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wasm_readme.md

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+## **license: apache-2.0**
+
+# **📚 Misraj Structured Data Dump (MSDD)**
+
+Misraj Structured Data Dump (MSDD) is a large-scale Arabic multimodal dataset created using our **WASM pipeline**. It is extracted and filtered from the [Common Crawl](https://commoncrawl.org/) dumps and uniquely preserves the structural integrity of web content by providing markdown output. This dataset aims to address the lack of high-quality, structured multimodal data for Arabic and accelerate research in large language and multimodal models.
+
+
+## **📌 Dataset Summary**
+
+- **Source:** Subset from multiple Common Crawl dumps, processed with the WASM pipeline.
+
+- **Documents:** 23 million documents.
+
+- **Timeframe:** \* 2024 Dump: Dump 10
+
+* 2025 Dump: Dump 13
+
+- **Languages:** Primarily Arabic (MSA and dialects).
+
+- **Format:** Multimodal format with interleaved text and images in Markdown.
+
+- **Domain Variety:** General web content.
+
+
+## **💡 Usage**
+
+### **📥 Loading the Dataset**
+
+from datasets import load\_dataset\
+\
+dataset = load\_dataset("Misraj/msdd")
+
+
+### **📋 Example Usage**
+
+\# Access the first example\
+example = dataset\['train']\[0]\
+print(f"Text: {example\['text']}")\
+print(f"Images: {example\['images']}")\
+print(f"Captions: {example\['image\_caption']}")
+
+
+## **⚙️ The WASM Processing Pipeline**
+
+The performance of large language models (LLMs) and large multimodal models (LMMs) depends heavily on the quality and scale of their pre-training datasets. For Arabic, the lack of high-quality multimodal datasets that preserve document structure has limited progress. Our **WASM pipeline** was developed to address this gap by processing Common Crawl and generating a structured, markdown-based multimodal dataset. The pipeline is designed to preserve the structural integrity of web content while maintaining flexibility for both text-only and multimodal pre-training scenarios.
+
+The core of the WASM pipeline involves careful filtering at both the paragraph and document level.
+
+
+### **✅ _Paragraph-Level Filtering_**
+
+Each paragraph in the corpus undergoes the following checks:
+
+- **Character Deduplication:** Removal of repeated characters beyond a threshold.
+
+- **Word Repetition Ratio:** Filtering paragraphs with excessive word repetitions.
+
+- **Special Character Ratio:** Filtering based on the proportion of non-standard characters.
+
+- **Language Identification:** Only Arabic paragraphs are retained.
+
+- **Perplexity Scoring:** Content scored using an in-house KenLM-based model trained on Wikipedia-like pages, Arabic Twitter data, and dialectal text (e.g., Lahjawi), to remove low-quality text.
+
+
+### **✅ _Document-Level Filtering_**
+
+Each full document must pass:
+
+- **Word Repetition Ratio:** Similar to paragraph level, but with different thresholds for full documents.
+
+- **Special Character Ratio:** Ensures no document is dominated by symbols, code snippets, or garbage text.
+
+- **Language Identification:** Verifies the document is primarily Arabic.
+
+- **Perplexity Score:** Documents are filtered based on perplexity thresholds to maintain fluent, natural text.
+
+
+## **📂 Dataset Structure**
+
+The dataset is structured with three main columns to support multimodal tasks. The text is interleaved with image placeholders, allowing for rich text-and-image documents.
+
+- **text**: A string containing the textual content. The special token \<image> is used to denote the position where an image should be inserted.
+
+- **images**: A list of image URLs (strings). These images correspond sequentially to the \<image> tokens in the text field.
+
+- **image\_caption**: A list of strings, where each string is a caption for the corresponding image in the images list. If an image does not have a caption, the list will contain an empty string '' at that position.
+
+The dataset has the following features:
+```json
+{\
+  "text": {\
+    "dtype": "string",\
+    "\_type": "Value"\
+  },\
+  "images": {\
+    "feature": {\
+      "dtype": "list",\
+      "\_type": "Value"\
+    },\
+    "\_type": "Sequence"\
+  },\
+  "image\_caption": {\
+    "feature": {\
+      "dtype": "list",\
+      "\_type": "Value"\
+    },\
+    "\_type": "Sequence"\
+  }\
+}
+```
+
+## **🚦 Quality Checks**
+
+The dataset quality was validated using:
+
+- In-house KenLM-based Arabic models for perplexity checks.
+
+- Manual inspection of samples.
+
+- A pipeline inspired by [OBELICS](https://github.com/huggingface/OBELICS), with custom enhancements.
+
+- Comparative analysis against major existing dataset processing pipelines to validate design choices.
+
+
+## **🔍 Intended Use**
+
+This dataset is intended for:
+
+- Training large-scale multimodal Arabic language models.
+
+- Research on Arabic NLP, including dialect modeling and low-resource language studies.
+
+
+## **🌐 Availability & Reproducibility**
+
+To support future research and ensure reproducibility, we are publicly releasing this representative dataset dump. The WASM processing pipeline for Arabic will also be made available to the community.
+
+
+## **📝 Citation**
+
+If you use this dataset, please cite:
+```bibtex
+@misc{misraj2025msdd,\
+  title        = {Misraj Structured Data Dump (MSDD)},\
+  author       = {Khalil Hennara, Muhammad Hreden, Mohamed Motasim Hamed, Zeina Aldallal, Sara Chrouf, Safwan AlModhayan, Ahmed Bustati},\
+  year         = {2025},\
+  publisher    = {MisrajAI},\
+  howpublished = {\url{\[https\://huggingface.co/datasets/Misraj/msdd]\(https\://huggingface.co/datasets/Misraj/msdd)}}\
+}
+```