Publish v2-rc3 release

.gitattributes

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.gitignore

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+__pycache__/
+.venv/
+*.pyc

LICENSE

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NOTICE

@@ -0,0 +1,17 @@
+OpenMed PPSN Extension
+Copyright 2026 Contributors
+
+This project includes fine-tuned/derived model artifacts from:
+- OpenMed/OpenMed-PII-SuperClinical-Large-434M-v1 (Hugging Face)
+  Declared license: Apache-2.0.
+
+This project uses evaluation/training data sources including:
+- nvidia/Nemotron-PII (Hugging Face dataset)
+  Declared license: CC-BY-4.0.
+
+Attribution and links:
+- OpenMed base model: https://huggingface.co/OpenMed/OpenMed-PII-SuperClinical-Large-434M-v1
+- Nemotron-PII dataset: https://huggingface.co/datasets/nvidia/Nemotron-PII
+
+If you redistribute models or checkpoints produced here, keep this NOTICE,
+retain upstream license notices, and provide dataset attribution where required.

README.md

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+---
+language:
+- en
+- ga
+license: apache-2.0
+library_name: transformers
+pipeline_tag: token-classification
+tags:
+- pii
+- token-classification
+- de-identification
+- ireland
+- irish
+- gaelic
+- ppsn
+- eircode
+- phone-number
+- iban
+- passport
+- onnx
+- quantized
+- release-candidate
+base_model:
+- OpenMed/OpenMed-PII-mLiteClinical-Base-135M-v1
+---
+
+# temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc3
+
+Third QA release candidate for Irish core PII detection with OpenMed mLiteClinical.
+
+This repository should be evaluated against:
+
+- current public release: `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v1`
+- previous RC: `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc2`
+- this repository: `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc3`
+
+This RC is the first version in the IrishCorePII line that is stronger than `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc2` across the main suites used in this workspace for release gating:
+
+- Irish core multilabel suite
+- Irish PPSN and phone edge suite
+- exact numeric QA suite
+- exact gap-feedback QA suite
+- user PPSN regression suite
+- multilingual PPSN suite
+- strict exact-span boundary suite
+
+This repository also includes a stronger bundled ONNX q8 artifact for CPU inference in `onnx/model_quantized.onnx`.
+
+## Included Variants
+
+| Variant | Artifact | Backend | Recommended Thresholds | Intended Use |
+|---|---|---|---|---|
+| Full checkpoint | repo root | `transformers` | `ppsn=0.55`, `other=0.40` | highest-fidelity evaluation and deployment |
+| Quantized checkpoint | `onnx/model_quantized.onnx` | ONNX Runtime dynamic int8 | `ppsn=0.60`, `other=0.45` | CPU-oriented deployment |
+
+## Coverage
+
+- `PPSN`
+- `account_number`
+- `bank_routing_number`
+- `credit_debit_card`
+- `PASSPORT_NUMBER`
+- `postcode`
+- `phone_number`
+- `email`
+- `first_name`
+- `last_name`
+- `swift_bic`
+
+## Recommended Inference
+
+Full checkpoint:
+
+```bash
+python3 inference_mask.py   --model temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc3   --ppsn-min-score 0.55   --other-min-score 0.40   --text "My sort code is 90-00-17 for AIB."   --json
+```
+
+Fast CPU path with the bundled ONNX q8 artifact:
+
+```bash
+python3 inference_mask_onnx.py   --model temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc3   --ppsn-min-score 0.60   --other-min-score 0.45   --text "Please provide your passport: NN5123456."   --json
+```
+
+## What Improved Versus `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc2`
+
+Full checkpoint:
+
+- core suite F1: `0.9554` -> `0.9806`
+- edge suite F1: `0.9500` -> `1.0000`
+- exact numeric QA F1: `0.8966` -> `0.9333`
+- exact gap-feedback QA F1: `0.8696` -> `0.9167`
+- user PPSN regression F1: `0.8571` -> `1.0000`
+- multilingual PPSN F1: `0.8038` -> `0.9333`
+
+Bundled ONNX q8:
+
+- core suite F1: `0.9677` -> `0.9677`
+- edge suite F1: `0.9500` -> `1.0000`
+- exact numeric QA F1: `0.8667` -> `0.9333`
+- exact gap-feedback QA F1: `0.8696` -> `0.9167`
+- user PPSN regression F1: `0.8571` -> `1.0000`
+- multilingual PPSN F1: `0.8077` -> `0.9333`
+
+## Quality Snapshot
+
+Key label changes on the Irish core suite for the full checkpoint:
+
+- `PASSPORT_NUMBER`: `0.8000` -> `1.0000`
+- `POSTCODE`: `0.7500` -> `1.0000`
+- `PPSN`: `0.8571` -> `0.9231`
+- `PHONE_NUMBER` on the numeric QA suite: `0.7500` -> `0.8889`
+
+## Benchmark Tables
+
+Broader CPU benchmarks:
+
+| Variant | User PPSN | Core | Edge | Multilingual PPSN | Strict Remaining IoU=1.0 |
+|---|---:|---:|---:|---:|---:|
+| `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc2` full | 0.8571 | 0.9554 | 0.9500 | 0.8038 | 0.4000 |
+| `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc3` full | 1.0000 | 0.9806 | 1.0000 | 0.9333 | 0.4444 |
+| `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc2` ONNX q8 | 0.8571 | 0.9677 | 0.9500 | 0.8077 | 0.6000 |
+| `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc3` ONNX q8 | 1.0000 | 0.9677 | 1.0000 | 0.9333 | 0.6667 |
+
+Exact QA suites:
+
+| Variant | Numeric v2 | Passport | Routing | Phone | Gap | Passport | Routing | Phone |
+|---|---:|---:|---:|---:|---:|---:|---:|---:|
+| `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc2` full | 0.8966 | 0.9091 | 1.0000 | 0.7500 | 0.8696 | 0.8889 | 1.0000 | 0.6667 |
+| `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc3` full | 0.9333 | 0.9091 | 1.0000 | 0.8889 | 0.9167 | 0.8889 | 1.0000 | 0.8571 |
+| `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc2` ONNX q8 | 0.8667 | 0.8333 | 1.0000 | 0.7500 | 0.8696 | 0.8889 | 1.0000 | 0.6667 |
+| `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc3` ONNX q8 | 0.9333 | 0.9091 | 1.0000 | 0.8889 | 0.9167 | 0.8889 | 1.0000 | 0.8571 |
+
+CPU throughput:
+
+| Variant | Core ex/s | Edge ex/s | Multilingual PPSN ex/s |
+|---|---:|---:|---:|
+| `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc2` full | 58.3521 | 63.1592 | 128.1891 |
+| `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc3` full | 64.8768 | 62.4612 | 126.4996 |
+| `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc2` ONNX q8 | 44.9578 | 45.5786 | 297.5262 |
+| `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc3` ONNX q8 | 44.9398 | 41.7212 | 190.8299 |
+
+## Quantized Artifact
+
+The bundled quantized artifact is:
+
+- `onnx/model_quantized.onnx`
+
+For this release line, the promoted q8 recipe remains the standard dynamic int8 ONNX export with per-channel quantization. Two alternative q8 recipes were tested locally and not promoted because they reduced multilingual PPSN quality.
+
+## Known Limits
+
+This is still a raw-model-only release candidate. QA should still test these carefully:
+
+- `Passport PA 1234567 was used to board the flight.`
+- `Usaideadh pas PA 1234567 chun dul ar bord an eitilt.`
+- the full checkpoint can still trim the final digit in some short English compact-phone contexts such as `Call me on 0851234567 tomorrow.`
+- multilingual PPSN quality is stronger than `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc2`, but still below `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v1` on the multilingual PPSN suite
+
+## Included Files
+
+- full `transformers` checkpoint in the repo root
+- dynamic int8 ONNX artifact in `onnx/model_quantized.onnx`
+- `inference_mask.py`
+- `inference_mask_onnx.py`
+- `qa_config.json`
+- `training_sources.json`
+- benchmark summaries in `eval/`
+
+## License And Attribution
+
+- release license: Apache-2.0
+- base model: `OpenMed/OpenMed-PII-mLiteClinical-Base-135M-v1`
+- upstream attributed data: `joelniklaus/mapa`, `gretelai/synthetic_pii_finance_multilingual`
+- synthetic Irish training and replay data created in this workspace
+
+See `NOTICE` for attribution details.

