README.md

---
license: apache-2.0
base_model: OpenMed/privacy-filter-multilingual
datasets:
  - ai4privacy/pii-masking-200k
  - ai4privacy/pii-masking-400k
  - ai4privacy/open-pii-masking-500k-ai4privacy
pipeline_tag: token-classification
library_name: openmed
tags:
  - openmed
  - mlx
  - apple-silicon
  - token-classification
  - pii
  - de-identification
  - privacy-filter
  - multilingual
language:
  - ar
  - bn
  - de
  - en
  - es
  - fr
  - hi
  - it
  - ja
  - ko
  - nl
  - pt
  - te
  - tr
  - vi
  - zh
---

# OpenMed Privacy Filter (Multilingual) — MLX BF16

A native [MLX](https://github.com/maziyarpanahi/openmed/) port of
[`OpenMed/privacy-filter-multilingual`](https://huggingface.co/OpenMed/privacy-filter-multilingual)
for fast, on-device fine-grained PII detection across **54 categories**
and **16 languages** on Apple Silicon.
This BF16 artifact preserves the full source precision; for a smaller / faster sibling, see [`OpenMed/privacy-filter-multilingual-mlx-8bit`](https://huggingface.co/OpenMed/privacy-filter-multilingual-mlx-8bit).

> **Family at a glance.** Same architecture and training data, three runtimes:
> - **PyTorch** — [`OpenMed/privacy-filter-multilingual`](https://huggingface.co/OpenMed/privacy-filter-multilingual) — CPU + CUDA.
> - **MLX BF16** (this repo) — [`OpenMed/privacy-filter-multilingual-mlx`](https://huggingface.co/OpenMed/privacy-filter-multilingual-mlx) — Apple Silicon, full precision (~2.6 GB).
> - **MLX 8-bit** — [`OpenMed/privacy-filter-multilingual-mlx-8bit`](https://huggingface.co/OpenMed/privacy-filter-multilingual-mlx-8bit) — Apple Silicon, ~1.4 GB.

## What it does

The model is a token classifier built on the OpenAI Privacy Filter
architecture (`openai_privacy_filter`). It tags each token with a BIOES
label across **54 PII span classes**, then a Viterbi pass over the BIOES
grammar yields clean entity spans. Languages covered: Arabic, Bengali,
Chinese, Dutch, English, French, German, Hindi, Italian, Japanese,
Korean, Portuguese, Spanish, Telugu, Turkish, Vietnamese.

<details>
<summary>Full label schema (217 BIOES labels)</summary>

The output space is `O` plus `B-`, `I-`, `E-`, `S-` for each of the 54
span classes (4 × 54 + 1 = 217). The runtime `PrivacyFilterMLXPipeline`
runs Viterbi over this BIOES grammar, so the consumer sees clean grouped
entities rather than raw token tags. The full `id2label` mapping is
shipped alongside the weights in this repo.
</details>

For per-label accuracy, training recipe, and dataset details, see the
[base PyTorch checkpoint](https://huggingface.co/OpenMed/privacy-filter-multilingual).

## Architecture

| Field | Value |
| --- | --- |
| Source model type | `openai_privacy_filter` |
| Source architecture | `OpenAIPrivacyFilterForTokenClassification` |
| Hidden size | 640 |
| Transformer layers | 8 |
| Attention | Grouped-Query (14 query heads / 2 KV heads, head_dim=64) with attention sinks |
| FFN | Sparse Mixture-of-Experts — 128 experts, top-4 routing, SwiGLU |
| Position encoding | YARN-scaled RoPE (`rope_theta=150_000`, factor=32) |
| Context length | 131,072 tokens (initial 4,096) |
| Tokenizer | `o200k_base` (tiktoken) — vocab 200,064 |
| Output head | Linear(640 → 217) with bias |

## File set

| File | Size | Purpose |
| --- | --- | --- |
| `weights.safetensors` | ~2.6 GB | Model weights in OpenMed-MLX layout |
| `config.json` | ~19 KB | Model + MLX runtime config |
| `id2label.json` | ~5 KB | Numeric ID → BIOES label string |
| `openmed-mlx.json` | ~1 KB | OpenMed MLX manifest (task, family, runtime hints) |
| `tokenizer.json`, `tokenizer_config.json` | ~28 MB | Source tokenizer files (kept for reference) |

The MLX runtime uses `tiktoken` `o200k_base` directly for tokenization;
the `tokenizer.json` is kept so consumers can inspect or re-tokenize via
`transformers` if desired.

