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README.md

@@ -1,150 +1,141 @@
-## **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)}}\
-}
-```
+---
+license: apache-2.0
+language:
+- ar
+tags:
+- multimodal
+- arabic
+- common-crawl
+pretty_name: Misraj Structured Data Dump (MSDD)
+---
+
+# **📚 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**
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("Misraj/msdd")
+```
+
+### **📋 Example Usage**
+
+```python
+# 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)}}
+}
+```