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Datasets:
torchsight
/
beam-training-data

Tasks:
Text Classification
Languages:
English
Size:
10K<n<100K
Tags:
cybersecurity
document-classification
sft
lora
License:
Dataset card Data Studio Files
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metadata 
license: apache-2.0
task_categories:
  - text-classification
language:
  - en
tags:
  - cybersecurity
  - document-classification
  - sft
  - lora
size_categories:
  - 10K<n<100K

Beam Training Data

Supervised fine-tuning (SFT) dataset used to train TorchSight Beam — a cybersecurity document classifier built by LoRA-fine-tuning Qwen 3.5 27B.

Dobrovolskyi, I. Security Document Classification with a Fine-Tuned Local Large Language Model: Benchmark Data and an Open-Source System. Journal of Information Security and Applications, 2026.

Dataset

  • 78,358 balanced samples (95 / 5 split → 74,441 train + 3,917 validation)
  • Alpaca format: instruction, input, output
  • Output is a JSON array of findings, each with category, subcategory, severity, explanation
  • Stratified across 7 categories × 51 subcategories

Composition

The initial corpus contained 116,956 raw samples drawn from 13 publicly available sources. To remove the NVD-dominated skew, each subcategory was capped at 5,000 samples and underrepresented subcategories were augmented with GPT-4–generated synthetic data and hard-negative boundary cases.

Source Raw Balanced License Categories
NVD CVE Database 50,000 8,475 Public domain malicious.exploit
Synthetic augmentation 33,100 39,754 Generated (GPT-4) All categories
Hard negatives 6,400 6,134 Generated (GPT-4) Boundary cases
AI4Privacy 5,000 4,851 Apache 2.0 pii.*
Fenrir v2.0 5,000 4,573 Apache 2.0 malicious.*
SEC EDGAR 3,000 3,000 Public domain financial.*
SecLists 3,229 1,708 MIT malicious.injection
Phishing Dataset 3,000 2,796 Apache 2.0 malicious.phishing
NIST Training 3,000 2,761 Public domain safe.documentation
Enron Email Corpus 2,000 1,902 Public domain pii.*, credentials.*
MITRE ATT&CK v14 1,620 871 Royalty-free malicious.malware
Loghub 1,280 1,280 Research-free safe.config
Other (3 sources) 327 253 Permissive Multiple
Total 116,956 78,358

All sources have been verified safe for AI training. Copyleft-licensed (GPL/LGPL) and ShareAlike-licensed (CC BY-SA) materials are excluded so the corpus is suitable for commercial training use.

Of the final 78,358 samples, 39,754 (50.7%) are GPT-4–generated synthetic augmentation and 6,134 (7.8%) are hard-negative boundary cases. The remaining 32,470 (41.5%) come from the 13 external sources listed above.

Structure 

sft/
├── train_alpaca.jsonl   # 74,441 samples
└── val_alpaca.jsonl     #  3,917 samples
processed/               # intermediate per-source files
synthetic/               # GPT-4 generations and hard negatives

LoRA training configuration

Parameter Value
Base model Qwen 3.5 27B (dense)
LoRA rank (r) 128
LoRA alpha (α) 256
Target modules q/k/v/o_proj, gate/up/down_proj
Dropout 0.05
Learning rate 2 × 10⁻⁵, cosine decay, 10% warmup
Effective batch size 16 (4 × 4 gradient accumulation)
Epochs 5
Precision bf16
Max sequence length 4,096 tokens
Hardware 8× NVIDIA A100 80GB SXM4
Wall-clock time 10.5 hours

Library versions: trl == 0.11.4, transformers == 4.45.2, peft == 0.13.2.

License

Apache 2.0.

Companion artifacts