Text Classification
Transformers
Safetensors
English
roberta
data-classification
sensitivity
data-governance
Synthetic
text-embeddings-inference
Instructions to use aitrainingdatabroker/document-sensitivity-tier-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aitrainingdatabroker/document-sensitivity-tier-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aitrainingdatabroker/document-sensitivity-tier-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aitrainingdatabroker/document-sensitivity-tier-classifier") model = AutoModelForSequenceClassification.from_pretrained("aitrainingdatabroker/document-sensitivity-tier-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 3,264 Bytes
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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.
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