config.json

@@ -0,0 +1,250 @@
+{
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForTokenClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "dtype": "float32",
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "O",
+    "1": "B-account_number",
+    "2": "B-age",
+    "3": "B-api_key",
+    "4": "B-bank_routing_number",
+    "5": "B-biometric_identifier",
+    "6": "B-blood_type",
+    "7": "B-certificate_license_number",
+    "8": "B-city",
+    "9": "B-company_name",
+    "10": "B-coordinate",
+    "11": "B-country",
+    "12": "B-county",
+    "13": "B-credit_debit_card",
+    "14": "B-customer_id",
+    "15": "B-cvv",
+    "16": "B-date",
+    "17": "B-date_of_birth",
+    "18": "B-date_time",
+    "19": "B-device_identifier",
+    "20": "B-education_level",
+    "21": "B-email",
+    "22": "B-employee_id",
+    "23": "B-employment_status",
+    "24": "B-fax_number",
+    "25": "B-first_name",
+    "26": "B-gender",
+    "27": "B-health_plan_beneficiary_number",
+    "28": "B-http_cookie",
+    "29": "B-ipv4",
+    "30": "B-ipv6",
+    "31": "B-language",
+    "32": "B-last_name",
+    "33": "B-license_plate",
+    "34": "B-mac_address",
+    "35": "B-medical_record_number",
+    "36": "B-occupation",
+    "37": "B-password",
+    "38": "B-phone_number",
+    "39": "B-pin",
+    "40": "B-political_view",
+    "41": "B-postcode",
+    "42": "B-race_ethnicity",
+    "43": "B-religious_belief",
+    "44": "B-sexuality",
+    "45": "B-ssn",
+    "46": "B-state",
+    "47": "B-street_address",
+    "48": "B-swift_bic",
+    "49": "B-tax_id",
+    "50": "B-time",
+    "51": "B-unique_id",
+    "52": "B-url",
+    "53": "B-user_name",
+    "54": "B-vehicle_identifier",
+    "55": "I-account_number",
+    "56": "I-api_key",
+    "57": "I-biometric_identifier",
+    "58": "I-blood_type",
+    "59": "I-certificate_license_number",
+    "60": "I-city",
+    "61": "I-company_name",
+    "62": "I-coordinate",
+    "63": "I-country",
+    "64": "I-county",
+    "65": "I-credit_debit_card",
+    "66": "I-customer_id",
+    "67": "I-date",
+    "68": "I-date_of_birth",
+    "69": "I-date_time",
+    "70": "I-device_identifier",
+    "71": "I-education_level",
+    "72": "I-email",
+    "73": "I-employee_id",
+    "74": "I-employment_status",
+    "75": "I-fax_number",
+    "76": "I-first_name",
+    "77": "I-gender",
+    "78": "I-health_plan_beneficiary_number",
+    "79": "I-http_cookie",
+    "80": "I-ipv4",
+    "81": "I-ipv6",
+    "82": "I-language",
+    "83": "I-last_name",
+    "84": "I-license_plate",
+    "85": "I-mac_address",
+    "86": "I-medical_record_number",
+    "87": "I-occupation",
+    "88": "I-password",
+    "89": "I-phone_number",
+    "90": "I-pin",
+    "91": "I-political_view",
+    "92": "I-postcode",
+    "93": "I-race_ethnicity",
+    "94": "I-religious_belief",
+    "95": "I-sexuality",
+    "96": "I-ssn",
+    "97": "I-state",
+    "98": "I-street_address",
+    "99": "I-swift_bic",
+    "100": "I-tax_id",
+    "101": "I-time",
+    "102": "I-unique_id",
+    "103": "I-url",
+    "104": "I-user_name",
+    "105": "I-vehicle_identifier",
+    "106": "B-PPSN",
+    "107": "I-PPSN",
+    "108": "B-PASSPORT_NUMBER",
+    "109": "I-PASSPORT_NUMBER",
+    "110": "I-bank_routing_number"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "B-PASSPORT_NUMBER": 108,
+    "B-PPSN": 106,
+    "B-account_number": 1,
+    "B-age": 2,
+    "B-api_key": 3,
+    "B-bank_routing_number": 4,
+    "B-biometric_identifier": 5,
+    "B-blood_type": 6,
+    "B-certificate_license_number": 7,
+    "B-city": 8,
+    "B-company_name": 9,
+    "B-coordinate": 10,
+    "B-country": 11,
+    "B-county": 12,
+    "B-credit_debit_card": 13,
+    "B-customer_id": 14,
+    "B-cvv": 15,
+    "B-date": 16,
+    "B-date_of_birth": 17,
+    "B-date_time": 18,
+    "B-device_identifier": 19,
+    "B-education_level": 20,
+    "B-email": 21,
+    "B-employee_id": 22,
+    "B-employment_status": 23,
+    "B-fax_number": 24,
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+}

eval/benchmark_summary.json

@@ -0,0 +1,2123 @@
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+      "remaining_iou1_f1": 0.6,
+      "remaining_iou1_eps": 66.84971455324192
+    },
+    {
+      "variant": "q8_mg_reduce",
+      "core_f1": 0.9554140127388535,
+      "core_eps": 111.07958753347033,
+      "edge_f1": 0.9500000000000001,
+      "edge_eps": 50.58773730012114,
+      "numeric_f1": 0.7878787878787878,
+      "numeric_eps": 42.08249226466411,
+      "gap_f1": 0.7692307692307692,
+      "gap_eps": 112.74792550347136,
+      "multilingual_f1": 0.7336244541484717,
+      "multilingual_eps": 105.25341259054518,
+      "remaining_iou1_f1": 0.4000000000000001,
+      "remaining_iou1_eps": 54.19501381319339
+    }
+  ]
+}

