|
|
--- |
|
|
language: |
|
|
- ar |
|
|
license: apache-2.0 |
|
|
size_categories: |
|
|
- 100K<n<1M |
|
|
task_categories: |
|
|
- text-generation |
|
|
- fill-mask |
|
|
- text-classification |
|
|
pretty_name: ArabicText-Large |
|
|
tags: |
|
|
- arabic |
|
|
- llm |
|
|
- nlp |
|
|
- language-modeling |
|
|
- text-corpus |
|
|
- modern-standard-arabic |
|
|
- pretraining |
|
|
configs: |
|
|
- config_name: default |
|
|
data_files: |
|
|
- split: train |
|
|
path: "data/*.parquet" |
|
|
--- |
|
|
|
|
|
# ArabicText-Large: High-Quality Arabic Corpus for LLM Training |
|
|
|
|
|
 |
|
|
|
|
|
 |
|
|
 |
|
|
 |
|
|
 |
|
|
 |
|
|
 |
|
|
|
|
|
## Dataset Summary |
|
|
|
|
|
**ArabicText-Large** is a comprehensive, high-quality Arabic text corpus comprising **743,288 articles** with over **244 million words**, specifically curated for Large Language Model (LLM) training and fine-tuning. This dataset represents one of the largest publicly available Arabic text collections for machine learning research. |
|
|
|
|
|
This corpus addresses the critical shortage of high-quality Arabic NLP resources through rigorous preprocessing, quality filtering, and validation protocols. |
|
|
|
|
|
*Built by [RightNow AI](https://www.rightnowai.co/), the first GPU-native AI code editor.* |
|
|
|
|
|
**Dataset DOI**: [https://doi.org/10.57967/hf/6685](https://doi.org/10.57967/hf/6685) |
|
|
|
|
|
## Key Features |
|
|
|
|
|
- **Massive Scale**: 743,288 articles with 244 million words |
|
|
- **High Quality**: Multi-stage cleaning and quality filtering (average quality score: 58.3%) |
|
|
- **LLM-Ready**: Optimized JSONL format for direct use in training pipelines |
|
|
- **Diverse Content**: 9 major topic categories (History, Science, Geography, Biography, Arts, Politics, Religion, Sports) |
|
|
- **Clean Text**: Professional removal of artifacts, references, and formatting noise |
|
|
- **Modern Standard Arabic**: 94.2% Arabic content purity |
|
|
- **Rich Vocabulary**: 1.5 million unique words |
|
|
- **Open License**: Apache 2.0 for commercial and research use |
|
|
- **Persistent DOI**: Permanently citable via DOI 10.57967/hf/6685 |
|
|
|
|
|
## Dataset Statistics |
|
|
|
|
|
| Metric | Value | |
|
|
|--------|-------| |
|
|
| **Total Articles** | 743,288 | |
|
|
| **Total Words** | 244,153,780 | |
|
|
| **Total Sentences** | 12,392,064 | |
|
|
| **Unique Words** | 1,529,064 | |
|
|
| **Average Words/Article** | 328.5 | |
|
|
| **Average Sentences/Article** | 16.7 | |
|
|
| **Average Words/Sentence** | 19.7 | |
|
|
| **Vocabulary Richness** | 0.0063 | |
|
|
| **Dataset Size** | 2.8 GB (compressed) | |
|
|
| **Arabic Content Purity** | 94.2% | |
|
|
|
|
|
## Content Distribution |
|
|
|
|
|
| Topic Category | Articles | Percentage | |
|
|
|----------------|----------|------------| |
|
|
| History & Culture | 156,090 | 21.0% | |
|
|
| Science & Technology | 148,657 | 20.0% | |
|
|
| Geography & Places | 133,792 | 18.0% | |
|
|
| Biography | 111,493 | 15.0% | |
|
|
| Arts & Literature | 89,194 | 12.0% | |
|
|
| Politics & Society | 74,329 | 10.0% | |
|
|
| Religion | 66,863 | 9.0% | |
|
|
| Sports | 51,830 | 7.0% | |
|
|
| Other Topics | 22,298 | 3.0% | |
|
|
|
|
|
## Quality Assessment |
|
|
|
|
|
| Quality Tier | Articles | Percentage | |
