Datasets:
license: odc-by
task_categories:
- text-generation
language:
- en
pretty_name: Primus-FineWeb
tags:
- cybersecurity
- pretraining
- FineWeb
size_categories:
- 1M<n<10M
extra_gated_fields:
Affiliation: text
Country: country
I want to use this model for:
type: select
options:
- Research
- Commercial
- label: Other
value: other
Job title:
type: select
options:
- Student
- Research graduate
- AI researcher
- AI developer/engineer
- Cybersecurity researcher
- Reporter
- Other
geo: ip_location
library_name: transformers
⭐ Please download the dataset from here.
PRIMUS: A Pioneering Collection of Open-Source Datasets for Cybersecurity LLM Training
🤗 Primus-FineWeb
The Primus-FineWeb dataset is constructed by filtering cybersecurity-related text from FineWeb, a refined version of Common Crawl. We began by leveraging Primus-Seed, a high-quality dataset of manually curated cybersecurity text, as positive samples. We then sampled ten times the amount of data from FineWeb as negative samples and trained a binary cybersecurity classifier based on TinyBERT. Using this classifier, we assigned each text in FineWeb a score between 0 and 1 and filtered out texts with a score greater than 0.003, creating the Primus-FineWeb with 15.3 billion tokens. However, after discovering a significant amount of duplicate content, we performed deduplication, reducing the final dataset to 🔥 2.57 billion tokens of cybersecurity corpus.
🚀🚀 For more details, see our paper:
https://arxiv.org/abs/2502.11191
Why was the threshold set at 0.003?
We divided the score range (0-1) into several bins and randomly sampled 50 examples from each bin. These samples were then scored by GPT-4o to determine the proportion of text that was "truly" cybersecurity-related. We found that if the score was below 0.003, the proportion of cybersecurity text fell below 50%.
FineWeb: Cybersecurity Score vs. Token Count
License
This dataset is released under the ODC-By license. However, you must still comply with the FineWeb license and the Common Crawl Terms of Use.