eval/benchmark_summary.md

@@ -0,0 +1,42 @@
+# Benchmark Summary
+
+This release packages a stronger full checkpoint and a stronger bundled ONNX q8 artifact than `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc2`.
+
+Recommended thresholds:
+
+- full checkpoint: `ppsn_min_score = 0.55`, `other_min_score = 0.40`
+- ONNX q8: `ppsn_min_score = 0.60`, `other_min_score = 0.45`
+
+## Broader CPU Benchmarks
+
+| Variant | User PPSN | Core | Edge | Multilingual PPSN | Strict Remaining IoU=1.0 |
+|---|---:|---:|---:|---:|---:|
+| `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc2` full | 0.8571 | 0.9554 | 0.9500 | 0.8038 | 0.4000 |
+| `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc3` full | 1.0000 | 0.9806 | 1.0000 | 0.9333 | 0.4444 |
+| `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc2` ONNX q8 | 0.8571 | 0.9677 | 0.9500 | 0.8077 | 0.6000 |
+| `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc3` ONNX q8 | 1.0000 | 0.9677 | 1.0000 | 0.9333 | 0.6667 |
+
+## Exact QA Suites
+
+| Variant | Numeric v2 | Passport | Routing | Phone | Gap | Passport | Routing | Phone |
+|---|---:|---:|---:|---:|---:|---:|---:|---:|
+| `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc2` full | 0.8966 | 0.9091 | 1.0000 | 0.7500 | 0.8696 | 0.8889 | 1.0000 | 0.6667 |
+| `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc3` full | 0.9333 | 0.9091 | 1.0000 | 0.8889 | 0.9167 | 0.8889 | 1.0000 | 0.8571 |
+| `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc2` ONNX q8 | 0.8667 | 0.8333 | 1.0000 | 0.7500 | 0.8696 | 0.8889 | 1.0000 | 0.6667 |
+| `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc3` ONNX q8 | 0.9333 | 0.9091 | 1.0000 | 0.8889 | 0.9167 | 0.8889 | 1.0000 | 0.8571 |
+
+## CPU Throughput
+
+| Variant | Core ex/s | Edge ex/s | Multilingual PPSN ex/s |
+|---|---:|---:|---:|
+| `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc2` full | 58.3521 | 63.1592 | 128.1891 |
+| `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc3` full | 64.8768 | 62.4612 | 126.4996 |
+| `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc2` ONNX q8 | 44.9578 | 45.5786 | 297.5262 |
+| `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc3` ONNX q8 | 44.9398 | 41.7212 | 190.8299 |
+
+## Reading These Numbers
+
+- The new full checkpoint improves the previous RC on every critical suite measured here.
+- The new ONNX q8 artifact also improves the previous ONNX q8 on every critical suite measured here.
+- The best promoted q8 recipe remains the standard dynamic int8 export used in this repo. Two alternative recipes were tested locally and rejected because they hurt multilingual PPSN quality.
+- The strict exact-span boundary suite is still not solved. The remaining failures are mostly passport values written as `prefix + space + digits`.

eval/multilabel_summary.json

@@ -0,0 +1,1298 @@
+{
+  "full": {
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+      "ppsn_min_score": 0.55
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+          "PASSPORT_NUMBER",
+          "PHONE_NUMBER",
+          "POSTCODE",
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+          "SWIFT_BIC"
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+}

eval/ppsn_only_summary.json

@@ -0,0 +1,812 @@
+{
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+}

inference_mask.py

@@ -0,0 +1,181 @@
+#!/usr/bin/env python3
+import argparse
+import json
+import os
+
+os.environ.setdefault("TRANSFORMERS_NO_TF", "1")
+os.environ.setdefault("TRANSFORMERS_NO_FLAX", "1")
+os.environ.setdefault("TRANSFORMERS_NO_TORCHVISION", "1")
+os.environ["USE_TF"] = "0"
+os.environ["USE_FLAX"] = "0"
+os.environ["USE_TORCH"] = "1"
+
+import torch
+from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline
+
+import regex as re
+
+
+TOKEN_RE = re.compile(r"[A-Za-z0-9]+|[^\w\s]", re.UNICODE)
+EIRCODE_RE = re.compile(r"^(?:[ACDEFHKNPRTVWXY]\d{2}|D6W)\s?[0-9ACDEFHKNPRTVWXY]{4}$", re.IGNORECASE)
+ALLOWED = {
+    "PPSN",
+    "ACCOUNT_NUMBER",
+    "BANK_ROUTING_NUMBER",
+    "CREDIT_DEBIT_CARD",
+    "PASSPORT_NUMBER",
+    "POSTCODE",
+    "PHONE_NUMBER",
+    "EMAIL",
+    "FIRST_NAME",
+    "LAST_NAME",
+    "SWIFT_BIC",
+}
+
+
+def tokenize_with_spans(text: str):
+    return [(m.group(0), m.start(), m.end()) for m in TOKEN_RE.finditer(text)]
+
+
+def normalize_label(label: str) -> str:
+    label = (label or "").strip()
+    if label.startswith("B-") or label.startswith("I-"):
+        label = label[2:]
+    return label.upper()
+
+
+def looks_like_eircode(value: str) -> bool:
+    return EIRCODE_RE.match(value.strip()) is not None
+
+
+def ppsn_label_ids(model):
+    ids = []
+    for raw_id, raw_label in model.config.id2label.items():
+        label_id = int(raw_id)
+        label = str(raw_label or "").strip()
+        if label.endswith("PPSN"):
+            ids.append(label_id)
+    return sorted(ids)
+
+
+def word_aligned_ppsn_spans(text: str, model, tokenizer, threshold: float):
+    pieces = tokenize_with_spans(text)
+    if not pieces:
+        return []
+    words = [word for word, _, _ in pieces]
+    encoded = tokenizer(words, is_split_into_words=True, return_tensors="pt", truncation=True)
+    word_ids = encoded.word_ids(batch_index=0)
+    device = next(model.parameters()).device
+    encoded = {k: v.to(device) for k, v in encoded.items()}
+    with torch.no_grad():
+        logits = model(**encoded).logits[0]
+    probs = torch.softmax(logits, dim=-1)
+    label_ids = ppsn_label_ids(model)
+    word_scores = []
+    for word_index in range(len(pieces)):
+        score = 0.0
+        for token_index, wid in enumerate(word_ids):
+            if wid != word_index:
+                continue
+            for label_id in label_ids:
+                score = max(score, float(probs[token_index, label_id]))
+        word_scores.append(score)
+    spans = []
+    active = None
+    for (_, start, end), score in zip(pieces, word_scores):
+        if score >= threshold:
+            if active is None:
+                active = {"start": start, "end": end, "score": score}
+            else:
+                active["end"] = end
+                active["score"] = max(active["score"], score)
+        elif active is not None:
+            spans.append(active)
+            active = None
+    if active is not None:
+        spans.append(active)
+    for span in spans:
+        span["label"] = "PPSN"
+        span["text"] = text[span["start"]:span["end"]]
+    return spans
+
+
+def merge_spans(text: str, general_spans: list[dict], ppsn_spans: list[dict], other_min_score: float):
+    out = []
+    for span in general_spans:
+        label = normalize_label(span.get("entity_group") or span.get("entity") or "")
+        if label not in ALLOWED or label == "PPSN":
+            continue
+        if float(span.get("score", 0.0)) < other_min_score:
+            continue
+        out.append({
+            "label": label,
+            "start": int(span["start"]),
+            "end": int(span["end"]),
+            "score": float(span["score"]),
+            "text": text[int(span["start"]):int(span["end"])],
+        })
+    def overlaps(a, b):
+        return not (a["end"] <= b["start"] or b["end"] <= a["start"])
+    for span in ppsn_spans:
+        if looks_like_eircode(span["text"]):
+            continue
+        if any(overlaps(span, existing) for existing in out):
+            continue
+        out.append(span)
+    out.sort(key=lambda item: (item["start"], item["end"]))
+    return out
+
+
+def mask_text(text: str, spans: list[dict]) -> str:
+    out = text
+    for span in sorted(spans, key=lambda item: (item["start"], item["end"]), reverse=True):
+        out = out[:span["start"]] + f"[{span['label']}]" + out[span["end"]:]
+    return out
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model", default=".")
+    parser.add_argument("--text", required=True)
+    parser.add_argument("--device", choices=["auto", "cpu", "cuda"], default="auto")
+    parser.add_argument("--ppsn-min-score", type=float, default=0.55)
+    parser.add_argument("--other-min-score", type=float, default=0.40)
+    parser.add_argument("--json", action="store_true")
+    args = parser.parse_args()
+
+    try:
+        tokenizer = AutoTokenizer.from_pretrained(args.model, use_fast=True, fix_mistral_regex=True)
+    except Exception:
+        try:
+            tokenizer = AutoTokenizer.from_pretrained(args.model, use_fast=True, fix_mistral_regex=False)
+        except TypeError:
+            tokenizer = AutoTokenizer.from_pretrained(args.model, use_fast=True)
+    model = AutoModelForTokenClassification.from_pretrained(args.model)
+    if args.device == "auto":
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+    else:
+        device = args.device
+    model.to(device)
+    model.eval()
+
+    nlp = pipeline("token-classification", model=model, tokenizer=tokenizer, aggregation_strategy="simple", device=0 if device == "cuda" else -1)
+    general = nlp(args.text)
+    ppsn = word_aligned_ppsn_spans(args.text, model, tokenizer, threshold=args.ppsn_min_score)
+    spans = merge_spans(args.text, general, ppsn, other_min_score=args.other_min_score)
+    result = {
+        "model": args.model,
+        "masked_text": mask_text(args.text, spans),
+        "spans": spans,
+        "ppsn_decoder": "word_aligned",
+        "ppsn_min_score": args.ppsn_min_score,
+        "other_min_score": args.other_min_score,
+    }
+    if args.json:
+        print(json.dumps(result, indent=2, ensure_ascii=False))
+    else:
+        print(result["masked_text"])
+
+
+if __name__ == "__main__":
+    main()

