| --- |
| license: apache-2.0 |
| language: |
| - en |
| - de |
| - fr |
| - it |
| - es |
| - nl |
| - pt |
| - pl |
| - cs |
| - da |
| - fi |
| - sv |
| pipeline_tag: token-classification |
| tags: |
| - pii |
| - ner |
| - privacy |
| - token-classification |
| - transformers |
| - pytorch |
| - safetensors |
| --- |
| |
| # Sarden1: Multilingual PII Detection & Redaction Model |
|
|
| ## Model Description |
|
|
| Sarden1 is a high-performance token classification model built from scratch for |
| personally identifiable information (PII) detection and redaction. It identifies and |
| labels sensitive entity spans in text across 15 locales, making it suitable for |
| GDPR/HIPAA compliance pipelines, log scrubbing, and document redaction at production scale. |
|
|
| * **Developed by:** Surpem |
| * **Model Type:** Token Classifier (BIO tagging) |
| * **Architecture:** Custom Decoder-style Transformer |
| * **Base Model:** Trained from scratch — no pretrained base |
| * **License:** Apache 2.0 |
| * **Languages:** en, de, fr, it, es, nl, pt, pl, cs, da, fi, sv (+ en_GB, en_CA, en_AU) |
| |
| ## Architecture |
| |
| | Component | Detail | |
| | :--- | :--- | |
| | Parameters | ~300M | |
| | Layers | 18 transformer layers | |
| | Hidden size | 1024 | |
| | Attention | Grouped Query Attention (16Q / 4KV heads) | |
| | FFN | SwiGLU (2730 intermediate) | |
| | Positional encoding | RoPE (θ = 500,000) | |
| | Normalisation | RMSNorm (no bias) | |
| | Tokeniser | GPT-2 BPE (vocab 50,257) | |
| | Precision | bfloat16 | |
| |
| ## Entity Types |
| |
| Sarden1 detects 12 PII entity types using BIO span labelling: |
| |
| | Category | Entity Types | |
| | :--- | :--- | |
| | Identity | `PERSON`, `USERNAME`, `DATE` | |
| | Contact | `EMAIL`, `PHONE`, `ADDRESS` | |
| | Financial | `CREDITCARD`, `SSN` | |
| | Documents | `PASSPORT`, `DRIVERSLICENSE` | |
| | Technical | `IP` | |
| | Organisational | `ORG` | |
| |
| ## Get Started |
| |
| ```python |
| import json, torch |
| from safetensors.torch import load_file |
| from transformers import AutoTokenizer |
|
|
| # Load weights and config |
| sd = load_file("model.safetensors") |
| cfg = json.load(open("config.json")) |
| id2label = {int(k): v for k, v in cfg["id2label"].items()} |
| |
| # Load tokeniser |
| tok = AutoTokenizer.from_pretrained(".") |
|
|
| # (Rebuild model from architecture, then:) |
| model.load_state_dict(sd) |
| model.eval() |
|
|
| # Inference |
| text = "Hi, I'm Jane Smith. Reach me at jane@example.com or 555-1234." |
| enc = tok(text, return_offsets_mapping=True, return_tensors="pt") |
| with torch.no_grad(): |
| logits = model(enc["input_ids"])["logits"] |
| |
| preds = logits.argmax(-1)[0].tolist() |
| offsets = enc["offset_mapping"][0].tolist() |
|
|
| for pred, (cs, ce) in zip(preds, offsets): |
| if cs != ce and id2label.get(pred, "O") != "O": |
| print(f"{id2label[pred]:<20} {repr(text[cs:ce])}") |
| ``` |
| |
| Example output: |
| ``` |
| PERSON 'Jane Smith' |
| EMAIL 'jane@example.com' |
| PHONE '555-1234' |
| ``` |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{surpem2026sarden1, |
| title = {Sarden1-300M: Multilingual PII Detection \& Redaction Model}, |
| author = {Surpem}, |
| year = {2026}, |
| url = {https://huggingface.co/surpem/sarden1-300m}, |
| } |
| ``` |
