| --- |
| annotations_creators: |
| - machine-generated |
| language: |
| - en |
| license: odc-by |
| multilinguality: |
| - monolingual |
| size_categories: |
| - 10M<n<100M |
| source_datasets: |
| - HuggingFaceFW/fineweb |
| task_categories: |
| - text-generation |
| - text-classification |
| task_ids: [] |
| pretty_name: RedSage-CFW Full (Dedup) |
| tags: |
| - cybersecurity |
| - pretraining |
| - continual-pretraining |
| - fineweb |
| - common-crawl |
| - deduplication |
| - datatrove |
| dataset_info: |
| features: |
| - name: probability |
| dtype: float64 |
| - name: relevant |
| dtype: bool |
| - name: text |
| dtype: string |
| - name: id |
| dtype: string |
| - name: dump |
| dtype: string |
| - name: url |
| dtype: string |
| - name: date |
| dtype: string |
| - name: file_path |
| dtype: string |
| - name: language |
| dtype: string |
| - name: language_score |
| dtype: float64 |
| - name: token_count |
| dtype: int64 |
| - name: idx |
| dtype: int64 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: "*.jsonl.gz" |
| --- |
| |
| # Dataset Card for RedSage-CFW Full (Dedup) |
|
|
| <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>University of Bonn, <sup>3</sup>University of Milan |
| <br> |
| <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> | |
| 🤖 <a href="https://huggingface.co/collections/RISys-Lab/redsage-models">Model Collection</a> | |
| 📊 <a href="https://huggingface.co/collections/RISys-Lab/redsage-benchmarks">Benchmark Collection</a> | |
| 📘 <a href="https://huggingface.co/collections/RISys-Lab/redsage-datasets">Data Collection</a> |
| </p> |
|
|
| **** |
|
|
| ## Dataset Description |
|
|
| - **Developed by:** RISys-Lab |
| - **Repository:** [GitHub](https://github.com/RISys-Lab/RedSage) |
| - **Paper:** [RedSage: A Cybersecurity Generalist LLM (ICLR 2026)](https://openreview.net/forum?id=W4FAenIrQ2) |
| - **Arxiv:** https://arxiv.org/abs/2601.22159 |
| - **Curated 5-Chunk Pretraining Release:** [RISys-Lab/RedSage-CFW](https://huggingface.co/datasets/RISys-Lab/RedSage-CFW) |
| - **Raw Non-Deduplicated Release:** [RISys-Lab/RedSage-CFW-Full-NonDedup](https://huggingface.co/datasets/RISys-Lab/RedSage-CFW-Full-NonDedup) |
|
|
| ### Dataset Summary |
|
|
| **RedSage-CFW Full (Dedup)** is the globally deduplicated cybersecurity web corpus extracted from **FineWeb** across all 103 Common Crawl dumps from **CC-MAIN-2013-20** through **CC-MAIN-2024-51**. |
|
|
| Starting from the raw \~122.15M classified documents (\~89.8B tokens), this corpus was processed using global MinHash-LSH deduplication via Hugging Face's `datatrove` library, removing \~47.9% redundant tokens across dumps. The result is packaged as 40 sharded `jsonl.gz` files totaling \~88.8 GB of compressed data, containing clean, deduplicated domain-specific text spanning over a decade of public cybersecurity discourse. |
|
|
| > **Note on CFW Versions:** |
| > - **[RedSage-CFW Full (Non-Dedup)](https://huggingface.co/datasets/RISys-Lab/RedSage-CFW-Full):** Complete raw classifier outputs across 103 dumps (122.15M rows, \~267 GB Parquet). No deduplication applied. |
| > - **[RedSage-CFW Full (Dedup)](https://huggingface.co/datasets/RISys-Lab/RedSage-CFW-Full-Dedup) [This Dataset]:** Complete full-span filtered corpus after global MinHash-LSH deduplication (40 compressed `jsonl.gz` shards, \~88.8 GB compressed). No general replay mixed. |
| > - **[RedSage-CFW](https://huggingface.co/datasets/RISys-Lab/RedSage-CFW):** The final training-ready corpus combining the latest 5 chronological chunks with MinHash deduplication and 30% FineWeb-Edu general knowledge replay (\~13M documents, \~11.7B tokens). |
|
|
| ### Supported Tasks |
|
|
| - **Continual Domain Pretraining:** Train or continue pretraining foundational LLMs on clean, non-redundant cybersecurity web text. |
| - **Custom Replay & Mixing Experiments:** Mix with custom general-purpose datasets (e.g., FineWeb-Edu, SlimPajama) at bespoke ratios without pre-baked replay constraints. |
| - **Cybersecurity Text Evolution & Corpus Linguistics:** Track the emergence of domain-specific terminology, public security disclosures, and changing community practices across more than ten years of web discourse. |
|
|
| ### Languages |
|
|
| The dataset consists primarily of English text filtered from the FineWeb Common Crawl archive. |
|
|
| --- |
|
|
| ## Dataset Structure |
|
|
| ### Data Instances |
|
|