inference_mask_onnx.py

@@ -0,0 +1,78 @@
+#!/usr/bin/env python3
+import argparse
+import json
+
+from onnx_token_classifier import (
+    load_onnx_token_classifier,
+    looks_like_eircode,
+    normalize_label,
+    simple_aggregate_spans_onnx,
+    word_aligned_ppsn_spans_onnx,
+)
+
+
+ALLOWED = {
+    "PPSN",
+    "ACCOUNT_NUMBER",
+    "BANK_ROUTING_NUMBER",
+    "CREDIT_DEBIT_CARD",
+    "PASSPORT_NUMBER",
+    "POSTCODE",
+    "PHONE_NUMBER",
+    "EMAIL",
+    "FIRST_NAME",
+    "LAST_NAME",
+    "SWIFT_BIC",
+}
+
+
+def mask_text(text: str, spans: list[dict]) -> str:
+    out = text
+    for span in sorted(spans, key=lambda item: (item["start"], item["end"]), reverse=True):
+        out = out[:span["start"]] + f"[{span['label']}]" + out[span["end"]:]
+    return out
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model", default=".")
+    parser.add_argument("--text", required=True)
+    parser.add_argument("--ppsn-min-score", type=float, default=0.60)
+    parser.add_argument("--other-min-score", type=float, default=0.45)
+    parser.add_argument("--json", action="store_true")
+    args = parser.parse_args()
+
+    session, tokenizer, config = load_onnx_token_classifier(args.model)
+    general = simple_aggregate_spans_onnx(args.text, session, tokenizer, config, min_score=args.other_min_score)
+    ppsn = word_aligned_ppsn_spans_onnx(args.text, session, tokenizer, config, threshold=args.ppsn_min_score)
+    spans = []
+    for span in general:
+        label = normalize_label(span["label"])
+        if label in ALLOWED and label != "PPSN":
+            spans.append(span)
+    def overlaps(a, b):
+        return not (a["end"] <= b["start"] or b["end"] <= a["start"])
+    for span in ppsn:
+        if looks_like_eircode(span["text"]):
+            continue
+        if any(overlaps(span, existing) for existing in spans):
+            continue
+        spans.append(span)
+    spans.sort(key=lambda item: (item["start"], item["end"]))
+    result = {
+        "model": args.model,
+        "masked_text": mask_text(args.text, spans),
+        "spans": spans,
+        "ppsn_decoder": "word_aligned",
+        "ppsn_min_score": args.ppsn_min_score,
+        "other_min_score": args.other_min_score,
+        "backend": "onnx",
+    }
+    if args.json:
+        print(json.dumps(result, indent=2, ensure_ascii=False))
+    else:
+        print(result["masked_text"])
+
+
+if __name__ == "__main__":
+    main()

