Update README.md

README.md

@@ -211,4 +211,139 @@ configs:
   data_files:
   - split: train
     path: chunk_5/train-*
+license: odc-by
+task_categories:
+- text-generation
+language:
+- en
+tags:
+- cybersecurity
+- pretraining
+pretty_name: RedSage-CFW
+size_categories:
+- 10M<n<100M
 ---
+
+# Dataset Card for RedSage-CFW
+
+<p align="center">
+  <b> RedSage: A Cybersecurity Generalist LLM" (ICLR 2026). </b>
+  <br>
+  <b>Authors:</b> Naufal Suryanto<sup>1</sup>, Muzammal Naseer<sup>1†</sup>, Pengfei Li<sup>1</sup>, Syed Talal Wasim<sup>2</sup>, Jinhui Yi<sup>2</sup>, Juergen Gall<sup>2</sup>, Paolo Ceravolo<sup>3</sup>, Ernesto Damiani<sup>3</sup>
+  <br>
+  <sup>1</sup>Khalifa University, <sup>2</sup>Universität Bonn, <sup>3</sup>University of Milan  
+  <sup>†</sup>Project Lead
+  <br>
+  <br>
+  <a href="https://openreview.net/forum?id=W4FAenIrQ2"><img src="https://img.shields.io/badge/Paper-OpenReview-B31B1B.svg"></a>
+  <a href="https://huggingface.co/RISys-Lab"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-RISys--Lab-orange"></a>
+  <br>
+  🌐 <a href="https://risys-lab.github.io/RedSage/">Project Page</a>&nbsp;&nbsp;|&nbsp;&nbsp;
+  🤖 <a href="https://huggingface.co/collections/RISys-Lab/redsage-models">Model Collection</a>&nbsp;&nbsp;|&nbsp;&nbsp;
+  📊 <a href="https://huggingface.co/collections/RISys-Lab/redsage-benchmarks">Benchmark Collection</a>&nbsp;&nbsp;|&nbsp;&nbsp;
+  📘 <a href="https://huggingface.co/collections/RISys-Lab/redsage-datasets">Data Collection </a>
+  
+  
+</p>
+
+****
+
+
+## Dataset Description
+
+* **Developed by:** RISysLab
+* **Repository:** [GitHub](https://github.com/RISys-Lab/RedSage)
+* **Paper:** [RedSage: A Cybersecurity Generalist LLM](https://openreview.net/forum?id=W4FAenIrQ2)
+* **Arxiv:** https://arxiv.org/abs/2601.22159
+
+### Dataset Summary
+
+**RedSage-CFW** (CyberFineWeb) is a large-scale, cybersecurity dataset designed for the continual pretraining of Large Language Models (LLMs). It consists of approximately **11.7 billion tokens** spanning **13 million documents**.
+
+The dataset was constructed by filtering the **FineWeb** corpus (Common Crawl 2013–2024) using a custom ModernBERT-based classifier to identify cybersecurity-relevant content. To prevent catastrophic forgetting of general capabilities during pretraining, the cybersecurity data is mixed with general educational content from **FineWeb-Edu**.
+
+### Supported Tasks
+
+* **Continual Pretraining:** Designed to adapt general-purpose LLMs (e.g., Qwen, Llama) to the cybersecurity domain.
+* **Domain Adaptation:** Enhances model performance on cybersecurity knowledge, skills, and tool usage
+
+### Languages
+
+The dataset primarily consists of English text, derived from Common Crawl sources.
+
+## Dataset Structure
+
+### Data Instances
+
+The dataset is partitioned into 5 chunks (config names: `chunk_1` through `chunk_5`). Each instance represents a single document (e.g., a web page, article, or forum post).
+
+### Data Fields
+
+Based on the provided configuration, the data fields are:
+
+* **`text`** (string): The full text content of the document.
+* **`id`** (string): A unique identifier for the document.
+* **`metadata`** (struct): Contains detailed attributes about the source and filtering:
+* `probability` (float64): The confidence score from the cybersecurity classifier.
+* `relevant` (bool): A flag indicating if the document passed the relevance filter.
+* `url` (string): The source URL of the document.
+* `date` (timestamp): The crawl or publication date.
+* `dump` (string): The Common Crawl dump identifier (e.g., `CC-MAIN-2024-51`).
+* `file_path` (string): Path information for the original file.
+* `language` (string): The detected language of the text.
+* `language_score` (float64): Confidence score of the language detection.
+* `token_count` (int64): The number of tokens in the document.
+* `score`, `int_score`: Additional quality or relevance metrics.
+
+### Data Splits
+
+The dataset is segmented into 5 chunks. The paper notes that the final corpus consists of the "latest 5 chunks" from the filtered pipeline to fit training budgets.
+
+* **Total Size:** ~11.7B tokens.
+* **Total Documents:** ~13M documents.
+
+## Dataset Creation
+
+### Curation Rationale
+
+Existing cybersecurity solutions often rely on proprietary APIs or lack domain adaptation. RedSage-CFW bridges this gap by providing a transparent, open-source corpus for training local, privacy-preserving cybersecurity assistants.
+
+### Source Data
+
+* **FineWeb:** The base corpus is FineWeb, aggregated from 104 Common Crawl subsets between Summer 2013 and December 2024 (~17.2T tokens).
+* **FineWeb-Edu:** Used for mixing general knowledge to maintain reasoning capabilities.
+
+### Data Processing & Filtering
+
+1. **Classifier Training:** A binary classifier based on **ModernBERT-base** was trained on the "Cybersecurity Topic Classification" dataset (sourced from Reddit, StackExchange, and arXiv). It achieved 97.3% accuracy on validation.
+2. **Filtering:** This classifier was applied to FineWeb, identifying ~125M cybersecurity-relevant documents (~89.8B tokens).
+3. **General Knowledge Replay:** To avoid catastrophic forgetting, the cybersecurity data was mixed with FineWeb-Edu samples at a **30% replay ratio**.
+4. **Deduplication:** Global deduplication was performed using MinHash-LSH (via DataTrove), reducing the corpus size by ~47.9% in tokens.
+5. **Chunking:** The final dataset comprises the latest 5 chronological chunks from the processed data to manage computational costs.
+
+
+## Considerations for Using the Data
+
+### Social Impact
+
+The dataset enables the development of open-source cybersecurity assistants, potentially helping to bridge the global skills shortage in the field.
+
+### Discussion of Biases and Limitations
+
+* **Source Bias:** As a web-crawled dataset, it may inherit biases present in Common Crawl and online cybersecurity discussions.
+* **Dual Use:** The dataset contains offensive security knowledge (e.g., penetration testing techniques). While intended for defense, there is an inherent risk of misuse.
+
+---
+
+## Citation
+
+```bibtex
+@inproceedings{suryanto2026redsage,
+  title={RedSage: A Cybersecurity Generalist {LLM}},
+  author={Naufal Suryanto and Muzammal Naseer and Pengfei Li and Syed Talal Wasim and Jinhui Yi and Juergen Gall and Paolo Ceravolo and Ernesto Damiani},
+  booktitle={The Fourteenth International Conference on Learning Representations},
+  year={2026},
+  url={https://openreview.net/forum?id=W4FAenIrQ2}
+}
+
+```