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|
---
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dataset_info:
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features:
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|
- name: id
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|
dtype: string
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|
- name: Title
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|
dtype: string
|
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|
- name: Author
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|
dtype: string
|
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|
- name: Category
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|
|
dtype: string
|
|
|
- name: Poem
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|
|
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:
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|
|
- config_name: default
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|
|
data_files:
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|
|
- split: train
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|
path: train-*.parquet
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|
|
- split: validation
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|
path: validation-*.parquet
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|
|
---
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|
# Arabic Poetry Periods Classification
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This dataset contains Arabic poetry with period classification. Each poem is classified into a specific historical period (Category).
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## Dataset Information
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The dataset is split into train (80%) and validation (20%) sets with stratified sampling based on the Category (period) to ensure balanced distribution.
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- **Train set**: 20000 examples
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- **Validation set**: 5000 examples
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|
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- **Total**: 25000 examples
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## Data Format
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The dataset is stored in Parquet format with the following fields:
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|
- `id`: Unique identifier
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- `Title`: Poem title
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- `Author`: Author name
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|
|
- `Category`: Period category (historical period)
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- `Poem`: Poem text
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## Usage
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```python
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|
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from datasets import load_dataset
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|
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dataset = load_dataset("PoetryMTEB/ArabicPoetryPeriodsClassification")
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|
|
# Access train and validation splits
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|
|
train_data = dataset['train']
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|
|
validation_data = dataset['validation']
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|
|
```
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## Citation
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|
|
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|
If you use this dataset, please cite the following paper:
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|
|
|
```
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|
|
@INPROCEEDINGS{10638967,
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|
|
author={Ba Alawi, Abdulfattah E. and Bozkurt, Ferhat and Yağanoğlu, Mete},
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|
|
booktitle={2024 4th International Conference on Emerging Smart Technologies and Applications (eSmarTA)},
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|
|
title={BERT-AraPeotry: BERT-based Arabic Poems Classification Model},
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|
|
year={2024},
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|
|
volume={},
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|
|
number={},
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|
|
pages={1-5},
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|
|
keywords={Training;Text analysis;Computational modeling;Bidirectional control;Writing;Transformers;Natural language processing;Arabic Text;Poem;Sentiment Analysis;Natural Language Processing},
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|
|
doi={10.1109/eSmarTA62850.2024.10638967}
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}
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|
```
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|