label_meta.json

@@ -0,0 +1,131 @@
+{
+  "base_model": "models/openmed-mliteclinical-irish-core-v17_full_s180",
+  "label_list": [
+    "O",
+    "B-account_number",
+    "B-age",
+    "B-api_key",
+    "B-bank_routing_number",
+    "B-biometric_identifier",
+    "B-blood_type",
+    "B-certificate_license_number",
+    "B-city",
+    "B-company_name",
+    "B-coordinate",
+    "B-country",
+    "B-county",
+    "B-credit_debit_card",
+    "B-customer_id",
+    "B-cvv",
+    "B-date",
+    "B-date_of_birth",
+    "B-date_time",
+    "B-device_identifier",
+    "B-education_level",
+    "B-email",
+    "B-employee_id",
+    "B-employment_status",
+    "B-fax_number",
+    "B-first_name",
+    "B-gender",
+    "B-health_plan_beneficiary_number",
+    "B-http_cookie",
+    "B-ipv4",
+    "B-ipv6",
+    "B-language",
+    "B-last_name",
+    "B-license_plate",
+    "B-mac_address",
+    "B-medical_record_number",
+    "B-occupation",
+    "B-password",
+    "B-phone_number",
+    "B-pin",
+    "B-political_view",
+    "B-postcode",
+    "B-race_ethnicity",
+    "B-religious_belief",
+    "B-sexuality",
+    "B-ssn",
+    "B-state",
+    "B-street_address",
+    "B-swift_bic",
+    "B-tax_id",
+    "B-time",
+    "B-unique_id",
+    "B-url",
+    "B-user_name",
+    "B-vehicle_identifier",
+    "I-account_number",
+    "I-api_key",
+    "I-biometric_identifier",
+    "I-blood_type",
+    "I-certificate_license_number",
+    "I-city",
+    "I-company_name",
+    "I-coordinate",
+    "I-country",
+    "I-county",
+    "I-credit_debit_card",
+    "I-customer_id",
+    "I-date",
+    "I-date_of_birth",
+    "I-date_time",
+    "I-device_identifier",
+    "I-education_level",
+    "I-email",
+    "I-employee_id",
+    "I-employment_status",
+    "I-fax_number",
+    "I-first_name",
+    "I-gender",
+    "I-health_plan_beneficiary_number",
+    "I-http_cookie",
+    "I-ipv4",
+    "I-ipv6",
+    "I-language",
+    "I-last_name",
+    "I-license_plate",
+    "I-mac_address",
+    "I-medical_record_number",
+    "I-occupation",
+    "I-password",
+    "I-phone_number",
+    "I-pin",
+    "I-political_view",
+    "I-postcode",
+    "I-race_ethnicity",
+    "I-religious_belief",
+    "I-sexuality",
+    "I-ssn",
+    "I-state",
+    "I-street_address",
+    "I-swift_bic",
+    "I-tax_id",
+    "I-time",
+    "I-unique_id",
+    "I-url",
+    "I-user_name",
+    "I-vehicle_identifier",
+    "B-PPSN",
+    "I-PPSN",
+    "B-PASSPORT_NUMBER",
+    "I-PASSPORT_NUMBER",
+    "I-bank_routing_number"
+  ],
+  "num_labels": 111,
+  "target_label": "PPSN",
+  "extra_labels": [
+    "PPSN",
+    "PASSPORT_NUMBER",
+    "PHONE_NUMBER",
+    "POSTCODE",
+    "BANK_ROUTING_NUMBER",
+    "ACCOUNT_NUMBER",
+    "CREDIT_DEBIT_CARD",
+    "EMAIL",
+    "FIRST_NAME",
+    "LAST_NAME",
+    "SWIFT_BIC"
+  ]
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cad5c4d1647a3e45d14665f42167611f1a4ca0bdb298199f0f0d9d1da834dafd
+size 539290124

onnx/config.json

@@ -0,0 +1,250 @@
+{
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForTokenClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "dtype": "float32",
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "O",
+    "1": "B-account_number",
+    "2": "B-age",
+    "3": "B-api_key",
+    "4": "B-bank_routing_number",
+    "5": "B-biometric_identifier",
+    "6": "B-blood_type",
+    "7": "B-certificate_license_number",
+    "8": "B-city",
+    "9": "B-company_name",
+    "10": "B-coordinate",
+    "11": "B-country",
+    "12": "B-county",
+    "13": "B-credit_debit_card",
+    "14": "B-customer_id",
+    "15": "B-cvv",
+    "16": "B-date",
+    "17": "B-date_of_birth",
+    "18": "B-date_time",
+    "19": "B-device_identifier",
+    "20": "B-education_level",
+    "21": "B-email",
+    "22": "B-employee_id",
+    "23": "B-employment_status",
+    "24": "B-fax_number",
+    "25": "B-first_name",
+    "26": "B-gender",
+    "27": "B-health_plan_beneficiary_number",
+    "28": "B-http_cookie",
+    "29": "B-ipv4",
+    "30": "B-ipv6",
+    "31": "B-language",
+    "32": "B-last_name",
+    "33": "B-license_plate",
+    "34": "B-mac_address",
+    "35": "B-medical_record_number",
+    "36": "B-occupation",
+    "37": "B-password",
+    "38": "B-phone_number",
+    "39": "B-pin",
+    "40": "B-political_view",
+    "41": "B-postcode",
+    "42": "B-race_ethnicity",
+    "43": "B-religious_belief",
+    "44": "B-sexuality",
+    "45": "B-ssn",
+    "46": "B-state",
+    "47": "B-street_address",
+    "48": "B-swift_bic",
+    "49": "B-tax_id",
+    "50": "B-time",
+    "51": "B-unique_id",
+    "52": "B-url",
+    "53": "B-user_name",
+    "54": "B-vehicle_identifier",
+    "55": "I-account_number",
+    "56": "I-api_key",
+    "57": "I-biometric_identifier",
+    "58": "I-blood_type",
+    "59": "I-certificate_license_number",
+    "60": "I-city",
+    "61": "I-company_name",
+    "62": "I-coordinate",
+    "63": "I-country",
+    "64": "I-county",
+    "65": "I-credit_debit_card",
+    "66": "I-customer_id",
+    "67": "I-date",
+    "68": "I-date_of_birth",
+    "69": "I-date_time",
+    "70": "I-device_identifier",
+    "71": "I-education_level",
+    "72": "I-email",
+    "73": "I-employee_id",
+    "74": "I-employment_status",
+    "75": "I-fax_number",
+    "76": "I-first_name",
+    "77": "I-gender",
+    "78": "I-health_plan_beneficiary_number",
+    "79": "I-http_cookie",
+    "80": "I-ipv4",
+    "81": "I-ipv6",
+    "82": "I-language",
+    "83": "I-last_name",
+    "84": "I-license_plate",
+    "85": "I-mac_address",
+    "86": "I-medical_record_number",
+    "87": "I-occupation",
+    "88": "I-password",
+    "89": "I-phone_number",
+    "90": "I-pin",
+    "91": "I-political_view",
+    "92": "I-postcode",
+    "93": "I-race_ethnicity",
+    "94": "I-religious_belief",
+    "95": "I-sexuality",
+    "96": "I-ssn",
+    "97": "I-state",
+    "98": "I-street_address",
+    "99": "I-swift_bic",
+    "100": "I-tax_id",
+    "101": "I-time",
+    "102": "I-unique_id",
+    "103": "I-url",
+    "104": "I-user_name",
+    "105": "I-vehicle_identifier",
+    "106": "B-PPSN",
+    "107": "I-PPSN",
+    "108": "B-PASSPORT_NUMBER",
+    "109": "I-PASSPORT_NUMBER",
+    "110": "I-bank_routing_number"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "B-PASSPORT_NUMBER": 108,
+    "B-PPSN": 106,
+    "B-account_number": 1,
+    "B-age": 2,
+    "B-api_key": 3,
+    "B-bank_routing_number": 4,
+    "B-biometric_identifier": 5,
+    "B-blood_type": 6,
+    "B-certificate_license_number": 7,
+    "B-city": 8,
+    "B-company_name": 9,
+    "B-coordinate": 10,
+    "B-country": 11,
+    "B-county": 12,
+    "B-credit_debit_card": 13,
+    "B-customer_id": 14,
+    "B-cvv": 15,
+    "B-date": 16,
+    "B-date_of_birth": 17,
+    "B-date_time": 18,
+    "B-device_identifier": 19,
+    "B-education_level": 20,
+    "B-email": 21,
+    "B-employee_id": 22,
+    "B-employment_status": 23,
+    "B-fax_number": 24,
+    "B-first_name": 25,
+    "B-gender": 26,
+    "B-health_plan_beneficiary_number": 27,
+    "B-http_cookie": 28,
+    "B-ipv4": 29,
+    "B-ipv6": 30,
+    "B-language": 31,
+    "B-last_name": 32,
+    "B-license_plate": 33,
+    "B-mac_address": 34,
+    "B-medical_record_number": 35,
+    "B-occupation": 36,
+    "B-password": 37,
+    "B-phone_number": 38,
+    "B-pin": 39,
+    "B-political_view": 40,
+    "B-postcode": 41,
+    "B-race_ethnicity": 42,
+    "B-religious_belief": 43,
+    "B-sexuality": 44,
+    "B-ssn": 45,
+    "B-state": 46,
+    "B-street_address": 47,
+    "B-swift_bic": 48,
+    "B-tax_id": 49,
+    "B-time": 50,
+    "B-unique_id": 51,
+    "B-url": 52,
+    "B-user_name": 53,
+    "B-vehicle_identifier": 54,
+    "I-PASSPORT_NUMBER": 109,
+    "I-PPSN": 107,
+    "I-account_number": 55,
+    "I-api_key": 56,
+    "I-bank_routing_number": 110,
+    "I-biometric_identifier": 57,
+    "I-blood_type": 58,
+    "I-certificate_license_number": 59,
+    "I-city": 60,
+    "I-company_name": 61,
+    "I-coordinate": 62,
+    "I-country": 63,
+    "I-county": 64,
+    "I-credit_debit_card": 65,
+    "I-customer_id": 66,
+    "I-date": 67,
+    "I-date_of_birth": 68,
+    "I-date_time": 69,
+    "I-device_identifier": 70,
+    "I-education_level": 71,
+    "I-email": 72,
+    "I-employee_id": 73,
+    "I-employment_status": 74,
+    "I-fax_number": 75,
+    "I-first_name": 76,
+    "I-gender": 77,
+    "I-health_plan_beneficiary_number": 78,
+    "I-http_cookie": 79,
+    "I-ipv4": 80,
+    "I-ipv6": 81,
+    "I-language": 82,
+    "I-last_name": 83,
+    "I-license_plate": 84,
+    "I-mac_address": 85,
+    "I-medical_record_number": 86,
+    "I-occupation": 87,
+    "I-password": 88,
+    "I-phone_number": 89,
+    "I-pin": 90,
+    "I-political_view": 91,
+    "I-postcode": 92,
+    "I-race_ethnicity": 93,
+    "I-religious_belief": 94,
+    "I-sexuality": 95,
+    "I-ssn": 96,
+    "I-state": 97,
+    "I-street_address": 98,
+    "I-swift_bic": 99,
+    "I-tax_id": 100,
+    "I-time": 101,
+    "I-unique_id": 102,
+    "I-url": 103,
+    "I-user_name": 104,
+    "I-vehicle_identifier": 105,
+    "O": 0
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "output_past": true,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "transformers_version": "4.57.6",
+  "vocab_size": 119547
+}

