README.md

metadata

license: cc-by-sa-4.0
language:
  - ar
pretty_name: BAREC-X
task_categories:
  - text-classification
size_categories:
  - 1K<n<10K
tags:
  - readability
  - arabic
  - explanations
configs:
  - config_name: annotations
    default: true
    data_files: annotations.parquet
  - config_name: gpt
    data_files: gpt.parquet
  - config_name: gemini
    data_files: gemini.parquet
  - config_name: rule_based
    data_files: rule_based.parquet
  - config_name: labels
    data_files: labels.parquet
source_datasets:
  - CAMeL-Lab/BAREC-Corpus-v1.0

BAREC-X

BAREC-X is the first explanation-annotated Arabic readability dataset. It contains 1,793 sentences from the Balanced Arabic Readability Evaluation Corpus (BAREC), each with its BAREC readability level (1–19, from early literacy to postgraduate comprehension) and the linguistic reasons that justify that level, selected by expert annotators from the BAREC annotation guidelines along six dimensions: spelling, word count, morphology, syntax, vocabulary and content.

The sentences come from the inter-annotator agreement portion of BAREC and were annotated by three native Arabic-speaking educators. About 300 sentences were labelled by all three; for those, the released labels are the reasons at least two annotators agreed on.

The release also includes the labels predicted for the same sentences by GPT-5.5 and Gemini-3-Flash (four-shot prompting) and by a training-free rule-based system, plus the full label inventory.

BAREC-X was introduced in Benchmarking Fine-Grained Readability Explanations for Arabic (Rabih, Elmadani, Taha-Thomure and Habash). Project page: https://barec.camel-lab.com/

Content

Config	Rows	What it holds
annotations (default)	1,793	Human annotations: sentence, readability level, labels.
gpt	1,793	Labels predicted by GPT-5.5 with four-shot prompting.
gemini	1,793	Labels predicted by Gemini-3-Flash with four-shot prompting.
rule_based	1,793	Labels predicted by the training-free rule-based system.
labels	148	The label inventory: code, dimension, level, Arabic description.

The four sentence tables have the same 1,793 rows in the same order and share the id column, so they can be joined on id or compared row by row.

All three systems receive the sentence and its gold readability level and return the reasons that justify that level. The LLM prompts contain a task description, the BAREC guideline excerpt for the sentence's level, and four example sentences with their gold reasons. The rule-based system extracts its features with CAMeL Tools 1.5.6 and CamelParser 2.0 and returns the activated reasons whose guideline level matches the given level.

Loading

from datasets import load_dataset

repo = "CAMeL-Lab/BAREC-X"
gold = load_dataset(repo)                 # human annotations
gpt = load_dataset(repo, "gpt")           # also "gemini", "rule_based"
labels = load_dataset(repo, "labels")     # code -> Arabic description

annotations

Column	Type	Description
id	string	Unique 11-digit sentence identifier.
sentence	string	The Arabic sentence.
readability_level	int	Readability level, 1–19.
spelling	list of string	Spelling label codes (prefix O).
word_count	list of string	Word-count label codes (prefix W).
morphology	list of string	Morphology label codes (prefix M).
syntax	list of string	Syntax label codes (prefix S).
vocabulary	list of string	Vocabulary label codes (prefix V).
content	list of string	Content label codes (prefix C).

A dimension with no label is an empty list. A sentence can carry more than one label, in the same dimension or across dimensions.

Example row:

{"id": "10600010006", "sentence": "عالم غريب المعالم", "readability_level": 11, "spelling": [], "word_count": [], "morphology": [], "syntax": ["S11-2"], "vocabulary": [], "content": []}

gpt, gemini, rule_based

These are the outputs of the systems. They contain the same columns as annotations without readability_level: id, sentence, and the six label lists holding that system's predictions. The sentence text is identical to annotations.

labels

Column	Type	Description
code	string	Label code, e.g. S10-2.
dimension	string	One of spelling, word_count, morphology, syntax, vocabulary, content.
level	int or null	The BAREC readability level the label belongs to. Null for the "other" codes.
description_ar	string	The label's description in Arabic.
num_sentences	int	Number of sentences carrying this label in the human annotations.

A code has the form <dimension letter><level>-<index>: S10-2 is the second syntax label of level 10. Codes ending in -O (M-O, S-O, V-O, C-O) mean "other" and have no level.

Sentences per readability level:

Level	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19
Sentences	4	12	34	10	49	55	134	130	61	209	89	423	93	337	75	47	23	3	5

Citation

@inproceedings{rabih-etal-2026-benchmarking,
    title = "Benchmarking Fine-Grained Readability Explanations for Arabic",
    author = "Rabih, Nour  and
      Elmadani, Khalid N. and
      Taha-Thomure, Hanada  and
      Habash, Nizar",
    booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics",
    month = nov,
    year = "2026",
    address = "Hengqin, China",
    publisher = "Association for Computational Linguistics",
}