Initial upload: privacy-filter fine-tuned for 55 Nemotron-PII labels

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+license: apache-2.0
+library_name: transformers
+base_model: openai/privacy-filter
+datasets:
+  - nvidia/Nemotron-PII
+pipeline_tag: token-classification
+tags:
+  - token-classification
+  - pii
+  - ner
+  - privacy
+  - redaction
+  - nemotron
+  - privacy-filter
+language:
+  - en
+---
+
+# privacy-filter-nemotron-55
+
+Fine-tuned [openai/privacy-filter](https://huggingface.co/openai/privacy-filter)
+for **fine-grained PII extraction** across **55 categories** from
+[nvidia/Nemotron-PII](https://huggingface.co/datasets/nvidia/Nemotron-PII).
+
+- **Base model**: [`openai/privacy-filter`](https://huggingface.co/openai/privacy-filter) — 1.4B-parameter MoE (50M active per token), BIOES token-classification head
+- **Task**: Token classification for PII detection (BIOES scheme)
+- **Training data**: Full 100K rows of `nvidia/Nemotron-PII` train split
+- **Held-out val**: 10K label-stratified rows from the Nemotron `test` split (every label has ≥229 entities)
+- **Recipe**: `opf train` (OpenAI's official fine-tuning CLI) — full fine-tune, AdamW, lr=1e-4, 5 epochs, bf16, weight decay 0.0
+- **Labels**: 55 fine-grained PII categories → 221 BIOES classes (1 `O` + 55 × B/I/E/S)
+
+The base model ships with 8 coarse PII categories (`private_person`,
+`private_email`, etc.). This model trades that coarse vocabulary for a
+**5× more granular one** — `first_name`, `last_name`, `medical_record_number`,
+`credit_debit_card`, `ssn`, and so on — matching what downstream redaction
+and masking pipelines typically need.
+
+## Quick start
+
+### With `opf` (official CLI from OpenAI, recommended)
+
+```bash
+pip install 'opf @ git+https://github.com/openai/privacy-filter.git'
+
+opf redact \
+  --checkpoint OpenMed/privacy-filter-nemotron-55 \
+  --text "Patient Sarah Johnson (DOB 03/15/1985), MRN 4872910, phone 415-555-0123."
+```
+
+### With `transformers`
+
+```python
+import torch
+from transformers import AutoModelForTokenClassification, AutoTokenizer
+
+model_id = "OpenMed/privacy-filter-nemotron-55"
+tok = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForTokenClassification.from_pretrained(
+    model_id, trust_remote_code=True, dtype=torch.bfloat16
+).to("cuda")
+model.eval()
+
+text = "Patient Sarah Johnson (DOB 03/15/1985), MRN 4872910, phone 415-555-0123."
+enc = tok(text, return_tensors="pt").to("cuda")
+with torch.no_grad():
+    out = model(**enc).logits.argmax(-1).cpu()[0].tolist()
+
+id2label = {int(k): v for k, v in model.config.id2label.items()}
+tokens = tok.convert_ids_to_tokens(enc["input_ids"][0].cpu().tolist())
+for t, l in zip(tokens, out):
+    if l != 0:
+        print(f"{t}\t{id2label[l]}")
+```
+
+For best results use Viterbi decoding (not argmax) — the `opf` CLI does this by
+default. If you're doing argmax with the HF transformers API, you'll see
+slightly more boundary errors but still excellent label accuracy.
+
+## Performance
+
+Evaluated with `opf eval --decode-mode viterbi --eval-mode typed --span-metrics-space char`
+on the 10K label-stratified held-out val from `nvidia/Nemotron-PII:test`.
