Upload clinical NER model for PHI/PII de-identification

README.md

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+---
+license: apache-2.0
+language:
+  - en
+library_name: transformers
+tags:
+  - token-classification
+  - ner
+  - clinical
+  - phi
+  - pii
+  - de-identification
+  - medical
+  - genzeon-platform
+  - cliniguard
+  - healthcare
+pipeline_tag: token-classification
+model-index:
+  - name: cliniguard-ner
+    results:
+      - task:
+          type: token-classification
+          name: Named Entity Recognition
+        metrics:
+          - type: f1
+            value: 0.9695
+            name: Micro F1
+          - type: precision
+            value: 0.9659
+            name: Micro Precision
+          - type: recall
+            value: 0.9732
+            name: Micro Recall
+---
+
+# CliniGuard NER -- PHI/PII De-identification by Genzeon Platform
+
+**CliniGuard NER** is a clinical Named Entity Recognition model developed by
+[Genzeon Platform](https://genzeon.one/) for automated detection and
+de-identification of Protected Health Information (PHI) and Personally
+Identifiable Information (PII) in clinical text. Built on a domain-specialized
+BERT architecture fine-tuned on healthcare corpora, CliniGuard delivers
+production-grade entity recognition across 20 PHI categories.
+
+## Model Details
+
+| Property | Value |
+|---|---|
+| Developed by | **Genzeon Platform** |
+| Architecture | BertForTokenClassification |
+| Parameters | ~110M |
+| Tagging scheme | BIO (41 labels) |
+| Max sequence length | 512 tokens |
+| License | Apache-2.0 |
+
+## Intended Use
+
+CliniGuard NER is designed for enterprise healthcare environments where patient
+data privacy is critical. Primary use cases include:
+
+- **Clinical text de-identification** -- removing or masking patient identifiers
+  before sharing medical records for research.
+- **PII detection** -- flagging sensitive information in healthcare documents,
+  EHRs, and discharge summaries.
+- **Regulatory compliance** -- supporting HIPAA Safe Harbor de-identification
+  requirements.
+- **Healthcare AI pipelines** -- preprocessing clinical text for downstream NLP
+  tasks while ensuring patient privacy.
+
+## Entity Types
+
+The model recognizes **20 PHI entity types** using BIO tagging (41 labels total):
+
+| Category | Entity Types |
+|---|---|
+| Patient identifiers | PATIENT_NAME, DATE_OF_BIRTH, AGE, GENDER, SSN, MRN |
+| Contact information | PHONE, FAX, EMAIL |
+| Location | ADDRESS, CITY, STATE, ZIP, COUNTRY |
+| Organization | HOSPITAL |
+| Provider | DOCTOR_NAME |
+| Digital identifiers | USERNAME, ID_NUMBER, IP_ADDRESS, URL |
+
+## Performance
+
+### Overall Metrics
+
+| Metric | Precision | Recall | F1 |
+|---|---|---|---|
+| **Micro avg** | 0.9659 | 0.9732 | 0.9695 |
+| Macro avg | 0.9609 | 0.9706 | 0.9656 |
+
+### Per-Entity Metrics
+
+| Entity | Precision | Recall | F1 | Support |
+|---|---|---|---|---|
+| PATIENT_NAME | 0.9817 | 0.9853 | 0.9835 | 14335 |
+| DATE_OF_BIRTH | 0.9798 | 0.9740 | 0.9769 | 9818 |
+| AGE | 0.9028 | 0.9854 | 0.9423 | 1508 |
+| GENDER | 0.9596 | 0.9885 | 0.9738 | 1562 |
+| SSN | 0.9513 | 0.9935 | 0.9719 | 766 |
+| MRN | 0.9938 | 0.9923 | 0.9930 | 1943 |
+| PHONE | 0.9730 | 0.9869 | 0.9799 | 2590 |
+| FAX | 0.9481 | 0.9454 | 0.9468 | 696 |
+| EMAIL | 0.9965 | 0.9936 | 0.9950 | 4543 |
+| ADDRESS | 0.9746 | 0.9844 | 0.9794 | 1985 |
+| CITY | 0.9086 | 0.8891 | 0.8988 | 2047 |
+| STATE | 0.9103 | 0.9060 | 0.9082 | 2734 |
+| ZIP | 0.9770 | 0.9832 | 0.9801 | 951 |
+| COUNTRY | 0.9485 | 0.9504 | 0.9495 | 2056 |
+| HOSPITAL | 0.9033 | 0.9345 | 0.9186 | 5267 |
+| DOCTOR_NAME | 0.9865 | 1.0000 | 0.9932 | 802 |
+| USERNAME | 0.9689 | 0.9431 | 0.9559 | 1917 |
+| ID_NUMBER | 0.9724 | 0.9898 | 0.9811 | 8555 |
+| IP_ADDRESS | 0.9892 | 0.9924 | 0.9908 | 926 |
+| URL | 0.9910 | 0.9947 | 0.9928 | 3001 |
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
+
+model_name = "genzeonplatform/cliniguard-ner"
+
+# Option 1: Use the transformers pipeline
+nlp = pipeline("token-classification", model=model_name, aggregation_strategy="simple")
+text = "Patient John Smith, DOB 03/15/1960, was seen at Springfield General Hospital by Dr. Jane Doe."
+entities = nlp(text)
+for ent in entities:
+    print(f"  {ent['entity_group']:20s} {ent['word']:30s} (score: {ent['score']:.3f})")
+
+# Option 2: Manual inference
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForTokenClassification.from_pretrained(model_name)
+
+import torch
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+with torch.no_grad():
+    outputs = model(**inputs)
+predictions = torch.argmax(outputs.logits, dim=2)
+tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])
+for token, pred in zip(tokens, predictions[0]):
+    label = model.config.id2label[str(pred.item())]
+    if label != "O":
+        print(f"  {token:20s} -> {label}")
+```
+
+## Training Details
+
+- **Developed by**: Genzeon Platform
+- **Architecture**: Domain-specialized BERT fine-tuned on clinical corpora
+- **Training data**: Genzeon Platform's proprietary clinical NER dataset with diverse healthcare note formats
+- **Epochs**: 15 (with early stopping, patience=3)
+- **Learning rate**: 3e-5 (linear schedule with warmup)
+- **Batch size**: 16 (train) / 32 (eval)
+- **Max sequence length**: 512 tokens
+- **Optimizer**: AdamW (weight decay 0.01)
+
+## Limitations
+
+- **English only**: Currently optimized for English clinical text. Multilingual
+  support is on the Genzeon Platform roadmap.
+- **Recommended with human-in-the-loop**: For high-stakes de-identification
+  workflows, Genzeon Platform recommends pairing CliniGuard with human review for
+  maximum safety.
+- **Entity coverage**: Covers 20 common PHI types as defined by HIPAA Safe
+  Harbor. Rare or domain-specific identifiers may require custom fine-tuning --
+  contact Genzeon Platform for enterprise support.
+- **Context window**: Limited to 512 tokens per input. Longer documents should
+  be chunked with overlap for best results.
+
+## About Genzeon Platform
+
+[Genzeon Platform](https://genzeon.one/) is a healthcare technology
+company specializing in AI-powered solutions for clinical data management,
+regulatory compliance, and healthcare interoperability. CliniGuard NER is part
+of Genzeon Platform's suite of healthcare AI tools designed to accelerate clinical
+research while safeguarding patient privacy.
+
+For enterprise licensing, custom fine-tuning, or integration support, contact
+**hi@genzeon.one**.

