---
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>&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:** 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.