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---
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
```python
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}
}
```
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