Add raw-only rc8 release with ONNX dynamic q8

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+*.pyc
+.venv/

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NOTICE

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+This release is derived from OpenMed/OpenMed-PII-mLiteClinical-Base-135M-v1 (Apache-2.0).
+
+Additional training data attribution:
+- temsa/OpenMed-Irish-CorePII-TrainMix-v1 (composite train mix)
+- temsa/OpenMed-Irish-PPSN-Eircode-Spec-v1 (synthetic dataset)
+- joelniklaus/mapa (CC-BY-4.0)
+- gretelai/synthetic_pii_finance_multilingual (Apache-2.0)
+
+This repo distributes model artifacts and synthetic benchmark files. It does not redistribute third-party dataset rows from upstream datasets.

README.md

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+---
+language:
+- en
+- ga
+license: apache-2.0
+library_name: transformers
+pipeline_tag: token-classification
+tags:
+- pii
+- de-identification
+- token-classification
+- ireland
+- irish
+- gaelic
+- raw-only
+- ppsn
+- eircode
+- passport
+- phone-number
+- onnx
+- int8
+- dynamic-quantization
+base_model:
+- OpenMed/OpenMed-PII-mLiteClinical-Base-135M-v1
+datasets:
+- temsa/OpenMed-Irish-CorePII-TrainMix-v1
+- temsa/OpenMed-Irish-PPSN-Eircode-Spec-v1
+- joelniklaus/mapa
+- gretelai/synthetic_pii_finance_multilingual
+---
+
+# OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc8
+
+Raw-only Irish core PII release derived from `OpenMed/OpenMed-PII-mLiteClinical-Base-135M-v1`.
+
+This repo does **not** require the scanner / validator layer used by `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc7`.
+Both the full checkpoint and the ONNX q8 artifact use the same learned score-only decoder over token-presence and typed boundary heads.
+
+## Coverage
+
+- `PPSN`
+- `ACCOUNT_NUMBER`
+- `BANK_ROUTING_NUMBER`
+- `CREDIT_DEBIT_CARD`
+- `PASSPORT_NUMBER`
+- `POSTCODE`
+- `PHONE_NUMBER`
+- `EMAIL`
+- `FIRST_NAME`
+- `LAST_NAME`
+- `SWIFT_BIC`
+
+## Included Variants
+
+- Full `transformers` checkpoint in the repo root
+- Unquantized ONNX export in `onnx/model.onnx`
+- Dynamic q8 ONNX artifact in `onnx/model_quantized.onnx`
+- `inference_mask.py` for the full checkpoint
+- `inference_mask_onnx.py` for the ONNX q8 artifact
+- `common.py`, `model.py`, and `multitask_model.py` implementing the raw-only decoder
+- benchmark files in `eval/`
+
+Artifact sizes:
+
+- Full checkpoint: `515 MB` (`model.safetensors`)
+- Dynamic q8 ONNX: `393 MB` (`onnx/model_quantized.onnx`)
+
+## What Changed From rc7
+
+`rc7` achieves its public quality with a bundled scanner / validator inference stack.
+
+`rc8` removes that layer completely:
+
+- no regex-based candidate extraction
+- no checksum validator dependency at inference time
+- no separate scanner spec or generated scanner code
+- same compact `mLiteClinical` encoder family, but with a raw-only multi-head decoder
+
+The tradeoff is explicit:
+
+- `rc7` is still stronger on the broad manual Irish core suite
+- `rc8` is easier to embed, simpler to maintain, and its ONNX q8 path stays very close to the full checkpoint
+
+## Architecture
+
+`rc8` keeps the DistilBERT-size encoder class of the 135M `mLiteClinical` base and adds:
+
+- a token-presence head for each released label
