Update README.md

README.md

@@ -32,24 +32,24 @@ configs:
 ![Words](https://img.shields.io/badge/Words-244M-orange)
 ![License](https://img.shields.io/badge/License-Apache%202.0-yellow)
 
-## 📋 Dataset Summary
+## 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.
 
-## 🎯 Key Features
+## Key Features
 
-- ✅ **Massive Scale**: 743K articles with 244M words
-- ✅ **High Quality**: Multi-stage cleaning and quality filtering (avg. quality score: 58.3%)
-- ✅ **LLM-Ready**: Optimized JSONL format for direct use in training pipelines
-- ✅ **Diverse Content**: 9 major topic categories (History, Science, Geography, etc.)
-- ✅ **Clean Text**: Professional removal of artifacts, references, and formatting noise
-- ✅ **Modern Standard Arabic**: 94.2% Arabic content purity
-- ✅ **Rich Vocabulary**: 1.5M+ unique words
-- ✅ **Open License**: Apache 2.0 for commercial and research use
+- **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
 
-## 📊 Dataset Statistics
+## Dataset Statistics
 
 | Metric | Value |
 |--------|-------|
@@ -64,7 +64,7 @@ This corpus addresses the critical shortage of high-quality Arabic NLP resources
 | **Dataset Size** | 2.8 GB (compressed) |
 | **Arabic Content Purity** | 94.2% |
 
-## 🏷️ Content Distribution
+## Content Distribution
 
 | Topic Category | Articles | Percentage |
 |----------------|----------|------------|
@@ -78,7 +78,7 @@ This corpus addresses the critical shortage of high-quality Arabic NLP resources
 | Sports | 51,830 | 7.0% |
 | Other Topics | 22,298 | 3.0% |
 
-## ⭐ Quality Assessment
+## Quality Assessment
 
 | Quality Tier | Articles | Percentage |
 |--------------|----------|------------|
@@ -89,7 +89,7 @@ This corpus addresses the critical shortage of high-quality Arabic NLP resources
 **Average Quality Score**: 58.3%
 **High-Quality Articles (≥60%)**: 58.7%
 
-## 💻 Usage
+## Usage
 
 ### Loading with Hugging Face Datasets
 
@@ -97,7 +97,7 @@ This corpus addresses the critical shortage of high-quality Arabic NLP resources
 from datasets import load_dataset
 
 # Load the dataset
-dataset = load_dataset("htu-ai/ArabicText-Large")
+dataset = load_dataset("Jr23xd23/ArabicText-Large")
 
