README.md

---
license: cc-by-4.0
language:
  - en
library_name: mlx
pipeline_tag: token-classification
tags:
  - mlx
  - bert
  - token-classification
  - pii
  - ner
  - privacy
base_model: nationaldesignstudio/rampart
---

# Rampart PII NER — MLX

An MLX build of **Rampart**, a compact encoder-only BERT (MiniLM-L6, hidden 384,
6 layers, ~18.5M params) with a 35-label BIO token-classification head for
detecting personally identifiable information (PII). Intended for on-device,
client-side PII redaction on Apple Silicon.

This repository ships **float (fp16) MLX weights** in `model.safetensors` plus a
small self-contained MLX implementation (`rampart_mlx.py`).

## Provenance

This is an **independent MLX conversion** of the original
[`nationaldesignstudio/rampart`](https://huggingface.co/nationaldesignstudio/rampart).
The original is distributed as a 4-bit quantized ONNX export; the float weights
here were recovered directly from that export (4-bit `MatMulNBits` linears and
INT8 embeddings dequantized to float) and then stored in MLX `safetensors`.

The conversion was verified to reproduce the original ONNX model **exactly**:
on the validation prompts, MLX vs. ONNX Runtime token-label agreement is
**100%** with a maximum logit difference of ~1e-5 (floating-point rounding).

No third-party MLX port was used in producing these weights.

## Labels

17 entity types in BIO format (35 classes incl. `O`): `GIVEN_NAME`, `SURNAME`,
`EMAIL`, `PHONE`, `URL`, `TAX_ID`, `BANK_ACCOUNT`, `ROUTING_NUMBER`,
`GOVERNMENT_ID`, `PASSPORT`, `DRIVERS_LICENSE`, `BUILDING_NUMBER`, `STREET_NAME`,
`SECONDARY_ADDRESS`, `CITY`, `STATE`, `ZIP_CODE`.

## Usage

```bash
pip install mlx tokenizers
python demo.py "My name is John Smith, email john.smith@example.com"
```

```python
import mlx.core as mx
from tokenizers import Tokenizer
from rampart_mlx import load

model, cfg = load(".")
tok = Tokenizer.from_file("tokenizer.json")
enc = tok.encode("Call me at (555) 123-4567")
logits = model(mx.array([enc.ids]), mx.array([enc.attention_mask]))
label_ids = mx.argmax(logits[0], axis=-1).tolist()
labels = [cfg.id2label[i] for i in label_ids]
```

See `demo.py` for BIO span decoding using the tokenizer's char offsets (needed to
map predicted labels back onto the original text for redaction).

## Files

| File | Purpose |
|------|---------|
| `model.safetensors` | fp16 MLX weights (HuggingFace-style key names) |
| `config.json` | model architecture + `id2label` |
| `rampart_mlx.py` | self-contained MLX model + loader |
| `demo.py` | tokenize → infer → decode spans |
| `tokenizer.json`, `vocab.txt`, `tokenizer_config.json`, `special_tokens_map.json` | WordPiece tokenizer |

## License & attribution

Released under **CC-BY-4.0**, the same license as the upstream model. Attribution:

- Original model: [`nationaldesignstudio/rampart`](https://huggingface.co/nationaldesignstudio/rampart) (CC-BY-4.0)
- Base encoder: [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased)
- Training data: [`ai4privacy/pii-masking-openpii-1.5m`](https://huggingface.co/datasets/ai4privacy/pii-masking-openpii-1.5m) (CC-BY-4.0)