+- a typed start-boundary head
+- a typed end-boundary head
+- a score-only decoder that uses model scores, token offsets, continuity priors, and minimum-length priors from config
+
+There is no scanner or external validator in the release path.
+
+Design references that informed this direction:
+
+- Split-NER: https://aclanthology.org/2023.acl-short.36/
+- SpanNER: https://aclanthology.org/2021.acl-long.558/
+- Boundary Smoothing for Named Entity Recognition: https://aclanthology.org/2022.acl-long.490/
+- TinyBERT: https://aclanthology.org/2020.findings-emnlp.372/
+
+## How To Use It
+
+Full checkpoint:
+
+```bash
+uv run python inference_mask.py \
+  --model temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc8 \
+  --min-score 0.5 \
+  --text "My PPSN is 1234567TW, my Eircode is D02 X285, and my phone is 087 123 4567." \
+  --json
+```
+
+Dynamic q8 ONNX:
+
+```bash
+uv run python inference_mask_onnx.py \
+  --model temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc8 \
+  --min-score 0.5 \
+  --text "Please provide your passport NN5123456 and call me on 0851234567." \
+  --json
+```
+
+## Benchmarks
+
+Main comparison:
+
+| Model | Irish core F1 | Edge F1 | Finance F1 | Finance-boundary F1 | User PPSN F1 | GA weak PPSN F1 | Multilingual PPSN F1 | Core CPU ex/s | Multilingual CPU ex/s |
+|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
+| Base OpenMed | 0.3743 | 0.0556 | - | - | - | - | 0.0000 | 15.1811 | 37.4666 |
+| Previous public `rc7` full | 1.0000 | - | 1.0000 | 1.0000 | - | 1.0000 | - | 3.5394 | - |
+| Previous public `rc7` ONNX q8 | 0.9934 | - | 1.0000 | 1.0000 | - | 1.0000 | - | 12.1653 | - |
+| `rc8` full | 0.9737 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 0.9176 | 2.2965 | 6.3095 |
+| `rc8` ONNX q8 | 0.9737 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 0.9176 | 46.1420 | 99.7166 |
+
+Irish core label breakdown:
+
+| Label | `rc8` full | `rc8` ONNX q8 |
+|---|---:|---:|
+| PPSN | 1.0000 | 1.0000 |
+| PHONE_NUMBER | 0.9565 | 0.9565 |
+| POSTCODE | 1.0000 | 1.0000 |
+| ACCOUNT_NUMBER | 0.8000 | 0.8000 |
+| PASSPORT_NUMBER | 1.0000 | 1.0000 |
+| EMAIL | 1.0000 | 1.0000 |
+| FIRST_NAME | 0.9744 | 0.9744 |
+| LAST_NAME | 0.9744 | 0.9744 |
+
+## Dynamic q8 Artifact
+
+Artifact paths:
+
+- unquantized: `onnx/model.onnx`
+- preprocessed: `onnx/model.preprocessed.onnx`
+- quantized: `onnx/model_quantized.onnx`
+
+Quantization recipe used here:
+
+- ONNX pre-processing before quantization
+- ONNX Runtime dynamic int8
+- `qint8`
+- `per_channel=true`
+- `op_types=MatMul,Gemm,Attention`
+
+For CPU deployment, the ONNX q8 artifact is the recommended default.
+
+## Limits
+
+- `rc8` is raw-only. It intentionally gives up the scanner/validator stack used by `rc7`, so its broad manual-suite ceiling is lower.
+- The current remaining local misses are a bare 8-digit account-number case and one Gaelic phone-number case in the manual core suite.
+- If you need the exact `rc8` behavior, use the bundled inference scripts or import `decode_token_presence_segments` from `common.py`.
+
+## License And Attribution
+
+- Release license: Apache-2.0
+- Base model: `OpenMed/OpenMed-PII-mLiteClinical-Base-135M-v1`
+- See `NOTICE` and `training_sources.json` for attribution and training details.