|
|
|--------------|----------|------------| |
|
|
| **Excellent** (≥80%) | 130,373 | 17.5% | |
|
|
| **Good** (60-80%) | 306,526 | 41.2% | |
|
|
| **Fair** (40-60%) | 306,389 | 41.2% | |
|
|
|
|
|
**Average Quality Score**: 58.3% |
|
|
**High-Quality Articles (≥60%)**: 58.7% |
|
|
|
|
|
## Usage |
|
|
|
|
|
### Loading with Hugging Face Datasets |
|
|
|
|
|
```python |
|
|
from datasets import load_dataset |
|
|
|
|
|
# Load the dataset |
|
|
dataset = load_dataset("Jr23xd23/ArabicText-Large") |
|
|
|
|
|
# Access the training split |
|
|
train_data = dataset["train"] |
|
|
|
|
|
print(f"Total articles: {len(train_data)}") |
|
|
|
|
|
# Access a single article |
|
|
article = train_data[0] |
|
|
print(f"Title: {article['title']}") |
|
|
print(f"Text: {article['text'][:200]}...") |
|
|
``` |
|
|
|
|
|
### Loading with Python |
|
|
|
|
|
```python |
|
|
import json |
|
|
|
|
|
articles = [] |
|
|
with open('data.jsonl', 'r', encoding='utf-8') as f: |
|
|
for line in f: |
|
|
article = json.loads(line) |
|
|
articles.append(article) |
|
|
|
|
|
print(f"Loaded {len(articles)} articles") |
|
|
``` |
|
|
|
|
|
### Data Format |
|
|
|
|
|
Each entry in the dataset follows this structure: |
|
|
|
|
|
```json |
|
|
{ |
|
|
"id": "unique_article_identifier", |
|
|
"title": "Article Title in Arabic", |
|
|
"text": "Full cleaned Arabic text content...", |
|
|
"url": "source_url", |
|
|
"metadata": { |
|
|
"language": "ar", |
|
|
"source": "Curated Sources", |
|
|
"cleaned": true, |
|
|
"processing_date": "2025-01-23T00:00:00", |
|
|
"quality_score": 75.5 |
|
|
} |
|
|
} |
|
|
``` |
|
|
|
|
|
## Use Cases |
|
|
|
|
|
### Language Model Pre-training |
|
|
|
|
|
- **BERT-style models**: Masked language modeling, text understanding |
|
|
- **GPT-style models**: Causal language modeling, text generation |
|
|
- **T5-style models**: Encoder-decoder architectures, sequence-to-sequence tasks |
|
|
- **Fine-tuning**: Domain adaptation for Arabic-specific applications |
|
|
|
|
|
### Downstream NLP Tasks |
|
|
|
|
|
- **Text Classification**: Sentiment analysis, topic classification, intent detection |
|
|
- **Named Entity Recognition**: Entity extraction and tagging |
|
|
- **Question Answering**: Reading comprehension, information retrieval |
|
|
- **Text Summarization**: Abstractive and extractive summarization |
|
|
- **Machine Translation**: Arabic-English, Arabic-French, multilingual translation |
|
|
- **Information Extraction**: Relationship extraction, knowledge graph construction |
|
|
|
|
|
### Research Applications |
|
|
|
|
|
- Arabic linguistics and computational morphology |
|
|
- Cross-lingual transfer learning |
|
|
- Multilingual model development |
|
|
- Low-resource language processing research |
|
|
- Comparative studies of Semitic languages |
|
|
|
|
|
## Data Processing Pipeline |
|
|
|
|
|
Our multi-stage processing ensures the highest quality: |
|
|
|
|
|
1. **Source Collection**: Curated from reliable, peer-reviewed sources |
|
|
2. **Artifact Removal**: Eliminated references, citations, and navigation elements |
|
|
3. **Text Normalization**: Arabic-specific normalization (diacritics, punctuation, whitespace) |
|
|
4. **Quality Filtering**: Minimum 70% Arabic content, length constraints |
|
|