+
+### Headline
+
+| Metric | Value |
+|---|---:|
+| **Macro B-F1** (across 55 labels) | **0.9533** |
+| **Token accuracy** | **0.9910** |
+| Strong labels (F1 ≥ 0.90) | 46 / 55 |
+| Acceptable (F1 0.70–0.89) | 7 / 55 |
+| Weak (F1 < 0.70) | 0 / 55 |
+
+### Per-label F1 (B-tag, sorted)
+
+| Label | Precision | Recall | F1 |
+|---|---:|---:|---:|
+| 🟢 `mac_address` | 1.000 | 1.000 | **1.000** |
+| 🟢 `biometric_identifier` | 0.999 | 0.998 | **0.999** |
+| 🟢 `bank_routing_number` | 0.995 | 0.999 | **0.997** |
+| 🟢 `credit_debit_card` | 0.999 | 0.993 | **0.996** |
+| 🟢 `ipv6` | 0.992 | 1.000 | **0.996** |
+| 🟢 `health_plan_beneficiary_number` | 1.000 | 0.990 | **0.995** |
+| 🟢 `coordinate` | 0.994 | 0.996 | **0.995** |
+| 🟢 `ipv4` | 0.993 | 0.996 | **0.994** |
+| 🟢 `url` | 0.989 | 0.999 | **0.994** |
+| 🟢 `email` | 0.994 | 0.993 | **0.994** |
+| 🟢 `date_of_birth` | 0.992 | 0.994 | **0.993** |
+| 🟢 `medical_record_number` | 0.997 | 0.989 | **0.993** |
+| 🟢 `street_address` | 0.996 | 0.989 | **0.993** |
+| 🟢 `vehicle_identifier` | 0.986 | 0.996 | **0.991** |
+| 🟢 `license_plate` | 0.987 | 0.993 | **0.990** |
+| 🟢 `customer_id` | 0.995 | 0.984 | **0.990** |
+| 🟢 `http_cookie` | 0.992 | 0.983 | **0.988** |
+| 🟢 `employee_id` | 0.987 | 0.988 | **0.988** |
+| 🟢 `account_number` | 0.992 | 0.982 | **0.987** |
+| 🟢 `certificate_license_number` | 0.989 | 0.984 | **0.987** |
+| 🟢 `swift_bic` | 0.975 | 0.998 | **0.987** |
+| 🟢 `postcode` | 0.991 | 0.981 | **0.986** |
+| 🟢 `api_key` | 0.980 | 0.990 | **0.985** |
+| 🟢 `password` | 0.999 | 0.968 | **0.983** |
+| 🟢 `tax_id` | 1.000 | 0.965 | **0.982** |
+| 🟢 `device_identifier` | 0.974 | 0.988 | **0.981** |
+| 🟢 `national_id` | 0.991 | 0.961 | **0.976** |
+| 🟢 `last_name` | 0.977 | 0.975 | **0.976** |
+| 🟢 `date_time` | 0.982 | 0.967 | **0.974** |
+| 🟢 `first_name` | 0.962 | 0.978 | **0.970** |
+| 🟢 `pin` | 0.973 | 0.967 | **0.970** |
+| 🟢 `phone_number` | 0.948 | 0.992 | **0.970** |
+| 🟢 `county` | 0.986 | 0.946 | **0.965** |
+| 🟢 `employment_status` | 0.960 | 0.968 | **0.964** |
+| 🟢 `user_name` | 0.959 | 0.964 | **0.961** |
+| 🟢 `date` | 0.967 | 0.955 | **0.961** |
+| 🟢 `blood_type` | 0.922 | 0.954 | **0.938** |
+| 🟢 `country` | 0.955 | 0.918 | **0.936** |
+| 🟢 `ssn` | 0.926 | 0.945 | **0.935** |
+| 🟢 `education_level` | 0.961 | 0.908 | **0.934** |
+| 🟢 `sexuality` | 0.908 | 0.956 | **0.931** |
+| 🟢 `company_name` | 0.967 | 0.894 | **0.929** |
+| 🟢 `religious_belief` | 0.912 | 0.941 | **0.926** |
+| 🟢 `unique_id` | 0.910 | 0.922 | **0.916** |
+| 🟢 `political_view` | 0.939 | 0.872 | **0.905** |
+| 🟢 `fax_number` | 0.978 | 0.841 | **0.904** |
+| 🟡 `city` | 0.917 | 0.876 | **0.896** |
+| 🟡 `time` | 0.933 | 0.802 | **0.863** |
+| 🟡 `race_ethnicity` | 0.821 | 0.906 | **0.861** |
+| 🟡 `gender` | 0.967 | 0.744 | **0.841** |
+| 🟡 `state` | 0.878 | 0.785 | **0.829** |
+| 🟡 `language` | 0.889 | 0.735 | **0.804** |
+| 🟡 `occupation` | 0.799 | 0.667 | **0.727** |
+
+## Label space (55 categories)
+
+| Category | Typical examples |
+|---|---|
+| **Identity** | `first_name`, `last_name`, `user_name`, `age`, `gender`, `race_ethnicity`, `sexuality`, `religious_belief`, `political_view`, `marital_status`, `nationality`, `education_level`, `occupation`, `employment_status`, `language`, `blood_type`, `biometric_identifier` |
+| **Contact** | `email`, `phone_number`, `fax_number`, `url` |
+| **Address** | `street_address`, `city`, `county`, `state`, `country`, `postcode`, `coordinate` |
+| **Dates** | `date`, `date_of_birth`, `date_time`, `time` |
+| **Government IDs** | `ssn`, `national_id`, `tax_id` |
+| **Financial** | `account_number`, `bank_routing_number`, `swift_bic`, `credit_debit_card`, `cvv`, `pin`, `password` |