common.py

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+#!/usr/bin/env python3
+from __future__ import annotations
+
+import json
+import tempfile
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+from huggingface_hub import HfApi, hf_hub_download
+from transformers import AutoConfig, AutoTokenizer
+
+TOKENIZER_FILES = [
+    "tokenizer_config.json",
+    "tokenizer.json",
+    "special_tokens_map.json",
+    "vocab.txt",
+    "vocab.json",
+    "merges.txt",
+    "added_tokens.json",
+    "sentencepiece.bpe.model",
+    "spiece.model",
+]
+DEFAULT_LABEL_MAX_SPAN_TOKENS = {
+    # Token-piece limits, not word limits. These need to reflect how the
+    # underlying tokenizer actually fragments compact identifiers.
+    "PPSN": 9,
+    "POSTCODE": 7,
+    "PHONE_NUMBER": 10,
+    "PASSPORT_NUMBER": 8,
+    "BANK_ROUTING_NUMBER": 5,
+    "ACCOUNT_NUMBER": 19,
+    "CREDIT_DEBIT_CARD": 12,
+    "SWIFT_BIC": 8,
+    "EMAIL": 15,
+    "FIRST_NAME": 5,
+    "LAST_NAME": 8,
+}
+DEFAULT_LABEL_MIN_NONSPACE_CHARS = {
+    "PPSN": 8,
+    "POSTCODE": 6,
+    "PHONE_NUMBER": 7,
+    "PASSPORT_NUMBER": 7,
+    "BANK_ROUTING_NUMBER": 6,
+    "ACCOUNT_NUMBER": 6,
+    "CREDIT_DEBIT_CARD": 12,
+    "SWIFT_BIC": 8,
+    "EMAIL": 6,
+    "FIRST_NAME": 2,
+    "LAST_NAME": 2,
+}
+WHITESPACE_BRIDGE_LABELS = {
+    "PPSN",
+    "POSTCODE",
+    "PHONE_NUMBER",
+    "PASSPORT_NUMBER",
+    "BANK_ROUTING_NUMBER",
+    "ACCOUNT_NUMBER",
+    "CREDIT_DEBIT_CARD",
+    "SWIFT_BIC",
+    "EMAIL",
+}
+CONSERVATIVE_BOUNDARY_REFINEMENT_LABELS = {
+    "PPSN",
+    "POSTCODE",
+    "PHONE_NUMBER",
+    "PASSPORT_NUMBER",
+    "BANK_ROUTING_NUMBER",
+    "ACCOUNT_NUMBER",
+    "CREDIT_DEBIT_CARD",
+    "SWIFT_BIC",
+    "EMAIL",
+}
+OUTPUT_PRIORITY = {
+    "PPSN": 0,
+    "PASSPORT_NUMBER": 1,
+    "ACCOUNT_NUMBER": 2,
+    "BANK_ROUTING_NUMBER": 3,
+    "CREDIT_DEBIT_CARD": 4,
+    "PHONE_NUMBER": 5,
+    "SWIFT_BIC": 6,
+    "POSTCODE": 7,
+    "EMAIL": 8,
+    "FIRST_NAME": 9,
+    "LAST_NAME": 10,
+}
+
+
+def normalize_entity_name(label: str) -> str:
+    label = (label or "").strip()
+    if label.startswith("B-") or label.startswith("I-"):
+        label = label[2:]
+    return label.upper()
+
+
+def _sanitize_tokenizer_dir(tokenizer_path: Path) -> str:
+    tokenizer_cfg_path = tokenizer_path / "tokenizer_config.json"
+    if not tokenizer_cfg_path.exists():
+        return str(tokenizer_path)
+    data = json.loads(tokenizer_cfg_path.read_text(encoding="utf-8"))
+    if "fix_mistral_regex" not in data:
+        return str(tokenizer_path)
+    tmpdir = Path(tempfile.mkdtemp(prefix="openmed_span_tokenizer_"))
+    keep = set(TOKENIZER_FILES)
+    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")
+    return str(tmpdir)
+
+
+def safe_auto_tokenizer(tokenizer_ref: str):
+    tokenizer_path = Path(tokenizer_ref)
+    if tokenizer_path.exists():
+        tokenizer_ref = _sanitize_tokenizer_dir(tokenizer_path)
+    else:
+        api = HfApi()
+        files = set(api.list_repo_files(repo_id=tokenizer_ref, repo_type="model"))
+        tmpdir = Path(tempfile.mkdtemp(prefix="openmed_remote_span_tokenizer_"))
+        copied = False
+        for name in TOKENIZER_FILES:
+            if name not in files:
+                continue
+            src = hf_hub_download(repo_id=tokenizer_ref, filename=name, repo_type="model")
+            (tmpdir / Path(name).name).write_bytes(Path(src).read_bytes())
+            copied = True
+        if copied:
+            tokenizer_ref = _sanitize_tokenizer_dir(tmpdir)
+
+    try:
+        return AutoTokenizer.from_pretrained(tokenizer_ref, use_fast=True, fix_mistral_regex=True)
+    except Exception:
+        pass
+    try:
+        return AutoTokenizer.from_pretrained(tokenizer_ref, use_fast=True, fix_mistral_regex=False)
+    except TypeError:
+        pass
+    try:
+        return AutoTokenizer.from_pretrained(tokenizer_ref, use_fast=True)
+    except Exception:
+        return AutoTokenizer.from_pretrained(tokenizer_ref, use_fast=False)
+
+
+def label_names_from_config(config) -> list[str]:
+    names = list(getattr(config, "span_label_names", []))
+    if not names:
+        raise ValueError("Missing span_label_names in config")
+    return [normalize_entity_name(name) for name in names]
+
+
+def label_thresholds_from_config(config, default_threshold: float) -> dict[str, float]:
+    raw = getattr(config, "span_label_thresholds", None) or {}
+    out = {normalize_entity_name(key): float(value) for key, value in raw.items()}