 # Access the training split
 train_data = dataset["train"]
@@ -144,23 +144,23 @@ Each entry in the dataset follows this structure:
 }
 ```
 
-## 🚀 Use Cases
+## 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, seq2seq tasks
-- **Fine-tuning**: Domain adaptation for Arabic-specific tasks
+- **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
+- **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 translation
-- **Information Extraction**: Relationship extraction, knowledge graphs
+- **Machine Translation**: Arabic-English, Arabic-French, multilingual translation
+- **Information Extraction**: Relationship extraction, knowledge graph construction
 
 ### Research Applications
 
@@ -168,52 +168,53 @@ Each entry in the dataset follows this structure:
 - Cross-lingual transfer learning
 - Multilingual model development
 - Low-resource language processing research
+- Comparative studies of Semitic languages
 
-## 🏗️ Data Processing Pipeline
+## 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, navigation elements
-3. **🔤 Text Normalization**: Arabic-specific normalization (diacritics, punctuation)
-4. **🎯 Quality Filtering**: Minimum 70% Arabic content, length constraints
-5. **📊 Quality Scoring**: Multi-dimensional assessment (structure, linguistics, coherence)
-6. **♻️ Deduplication**: Hash-based exact + MinHash LSH near-duplicate removal
-7. **✅ Validation**: Format verification, encoding checks, statistical validation
+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:
-- ✅ 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")
+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
+## Dataset Metrics
 
 ### Length Distributions
 
 **Article Lengths:**
-- Min: 50 words
-- Max: 20,757 words
+- Minimum: 50 words
+- Maximum: 20,757 words
 - Median: 106 words
 - Mean: 328.5 words
-- Std Dev: 584.2 words
+- Standard Deviation: 584.2 words
 
 **Sentence Lengths:**
-- Min: 1 word
-- Max: 247 words
+- Minimum: 1 word
+- Maximum: 247 words
 - Median: 16 words
 - Mean: 19.7 words
-- Std Dev: 12.3 words
+- Standard Deviation: 12.3 words
 
 **Word Lengths:**
-- Min: 1 character
-- Max: 42 characters
+- Minimum: 1 character
+- Maximum: 42 characters
 - Median: 4 characters
 - Mean: 4.9 characters
-- Std Dev: 2.8 characters
+- Standard Deviation: 2.8 characters
 
 ### Vocabulary Statistics
 
@@ -223,15 +224,15 @@ Articles are retained only if they meet:
 
 **Most Frequent Words:**
 
-| Rank | Word (Arabic) | Translation | Frequency | % |
-|------|---------------|-------------|-----------|---|
+| 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
+## Technical Specifications
 
 - **Format**: JSONL (JSON Lines)
 - **Encoding**: UTF-8
@@ -241,33 +242,33 @@ Articles are retained only if they meet:
 - **License**: Apache 2.0
 - **Python Compatibility**: 3.7+
 
-## 📊 Comparison with Other Arabic Datasets
+## Comparison with Other Arabic Datasets
 
 | Dataset | Words | Articles | Domain | Quality | Year | License |
 |---------|-------|----------|--------|---------|------|---------|
-| Arabic Gigaword | 848M | - | News | Moderate | 2011 | LDC |
-| AraBERT Corpus | 70M | - | Mixed | Good | 2020 | MIT |
-| OSCAR-Arabic | 22B | - | Web | Variable | 2019 | CC0 |
-| mC4-Arabic | 42B | - | Web | Variable | 2021 | ODC-BY |
+| 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
+## 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 (2025)
-- **Size Trade-off**: Smaller than billion-word web corpora but higher quality
+- **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
+## Future Enhancements
 
 Planned improvements include:
 - Dialectal Arabic expansion (Egyptian, Levantine, Gulf, Maghrebi)
-- Domain diversification (literature, technical documents, news)
+- 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
+- Regular updates with new content and quality improvements
 
-## 📄 License
+## License
 
 This dataset is released under the **Apache License 2.0**.
 
@@ -287,7 +288,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ```
 
-## 📚 Citation
+## Citation
 
 If you use this dataset in your research, please cite:
 
@@ -307,39 +308,40 @@ If you use this dataset in your research, please cite:
 @inproceedings{arabictext2025,
   title={ArabicText-Large: A Comprehensive 244M-Word Corpus for Arabic Language Model Training},
   author={Jaber, Jaber and Alkasasbeh, Bassam},
-  booktitle={Proceedings of [Conference]},
+  booktitle={Proceedings of [Conference Name]},
   year={2025}
 }
 ```
 
-## 🤝 Contributing
+## Contributing
 
 We welcome community contributions:
 
-- **Bug Reports**: Report data quality issues
-- **Feature Requests**: Suggest improvements
-- **Pull Requests**: Contribute preprocessing enhancements
-- **Feedback**: Share your usage experience
+- **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
+## Contact
 
-For questions or collaborations, please open an issue on the repository.
+For questions, collaborations, or research inquiries, please open an issue on the repository.
 
 **Authors:**
 - Jaber Jaber
 - Bassam Alkasasbeh
 
-## 🙏 Acknowledgments
+## Acknowledgments
 
-Special thanks to:
-- The Arabic NLP community for valuable feedback
-- Open-source contributors for tools and frameworks
-- Researchers and practitioners using this dataset
+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](https://huggingface.co/datasets/Jr23xd23/ArabicText-Large)
+**Dataset Homepage**: [ArabicText-Large on Hugging Face](https://huggingface.co/datasets/Jr23xd23/ArabicText-Large)
 **License**: Apache 2.0
 **Authors**: Jaber Jaber, Bassam Alkasasbeh
+**Year**: 2025
 
-*Built for advancing Arabic NLP research and development* 🚀
+*Advancing Arabic NLP research and development*