config.json

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+{
+  "add_cross_attention": false,
+  "architectures": [
+    "BertForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": null,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "O",
+    "1": "B-PATIENT_NAME",
+    "10": "I-SSN",
+    "11": "B-MRN",
+    "12": "I-MRN",
+    "13": "B-PHONE",
+    "14": "I-PHONE",
+    "15": "B-FAX",
+    "16": "I-FAX",
+    "17": "B-EMAIL",
+    "18": "I-EMAIL",
+    "19": "B-ADDRESS",
+    "2": "I-PATIENT_NAME",
+    "20": "I-ADDRESS",
+    "21": "B-CITY",
+    "22": "I-CITY",
+    "23": "B-STATE",
+    "24": "I-STATE",
+    "25": "B-ZIP",
+    "26": "I-ZIP",
+    "27": "B-COUNTRY",
+    "28": "I-COUNTRY",
+    "29": "B-HOSPITAL",
+    "3": "B-DATE_OF_BIRTH",
+    "30": "I-HOSPITAL",
+    "31": "B-DOCTOR_NAME",
+    "32": "I-DOCTOR_NAME",
+    "33": "B-USERNAME",
+    "34": "I-USERNAME",
+    "35": "B-ID_NUMBER",
+    "36": "I-ID_NUMBER",
+    "37": "B-IP_ADDRESS",
+    "38": "I-IP_ADDRESS",
+    "39": "B-URL",
+    "4": "I-DATE_OF_BIRTH",
+    "40": "I-URL",
+    "5": "B-AGE",
+    "6": "I-AGE",
+    "7": "B-GENDER",
+    "8": "I-GENDER",
+    "9": "B-SSN"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "is_decoder": false,
+  "label2id": {
+    "B-ADDRESS": 19,
+    "B-AGE": 5,
+    "B-CITY": 21,
+    "B-COUNTRY": 27,
+    "B-DATE_OF_BIRTH": 3,
+    "B-DOCTOR_NAME": 31,
+    "B-EMAIL": 17,
+    "B-FAX": 15,
+    "B-GENDER": 7,
+    "B-HOSPITAL": 29,
+    "B-ID_NUMBER": 35,
+    "B-IP_ADDRESS": 37,
+    "B-MRN": 11,
+    "B-PATIENT_NAME": 1,
+    "B-PHONE": 13,
+    "B-SSN": 9,
+    "B-STATE": 23,
+    "B-URL": 39,
+    "B-USERNAME": 33,
+    "B-ZIP": 25,
+    "I-ADDRESS": 20,
+    "I-AGE": 6,
+    "I-CITY": 22,
+    "I-COUNTRY": 28,
+    "I-DATE_OF_BIRTH": 4,
+    "I-DOCTOR_NAME": 32,
+    "I-EMAIL": 18,
+    "I-FAX": 16,
+    "I-GENDER": 8,
+    "I-HOSPITAL": 30,
+    "I-ID_NUMBER": 36,
+    "I-IP_ADDRESS": 38,
+    "I-MRN": 12,
+    "I-PATIENT_NAME": 2,
+    "I-PHONE": 14,
+    "I-SSN": 10,
+    "I-STATE": 24,
+    "I-URL": 40,
+    "I-USERNAME": 34,
+    "I-ZIP": 26,
+    "O": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.3.0",
+  "type_vocab_size": 2,
+  "use_cache": false,
+  "vocab_size": 28996
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f33aa459a32c0afae05d89d43617ac19d81561e77c4cb843b453ed33dba8916b
+size 431028148

tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "is_local": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}