Upload pii_hybrid_decode.py with huggingface_hub

pii_hybrid_decode.py

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+"""Self-contained hybrid decode for LiquidAI/pii-detect (v7).
+
+The token-classification head locates PII but, like all byte-BPE token classifiers,
+fragments the boundaries of format-bound entities (e.g. it tags `1969` inside a date,
+or `charite.de` inside an email). This module adds an inference-time regex layer — the
+decode the product is meant to use — which roughly DOUBLES exact-match F1 with no loss
+of precision/recall on real text:
+
+  AUTH types  : distinctive, validator-gated formats (email, IBAN, JWT, SSN, MAC, crypto,
+                api_key, private_key, connection_string, ip, url, credit_card, swift, imei,
+                gps). Regex ADDS these and owns their exact boundaries.
+  SNAP types  : FP-prone formats (phone, date_of_birth, amount, postal_code). The MODEL
+                must fire; regex only EXPANDS its fragment to the full match (no new FPs).
+
+Everything else (names, addresses, conditions, medications, org, special-category,
+username, national_id, passport, etc.) is left to the model.
+
+Usage:
+    import torch
+    from transformers import AutoTokenizer, AutoModelForTokenClassification
+    from pii_hybrid_decode import predict
+    tok = AutoTokenizer.from_pretrained("LiquidAI/pii-detect", trust_remote_code=True)
+    model = AutoModelForTokenClassification.from_pretrained("LiquidAI/pii-detect",
+                trust_remote_code=True).eval()
+    spans = predict("Email [email protected] or call +49 30 4505 1234.", tok, model)
+    # -> [{'start':6,'end':22,'type':'contact.email','text':'[email protected]'}, ...]
+"""
+from __future__ import annotations
+import re
+
+def _luhn_ok(num: str) -> bool:
+    ds = [int(c) for c in num if c.isdigit()]
+    if not (12 <= len(ds) <= 19): return False
+    tot, par = 0, len(ds) % 2
+    for i, d in enumerate(ds):
+        if i % 2 == par:
+            d *= 2; d = d - 9 if d > 9 else d
+        tot += d
+    return tot % 10 == 0
+
+def _iban_ok(s: str) -> bool:
+    s = s.replace(" ", "").upper()
+    if not re.fullmatch(r"[A-Z]{2}\d{2}[A-Z0-9]{11,30}", s): return False
+    r = s[4:] + s[:4]
+    return int("".join(str(int(c, 36)) for c in r)) % 97 == 1
+
+# (type, pattern, validator) — distinctive formats the regex layer ADDS + owns boundaries
+_AUTH = [
+    ("contact.email", re.compile(r"\b[A-Za-z0-9._%+\-]+@[A-Za-z0-9.\-]+\.[A-Za-z]{2,}\b"), None),
+    ("credential.jwt", re.compile(r"\beyJ[A-Za-z0-9_\-]+\.eyJ[A-Za-z0-9_\-]+\.[A-Za-z0-9_\-]+"), None),
+    ("credential.api_key", re.compile(r"\b(?:AKIA[0-9A-Z]{16}|sk-(?:proj-)?[A-Za-z0-9]{20,}|sk-ant-api03-[A-Za-z0-9_\-]{20,}|ghp_[A-Za-z0-9]{36}|AIza[0-9A-Za-z_\-]{35}|xox[baprs]-[A-Za-z0-9\-]{10,}|hf_[A-Za-z0-9]{30,})\b"), None),
+    ("credential.private_key", re.compile(r"-----BEGIN (?:RSA |EC |OPENSSH |PGP )?PRIVATE KEY-----"), None),
+    ("credential.connection_string", re.compile(r"\b(?:postgres(?:ql)?|mysql|mongodb(?:\+srv)?|redis|amqp)://[^\s:@/]+:[^\s:@/]+@[^\s/]+"), None),
+    ("financial.iban", re.compile(r"\b[A-Z]{2}\d{2}(?:[ ]?[A-Z0-9]{4}){2,7}[ ]?[A-Z0-9]{1,3}\b"), _iban_ok),
+    ("financial.crypto_wallet", re.compile(r"\b(?:0x[a-fA-F0-9]{40}|bc1[a-z0-9]{25,90}|[13][a-km-zA-HJ-NP-Z1-9]{25,34})\b"), None),
+    ("device.mac_address", re.compile(r"\b(?:[0-9A-Fa-f]{2}[:\-]){5}[0-9A-Fa-f]{2}\b"), None),
+    ("location.gps_coordinates", re.compile(r"[\-+]?\d{1,3}\.\d{3,}\s*,\s*[\-+]?\d{1,3}\.\d{3,}"), None),
+    ("online.url", re.compile(r"\bhttps?://[^\s]+"), None),
+    ("identity.ssn", re.compile(r"\b\d{3}-\d{2}-\d{4}\b"), None),
+    ("contact.ip_address", re.compile(r"\b(?:(?:25[0-5]|2[0-4]\d|1?\d?\d)\.){3}(?:25[0-5]|2[0-4]\d|1?\d?\d)\b"), None),
+    ("financial.credit_card", re.compile(r"\b(?:\d[ \-]?){13,19}\b"), _luhn_ok),
+    ("financial.swift_bic", re.compile(r"\b[A-Z]{4}[A-Z]{2}[A-Z0-9]{2}(?:[A-Z0-9]{3})?\b"), None),
+    ("device.imei", re.compile(r"\b\d{15}\b"), _luhn_ok),
+]