+| **Healthcare** | `medical_record_number`, `health_plan_beneficiary_number` |
+| **Enterprise IDs** | `customer_id`, `employee_id`, `unique_id`, `certificate_license_number` |
+| **Vehicle** | `license_plate`, `vehicle_identifier` |
+| **Digital** | `ipv4`, `ipv6`, `mac_address`, `device_identifier`, `api_key`, `http_cookie` |
+
+## Training details
+
+| Hyperparameter | Value |
+|---|---|
+| Optimizer | AdamW |
+| Learning rate | 1e-4 |
+| Weight decay | 0.0 |
+| Batch size (per GPU) | 4 |
+| Gradient accumulation | 1 |
+| Max grad norm | 1.0 |
+| Epochs | 5 |
+| Precision | bf16 |
+| Context length | 128K (YaRN RoPE, 128-token sliding window) |
+| Hardware | 1× NVIDIA A100 80GB |
+| Total optimizer steps | 125,000 |
+| Framework | `opf train` v0.1.0 |
+
+**Head initialization**: `opf`'s default "copy-from-matching-base" head init.
+Of the 221 new BIOES classes, 5 had exact matches in the base
+(`O`, `B/I/E/S-account_number`); the other 216 were copied from
+semantically-adjacent coarse rows and fine-tuned end-to-end.
+
+**Router**: base model has 128 MoE experts per layer with top-4 routing.
+Routers were kept trainable during full fine-tuning; no collapse was
+observed.
+
+## Limitations & intended use
+
+- **English-only training data.** Nemotron-PII is predominantly English
+  with a 50/50 US/international locale split. Performance on non-English
+  text is not guaranteed.
+- **`occupation`, `language`, `gender`, `state`, `race_ethnicity`,
+  `political_view`, `education_level` are fuzzier categories** than the
+  strict identifiers — F1 lands in 0.65–0.89 vs 0.95+ for formatted
+  identifiers. If your downstream only cares about strict PII, you can
+  ignore low-confidence predictions on these.
+- **Synthetic training data.** Nemotron-PII is a synthesized dataset; real
+  clinical notes, legal documents, and web text may show different
+  surface forms. For high-stakes deployments, collect a domain-specific
+  eval set and re-calibrate thresholds.
+- **Not a substitute for legal compliance review.** Use alongside a
+  governance layer (human review, deterministic regex pre-filters, etc.).
+
+## License
+
+Apache 2.0, same as the base model.
+
+## Citation
+
+If you use this model, please cite both the base model and the dataset:
+
+```bibtex
+@misc{openai_privacy_filter_2025,
+  author = {OpenAI},
+  title  = {{openai/privacy-filter}},
+  year   = {2025},
+  howpublished = {\url{https://huggingface.co/openai/privacy-filter}}
+}
+
+@misc{nemotron_pii_2025,
+  author = {NVIDIA},
+  title  = {{Nemotron-PII}},
+  year   = {2025},
+  howpublished = {\url{https://huggingface.co/datasets/nvidia/Nemotron-PII}}
+}
+```

config.json

@@ -0,0 +1,783 @@
+{
+  "architectures": [
+    "OpenAIPrivacyFilterForTokenClassification"
+  ],
+  "attention_bias": true,
+  "attention_dropout": 0.0,
+  "bos_token_id": null,
+  "classifier_dropout": 0.0,
+  "default_n_ctx": 128000,
+  "dtype": "bfloat16",
+  "eos_token_id": 199999,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 640,
+  "id2label": {
+    "0": "O",
+    "1": "B-account_number",
+    "2": "I-account_number",
+    "3": "E-account_number",
+    "4": "S-account_number",
+    "5": "B-age",
+    "6": "I-age",
+    "7": "E-age",
+    "8": "S-age",
+    "9": "B-api_key",
+    "10": "I-api_key",
+    "11": "E-api_key",
+    "12": "S-api_key",
+    "13": "B-bank_routing_number",
+    "14": "I-bank_routing_number",
+    "15": "E-bank_routing_number",
+    "16": "S-bank_routing_number",
+    "17": "B-biometric_identifier",
+    "18": "I-biometric_identifier",
+    "19": "E-biometric_identifier",