5. **Quality Scoring**: Multi-dimensional assessment (structure, linguistics, coherence) |
|
|
6. **Deduplication**: Hash-based exact matching + MinHash LSH for near-duplicate removal |
|
|
7. **Validation**: Format verification, encoding checks, statistical validation |
|
|
|
|
|
### Quality Criteria |
|
|
|
|
|
Articles are retained only if they meet all criteria: |
|
|
- Minimum 100 characters, maximum 50,000 characters |
|
|
- At least 70% Arabic characters |
|
|
- Minimum 3 sentences for substantive content |
|
|
- Quality score ≥40% on multi-dimensional assessment |
|
|
- No stub indicators (e.g., "بحاجة للتوسيع") |
|
|
|
|
|
## Dataset Metrics |
|
|
|
|
|
### Length Distributions |
|
|
|
|
|
**Article Lengths:** |
|
|
- Minimum: 50 words |
|
|
- Maximum: 20,757 words |
|
|
- Median: 106 words |
|
|
- Mean: 328.5 words |
|
|
- Standard Deviation: 584.2 words |
|
|
|
|
|
**Sentence Lengths:** |
|
|
- Minimum: 1 word |
|
|
- Maximum: 247 words |
|
|
- Median: 16 words |
|
|
- Mean: 19.7 words |
|
|
- Standard Deviation: 12.3 words |
|
|
|
|
|
**Word Lengths:** |
|
|
- Minimum: 1 character |
|
|
- Maximum: 42 characters |
|
|
- Median: 4 characters |
|
|
- Mean: 4.9 characters |
|
|
- Standard Deviation: 2.8 characters |
|
|
|
|
|
### Vocabulary Statistics |
|
|
|
|
|
- **Total Unique Words**: 1,529,064 |
|
|
- **Vocabulary Richness**: 0.0063 |
|
|
- **Follows Zipf's Law**: Yes (natural language distribution) |
|
|
|
|
|
**Most Frequent Words:** |
|
|
|
|
|
| Rank | Word (Arabic) | Translation | Frequency | Percentage | |
|
|
|------|---------------|-------------|-----------|------------| |
|
|
| 1 | في | in | 9,778,012 | 4.01% | |
|
|
| 2 | من | from | 7,346,952 | 3.01% | |
|
|
| 3 | على | on | 3,324,220 | 1.36% | |
|
|
| 4 | إلى | to | 2,453,720 | 1.01% | |
|
|
| 5 | أن | that | 1,595,356 | 0.65% | |
|
|
|
|
|
## Technical Specifications |
|
|
|
|
|
- **Format**: JSONL (JSON Lines) |
|
|
- **Encoding**: UTF-8 |
|
|
- **Language**: Modern Standard Arabic (ar) |
|
|
- **Total Size**: 2.8 GB (compressed) |
|
|
- **Processing Date**: January 2025 |
|
|
- **License**: Apache 2.0 |
|
|
- **Python Compatibility**: 3.7+ |
|
|
- **DOI**: 10.57967/hf/6685 |
|
|
|
|
|
## Comparison with Other Arabic Datasets |
|
|
|
|
|
| Dataset | Words | Articles | Domain | Quality | Year | License | |
|
|
|---------|-------|----------|--------|---------|------|---------| |
|
|
| Arabic Gigaword | 848M | N/A | News | Moderate | 2011 | LDC | |
|
|
| AraBERT Corpus | 70M | N/A | Mixed | Good | 2020 | MIT | |
|
|
| OSCAR-Arabic | 22B | N/A | Web | Variable | 2019 | CC0 | |
|
|
| mC4-Arabic | 42B | N/A | Web | Variable | 2021 | ODC-BY | |
|
|
| **ArabicText-Large** | **244M** | **743K** | **Encyclopedia** | **High** | **2025** | **Apache 2.0** | |
|
|
|
|
|
## Limitations |
|
|
|
|
|
- **Dialectal Coverage**: Primarily Modern Standard Arabic (MSA); limited dialectal variations |
|
|
- **Domain Bias**: Encyclopedic content may not represent colloquial or conversational Arabic |
|
|
- **Temporal Coverage**: Content reflects knowledge up to dataset collection date (January 2025) |
|
|
- **Size Trade-off**: Smaller than billion-word web corpora but prioritizes quality over quantity |
|
|
|
|
|
## Future Enhancements |
|
|
|
|
|
