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---
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:
```json
{
"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:
```bibtex
@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](https://github.com/sandstorm12/persian_readability_dataset) |