+    "20": "S-biometric_identifier",
+    "21": "B-blood_type",
+    "22": "I-blood_type",
+    "23": "E-blood_type",
+    "24": "S-blood_type",
+    "25": "B-certificate_license_number",
+    "26": "I-certificate_license_number",
+    "27": "E-certificate_license_number",
+    "28": "S-certificate_license_number",
+    "29": "B-city",
+    "30": "I-city",
+    "31": "E-city",
+    "32": "S-city",
+    "33": "B-company_name",
+    "34": "I-company_name",
+    "35": "E-company_name",
+    "36": "S-company_name",
+    "37": "B-coordinate",
+    "38": "I-coordinate",
+    "39": "E-coordinate",
+    "40": "S-coordinate",
+    "41": "B-country",
+    "42": "I-country",
+    "43": "E-country",
+    "44": "S-country",
+    "45": "B-county",
+    "46": "I-county",
+    "47": "E-county",
+    "48": "S-county",
+    "49": "B-credit_debit_card",
+    "50": "I-credit_debit_card",
+    "51": "E-credit_debit_card",
+    "52": "S-credit_debit_card",
+    "53": "B-customer_id",
+    "54": "I-customer_id",
+    "55": "E-customer_id",
+    "56": "S-customer_id",
+    "57": "B-cvv",
+    "58": "I-cvv",
+    "59": "E-cvv",
+    "60": "S-cvv",
+    "61": "B-date",
+    "62": "I-date",
+    "63": "E-date",
+    "64": "S-date",
+    "65": "B-date_of_birth",
+    "66": "I-date_of_birth",
+    "67": "E-date_of_birth",
+    "68": "S-date_of_birth",
+    "69": "B-date_time",
+    "70": "I-date_time",
+    "71": "E-date_time",
+    "72": "S-date_time",
+    "73": "B-device_identifier",
+    "74": "I-device_identifier",
+    "75": "E-device_identifier",
+    "76": "S-device_identifier",
+    "77": "B-education_level",
+    "78": "I-education_level",
+    "79": "E-education_level",
+    "80": "S-education_level",
+    "81": "B-email",
+    "82": "I-email",
+    "83": "E-email",
+    "84": "S-email",
+    "85": "B-employee_id",
+    "86": "I-employee_id",
+    "87": "E-employee_id",
+    "88": "S-employee_id",
+    "89": "B-employment_status",
+    "90": "I-employment_status",
+    "91": "E-employment_status",
+    "92": "S-employment_status",
+    "93": "B-fax_number",
+    "94": "I-fax_number",
+    "95": "E-fax_number",
+    "96": "S-fax_number",
+    "97": "B-first_name",
+    "98": "I-first_name",
+    "99": "E-first_name",
+    "100": "S-first_name",
+    "101": "B-gender",
+    "102": "I-gender",
+    "103": "E-gender",
+    "104": "S-gender",
+    "105": "B-health_plan_beneficiary_number",
+    "106": "I-health_plan_beneficiary_number",
+    "107": "E-health_plan_beneficiary_number",
+    "108": "S-health_plan_beneficiary_number",
+    "109": "B-http_cookie",
+    "110": "I-http_cookie",
+    "111": "E-http_cookie",
+    "112": "S-http_cookie",
+    "113": "B-ipv4",
+    "114": "I-ipv4",
+    "115": "E-ipv4",
+    "116": "S-ipv4",
+    "117": "B-ipv6",
+    "118": "I-ipv6",
+    "119": "E-ipv6",
+    "120": "S-ipv6",
+    "121": "B-language",
+    "122": "I-language",
+    "123": "E-language",
+    "124": "S-language",
+    "125": "B-last_name",
+    "126": "I-last_name",
+    "127": "E-last_name",
+    "128": "S-last_name",
+    "129": "B-license_plate",
+    "130": "I-license_plate",
+    "131": "E-license_plate",
+    "132": "S-license_plate",
+    "133": "B-mac_address",
+    "134": "I-mac_address",
+    "135": "E-mac_address",
+    "136": "S-mac_address",
+    "137": "B-medical_record_number",
+    "138": "I-medical_record_number",
+    "139": "E-medical_record_number",
+    "140": "S-medical_record_number",
+    "141": "B-national_id",
+    "142": "I-national_id",
+    "143": "E-national_id",
+    "144": "S-national_id",
+    "145": "B-occupation",
+    "146": "I-occupation",
+    "147": "E-occupation",
