquet format with the following fields:

id: Unique identifier
Title: Poem title
Author: Author name
Category: Period category (historical period)
Poem: Poem text

Usage

from datasets import load_dataset

dataset = load_dataset("PoetryMTEB/ArabicPoetryPeriodsClassification")

# Access train and validation splits
train_data = dataset['train']
validation_data = dataset['validation']

Citation

If you use this dataset, please cite the following paper:

@INPROCEEDINGS{10638967,
  author={Ba Alawi, Abdulfattah E. and Bozkurt, Ferhat and Yağanoğlu, Mete},
  booktitle={2024 4th International Conference on Emerging Smart Technologies and Applications (eSmarTA)}, 
  title={BERT-AraPeotry: BERT-based Arabic Poems Classification Model}, 
  year={2024},
  volume={},
  number={},
  pages={1-5},
  keywords={Training;Text analysis;Computational modeling;Bidirectional control;Writing;Transformers;Natural language processing;Arabic Text;Poem;Sentiment Analysis;Natural Language Processing},
  doi={10.1109/eSmarTA62850.2024.10638967}
}