+    for label in label_names_from_config(config):
+        out.setdefault(label, float(default_threshold))
+    return out
+
+
+def token_label_thresholds_from_config(config, default_threshold: float) -> dict[str, float]:
+    raw = getattr(config, "token_label_thresholds", None) or {}
+    out = {normalize_entity_name(key): float(value) for key, value in raw.items()}
+    for label in label_names_from_config(config):
+        out.setdefault(label, float(default_threshold))
+    return out
+
+
+def token_extend_thresholds_from_config(config, default_fraction: float = 0.6) -> dict[str, float]:
+    raw = getattr(config, "token_extend_thresholds", None) or {}
+    out = {normalize_entity_name(key): float(value) for key, value in raw.items()}
+    for label in label_names_from_config(config):
+        out.setdefault(label, max(0.0, min(1.0, float(token_label_thresholds_from_config(config, 0.5).get(label, 0.5)) * default_fraction)))
+    return out
+
+
+def boundary_label_thresholds_from_config(config, default_threshold: float = 0.0) -> dict[str, float]:
+    raw = getattr(config, "boundary_label_thresholds", None) or {}
+    out = {normalize_entity_name(key): float(value) for key, value in raw.items()}
+    for label in label_names_from_config(config):
+        out.setdefault(label, float(default_threshold))
+    return out
+
+
+def label_max_span_tokens_from_config(config) -> dict[str, int]:
+    raw = getattr(config, "span_label_max_span_tokens", None) or {}
+    out = {normalize_entity_name(key): int(value) for key, value in raw.items()}
+    for label, value in DEFAULT_LABEL_MAX_SPAN_TOKENS.items():
+        out.setdefault(label, value)
+    for label in label_names_from_config(config):
+        out.setdefault(label, 8)
+    return out
+
+
+def label_min_nonspace_chars_from_config(config) -> dict[str, int]:
+    raw = getattr(config, "span_label_min_nonspace_chars", None) or {}
+    out = {normalize_entity_name(key): int(value) for key, value in raw.items()}
+    for label, value in DEFAULT_LABEL_MIN_NONSPACE_CHARS.items():
+        out.setdefault(label, value)
+    for label in label_names_from_config(config):
+        out.setdefault(label, 1)
+    return out
+
+
+def overlaps(a: dict, b: dict) -> bool:
+    return not (a["end"] <= b["start"] or b["end"] <= a["start"])
+
+
+def dedupe_spans(spans: list[dict]) -> list[dict]:
+    ordered = sorted(
+        spans,
+        key=lambda item: (-float(item.get("score", 0.0)), item["start"], item["end"], OUTPUT_PRIORITY.get(item["label"], 99)),
+    )
+    kept = []
+    for span in ordered:
+        if any(overlaps(span, other) for other in kept):
+            continue
+        kept.append(span)
+    kept.sort(key=lambda item: (item["start"], item["end"], OUTPUT_PRIORITY.get(item["label"], 99)))
+    return kept
+
+
+def _valid_offset(offset: tuple[int, int]) -> bool:
+    return bool(offset) and offset[1] > offset[0]
+
+
+def _has_skippable_bridge(text: str, left: tuple[int, int], right: tuple[int, int], label: str) -> bool:
+    bridge = text[int(left[1]) : int(right[0])]
+    if bridge == "":
+        return True
+    return label in WHITESPACE_BRIDGE_LABELS and bridge.isspace()
+
+
+def _has_left_extension_bridge(text: str, left: tuple[int, int], right: tuple[int, int]) -> bool:
+    bridge = text[int(left[1]) : int(right[0])]
+    return bridge == ""
+
+
+def _nonspace_length(text: str, start: int, end: int) -> int:
+    return sum(0 if ch.isspace() else 1 for ch in text[int(start) : int(end)])
+
+
+def decode_span_logits(
+    text: str,
+    offsets: list[tuple[int, int]],
+    start_scores: np.ndarray,
+    end_scores: np.ndarray,
+    label_names: list[str],
+    default_threshold: float,
+    label_thresholds: dict[str, float] | None = None,
+    label_max_span_tokens: dict[str, int] | None = None,
+) -> list[dict]:
+    thresholds = {label: float(default_threshold) for label in label_names}
+    if label_thresholds:
+        thresholds.update({normalize_entity_name(key): float(value) for key, value in label_thresholds.items()})
+    max_tokens = dict(DEFAULT_LABEL_MAX_SPAN_TOKENS)
+    if label_max_span_tokens:
+        max_tokens.update({normalize_entity_name(key): int(value) for key, value in label_max_span_tokens.items()})
+
+    spans: list[dict] = []
+    for label_index, label in enumerate(label_names):
+        threshold = thresholds.get(label, float(default_threshold))
+        max_span = max_tokens.get(label, 8)