| The dataset is partitioned into 40 compressed JSON Lines files (`00000.jsonl.gz` to `00039.jsonl.gz`). Each JSON record represents a unique deduplicated document identified as cybersecurity-relevant. |
|
|
| ### Data Fields |
|
|
| The dataset contains the following 12 fields per record: |
|
|
| - **`probability`** (*float*): Confidence score generated by the ModernBERT cybersecurity classifier. |
| - **`relevant`** (*bool*): Binary classification flag (`true`/`false`) confirming domain relevance. |
| - **`text`** (*string*): Full extracted text of the document. |
| - **`id`** (*string*): Unique Common Crawl URN document identifier (e.g., `<urn:uuid:...>`). |
| - **`dump`** (*string*): Source Common Crawl snapshot identifier (e.g., `CC-MAIN-2024-51`). |
| - **`url`** (*string*): Source web URL. |
| - **`date`** (*string*): Crawl / publication timestamp string. |
| - **`file_path`** (*string*): Source path within the FineWeb archive. |
| - **`language`** (*string*): Detected language tag (e.g., `en`). |
| - **`language_score`** (*float*): Confidence score from the language identifier. |
| - **`token_count`** (*int*): Document token count. |
| - **`idx`** (*int*): Record index identifier. |
| |
| ### Files & Storage Summary |
| |
| The corpus is packaged into 40 gzipped JSONL files totaling approximately **88.8 GB compressed**: |
| |
| | File Range | Shard Count | Total Compressed Size | Format | |
| | :--- | :--- | :--- | :--- | |
| | `00000.jsonl.gz` – `00039.jsonl.gz` | 40 shards | \~88.8 GB | Gzip-compressed JSONL (`.jsonl.gz`) | |
| |
| --- |
| |
| ## Usage |
| |
| ### Loading via `datasets` |
| |
| ```python |
| from datasets import load_dataset |
| |
| # Load a specific shard |
| shard_0 = load_dataset( |
| "RISys-Lab/RedSage-CFW-Full-Dedup", |
| data_files="00000.jsonl.gz", |
| split="train" |
| ) |
| print(shard_0[0]) |
| |
| # Stream across the entire deduplicated dataset |
| full_stream = load_dataset( |
| "RISys-Lab/RedSage-CFW-Full-Dedup", |
| split="train", |
| streaming=True |
| ) |
| |
| for sample in full_stream: |
| print(sample["id"], sample["token_count"]) |
| break |
| ``` |
| |
| ### Processing Directly in Python |
| |
| ```python |
| import gzip |
| import json |
| |
| with gzip.open("00000.jsonl.gz", "rt", encoding="utf-8") as f: |
| for line in f: |
| doc = json.loads(line) |
| print(doc["id"], doc["probability"]) |
| break |
| ``` |
| |
| --- |
| |
| ## Dataset Creation |
| |
| ### Curation Rationale |
| |
| Continual pretraining on redundant web documents can degrade training efficiency and trigger verbatim memorization. While `RedSage-CFW Full (Non-Dedup)` provides the complete raw classifier outputs for cross-crawl auditing, this deduplicated release provides a cleaner, computation-ready base across the entire 2013–2024 time span. |
| |
| ### Source Data |
| |
| - **Base Corpus:** [HuggingFaceFW/fineweb](https://huggingface.co/datasets/HuggingFaceFW/fineweb), aggregated across 103 Common Crawl snapshots between 2013 and 2024 (\~17.2T tokens). |
|
|
| ### Data Processing Pipeline |
|
|
| 1. **Classifier Filtering:** A binary classifier based on **ModernBERT-base** (achieving 97.3% validation accuracy) was run across 103 FineWeb dumps, retrieving \~122.15M cybersecurity-relevant documents (\~89.8B tokens). |
| 2. **Global Deduplication:** MinHash-LSH was executed across the corpus using **DataTrove**, identifying and removing duplicate and near-duplicate text. This reduced total token volume by \~47.9%. |
| 3. **Partitioning:** The deduplicated records were serialized into 40 gzip-compressed JSON Lines shards (`.jsonl.gz`). |
|
|
| --- |
|
|
| ## Considerations for Using the Data |
|
|
| ### Social Impact |
|
|
| This dataset provides an accessible, non-redundant training resource for developing open-source cybersecurity foundational models, transparent assistants, and automated defensive tooling. |
|
|
| ### Discussion of Biases and Limitations |
|
|
| - **Source Biases:** As with all web-scale scrapes, the dataset contains informal syntax, forum noise, and potential biases from public discussions. |
| - **Dual-Use Considerations:** Contains authentic technical discussions, exploit mechanics, and vulnerability disclosures. Safe deployment requires post-training alignment and guardrails. |
|
|
| --- |
|
|
| ## 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} |
| } |
| ``` |
|
|
| ### Acknowledgements |
|
|
| Global deduplication was conducted using the open-source [datatrove](https://github.com/huggingface/datatrove) library. |
