README.md

---
license: mit
task_categories:
- text-classification
language:
- ar
tags:
- readability
size_categories:
- 10K<n<100K
pretty_name: 'BAREC 2025: Readability Assessment Shared Task'
---

# BAREC Shared Task 2025

## Dataset Summary

**BAREC** (the Balanced Arabic Readability Evaluation Corpus) is a large-scale dataset developed for the **BAREC Shared Task 2025**, focused on **fine-grained Arabic readability assessment**. The dataset includes over **1M words**, annotated across **19 readability levels**, with additional mappings to coarser 7, 5, and 3 level schemes.

The dataset is **annotated at the sentence level**. Document-level readability scores are derived by assigning each document the readability level of its **most difficult sentence**, based on the 19-level scheme. This provides both **sentence-level** and **document-level** readability information.

---

## Supported Tasks and Leaderboards

The dataset supports **multi-class readability classification** in the following formats:

- **19 levels** (default)
- **7 levels**
- **5 levels**
- **3 levels**

For details on the shared task, evaluation setup, and leaderboards, visit the [Shared Task Website](https://barec.camel-lab.com/sharedtask2025).

---

### Languages

- **Arabic** (Modern Standard Arabic)

---

## How to Use

You can load the dataset using Hugging Face Datasets by specifying the appropriate `data_files`.

### Sentence-level dataset

```python
data_files={"train": "Sent_Train.csv", "dev": "Sent_Dev.csv", "test": "Sent_Test.csv"}
barec = load_dataset("CAMeL-Lab/BAREC-Shared-Task-2025", data_files=data_files, token=token)
barec_train = barec["train"]
barec_dev = barec["dev"]
barec_test = barec["test"]
```

### Document-level dataset

```python
data_files={"train": "Doc_Train.csv", "dev": "Doc_Dev.csv", "test": "Doc_Test.csv"}
barec = load_dataset("CAMeL-Lab/BAREC-Shared-Task-2025", data_files=data_files, token=token)
barec_train = barec["train"]
barec_dev = barec["dev"]
barec_test = barec["test"]
```

---

## Dataset Structure (Sentence-level)

### Data Instances

`{'ID': 10100010008, 'Sentence': 'عيد سعيد', 'Word_Count': 2, 'Readability_Level': '2-ba', 'Readability_Level_19': 2, 'Readability_Level_7': 1, 'Readability_Level_5': 1, 'Readability_Level_3': 1, 'Annotator': 'A4', 'Document': 'BAREC_Majed_0229_1983_001.txt', 'Source': 'Majed', 'Book': 'Edition: 229', 'Author': '#', 'Domain': 'Arts & Humanities', 'Text_Class': 'Foundational'}`

### Data Fields

- **ID**: Unique sentence identifier.
- **Sentence**: The sentence text.
- **Word_Count**: Number of words in the sentence.
- **Readability_Level**: The readability level in `19-levels` scheme, ranging from `1-alif` to `19-qaf`.
- **Readability_Level_19**: The readability level in `19-levels` scheme, ranging from `1` to `19`.
- **Readability_Level_7**: The readability level in `7-levels` scheme, ranging from `1` to `7`.
- **Readability_Level_5**: The readability level in `5-levels` scheme, ranging from `1` to `5`.
- **Readability_Level_3**: The readability level in `3-levels` scheme, ranging from `1` to `3`.
- **Annotator**: The annotator ID (`A1-A5` or `IAA`).
- **Document**: Source document file name.
- **Source**: Document source.
- **Book**: Book name.
- **Author**: Author name.
- **Domain**: Domain (`Arts & Humanities`, `STEM` or `Social Sciences`).
- **Text_Class**: Readership group (`Foundational`, `Advanced` or `Specialized`).

---

## Dataset Structure (Document-level)

### Data Instances

`{'ID': 1010219, 'Document': 'BAREC_Majed_1481_2007_038.txt', 'Sentences': '"موزة الحبوبة وشقيقها رشود\nآيس كريم بالكريمة..\nأم كريمة بالآيس كريم؟!"', 'Sentence_Count': 3, 'Word_Count': 15, 'Readability_Level': '8-Ha', 'Readability_Level_19': 8, 'Readability_Level_7': 3, 'Readability_Level_5': 2, 'Readability_Level_3': 1, 'Source': 'Majed', 'Book': 'Edition: 1481', 'Author': '#', 'Domain': 'Arts & Humanities', 'Text_Class': 'Foundational'}`

### Data Fields

- **ID**: Unique document identifier.
- **Document**: Document file name.
- **Sentences**: Full text of the document.
- **Sentence_Count**: Number of sentences.
- **Word_Count**: Total word count.
- **Readability_Level**: The readability level in `19-levels` scheme, ranging from `1-alif` to `19-qaf`.
- **Readability_Level_19**: The readability level in `19-levels` scheme, ranging from `1` to `19`.
- **Readability_Level_7**: The readability level in `7-levels` scheme, ranging from `1` to `7`.
- **Readability_Level_5**: The readability level in `5-levels` scheme, ranging from `1` to `5`.
- **Readability_Level_3**: The readability level in `3-levels` scheme, ranging from `1` to `3`.
- **Source**: Document source.
- **Book**: Book name.
- **Author**: Author name.
- **Domain**: Domain (`Arts & Humanities`, `STEM` or `Social Sciences`).
- **Text_Class**: Readership group (`Foundational`, `Advanced` or `Specialized`).

---

## Data Splits

- The BAREC dataset has three splits: *Train* (80%), *Dev* (10%), and *Test* (10%).
- The splits are in the document level.
- The splits are balanced accross *Readability Levels*, *Domains*, and *Text Classes*.

---

## Evaluation

We define the Readability Assessment task as an ordinal classification task. The following metrics are used for evaluation:

- **Accuracy (Acc<sup>19</sup>):** The percentage of cases where reference and prediction classes match in the 19-level scheme.
- **Accuracy (Acc<sup>7</sup>, Acc<sup>5</sup>, Acc<sup>3</sup>):** The percentage of cases where reference and prediction classes match after collapsing the 19 levels into 7, 5, or 3 levels, respectively.
- **Adjacent Accuracy (±1 Acc<sup>19</sup>):** Also known as off-by-1 accuracy. The proportion of predictions that are either exactly correct or off by at most one level in the 19-level scheme.
- **Average Distance (Dist):** Also known as Mean Absolute Error (MAE). Measures the average absolute difference between predicted and true labels.
- **Quadratic Weighted Kappa (QWK):** An extension of Cohen’s Kappa that measures the agreement between predicted and true labels, applying a quadratic penalty to larger misclassifications (i.e., predictions farther from the true label are penalized more heavily).

We provide evaluation scripts [here](https://github.com/CAMeL-Lab/barec-shared-task-2025).

---

## Citation

If you use BAREC in your work, please cite the following papers:

```
@inproceedings{elmadani-etal-2025-readability,
    title = "A Large and Balanced Corpus for Fine-grained Arabic Readability Assessment",
    author = "Elmadani, Khalid N.  and
      Habash, Nizar  and
      Taha-Thomure, Hanada",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics"
}

@inproceedings{habash-etal-2025-guidelines,
    title = "Guidelines for Fine-grained Sentence-level Arabic Readability Annotation",
    author = "Habash, Nizar  and
      Taha-Thomure, Hanada  and
      Elmadani, Khalid N.  and
      Zeino, Zeina  and
      Abushmaes, Abdallah",
    booktitle = "Proceedings of the 19th Linguistic Annotation Workshop (LAW-XIX)",
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics"
}
```