# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("sentinelsage/arbiter-log-classifier")
model = AutoModelForSequenceClassification.from_pretrained("sentinelsage/arbiter-log-classifier", device_map="auto")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:
| Index | Action (app mapping) |
|---|---|
| 0 | block |
| 1 | quarantine |
| 2 | warn |
| 3 | none |
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Training data: custom-curated, unpublished project data.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
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sentinelsage/arbiter-log-classifier")