+    "148": "S-occupation",
+    "149": "B-password",
+    "150": "I-password",
+    "151": "E-password",
+    "152": "S-password",
+    "153": "B-phone_number",
+    "154": "I-phone_number",
+    "155": "E-phone_number",
+    "156": "S-phone_number",
+    "157": "B-pin",
+    "158": "I-pin",
+    "159": "E-pin",
+    "160": "S-pin",
+    "161": "B-political_view",
+    "162": "I-political_view",
+    "163": "E-political_view",
+    "164": "S-political_view",
+    "165": "B-postcode",
+    "166": "I-postcode",
+    "167": "E-postcode",
+    "168": "S-postcode",
+    "169": "B-race_ethnicity",
+    "170": "I-race_ethnicity",
+    "171": "E-race_ethnicity",
+    "172": "S-race_ethnicity",
+    "173": "B-religious_belief",
+    "174": "I-religious_belief",
+    "175": "E-religious_belief",
+    "176": "S-religious_belief",
+    "177": "B-sexuality",
+    "178": "I-sexuality",
+    "179": "E-sexuality",
+    "180": "S-sexuality",
+    "181": "B-ssn",
+    "182": "I-ssn",
+    "183": "E-ssn",
+    "184": "S-ssn",
+    "185": "B-state",
+    "186": "I-state",
+    "187": "E-state",
+    "188": "S-state",
+    "189": "B-street_address",
+    "190": "I-street_address",
+    "191": "E-street_address",
+    "192": "S-street_address",
+    "193": "B-swift_bic",
+    "194": "I-swift_bic",
+    "195": "E-swift_bic",
+    "196": "S-swift_bic",
+    "197": "B-tax_id",
+    "198": "I-tax_id",
+    "199": "E-tax_id",
+    "200": "S-tax_id",
+    "201": "B-time",
+    "202": "I-time",
+    "203": "E-time",
+    "204": "S-time",
+    "205": "B-unique_id",
+    "206": "I-unique_id",
+    "207": "E-unique_id",
+    "208": "S-unique_id",
+    "209": "B-url",
+    "210": "I-url",
+    "211": "E-url",
+    "212": "S-url",
+    "213": "B-user_name",
+    "214": "I-user_name",
+    "215": "E-user_name",
+    "216": "S-user_name",
+    "217": "B-vehicle_identifier",
+    "218": "I-vehicle_identifier",
+    "219": "E-vehicle_identifier",
+    "220": "S-vehicle_identifier"
+  },
+  "initial_context_length": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 640,
+  "label2id": {
+    "O": 0,
+    "B-account_number": 1,
+    "I-account_number": 2,
+    "E-account_number": 3,
+    "S-account_number": 4,
+    "B-age": 5,
+    "I-age": 6,
+    "E-age": 7,
+    "S-age": 8,
+    "B-api_key": 9,
+    "I-api_key": 10,
+    "E-api_key": 11,
+    "S-api_key": 12,
+    "B-bank_routing_number": 13,
+    "I-bank_routing_number": 14,
+    "E-bank_routing_number": 15,
+    "S-bank_routing_number": 16,
+    "B-biometric_identifier": 17,
+    "I-biometric_identifier": 18,
+    "E-biometric_identifier": 19,
+    "S-biometric_identifier": 20,
+    "B-blood_type": 21,
+    "I-blood_type": 22,
+    "E-blood_type": 23,
+    "S-blood_type": 24,
+    "B-certificate_license_number": 25,
+    "I-certificate_license_number": 26,
+    "E-certificate_license_number": 27,
+    "S-certificate_license_number": 28,
+    "B-city": 29,
+    "I-city": 30,
+    "E-city": 31,
+    "S-city": 32,
+    "B-company_name": 33,
+    "I-company_name": 34,
+    "E-company_name": 35,
+    "S-company_name": 36,
+    "B-coordinate": 37,
+    "I-coordinate": 38,
+    "E-coordinate": 39,
+    "S-coordinate": 40,
+    "B-country": 41,
+    "I-country": 42,
+    "E-country": 43,
+    "S-country": 44,
+    "B-county": 45,
+    "I-county": 46,
+    "E-county": 47,
+    "S-county": 48,
+    "B-credit_debit_card": 49,
+    "I-credit_debit_card": 50,
+    "E-credit_debit_card": 51,
+    "S-credit_debit_card": 52,
+    "B-customer_id": 53,
+    "I-customer_id": 54,
+    "E-customer_id": 55,
+    "S-customer_id": 56,
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