+        start_candidates = [idx for idx in range(len(offsets)) if _valid_offset(offsets[idx]) and float(start_scores[idx, label_index]) >= threshold]
+        for start_idx in start_candidates:
+            best = None
+            for end_idx in range(start_idx, min(len(offsets), start_idx + max_span)):
+                if not _valid_offset(offsets[end_idx]):
+                    continue
+                end_score = float(end_scores[end_idx, label_index])
+                if end_score < threshold:
+                    continue
+                score = min(float(start_scores[start_idx, label_index]), end_score)
+                if best is None or score > best["score"]:
+                    best = {
+                        "label": label,
+                        "start": int(offsets[start_idx][0]),
+                        "end": int(offsets[end_idx][1]),
+                        "score": score,
+                    }
+            if best is not None and best["start"] < best["end"]:
+                best["text"] = text[best["start"]:best["end"]]
+                spans.append(best)
+    return dedupe_spans(spans)
+
+
+def decode_token_presence_segments(
+    text: str,
+    offsets: list[tuple[int, int]],
+    token_scores: np.ndarray,
+    label_names: list[str],
+    default_threshold: float,
+    label_thresholds: dict[str, float] | None = None,
+    label_extend_thresholds: dict[str, float] | None = None,
+    label_max_span_tokens: dict[str, int] | None = None,
+    label_min_nonspace_chars: dict[str, int] | None = None,
+    boundary_label_thresholds: dict[str, float] | None = None,
+    start_scores: np.ndarray | None = None,
+    end_scores: np.ndarray | None = None,
+) -> list[dict]:
+    thresholds = {label: float(default_threshold) for label in label_names}
+    if label_thresholds:
+        thresholds.update({normalize_entity_name(key): float(value) for key, value in label_thresholds.items()})
+    extend_thresholds = {label: max(0.0, min(1.0, thresholds[label] * 0.6)) for label in label_names}
+    if label_extend_thresholds:
+        extend_thresholds.update({normalize_entity_name(key): float(value) for key, value in label_extend_thresholds.items()})
+    max_tokens = dict(DEFAULT_LABEL_MAX_SPAN_TOKENS)
+    if label_max_span_tokens:
+        max_tokens.update({normalize_entity_name(key): int(value) for key, value in label_max_span_tokens.items()})
+    min_nonspace_chars = dict(DEFAULT_LABEL_MIN_NONSPACE_CHARS)
+    if label_min_nonspace_chars:
+        min_nonspace_chars.update({normalize_entity_name(key): int(value) for key, value in label_min_nonspace_chars.items()})
+    boundary_thresholds = {label: 0.0 for label in label_names}
+    if boundary_label_thresholds:
+        boundary_thresholds.update({normalize_entity_name(key): float(value) for key, value in boundary_label_thresholds.items()})
+
+    spans: list[dict] = []
+    valid = [_valid_offset(offset) for offset in offsets]
+    num_tokens = len(offsets)
+    for label_index, label in enumerate(label_names):
+        threshold = thresholds.get(label, float(default_threshold))
+        extend_threshold = min(threshold, extend_thresholds.get(label, threshold))
+        max_span = max_tokens.get(label, 8)
+        idx = 0
+        while idx < num_tokens:
+            if not valid[idx] or float(token_scores[idx, label_index]) < threshold:
+                idx += 1
+                continue
+            start_idx = idx
+            end_idx = idx
+            while end_idx + 1 < num_tokens and valid[end_idx + 1] and float(token_scores[end_idx + 1, label_index]) >= threshold and (end_idx + 1 - start_idx + 1) <= max_span:
+                end_idx += 1
+            while (
+                start_idx - 1 >= 0
+                and valid[start_idx - 1]
+                and _has_left_extension_bridge(text, offsets[start_idx - 1], offsets[start_idx])
+                and float(token_scores[start_idx - 1, label_index]) >= extend_threshold
+                and (end_idx - (start_idx - 1) + 1) <= max_span
+            ):
+                start_idx -= 1
+            while (
+                end_idx + 1 < num_tokens
+                and valid[end_idx + 1]
+                and _has_skippable_bridge(text, offsets[end_idx], offsets[end_idx + 1], label)
+                and float(token_scores[end_idx + 1, label_index]) >= extend_threshold
+                and ((end_idx + 1) - start_idx + 1) <= max_span
+            ):
+                end_idx += 1
+            presence_slice = token_scores[start_idx : end_idx + 1, label_index]
