| --- |
| license: apache-2.0 |
| library_name: transformers |
| tags: |
| - distilbert |
| - text-classification |
| - security |
| - arbiter |
| pipeline_tag: text-classification |
| --- |
| |
| # Arbiter — Log threat classifier |
|
|
| ## What this is |
|
|
| A **DistilBERT** sequence-classification checkpoint fine-tuned for **[Arbiter](https://github.com/SentinelSage/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](https://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| **Training data:** custom-curated, **unpublished** project data.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): |
|
|
| ```text |
| 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](https://huggingface.co/distilbert-base-uncased) |
| - **Transformers:** Wolf, T., et al. (2020). *Transformers: State-of-the-Art Natural Language Processing.* EMNLP. |
| [huggingface/transformers](https://github.com/huggingface/transformers) |
| - **PyTorch:** Paszke, A., et al. (2019). *PyTorch: An Imperative Style, High-Performance Deep Learning Library.* NeurIPS. |
| [pytorch/pytorch](https://github.com/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 |
|
|
