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README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: transformers
+pipeline_tag: text-classification
+base_model: distilroberta-base
+datasets:
+- aitrainingdatabroker/synthetic-document-sensitivity-tiers
+tags:
+- data-classification
+- sensitivity
+- data-governance
+- text-classification
+- synthetic
+---
+
+# Document Sensitivity Tier Classifier
+
+Reads a short description of a document and suggests a sensitivity tier: `public`, `internal`, `confidential` or `restricted`.
+
+Every name, number and sentence it was trained on is made up. Nothing comes from a real person, customer or business.
+
+Related tool: [document-sensitivity-keyword-sorter](https://huggingface.co/spaces/aitrainingdatabroker/document-sensitivity-keyword-sorter) (a Space that runs in your browser).
+
+## Why it exists
+
+Before data leaves a company, someone sorts what it holds by how sensitive it is. This model gives that sort a first draft.
+
+## Use it
+
+```python
+from transformers import pipeline
+
+classify = pipeline("text-classification", model="aitrainingdatabroker/document-sensitivity-tier-classifier", top_k=3)
+print(classify("Customer database export with emails, phone numbers and home addresses."))
+```
+
+## How it was trained
+
+Fine-tuned from `distilroberta-base` for 6 epochs on one RTX 3070. Training data: 305 rows from [synthetic-document-sensitivity-tiers](https://huggingface.co/datasets/aitrainingdatabroker/synthetic-document-sensitivity-tiers), all invented, with test rows built from templates the model never saw in training. The scripts that built the data and trained the model are in `source/`; the full scores are in `scores.json`.
+
+## Test scores
+
+On 77 held-out rows: accuracy 0.831, macro F1 0.823.
+
+| Label | F1 | Count |
+|---|---|---|
+| `confidential` | 0.857 | 19 |
+| `internal` | 0.816 | 20 |
+| `public` | 0.974 | 20 |
+| `restricted` | 0.645 | 18 |
+
+## Limits
+
+- Trained only on invented text from templates, with test sentences built from templates it never saw. Real text varies far more.
+- Tiers differ between companies and laws. A model's tier is a suggestion; the owner of the data sets the tier.
+- This model is not legal advice and not a certification. A person should review its output before anything is shared or decided, and your counsel decides what a rule requires.
+
+## Read more
+
+- [De-identification before data is licensed](https://aitrainingdatabroker.com/de-identification/?utm_source=huggingface&utm_medium=model&utm_campaign=document-sensitivity-tier-classifier)
+- [Privacy rules by region](https://aitrainingdatabroker.com/regions/?utm_source=huggingface&utm_medium=model&utm_campaign=document-sensitivity-tier-classifier)
+- [Can you license SOPs and knowledge bases?](https://aitrainingdatabroker.com/data/sops-and-knowledge-bases/?utm_source=huggingface&utm_medium=model&utm_campaign=document-sensitivity-tier-classifier)
+
+## License and credit
+
+Apache 2.0. Please credit **AI Training Data Broker** and link to https://aitrainingdatabroker.com/ when you share or build on it.
+
+## About
+
+AI Training Data Broker is a broker and technical advisor for companies that sell or license their data to AI labs. It guides the technical side, including export, format, de-identification and compliance, and publishes free guides, checklists and tools.