+            score = float(presence_slice.mean())
+            out_start_idx = start_idx
+            out_end_idx = end_idx
+            if start_scores is not None and end_scores is not None:
+                refine_window = min(3, end_idx - start_idx + 1)
+                start_window = start_scores[start_idx : start_idx + refine_window, label_index]
+                best_start_rel = int(np.argmax(start_window))
+                best_start_idx = start_idx + best_start_rel
+                end_window_start = max(best_start_idx, end_idx - refine_window + 1)
+                end_window = end_scores[end_window_start : end_idx + 1, label_index]
+                best_end_rel = int(np.argmax(end_window))
+                best_end_idx = end_window_start + best_end_rel
+                if (
+                    float(start_scores[best_start_idx, label_index]) < boundary_thresholds.get(label, 0.0)
+                    or float(end_scores[best_end_idx, label_index]) < boundary_thresholds.get(label, 0.0)
+                ):
+                    idx = end_idx + 1
+                    continue
+                out_start_idx = best_start_idx
+                out_end_idx = best_end_idx
+                if label in CONSERVATIVE_BOUNDARY_REFINEMENT_LABELS and (
+                    best_start_idx != start_idx or best_end_idx != end_idx
+                ):
+                    outer_boundary = min(float(start_scores[start_idx, label_index]), float(end_scores[end_idx, label_index]))
+                    refined_boundary = min(
+                        float(start_scores[best_start_idx, label_index]),
+                        float(end_scores[best_end_idx, label_index]),
+                    )
+                    if refined_boundary < outer_boundary + 0.08:
+                        out_start_idx = start_idx
+                        out_end_idx = end_idx
+                score = (
+                    0.65 * score
+                    + 0.175 * float(start_scores[out_start_idx, label_index])
+                    + 0.175 * float(end_scores[out_end_idx, label_index])
+                )
+            min_chars = int(min_nonspace_chars.get(label, 1))
+            if _nonspace_length(text, offsets[out_start_idx][0], offsets[out_end_idx][1]) < min_chars:
+                idx = end_idx + 1
+                continue
+            spans.append(
+                {
+                    "label": label,
+                    "start": int(offsets[out_start_idx][0]),
+                    "end": int(offsets[out_end_idx][1]),
+                    "score": score,
+                    "text": text[int(offsets[out_start_idx][0]) : int(offsets[out_end_idx][1])],
+                }
+            )
+            idx = end_idx + 1
+    return dedupe_spans(spans)
+
+
+def load_onnx_session(model_ref: str, onnx_file: str = "model_quantized.onnx", onnx_subfolder: str = "onnx"):
+    import onnxruntime as ort
+
+    model_path = Path(model_ref)
+    if model_path.exists():
+        candidates = []
+        if onnx_subfolder:
+            candidates.append(model_path / onnx_subfolder / onnx_file)
+        candidates.append(model_path / onnx_file)
+        onnx_path = next((path for path in candidates if path.exists()), candidates[0])
+        config = AutoConfig.from_pretrained(model_ref)
+        tokenizer = safe_auto_tokenizer(model_ref)
+    else:
+        remote_name = f"{onnx_subfolder}/{onnx_file}" if onnx_subfolder else onnx_file
+        onnx_path = Path(hf_hub_download(repo_id=model_ref, filename=remote_name, repo_type="model"))
+        config = AutoConfig.from_pretrained(model_ref)
+        tokenizer = safe_auto_tokenizer(model_ref)
+    session = ort.InferenceSession(str(onnx_path), providers=["CPUExecutionProvider"])
+    return session, tokenizer, config
+
+
+def run_onnx(session, encoded: dict[str, Any]) -> tuple[np.ndarray, np.ndarray]:
+    feed = {}
+    input_names = {item.name for item in session.get_inputs()}
+    for key, value in encoded.items():
+        if key == "offset_mapping":
+            continue
+        if key in input_names:
+            feed[key] = value
+    outputs = session.run(None, feed)
+    return outputs[0], outputs[1]
+
+
+def run_onnx_all(session, encoded: dict[str, Any]) -> list[np.ndarray]:
+    feed = {}
+    input_names = {item.name for item in session.get_inputs()}
+    for key, value in encoded.items():
+        if key == "offset_mapping":
+            continue
+        if key in input_names:
+            feed[key] = value
+    return session.run(None, feed)

