Datasets:

CAMeL-Lab
/

BAREC-Corpus-v1.0

	---
	language:
	- ar
	license: cc-by-sa-4.0
	size_categories:
	- 10K<n<100K
	task_categories:
	- text-classification
	pretty_name: BAREC Corpus v1.0
	tags:
	- readability
	dataset_info:
	features:
	- name: ID
	dtype: int64
	- name: Sentence
	dtype: string
	- name: Word_Count
	dtype: int64
	- name: Word
	dtype: string
	- name: Lex
	dtype: string
	- name: D3Tok
	dtype: string
	- name: D3Lex
	dtype: string
	- name: Readability_Level
	dtype: string
	- name: Readability_Level_19
	dtype: int64
	- name: Readability_Level_7
	dtype: int64
	- name: Readability_Level_5
	dtype: int64
	- name: Readability_Level_3
	dtype: int64
	- name: Annotator
	dtype: string
	- name: Document
	dtype: string
	- name: Source
	dtype: string
	- name: Book
	dtype: string
	- name: Author
	dtype: string
	- name: Domain
	dtype: string
	- name: Text_Class
	dtype: string
	splits:
	- name: train
	num_bytes: 46655304
	num_examples: 54845
	- name: dev
	num_bytes: 5917980
	num_examples: 7310
	- name: test
	num_bytes: 6139540
	num_examples: 7286
	download_size: 23742307
	dataset_size: 58712824
	configs:
	- config_name: default
	data_files:
	- split: train
	path: data/train-*
	- split: dev
	path: data/dev-*
	- split: test
	path: data/test-*
	---

	# BAREC Corpus v1.0

	## Dataset Summary

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

	---

	## Supported Tasks

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

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

	---

	### Languages

	- Arabic (Modern Standard Arabic)

	---

	## Dataset Structure

	### Data Instances

	`{'ID': 10100010008, 'Sentence': 'عيد سعيد', 'Word_Count': 2, 'Word': 'عيد سعيد', 'Lex': 'عيد سعيد', 'D3Tok': 'عيد سعيد', 'D3Lex': 'عيد سعيد', '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.
	- Word: Simply tokenized and dediacritized sentences.
	- Lex: Each word is replaced by its predicited lemma (dediacritized).
	- D3Tok: We tokenize words into their base and clitics forms.
	- D3Lex: We replace the base forms in D3Tok with the predicited lemmas.
	- 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`).

	### 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).

	---

	## 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 {A}rabic Readability Assessment",
	author = "Elmadani, Khalid N. and
	Habash, Nizar and
	Taha-Thomure, Hanada",
	booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
	month = jul,
	year = "2025",
	address = "Vienna, Austria",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/2025.findings-acl.842/"
	}

	@inproceedings{habash-etal-2025-guidelines,
	title = "Guidelines for Fine-grained Sentence-level {A}rabic 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-2025)",
	month = jul,
	year = "2025",
	address = "Vienna, Austria",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/2025.law-1.30/"
	}
	```