+_SNAP = {
+    "contact.phone": re.compile(r"(?<!\d)(?:\+?\d{1,3}[ \-.]?)?(?:\(\d{2,4}\)[ \-.]?)?\d{2,4}[ \-.]?\d{3}[ \-.]?\d{3,4}(?!\d)"),
+    "identity.date_of_birth": re.compile(r"\b(?:\d{1,2}[\/.\-]\d{1,2}[\/.\-]\d{2,4}|\d{4}[\/.\-]\d{1,2}[\/.\-]\d{1,2}|(?:Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)[a-z]*\.?\s+\d{1,2},?\s+\d{4}|\d{1,2}\s+(?:Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)[a-z]*\.?\s+\d{4})\b"),
+    "financial.amount": re.compile(r"(?:[$€£¥]\s?\d[\d.,]*(?:\s?[KMB])?|\b(?:USD|EUR|GBP|JPY|CHF|CAD|AUD)\s?\d[\d.,]*(?:\s?[KMB])?\b|\b\d[\d.,]*\s?(?:USD|EUR|GBP|dollars|euros)\b)"),
+    "contact.postal_code": re.compile(r"\b(?:\d{5}(?:-\d{4})?|[A-Z]{1,2}\d[A-Z\d]?\s?\d[A-Z]{2})\b"),
+}
+_AUTH_TYPES = {t for t, _, _ in _AUTH}
+
+def hybrid_spans(text: str, model_spans: list[dict]) -> list[dict]:
+    """model_spans: [{'start','end','type'}...] from the token classifier. Returns the
+    hybrid-decoded spans (dicts with start/end/type/text)."""
+    # 1. AUTH regex spans — built INDEPENDENTLY of the model (regex is authoritative
+    #    for these distinctive formats; model fragments must not block them).
+    auth, claimed = [], [False] * len(text)
+    for t, pat, val in _AUTH:
+        for mm in pat.finditer(text):
+            s, e = mm.start(), mm.end()
+            if any(claimed[s:e]): continue
+            if val and not val(mm.group(0)): continue
+            for i in range(s, e): claimed[i] = True
+            auth.append({"start": s, "end": e, "type": t, "text": mm.group(0)})
+    # 2. model spans for non-AUTH types; SNAP types expand to overlapping regex match
+    out = []
+    for m in model_spans:
+        t = m["type"]
+        if t in _AUTH_TYPES:
+            continue  # regex owns these
+        if t in _SNAP:
+            snap = None
+            for mm in _SNAP[t].finditer(text):
+                if min(mm.end(), m["end"]) > max(mm.start(), m["start"]):
+                    snap = mm; break
+            if snap:
+                out.append({"start": snap.start(), "end": snap.end(), "type": t,
+                            "text": text[snap.start():snap.end()]}); continue
+        out.append({"start": m["start"], "end": m["end"], "type": t,
+                    "text": text[m["start"]:m["end"]]})
+    out.extend(auth)
+    seen, uniq = set(), []
+    for sp in sorted(out, key=lambda s: (s["start"], s["end"])):
+        k = (sp["start"], sp["end"], sp["type"])
+        if k not in seen:
+            seen.add(k); uniq.append(sp)
+    return uniq
+
+def model_spans(text: str, tok, model):
+    import torch
+    enc = tok(text, return_offsets_mapping=True, return_tensors="pt", truncation=True, max_length=2048)
+    off = enc.pop("offset_mapping")[0].tolist()
+    enc = {k: v.to(model.device) for k, v in enc.items()}
+    with torch.no_grad():
+        ids = model(**enc).logits[0].argmax(-1).tolist()
+    id2label = model.config.id2label
+    spans, cur = [], None
+    for (a, b), i in zip(off, ids):
+        lab = id2label[i]
+        if b <= a or lab == "O":
+            if cur: spans.append(cur); cur = None
+            continue
+        typ = lab.split("-", 1)[1] if "-" in lab else lab
+        if lab[:2] in ("B-", "S-") or cur is None or cur["type"] != typ:
+            if cur: spans.append(cur)
+            cur = {"start": a, "end": b, "type": typ}
+        else:
+            cur["end"] = b
+    if cur: spans.append(cur)
+    # trim leading/trailing whitespace
+    for sp in spans:
+        while sp["start"] < sp["end"] and text[sp["start"]].isspace(): sp["start"] += 1
+        while sp["end"] > sp["start"] and text[sp["end"] - 1].isspace(): sp["end"] -= 1
+    return [s for s in spans if s["end"] > s["start"]]
+
+def predict(text: str, tok, model, hybrid: bool = True) -> list[dict]:
+    ms = model_spans(text, tok, model)
+    if not hybrid:
+        return [{"start": s["start"], "end": s["end"], "type": s["type"],
+                 "text": text[s["start"]:s["end"]]} for s in ms]
+    return hybrid_spans(text, ms)