config.json

@@ -0,0 +1,353 @@
+{
+  "activation": "gelu",
+  "architectures": [
+    "IrishCoreTokenSpanModel"
+  ],
+  "attention_dropout": 0.1,
+  "boundary_loss_weight": 1.0,
+  "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,
+  "num_span_labels": 11,
+  "output_past": true,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "span_label_max_span_tokens": {
+    "ACCOUNT_NUMBER": 19,
+    "BANK_ROUTING_NUMBER": 6,
+    "CREDIT_DEBIT_CARD": 13,
+    "EMAIL": 16,
+    "FIRST_NAME": 5,
+    "LAST_NAME": 8,
+    "PASSPORT_NUMBER": 9,
+    "PHONE_NUMBER": 10,
+    "POSTCODE": 8,
+    "PPSN": 9,
+    "SWIFT_BIC": 8
+  },
+  "span_label_names": [
+    "ACCOUNT_NUMBER",
+    "BANK_ROUTING_NUMBER",
+    "CREDIT_DEBIT_CARD",
+    "EMAIL",
+    "FIRST_NAME",
+    "LAST_NAME",
+    "PASSPORT_NUMBER",
+    "PHONE_NUMBER",
+    "POSTCODE",
+    "PPSN",
+    "SWIFT_BIC"
+  ],
+  "span_label_thresholds": {
+    "ACCOUNT_NUMBER": 0.5,
+    "BANK_ROUTING_NUMBER": 0.5,
+    "CREDIT_DEBIT_CARD": 0.5,
+    "EMAIL": 0.5,
+    "FIRST_NAME": 0.5,
+    "LAST_NAME": 0.5,
+    "PASSPORT_NUMBER": 0.5,
+    "PHONE_NUMBER": 0.5,
+    "POSTCODE": 0.5,
+    "PPSN": 0.5,
+    "SWIFT_BIC": 0.5
+  },
+  "span_positive_weight": 6.0,
+  "tie_weights_": true,
+  "token_extend_thresholds": {
+    "ACCOUNT_NUMBER": 0.08,
+    "BANK_ROUTING_NUMBER": 0.3,
+    "CREDIT_DEBIT_CARD": 0.3,
+    "EMAIL": 0.3,
+    "FIRST_NAME": 0.3,
+    "LAST_NAME": 0.3,
+    "PASSPORT_NUMBER": 0.3,
+    "PHONE_NUMBER": 0.15,
+    "POSTCODE": 0.3,
+    "PPSN": 0.3,
+    "SWIFT_BIC": 0.3
+  },
+  "token_label_thresholds": {
+    "ACCOUNT_NUMBER": 0.18,
+    "BANK_ROUTING_NUMBER": 0.8,
+    "CREDIT_DEBIT_CARD": 0.8,
+    "EMAIL": 0.95,
+    "FIRST_NAME": 0.3,
+    "LAST_NAME": 0.4,
+    "PASSPORT_NUMBER": 0.8,
+    "PHONE_NUMBER": 0.65,
+    "POSTCODE": 0.9,
+    "PPSN": 0.7,
+    "SWIFT_BIC": 0.8
+  },
+  "token_positive_weight": 4.0,
+  "token_presence_weight": 1.0,
+  "transformers_version": "4.57.6",
+  "vocab_size": 119547,
+  "boundary_label_thresholds": {
+    "ACCOUNT_NUMBER": 0.1,
+    "BANK_ROUTING_NUMBER": 0.25,
+    "CREDIT_DEBIT_CARD": 0.25,
+    "EMAIL": 0.2,
+    "FIRST_NAME": 0.1,
+    "LAST_NAME": 0.1,
+    "PASSPORT_NUMBER": 0.25,
+    "PHONE_NUMBER": 0.25,
+    "POSTCODE": 0.4,
+    "PPSN": 0.35,
+    "SWIFT_BIC": 0.25
+  },
+  "span_label_min_nonspace_chars": {
+    "PPSN": 8,
+    "POSTCODE": 6,
+    "PHONE_NUMBER": 7,
+    "PASSPORT_NUMBER": 7,
+    "BANK_ROUTING_NUMBER": 6,
+    "ACCOUNT_NUMBER": 6,
+    "CREDIT_DEBIT_CARD": 12,
+    "SWIFT_BIC": 8,
+    "EMAIL": 6,
+    "FIRST_NAME": 2,
+    "LAST_NAME": 2
+  },
+  "raw_decoder": {
+    "name": "openmed_irish_core_token_span_v1",
+    "scanner_free": true,
+    "validator_free": true,
+    "default_min_score": 0.5,
+    "full_and_onnx_share_decoder": true
+  }
+}

