IndicPHI-MuRIL-PII

A MuRIL-base encoder (237M params) fine-tuned as a BIO token classifier for PII/PHI redaction in Indian clinical text — 23 languages, 13 scripts, 50 entity types including India-specific identifiers (ABHA ID, Aadhaar, BPL ration card, ASHA worker name).

Trained on Sidharth1743/indicphi. Evaluation code: IndicPHI-Bench.

Read this before you use the first number

setting	result
held-out synthetic clinical documents (same generator as training)	0.45% character leakage
held-out synthetic, document types never seen in training	2.0% leakage
real human-written Indic text (Naamapadam, person names)	31.8% detection recall (68.2% missed)
same architecture, trained on real news data instead	93.4% detection recall

The first number is the one people quote. It is real, but it is an in-distribution number on synthetic data, and it does not predict the third one. Six differently- pretrained encoders trained the identical way score within 0.34–0.64% on the synthetic benchmark and span 0.4–31.8% on real text — the benchmark alone does not tell you which model actually works. Full analysis, six-way comparison, and the trained-ceiling control that isolates why the real-text number is what it is: see the paper (link pending) and IndicPHI-Bench.

Practical read: this model is a strong choice for redacting documents similar to its synthetic training distribution. Its recall on genuinely novel real-world Indic text is meaningfully lower, and you should not assume the 0.45% figure transfers.

Usage

This uses a small custom head (LayerNorm + linear classifier) on top of the MuRIL backbone — not a standard AutoModelForTokenClassification shape, because a bare linear head could not learn at all on MuRIL's raw output scale (see "Why the custom head" below). Loading needs the included modeling_indicphi_bio.py:

from huggingface_hub import hf_hub_download
import sys, os

REPO = "sach3v/indicphi-muril-pii"
sys.path.insert(0, os.path.dirname(hf_hub_download(REPO, "modeling_indicphi_bio.py")))

from modeling_indicphi_bio import IndicPHIBioEncoder
from transformers import AutoTokenizer
import torch

model = IndicPHIBioEncoder.from_pretrained(REPO).eval()
tok = AutoTokenizer.from_pretrained(REPO)

text = "রোগীর নাম Manisha Brahma, বয়স 37, ফোন 9593541505, MRN-15FE5B1DAF"
enc = tok(text, return_tensors="pt", return_offsets_mapping=True)
with torch.no_grad():
    logits = model(enc["input_ids"], enc["attention_mask"])
pred = logits.argmax(-1)[0].tolist()

# decode BIO tags back to character spans
offsets = enc["offset_mapping"][0].tolist()
spans, cur = [], None
for (s, e), p in zip(offsets, pred):
    if s == e:
        continue
    tag = model.id2label[p]
    if tag == "O":
        if cur: spans.append(cur); cur = None
        continue
    pos, label = tag.split("-", 1)
    if pos == "B" or cur is None or cur[2] != label:
        if cur: spans.append(cur)
        cur = [s, e, label]
    else:
        cur[1] = e
if cur: spans.append(cur)

for s, e, label in spans:
    print(f"{label:<16} {text[s:e]!r}")
# PATIENT_NAME     'Manisha Brahma'
# AGE              '37'
# PHONE_NUMBER     '9593541505'
# MRN              'MRN-15FE5B1DAF'

from_pretrained verifies every backbone/head tensor loaded (the only permitted mismatch is MuRIL's unused NSP pooler head) and raises rather than silently returning an under-loaded model.

Training

• Base: google/muril-base-cased, output LayerNorm before the classifier (backbones vary by ~2 orders of magnitude in raw hidden-state scale; MuRIL's is small enough that a bare linear head trains at chance loss for the entire run without this).
• Head: BIO tagging over 50 entity types (101 tags incl. O).
• Data: Sidharth1743/indicphi train split, 22,554 documents, 23 languages.
• Schedule: 3 epochs, batch 8, max length 512, seed 42.
• Data-scaling: real-world transfer saturates at ~10k training documents; the last 2.25× of data only improved the in-distribution number.

Labels

50 PII/PHI types in BIO scheme (101 tags). Full list in bio_labels.json. Includes India-specific identifiers with no counterpart in general PII taxonomies: ABHA_ID, ABHA_ADDRESS, AADHAAR_NUMBER, BPL_RATION_CARD, ASHA_WORKER_NAME, VILLAGE, DISTRICT, WARD_NUMBER, CASTE, RELIGION.

Limitations

• Trained entirely on synthetic clinical text; the only real-text evaluation available (Naamapadam) is news, not clinical, so the 31.8% figure confounds synthetic-vs-real with domain shift. See GOLD_SET_SPEC.md in the harness repo for what would resolve this.
• Two scripts (Ol Chiki/Santali, Meetei Mayek/Manipuri) are outside MuRIL's pretraining and are the weakest languages for this model, and for every other encoder we tested.
• Right-to-left scripts (Sindhi, Urdu, Kashmiri) show a specific failure mode: boundary errors that redact only the last character or two of a name, which is a worse privacy failure than a full miss.

License

Apache 2.0 (inherited from google/muril-base-cased). Training data: Sidharth1743/indicphi, MIT.

Citation

Paper in preparation; citation to follow.