## Label space (54 categories)

| Category | Typical examples |
|---|---|
| **Identity** | `FIRSTNAME`, `MIDDLENAME`, `LASTNAME`, `PREFIX`, `AGE`, `GENDER`, `SEX`, `EYECOLOR`, `HEIGHT`, `USERNAME`, `OCCUPATION`, `JOBTITLE`, `JOBDEPARTMENT`, `ORGANIZATION`, `USERAGENT` |
| **Contact** | `EMAIL`, `PHONE`, `URL` |
| **Address** | `STREET`, `BUILDINGNUMBER`, `SECONDARYADDRESS`, `CITY`, `COUNTY`, `STATE`, `ZIPCODE`, `GPSCOORDINATES`, `ORDINALDIRECTION` |
| **Dates & time** | `DATE`, `DATEOFBIRTH`, `TIME` |
| **Government IDs** | `SSN` |
| **Financial** | `ACCOUNTNAME`, `BANKACCOUNT`, `IBAN`, `BIC`, `CREDITCARD`, `CREDITCARDISSUER`, `CVV`, `PIN`, `MASKEDNUMBER`, `AMOUNT`, `CURRENCY`, `CURRENCYCODE`, `CURRENCYNAME`, `CURRENCYSYMBOL` |
| **Crypto** | `BITCOINADDRESS`, `ETHEREUMADDRESS`, `LITECOINADDRESS` |
| **Vehicle** | `VIN`, `VRM` |
| **Digital** | `IPADDRESS`, `MACADDRESS`, `IMEI` |
| **Auth** | `PASSWORD` |


## Quick start

### With [OpenMed](https://github.com/maziyarpanahi/openmed) — recommended

OpenMed gives you a single `extract_pii()` / `deidentify()` API that
auto-selects MLX on Apple Silicon and PyTorch elsewhere — same code on
every host.

```bash
pip install -U "openmed[mlx]"
```

```python
from openmed import extract_pii, deidentify

text = (
    "Patient Sarah Johnson (DOB 03/15/1985), phone 415-555-0123, email sarah.johnson@example.com."
)

# Extract grouped entity spans (runs on MLX here, PyTorch fallback elsewhere)
result = extract_pii(text, model_name="OpenMed/privacy-filter-multilingual-mlx")
for ent in result.entities:
    print(f"{ent.label:30s} {ent.text!r}  conf={ent.confidence:.2f}")

# De-identify
masked = deidentify(text, method="mask",
                    model_name="OpenMed/privacy-filter-multilingual-mlx")
fake   = deidentify(
    text,
    method="replace",
    model_name="OpenMed/privacy-filter-multilingual-mlx",
    consistent=True,
    seed=42,   # deterministic locale-aware Faker surrogates
)
```

When MLX isn't available (Linux, Windows, Intel Mac, missing `mlx` package),
this exact same call automatically falls back to the PyTorch checkpoint
[`OpenMed/privacy-filter-multilingual`](https://huggingface.co/OpenMed/privacy-filter-multilingual) with a one-time warning. Family-aware fallback: a Multilingual
MLX request never substitutes an unrelated baseline.

### Direct MLX usage (lower-level)

```python
from huggingface_hub import snapshot_download
from openmed.mlx.inference import PrivacyFilterMLXPipeline

model_path = snapshot_download("OpenMed/privacy-filter-multilingual-mlx")
pipe = PrivacyFilterMLXPipeline(model_path)

print(pipe("Email me at alice.smith@example.com after 5pm."))
# [{'entity_group': 'EMAIL',
#   'score': 0.92,
#   'word': 'alice.smith@example.com',
#   'start': 12,
#   'end': 35}]
```

The pipeline returns a list of dicts with `entity_group`, `score`, `word`,
`start`, and `end` (character offsets into the input string).

## Hardware notes

- Designed for Apple Silicon (M-series GPUs); CPU inference works but is slower.
- Tested on macOS with `mlx>=0.18`. The MLX runtime in this repo is
  independent of `mlx_lm` (token classification, not causal LM).
- For lower latency or smaller memory footprint, use the [`-mlx-8bit`](https://huggingface.co/OpenMed/privacy-filter-multilingual-mlx-8bit) sibling instead.

## Credits & Acknowledgements

This artifact wouldn't exist without two open-source releases — sincere
thanks to both teams:

- **OpenAI** for [open-sourcing the Privacy Filter](https://huggingface.co/openai/privacy-filter)
  (architecture, modeling code, and `opf` training/eval CLI). The MLX
  port in this repo runs that same architecture under Apple's MLX
  framework.
- **AI4Privacy** for releasing the multilingual PII masking datasets
  used to fine-tune the source PyTorch checkpoint:
  [`pii-masking-200k`](https://huggingface.co/datasets/ai4privacy/pii-masking-200k),
  [`pii-masking-400k`](https://huggingface.co/datasets/ai4privacy/pii-masking-400k),
  and [`open-pii-masking-500k-ai4privacy`](https://huggingface.co/datasets/ai4privacy/open-pii-masking-500k-ai4privacy).

Additional thanks to **Apple** for [MLX](https://github.com/ml-explore/mlx)
and the **HuggingFace** team for the model-distribution ecosystem.

## License

Apache 2.0.