onnx/label_meta.json

@@ -0,0 +1,131 @@
+{
+  "base_model": "models/openmed-mliteclinical-irish-core-v17_full_s180",
+  "label_list": [
+    "O",
+    "B-account_number",
+    "B-age",
+    "B-api_key",
+    "B-bank_routing_number",
+    "B-biometric_identifier",
+    "B-blood_type",
+    "B-certificate_license_number",
+    "B-city",
+    "B-company_name",
+    "B-coordinate",
+    "B-country",
+    "B-county",
+    "B-credit_debit_card",
+    "B-customer_id",
+    "B-cvv",
+    "B-date",
+    "B-date_of_birth",
+    "B-date_time",
+    "B-device_identifier",
+    "B-education_level",
+    "B-email",
+    "B-employee_id",
+    "B-employment_status",
+    "B-fax_number",
+    "B-first_name",
+    "B-gender",
+    "B-health_plan_beneficiary_number",
+    "B-http_cookie",
+    "B-ipv4",
+    "B-ipv6",
+    "B-language",
+    "B-last_name",
+    "B-license_plate",
+    "B-mac_address",
+    "B-medical_record_number",
+    "B-occupation",
+    "B-password",
+    "B-phone_number",
+    "B-pin",
+    "B-political_view",
+    "B-postcode",
+    "B-race_ethnicity",
+    "B-religious_belief",
+    "B-sexuality",
+    "B-ssn",
+    "B-state",
+    "B-street_address",
+    "B-swift_bic",
+    "B-tax_id",
+    "B-time",
+    "B-unique_id",
+    "B-url",
+    "B-user_name",
+    "B-vehicle_identifier",
+    "I-account_number",
+    "I-api_key",
+    "I-biometric_identifier",
+    "I-blood_type",
+    "I-certificate_license_number",
+    "I-city",
+    "I-company_name",
+    "I-coordinate",
+    "I-country",
+    "I-county",
+    "I-credit_debit_card",
+    "I-customer_id",
+    "I-date",
+    "I-date_of_birth",
+    "I-date_time",
+    "I-device_identifier",
+    "I-education_level",
+    "I-email",
+    "I-employee_id",
+    "I-employment_status",
+    "I-fax_number",
+    "I-first_name",
+    "I-gender",
+    "I-health_plan_beneficiary_number",
+    "I-http_cookie",
+    "I-ipv4",
+    "I-ipv6",
+    "I-language",
+    "I-last_name",
+    "I-license_plate",
+    "I-mac_address",
+    "I-medical_record_number",
+    "I-occupation",
+    "I-password",
+    "I-phone_number",
+    "I-pin",
+    "I-political_view",
+    "I-postcode",
+    "I-race_ethnicity",
+    "I-religious_belief",
+    "I-sexuality",
+    "I-ssn",
+    "I-state",
+    "I-street_address",
+    "I-swift_bic",
+    "I-tax_id",
+    "I-time",
+    "I-unique_id",
+    "I-url",
+    "I-user_name",
+    "I-vehicle_identifier",
+    "B-PPSN",
+    "I-PPSN",
+    "B-PASSPORT_NUMBER",
+    "I-PASSPORT_NUMBER",
+    "I-bank_routing_number"
+  ],
+  "num_labels": 111,
+  "target_label": "PPSN",
+  "extra_labels": [
+    "PPSN",
+    "PASSPORT_NUMBER",
+    "PHONE_NUMBER",
+    "POSTCODE",
+    "BANK_ROUTING_NUMBER",
+    "ACCOUNT_NUMBER",
+    "CREDIT_DEBIT_CARD",
+    "EMAIL",
+    "FIRST_NAME",
+    "LAST_NAME",
+    "SWIFT_BIC"
+  ]
+}

onnx/model_quantized.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3e6d01052640d395078de109c3f194eef6d390fc5340e0d6a86bf8ca91baa166
+size 412032331

onnx/onnx_export.json

@@ -0,0 +1,18 @@
+{
+  "source_model": "models/openmed-mliteclinical-irish-core-v19_boundaryplus_full_s220",
+  "onnx_path": "models/openmed-mliteclinical-irish-core-v19_boundaryplus_full_s220_onnx/model.onnx",
+  "method": "exported_with_optimum",
+  "preferred_onnx_file": null,
+  "opset": 18,
+  "max_length": 256,
+  "copied_assets": [
+    "config.json",
+    "label_meta.json",
+    "special_tokens_map.json",
+    "tokenizer.json",
+    "tokenizer_config.json",
+    "vocab.txt"
+  ],
+  "external_data": false,
+  "task": "token-classification"
+}

