t4nishq

phi-redactor-qwen3-1.7b

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Results

Held-out synthetic test (1,590 docs incl. adversarial informal slices; 250-doc sample): 0.991 entity recall, 0.40% char leak rate (with the project repo's regex safety net).
Hand-written out-of-distribution set (24 adversarial docs): 3.7% char leak standard; 0.54% with the ensemble "paranoid" mode — the residue being a 5-character partial clip of one otherwise-masked address.
Microsoft Presidio on comparable clinical text leaks 22.6% and catches 0/315 MRNs.
Zero false positives on eponym-trap documents: Parkinson's, Foley catheter, and drug names stay unredacted.

v5 was built through five eval-driven rounds; notable lessons: single-generator synthetic data overfits on authorial style (v3), and placeholder-format drift can silently poison synthetic pipelines (caught by audit before the v5 retrain). Long documents are supported via chunked extraction in the project repo's redact.py.

Usage

python
import json, torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

SYSTEM_PROMPT = "You are a PHI de-identification engine for US clinical text. Find every personal identifier in the document. Respond with ONLY a JSON array where each item is {\"text\": \"<exact substring as it appears in the document>\", \"type\": \"<TYPE>\"}. Valid types: NAME, DATE, AGE_OVER_89, PHONE, FAX, EMAIL, SSN, MRN, HEALTH_PLAN_ID, ACCOUNT_NUMBER, LICENSE_NUMBER, DEVICE_ID, URL, IP_ADDRESS, LOCATION, HOSPITAL, VEHICLE_ID, USERNAME, PROFESSION. If the document contains no identifiers, respond with []. Never miss an identifier: when unsure whether something is PHI, include it."

tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-1.7B")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B", dtype=torch.bfloat16, device_map="auto")
model = PeftModel.from_pretrained(model, "t4nishq/phi-redactor-qwen3-1.7b")

text = "Pt Priya Raghunathan, MRN 4829171, seen 03/14/2026 at Northbridge Memorial."
msgs = [{"role": "system", "content": SYSTEM_PROMPT}, {"role": "user", "content": text}]
prompt = tok.apply_chat_template(msgs, add_generation_prompt=True, tokenize=False, enable_thinking=False)
ids = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**ids, max_new_tokens=1024, do_sample=False)
entities = json.loads(tok.decode(out[0][ids.input_ids.shape[1]:], skip_special_tokens=True))
masked = text
for e in sorted(entities, key=lambda e: -len(e["text"])):
    masked = masked.replace(e["text"], f"[{e['type']}]")
print(masked)  # Pt [NAME], MRN [MRN], seen [DATE] at [HOSPITAL].

For maximum recall, use the project repo's redact.py — it adds an OCR-tolerant regex safety net, long-document chunking, PDF input, and a --paranoid ensemble mode (sampled re-runs + Presidio + verify pass).

Training

LoRA r=16/alpha=32, all-linear, bf16, 2 epochs, lr 2e-4, effective batch 16. TRL SFT on an A100 (~50 min). 12,750 synthetic training documents from 1,050 templates written by three model families (Claude Sonnet, Llama 3.3, DeepSeek V3): clinical notes, lab reports, admin documents, informal patient messages, intake forms, OCR-noised variants, and adversarial hard negatives.

Limitations & intended use

Assistive tool, not a compliance guarantee. No automated redactor reaches 100% recall; use human review for high-stakes release. HIPAA de-identification also requires policy-level controls.
Synthetic-set numbers are optimistic by construction; the OOD sets are small. Validate on your own documents.
English, US identifier formats only. Known weak spots: compound ID formats (workers-comp claim numbers), long-address span boundaries.
Not for diagnosis or clinical decision-making.

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Results

Held-out synthetic test (1,590 docs incl. adversarial informal slices; 250-doc sample): 0.991 entity recall, 0.40% char leak rate (with the project repo's regex safety net).
Hand-written out-of-distribution set (24 adversarial docs): 3.7% char leak standard; 0.54% with the ensemble "paranoid" mode — the residue being a 5-character partial clip of one otherwise-masked address.
Microsoft Presidio on comparable clinical text leaks 22.6% and catches 0/315 MRNs.
Zero false positives on eponym-trap documents: Parkinson's, Foley catheter, and drug names stay unredacted.

Usage

python
import json, torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

SYSTEM_PROMPT = "You are a PHI de-identification engine for US clinical text. Find every personal identifier in the document. Respond with ONLY a JSON array where each item is {\"text\": \"<exact substring as it appears in the document>\", \"type\": \"<TYPE>\"}. Valid types: NAME, DATE, AGE_OVER_89, PHONE, FAX, EMAIL, SSN, MRN, HEALTH_PLAN_ID, ACCOUNT_NUMBER, LICENSE_NUMBER, DEVICE_ID, URL, IP_ADDRESS, LOCATION, HOSPITAL, VEHICLE_ID, USERNAME, PROFESSION. If the document contains no identifiers, respond with []. Never miss an identifier: when unsure whether something is PHI, include it."

tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-1.7B")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B", dtype=torch.bfloat16, device_map="auto")
model = PeftModel.from_pretrained(model, "t4nishq/phi-redactor-qwen3-1.7b")

text = "Pt Priya Raghunathan, MRN 4829171, seen 03/14/2026 at Northbridge Memorial."
msgs = [{"role": "system", "content": SYSTEM_PROMPT}, {"role": "user", "content": text}]
prompt = tok.apply_chat_template(msgs, add_generation_prompt=True, tokenize=False, enable_thinking=False)
ids = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**ids, max_new_tokens=1024, do_sample=False)
entities = json.loads(tok.decode(out[0][ids.input_ids.shape[1]:], skip_special_tokens=True))
masked = text
for e in sorted(entities, key=lambda e: -len(e["text"])):
    masked = masked.replace(e["text"], f"[{e['type']}]")
print(masked)  # Pt [NAME], MRN [MRN], seen [DATE] at [HOSPITAL].

Training

Limitations & intended use

Assistive tool, not a compliance guarantee. No automated redactor reaches 100% recall; use human review for high-stakes release. HIPAA de-identification also requires policy-level controls.
Synthetic-set numbers are optimistic by construction; the OOD sets are small. Validate on your own documents.
English, US identifier formats only. Known weak spots: compound ID formats (workers-comp claim numbers), long-address span boundaries.
Not for diagnosis or clinical decision-making.

phi-redactor-qwen3-1.7b

Get help setting up a custom Dedicated Endpoints.

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Results

Usage

Training

Limitations & intended use

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Results

Usage

Training

Limitations & intended use