arbiter-log-classifier

Arbiter — Log threat classifier

What this is

A DistilBERT sequence-classification checkpoint fine-tuned for Arbiter.

Given a short security-log style string, it predicts one of four actions:

The Arbiter app then scales a threat score from the predicted class / probabilities and may adjust the final recommendation with a separate rule-based layer (refine_action).

Project

Part of Arbiter (Django + React): paste a security log or ask a cybersecurity question.

This repository holds weights only. Application code: github.com/SentinelSage/arbiter.

Training note

This is a custom fine-tune, not an unmodified public DistilBERT base checkpoint. Do not treat Hub distilbert-base-uncased (or similar) as a drop-in substitute for this artifact.

Training data: custom-curated, unpublished project data. There is no public labelled cybersecurity corpus linked as a training set for this demo.

Intended use

• Running the Arbiter full-ML path locally
• Experimentation and educational use with the companion application

Not a production SOC product. No warranty. Outputs can be wrong; do not use for live blocking, compliance, or incident response without your own validation.

How Arbiter loads it

Local path after download (scripts/download_models.py or equivalent):

backend/models/log_based_model/

Loaded with Hugging Face DistilBertForSequenceClassification.from_pretrained(<local_or_hub_id>) when ARBITER_DEMO_MODE=false.

Files

Expect a standard Transformers export, e.g. model.safetensors, config.json, tokenizer files (vocab.txt, tokenizer_config.json, …).

Method / citations

If you reference this work:

• DistilBERT: Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.
  Base: distilbert-base-uncased
• Transformers: Wolf, T., et al. (2020). Transformers: State-of-the-Art Natural Language Processing. EMNLP.
  huggingface/transformers
• PyTorch: Paszke, A., et al. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. NeurIPS.
  pytorch/pytorch

Safety

Research / portfolio demo only. Classifier confidence is not calibrated. Do not paste production logs, PII, or credentials into demos that use these weights on a shared host.

License

Apache License 2.0. Demo only — provided as-is, without warranty of any kind.

Copyright 2026 Ryan Vong / Sentinel Sage