onnx/quantization.json

@@ -0,0 +1,25 @@
+{
+  "source_dir": "models/openmed-mliteclinical-irish-core-v19_boundaryplus_full_s220_onnx",
+  "input_model": "models/openmed-mliteclinical-irish-core-v19_boundaryplus_full_s220_onnx_q8/model.onnx",
+  "output_model": "models/openmed-mliteclinical-irish-core-v19_boundaryplus_full_s220_onnx_q8/model_quantized.onnx",
+  "weight_type": "qint8",
+  "per_channel": true,
+  "reduce_range": false,
+  "op_types": [
+    "MatMul",
+    "Gemm",
+    "Attention"
+  ],
+  "copied_assets": [
+    "models/openmed-mliteclinical-irish-core-v19_boundaryplus_full_s220_onnx/model.onnx",
+    "onnx_export.json",
+    "config.json",
+    "label_meta.json",
+    "special_tokens_map.json",
+    "tokenizer.json",
+    "tokenizer_config.json",
+    "vocab.txt"
+  ],
+  "format": "onnx_dynamic_quantized",
+  "task": "token-classification"
+}

onnx/special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

onnx/tokenizer_config.json

@@ -0,0 +1,61 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "extra_special_tokens": {},
+  "fix_mistral_regex": true,
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}

onnx_token_classifier.py

@@ -0,0 +1,207 @@
+#!/usr/bin/env python3
+import json
+import os
+import tempfile
+from pathlib import Path
+
+os.environ.setdefault("TRANSFORMERS_NO_TF", "1")
+os.environ.setdefault("TRANSFORMERS_NO_FLAX", "1")
+os.environ.setdefault("TRANSFORMERS_NO_TORCHVISION", "1")
+os.environ["USE_TF"] = "0"
+os.environ["USE_FLAX"] = "0"
+os.environ["USE_TORCH"] = "1"
+
+import numpy as np
+import regex as re
+from huggingface_hub import HfApi, hf_hub_download
+from transformers import AutoConfig, AutoTokenizer
+
+
+TOKEN_RE = re.compile(r"[A-Za-z0-9]+|[^\w\s]", re.UNICODE)
+DEFAULT_ONNX_FILES = ["onnx/model_quantized.onnx", "model_quantized.onnx"]
+EIRCODE_RE = re.compile(r"^(?:[ACDEFHKNPRTVWXY]\d{2}|D6W)\s?[0-9ACDEFHKNPRTVWXY]{4}$", re.IGNORECASE)
+
+
+def tokenize_with_spans(text: str):
+    return [(m.group(0), m.start(), m.end()) for m in TOKEN_RE.finditer(text)]
+
+
+def _load_tokenizer(tokenizer_ref: str):
+    tokenizer_path = Path(tokenizer_ref)
+    if tokenizer_path.exists():
+        tokenizer_cfg_path = tokenizer_path / "tokenizer_config.json"
+        if tokenizer_cfg_path.exists():
+            data = json.loads(tokenizer_cfg_path.read_text(encoding="utf-8"))
+            if "fix_mistral_regex" in data:
+                tmpdir = Path(tempfile.mkdtemp(prefix="openmed_onnx_tokenizer_"))
+                keep = {"tokenizer_config.json", "tokenizer.json", "special_tokens_map.json", "vocab.txt"}
+                for child in tokenizer_path.iterdir():
+                    if child.is_file() and child.name in keep:
+                        (tmpdir / child.name).write_bytes(child.read_bytes())
+                data.pop("fix_mistral_regex", None)
+                (tmpdir / "tokenizer_config.json").write_text(json.dumps(data, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
+                tokenizer_ref = str(tmpdir)
+    try:
+        return AutoTokenizer.from_pretrained(tokenizer_ref, use_fast=True, fix_mistral_regex=True)
+    except Exception:
+        try:
+            return AutoTokenizer.from_pretrained(tokenizer_ref, use_fast=True, fix_mistral_regex=False)
+        except TypeError:
+            return AutoTokenizer.from_pretrained(tokenizer_ref, use_fast=True)
+
+
+def load_onnx_token_classifier(model_ref: str, onnx_file: str | None = None):
+    import onnxruntime as ort
+    model_path = Path(model_ref)
+    if model_path.exists():
+        candidates = [onnx_file] if onnx_file else []
+        candidates += DEFAULT_ONNX_FILES
+        onnx_path = None
+        for candidate in candidates:
+            if not candidate:
+                continue
+            path = model_path / candidate
+            if path.exists():
+                onnx_path = path
+                break
+        if onnx_path is None:
+            raise FileNotFoundError("Missing ONNX artifact")
+        config = AutoConfig.from_pretrained(model_ref)
+        tokenizer = _load_tokenizer(model_ref)
+    else:
+        api = HfApi()
+        files = set(api.list_repo_files(repo_id=model_ref, repo_type="model"))
+        chosen = None
+        candidates = [onnx_file] if onnx_file else []
+        candidates += DEFAULT_ONNX_FILES
+        for candidate in candidates:
+            if candidate and candidate in files:
+                chosen = candidate
+                break
+        if chosen is None:
+            raise FileNotFoundError("No ONNX artifact published")
+        onnx_path = Path(hf_hub_download(repo_id=model_ref, filename=chosen, repo_type="model"))
+        config = AutoConfig.from_pretrained(model_ref)
+        tokenizer = _load_tokenizer(model_ref)
+    session = ort.InferenceSession(str(onnx_path), providers=["CPUExecutionProvider"])
+    return session, tokenizer, config
+
+
+def _softmax(logits):
+    shifted = logits - np.max(logits, axis=-1, keepdims=True)
+    exp = np.exp(shifted)
+    return exp / np.clip(np.sum(exp, axis=-1, keepdims=True), 1e-12, None)
+
+
+def _run(session, encoded):
+    input_names = {item.name for item in session.get_inputs()}
+    feed = {}
+    for key, value in encoded.items():
+        if key == "offset_mapping":
+            continue
+        if key in input_names:
+            feed[key] = value
+    return session.run(None, feed)[0]
+
+
+def normalize_label(label: str) -> str:
+    label = (label or "").strip()
+    if label.startswith("B-") or label.startswith("I-"):
+        label = label[2:]
+    return label.upper()
+
+
+def looks_like_eircode(value: str) -> bool:
+    return EIRCODE_RE.match(value.strip()) is not None
+
+
+def ppsn_label_ids_from_config(config):
+    ids = []
+    for raw_id, raw_label in config.id2label.items():
+        label_id = int(raw_id)
+        label = str(raw_label or "").strip()
+        if label.endswith("PPSN"):
+            ids.append(label_id)
+    return sorted(ids)
+
+
+def simple_aggregate_spans_onnx(text, session, tokenizer, config, min_score=0.5):
+    encoded = tokenizer(text, return_offsets_mapping=True, return_tensors="np", truncation=True)
+    logits = _run(session, encoded)[0]
+    probs = _softmax(logits)
+    pred_ids = probs.argmax(axis=-1)
+    id2label = {int(k): v for k, v in config.id2label.items()}
+    offsets = encoded["offset_mapping"][0].tolist()
+    attention = encoded["attention_mask"][0].tolist()
+    spans = []
+    active = None
+    for idx, ((start, end), keep) in enumerate(zip(offsets, attention)):
+        if not keep or start == end:
+            if active is not None:
+                spans.append(active)
+                active = None
+            continue
+        label = id2label[int(pred_ids[idx])]
+        if label == "O":
+            if active is not None:
+                spans.append(active)
+                active = None
+            continue
+        score = float(probs[idx, int(pred_ids[idx])])
+        if score < min_score:
+            if active is not None:
+                spans.append(active)
+                active = None
+            continue
+        prefix = label[:1] if label.startswith(("B-", "I-")) else "B"
+        entity = normalize_label(label)
+        if active is None or prefix == "B" or entity != active["label"] or int(start) > int(active["end"]) + 1:
+            if active is not None:
+                spans.append(active)
+            active = {"label": entity, "start": int(start), "end": int(end), "score": score}
+        else:
+            active["end"] = int(end)
+            active["score"] = max(float(active["score"]), score)
+    if active is not None:
+        spans.append(active)
+    for span in spans:
+        span["text"] = text[span["start"]:span["end"]]
+    return spans
+
+
+def word_aligned_ppsn_spans_onnx(text, session, tokenizer, config, threshold=0.4):
+    pieces = tokenize_with_spans(text)
+    if not pieces:
+        return []
+    words = [word for word, _, _ in pieces]
+    encoded = tokenizer(words, is_split_into_words=True, return_tensors="np", truncation=True)
+    word_ids = encoded.word_ids(batch_index=0)
+    logits = _run(session, encoded)[0]
+    probs = _softmax(logits)
+    label_ids = ppsn_label_ids_from_config(config)
+    word_scores = []
+    for word_index in range(len(pieces)):
+        score = 0.0
+        for token_index, wid in enumerate(word_ids):
+            if wid != word_index:
+                continue
+            for label_id in label_ids:
+                score = max(score, float(probs[token_index, label_id]))
+        word_scores.append(score)
+    spans = []
+    active = None
+    for (_, start, end), score in zip(pieces, word_scores):
+        if score >= threshold:
+            if active is None:
+                active = {"label": "PPSN", "start": start, "end": end, "score": score}
+            else:
+                active["end"] = end
+                active["score"] = max(float(active["score"]), score)
+        elif active is not None:
+            spans.append(active)
+            active = None
+    if active is not None:
+        spans.append(active)
+    for span in spans:
+        span["text"] = text[span["start"]:span["end"]]
+    return spans