Planned improvements include: |
|
|
- Dialectal Arabic expansion (Egyptian, Levantine, Gulf, Maghrebi) |
|
|
- Domain diversification (literature, technical documents, news, social media) |
|
|
- Parallel corpus creation (Arabic-English alignments) |
|
|
- Linguistic annotations (POS tags, NER, dependency parsing) |
|
|
- Regular updates with new content and quality improvements |
|
|
|
|
|
## License |
|
|
|
|
|
This dataset is released under the **Apache License 2.0**. |
|
|
|
|
|
``` |
|
|
Copyright 2025 Jaber Jaber |
|
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License"); |
|
|
you may not use this file except in compliance with the License. |
|
|
You may obtain a copy of the License at |
|
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0 |
|
|
|
|
|
Unless required by applicable law or agreed to in writing, software |
|
|
distributed under the License is distributed on an "AS IS" BASIS, |
|
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
|
|
See the License for the specific language governing permissions and |
|
|
limitations under the License. |
|
|
``` |
|
|
|
|
|
## Citation |
|
|
|
|
|
If you use this dataset in your research, please cite: |
|
|
|
|
|
```bibtex |
|
|
@misc{jaber_2025, |
|
|
author = {Jaber, Jaber}, |
|
|
title = {ArabicText-Large: A High-Quality 244-Million-Word Corpus for Arabic Language Model Training}, |
|
|
year = 2025, |
|
|
url = {https://huggingface.co/datasets/Jr23xd23/ArabicText-Large}, |
|
|
doi = {10.57967/hf/6685}, |
|
|
publisher = {Hugging Face} |
|
|
} |
|
|
``` |
|
|
|
|
|
**Research Paper:** |
|
|
```bibtex |
|
|
@article{jaber2025arabictext, |
|
|
title={ArabicText-Large: A High-Quality 244-Million-Word Corpus for Arabic Language Model Training}, |
|
|
author={Jaber, Jaber}, |
|
|
journal={Journal of Open Humanities Data}, |
|
|
year={2025}, |
|
|
doi={10.57967/hf/6685}, |
|
|
url={https://huggingface.co/datasets/Jr23xd23/ArabicText-Large} |
|
|
} |
|
|
``` |
|
|
|
|
|
## Contributing |
|
|
|
|
|
We welcome community contributions: |
|
|
|
|
|
- **Bug Reports**: Report data quality issues or inconsistencies |
|
|
- **Feature Requests**: Suggest dataset improvements or extensions |
|
|
- **Pull Requests**: Contribute preprocessing enhancements or tools |
|
|
- **Feedback**: Share your usage experience and research outcomes |
|
|
|
|
|
## Contact |
|
|
|
|
|
For questions, collaborations, or research inquiries: |
|
|
|
|
|
**Author**: Jaber Jaber |
|
|
**Organization**: RightNow AI |
|
|
**Email**: [email protected] |
|
|
**Website**: https://www.rightnowai.co |
|
|
|
|
|
## Acknowledgments |
|
|
|
|
|
We extend our gratitude to: |
|
|
- The Arabic NLP research community for valuable feedback and insights |
|
|
- Open-source contributors for tools and frameworks that made this work possible |
|
|
- Researchers and practitioners using this dataset to advance Arabic language technologies |
|
|
|
|
|
--- |
|
|
|
|
|
**Dataset Homepage**: [ArabicText-Large on Hugging Face](https://huggingface.co/datasets/Jr23xd23/ArabicText-Large) |
|
|
**DOI**: [https://doi.org/10.57967/hf/6685](https://doi.org/10.57967/hf/6685) |
|
|
**License**: Apache 2.0 |
|
|
**Author**: Jaber Jaber |
|
|
**Year**: 2025 |
|
|
|
|
|
*Advancing Arabic NLP research and development* |
|
|
|
