Add README with PII token-classification usage example

README.md

@@ -0,0 +1,44 @@
+---
+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-mxfp8
+
+The Model [mlx-community/openai-privacy-filter-mxfp8](https://huggingface.co/mlx-community/openai-privacy-filter-mxfp8) 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-mxfp8")
+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}")
+```