pyproject.toml

@@ -0,0 +1,18 @@
+[project]
+name = "openmed-mliteclinical-irishcorepii-rc"
+version = "0.2.0rc3"
+description = "Third QA release candidate for Irish core PII detection with OpenMed mLiteClinical"
+requires-python = ">=3.10"
+readme = "README.md"
+license = { text = "Apache-2.0" }
+dependencies = [
+  "transformers>=4.41.0",
+  "torch",
+  "regex>=2024.5.15",
+  "onnxruntime>=1.20.0",
+  "numpy>=1.26.0",
+  "huggingface_hub>=0.36.0",
+]
+
+[tool.uv]
+package = false

qa_config.json

@@ -0,0 +1,31 @@
+{
+  "repo_id": "temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc3",
+  "recommended_inference": {
+    "full_checkpoint": {
+      "ppsn_decoder": "word_aligned",
+      "ppsn_min_score": 0.55,
+      "other_min_score": 0.4,
+      "device": "cpu",
+      "script": "inference_mask.py"
+    },
+    "onnx_q8": {
+      "ppsn_decoder": "word_aligned",
+      "ppsn_min_score": 0.6,
+      "other_min_score": 0.45,
+      "device": "cpu",
+      "onnx_artifact": "onnx/model_quantized.onnx",
+      "script": "inference_mask_onnx.py"
+    }
+  },
+  "smoke_texts": [
+    "I was told to provide my number 1234567T when applying, what do I do next?",
+    "My sort code is 90-00-17 for AIB.",
+    "Please provide your passport: NN5123456.",
+    "Call me on 0851234567 tomorrow.",
+    "My Eircode is D02 X285 and my phone is 087 123 4567."
+  ],
+  "known_limit_texts": [
+    "Passport PA 1234567 was used to board the flight.",
+    "Usaideadh pas PA 1234567 chun dul ar bord an eitilt."
+  ]
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,61 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "extra_special_tokens": {},
+  "fix_mistral_regex": true,
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}

training_sources.json

@@ -0,0 +1,69 @@
+{
+  "base_model": "OpenMed/OpenMed-PII-mLiteClinical-Base-135M-v1",
+  "current_public_reference": "temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v1",
+  "previous_rc_reference": "temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc2",
+  "release_purpose": "Third v2 release candidate for the IrishCorePII line. This round improves the model across the board: stronger English and Irish core PII, exact QA regressions, multilingual PPSN precision, and a stronger bundled ONNX q8 artifact.",
+  "recommended_thresholds": {
+    "full_checkpoint": {
+      "ppsn_min_score": 0.55,
+      "other_min_score": 0.4
+    },
+    "onnx_q8": {
+      "ppsn_min_score": 0.6,
+      "other_min_score": 0.45
+    }
+  },
+  "training_mix_summary": [
+    {
+      "component": "previous Irish core release mix replay",
+      "weight": 4.0
+    },
+    {
+      "component": "spec-driven Irish boundary and format dataset for passports, compact mobiles, Eircodes, sort codes, cards, and PPSN/Eircode confusions",
+      "weight": 2.5
+    },
+    {
+      "component": "exact QA and user feedback replay from numeric, gap, PPSN, and edge suites",
+      "weight": 2.0
+    },
+    {
+      "component": "multilingual PPSN replay",
+      "weight": 1.5
+    },
+    {
+      "component": "weak-context PPSN recovery replay",
+      "weight": 1.5
+    },
+    {
+      "component": "spec-valid PPSN and Eircode synthetic mix",
+      "weight": 1.5
+    }
+  ],
+  "upstream_attribution": [
+    {
+      "name": "joelniklaus/mapa",
+      "license": "CC-BY-4.0"
+    },
+    {
+      "name": "gretelai/synthetic_pii_finance_multilingual",
+      "license": "Apache-2.0"
+    }
+  ],
+  "quantization_notes": {
+    "promoted_q8_recipe": "ONNX Runtime dynamic int8 with per-channel quantization over MatMul,Gemm,Attention",
+    "rejected_recipes": [
+      "MatMul,Gemm-only dynamic int8: faster on some suites, but lower multilingual PPSN quality",
+      "MatMul,Gemm-only dynamic int8 with reduce_range: quality regressed further"
+    ],
+    "not_promoted": [
+      "QAT in this DistilBERT token-classification stack",
+      "Mezzanine Qwen-focused weight transforms"
+    ]
+  },
+  "known_limitations": [
+    "The full checkpoint can still trim the final digit in some short English compact-phone contexts such as \"Call me on 0851234567 tomorrow.\"",
+    "Passport values written as prefix plus space plus digits are still the main strict-boundary failure mode.",
+    "The Irish Gaelic spaced-passport case remains weak in raw-model mode.",
+    "Multilingual PPSN quality is stronger than temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc2, but still below the PPSN-focused public release temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v1."
+  ]
+}