| --- |
| license: apache-2.0 |
| library_name: transformers |
| tags: |
| - t5 |
| - text2text-generation |
| - security |
| - arbiter |
| --- |
| |
| # Arbiter — T5 log Q&A |
|
|
| ## What this is |
|
|
| A **T5** conditional-generation checkpoint fine-tuned for **[Arbiter](https://github.com/SentinelSage/arbiter)**. |
|
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| Used for **log-oriented** prompts (e.g. explaining or answering questions about a security log string). The app generates a short text response via `T5ForConditionalGeneration.generate(...)`. |
|
|
| ## Project |
|
|
| Part of **Arbiter** (Django + React): paste a security log or ask a cybersecurity question. |
|
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| This repository holds **weights only**. Application code: [github.com/SentinelSage/arbiter](https://github.com/SentinelSage/arbiter). |
|
|
| ## Training note |
|
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| This is a **custom fine-tune**, not an unmodified public T5 base/small checkpoint. A generic Hub T5 model is not a guaranteed substitute for this artifact. |
|
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| **Training data:** custom-curated, **unpublished** project data. Training tables are not published with the app. |
|
|
| ## Intended use |
|
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| - Running the Arbiter full-ML path locally |
| - Experimentation and educational use with the companion application |
|
|
| **Not** a production SOC product. No warranty. Generated text can be incomplete or incorrect; do not use for live security operations without your own validation. |
|
|
| ## How Arbiter loads it |
|
|
| Local path after download: |
|
|
| ```text |
| backend/models/cyber_qa_t5_model_log/ |
| ``` |
|
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| Loaded with `T5ForConditionalGeneration.from_pretrained(<local_or_hub_id>)` when `ARBITER_DEMO_MODE=false`. |
|
|
| ## Files |
|
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| Expect a standard Transformers export, e.g. `model.safetensors`, `config.json`, `spiece.model`, tokenizer config files. |
|
|
| ## Method / citations |
|
|
| - **T5:** Raffel, C., et al. (2020). *Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer.* JMLR. |
| [T5 docs](https://huggingface.co/docs/transformers/model_doc/t5) |
| - **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 |
|
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| Research / portfolio demo only. Do not paste production logs, PII, or credentials into shared demos using these weights. |
|
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| ## License |
|
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| Apache License 2.0. Demo only — provided as-is, without warranty of any kind. |
|
|
| Copyright 2026 Ryan Vong / Sentinel Sage |
|
|
