README.md

---
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)}}
}
```