Datasets:

AlirezaF138
/

Persian-Readability-Dataset

dataset_info:
  features:
    - name: text
      dtype: string
    - name: label
      dtype: int64
    - name: rater_profile
      sequence: float64
  splits:
    - name: train
      num_bytes: 1198658
      num_examples: 3847
  download_size: 640645
  dataset_size: 1198658
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: cc-by-4.0
task_categories:
  - text-classification
language:
  - fa
pretty_name: Persian Text Readability Dataset
size_categories:
  - 1K<n<10K

Dataset Summary

This is a re-upload of the Persian Text Readability Dataset, originally created and published by Mohammadi & Khasteh (2020). It provides sentence-level readability annotations for Persian (Farsi) texts. Each data point includes:

A text in Persian
A label (readability level):
- 0 for easy
- 1 for medium
- 2 for hard
A rater profile: the average readability label distribution of the raters who annotated that specific text

All texts included have over 80% agreement between at least three human annotators. The dataset is intended for training and evaluating readability assessment models in the Persian language.

Supported Tasks and Leaderboards

Task: Readability Classification

Input: A Persian sentence
Output: A readability level (0, 1, or 2)

This dataset supports both standard classification models and those that take annotator bias into account (using the rater_profile).

Languages

Text Language: fa (Persian / Farsi)

Dataset Structure

Each data point is a dictionary with the following fields:

{
  "text": "متن فارسی نمونه",
  "label": 1,
  "rater_profile": [0.1, 0.5, 0.4]
}

text: A single Persian sentence or short passage.
label: The final readability level (0: easy, 1: medium, 2: hard).
rater_profile: A 3-element float list showing the average readability preferences of the annotators.

Dataset Stats

Level	# of texts	Avg. words per text
0 (easy)	2,953	28.8
1 (medium)	572	39.8
2 (hard)	322	62.1
Total	3,847	33.2

Source Data

Annotation Process

Texts were manually rated by undergraduate students at the K. N. Toosi University of Technology. Each text was rated by at least three annotators. Only texts where at least 80% of the raters agreed on the label were included.

Rater Profile

The rater_profile field helps capture rater bias. For example, [0.1, 0.5, 0.4] means the raters of that text tend to give:

10% of their scores as "easy"
50% as "medium"
40% as "hard"

This can be useful in modeling subjective readability with annotator-specific information.

Citation

Please cite the following if you use this dataset:

@inproceedings{mohammadi2020machine,
  title={A machine learning approach to Persian text readability assessment using a crowdsourced dataset},
  author={Mohammadi, Hamid and Khasteh, Seyed Hossein},
  booktitle={2020 28th Iranian Conference on Electrical Engineering (ICEE)},
  pages={1--7},
  year={2020},
  organization={IEEE}
}

Acknowledgements

We express deep appreciation to the undergraduate computer engineering students at the K. N. Toosi University of Technology who annotated the dataset.

Licensing

This dataset is a re-upload. Licensing terms are inherited from the original work. Please ensure compliance with any applicable usage conditions described in the original publication or source repository: https://github.com/sandstorm12/persian_readability_dataset