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license: apache-2.0
pipeline_tag: token-classification
library_name: mlx
base_model: openai/privacy-filter
tags:
- transformers.js
- mlx
- mlx-embeddings
---
# mlx-community/openai-privacy-filter-6bit
The Model [mlx-community/openai-privacy-filter-6bit](https://huggingface.co/mlx-community/openai-privacy-filter-6bit) was converted to MLX format from [openai/privacy-filter](https://huggingface.co/openai/privacy-filter) using mlx-embeddings version **0.1.1**.
`openai/privacy-filter` is a bidirectional 1.5B-parameter / 50M-active sparse-MoE token classifier that tags personally identifiable information (PII) with BIOES spans over 8 categories (person, email, phone, URL, address, date, account number, secret).
## Use with mlx
```bash
pip install mlx-embeddings
```
```python
from itertools import groupby
import mlx.core as mx
from mlx_embeddings.utils import load
model, tokenizer = load("mlx-community/openai-privacy-filter-6bit")
id2label = model.config.id2label
text = "My name is Alice Smith and my email is alice@example.com. Phone: 555-1234."
inputs = tokenizer(text, return_tensors="mlx")
outputs = model(inputs["input_ids"], attention_mask=inputs["attention_mask"])
preds = mx.argmax(outputs.logits, axis=-1)[0].tolist()
entity = lambda p: id2label[str(p)].split("-", 1)[-1] if id2label[str(p)] != "O" else None
for ent, group in groupby(zip(inputs["input_ids"][0].tolist(), preds), key=lambda x: entity(x[1])):
if ent:
span = tokenizer.decode([tid for tid, _ in group]).strip()
print(f"{ent:18s} -> {span!r}")
```
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