Datasets:
The viewer is disabled because this dataset repo requires arbitrary Python code execution. Please consider
removing the
loading script
and relying on
automated data support
(you can use
convert_to_parquet
from the datasets
library). If this is not possible, please
open a discussion
for direct help.
Dataset Card for MetRec
Dataset Summary
The dataset contains the verses and their corresponding meter classes. Meter classes are represented as numbers from 0 to 13. The dataset can be highly useful for further research in order to improve the field of Arabic poems’ meter classification. The train dataset contains 47,124 records and the test dataset contains 8,316 records.
Supported Tasks and Leaderboards
The dataset was published on this paper. A benchmark is acheived on this paper.
Languages
The dataset is based on Arabic.
Dataset Structure
Data Instances
A typical data point comprises a label which is out of 13 classes and a verse part of poem.
Data Fields
[N/A]
Data Splits
The data is split into a training and testing. The split is organized as the following
train | test | |
---|---|---|
data split | 47,124 | 8,316 |
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
[More Information Needed]
Initial Data Collection and Normalization
The dataset was collected from Aldiwan.
Who are the source language producers?
The poems are from different poets.
Annotations
The dataset does not contain any additional annotations.
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
[More Information Needed]
Licensing Information
[More Information Needed]
Citation Information
[More Information Needed]
@article{metrec2020,
title={MetRec: A dataset for meter classification of arabic poetry},
author={Al-shaibani, Maged S and Alyafeai, Zaid and Ahmad, Irfan},
journal={Data in Brief},
year={2020},
publisher={Elsevier}
}
Contributions
Thanks to @zaidalyafeai for adding this dataset.
- Downloads last month
- 0