eval/benchmark_manual_suite_multilabel_base_irish_core_pii_v1.json

@@ -0,0 +1,197 @@
+{
+  "model": "OpenMed/OpenMed-PII-mLiteClinical-Base-135M-v1",
+  "input": "eval/irish_core_pii_v1.jsonl",
+  "loader_type": "standard",
+  "device": "cpu",
+  "examples": 37,
+  "batch_size": 8,
+  "min_score": 0.5,
+  "ppsn_min_score": 0.5,
+  "iou_threshold": 0.5,
+  "ppsn_decoder": "word_aligned",
+  "labels_evaluated": [
+    "ACCOUNT_NUMBER",
+    "BANK_ROUTING_NUMBER",
+    "CREDIT_DEBIT_CARD",
+    "EMAIL",
+    "FIRST_NAME",
+    "LAST_NAME",
+    "PASSPORT_NUMBER",
+    "PHONE_NUMBER",
+    "POSTCODE",
+    "PPSN",
+    "SWIFT_BIC"
+  ],
+  "elapsed_seconds": 2.4372421499574557,
+  "examples_per_second": 15.181093105847472,
+  "overall": {
+    "precision": 0.3153153153153153,
+    "recall": 0.4605263157894737,
+    "f1": 0.37433155080213903,
+    "tp": 35,
+    "fp": 76,
+    "fn": 41
+  },
+  "by_label": {
+    "ACCOUNT_NUMBER": {
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+      "recall": 0.3333333333333333,
+      "f1": 0.3333333333333333,
+      "tp": 1,
+      "fp": 2,
+      "fn": 2
+    },
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+      "f1": 0.0,
+      "tp": 0,
+      "fp": 0,
+      "fn": 1
+    },
+    "BIOMETRIC_IDENTIFIER": {
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+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 3,
+      "fn": 0
+    },
+    "CERTIFICATE_LICENSE_NUMBER": {
+      "precision": 0.0,
+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 1,
+      "fn": 0
+    },
+    "COORDINATE": {
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+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 1,
+      "fn": 0
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+    "CREDIT_DEBIT_CARD": {
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+      "recall": 0.5,
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+      "tp": 1,
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+    "DATE": {
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+      "tp": 6,
+      "fp": 5,
+      "fn": 0
+    },
+    "EMPLOYEE_ID": {
+      "precision": 0.0,
+      "recall": 0.0,
+      "f1": 0.0,
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+      "fp": 1,
+      "fn": 0
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+    "FIRST_NAME": {
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+      "recall": 0.8421052631578947,
+      "f1": 0.5333333333333333,
+      "tp": 16,
+      "fp": 25,
+      "fn": 3
+    },
+    "HEALTH_PLAN_BENEFICIARY_NUMBER": {
+      "precision": 0.0,
+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 1,
+      "fn": 0
+    },
+    "LAST_NAME": {
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+      "f1": 0.46808510638297873,
+      "tp": 11,
+      "fp": 17,
+      "fn": 8
+    },
+    "PASSPORT_NUMBER": {
+      "precision": 0.0,
+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 0,
+      "fn": 2
+    },
+    "PHONE_NUMBER": {
+      "precision": 0.0,
+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 1,
+      "fn": 12
+    },
+    "POSTCODE": {
+      "precision": 0.0,
+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 1,
+      "fn": 4
+    },
+    "PPSN": {
+      "precision": 0.0,
+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 0,
+      "fn": 6
+    },
+    "SSN": {
+      "precision": 0.0,
+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 1,
+      "fn": 0
+    },
+    "STREET_ADDRESS": {
+      "precision": 0.0,
+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 3,
+      "fn": 0
+    },
+    "SWIFT_BIC": {
+      "precision": 0.0,
+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 0,
+      "fn": 2
+    },
+    "UNIQUE_ID": {
+      "precision": 0.0,
+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 2,
+      "fn": 0
+    }
+  }
+}

eval/benchmark_manual_suite_multilabel_base_irish_ppsn_phone_edge_v1.json

@@ -0,0 +1,76 @@
+{
+  "model": "OpenMed/OpenMed-PII-mLiteClinical-Base-135M-v1",
+  "input": "eval/irish_ppsn_phone_edge_v1.jsonl",
+  "loader_type": "standard",
+  "device": "cpu",
+  "examples": 22,
+  "batch_size": 8,
+  "min_score": 0.5,
+  "ppsn_min_score": 0.5,
+  "iou_threshold": 0.5,
+  "ppsn_decoder": "word_aligned",
+  "labels_evaluated": [
+    "PHONE_NUMBER",
+    "PPSN"
+  ],
+  "elapsed_seconds": 1.2349001580150798,
+  "examples_per_second": 17.815205429531858,
+  "overall": {
+    "precision": 0.058823529411764705,
+    "recall": 0.05263157894736842,
+    "f1": 0.05555555555555555,
+    "tp": 1,
+    "fp": 16,
+    "fn": 18
+  },
+  "by_label": {
+    "CREDIT_DEBIT_CARD": {
+      "precision": 0.0,
+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 9,
+      "fn": 0
+    },
+    "DATE": {
+      "precision": 0.0,
+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 2,
+      "fn": 0
+    },
+    "PHONE_NUMBER": {
+      "precision": 1.0,
+      "recall": 0.07692307692307693,
+      "f1": 0.14285714285714288,
+      "tp": 1,
+      "fp": 0,
+      "fn": 12
+    },
+    "PPSN": {
+      "precision": 0.0,
+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 0,
+      "fn": 6
+    },
+    "STREET_ADDRESS": {
+      "precision": 0.0,
+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 2,
+      "fn": 0
+    },
+    "UNIQUE_ID": {
+      "precision": 0.0,
+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 3,
+      "fn": 0
+    }
+  }
+}

eval/benchmark_multilingual_ppsn_v1_all_openmed_mliteclinical_base_cpu.json

@@ -0,0 +1,159 @@
+{
+  "model": "OpenMed/OpenMed-PII-mLiteClinical-Base-135M-v1",
+  "input": "eval/multilingual_ppsn_v1_all.jsonl",
+  "loader_type": "standard",
+  "device": "cpu",
+  "examples": 168,
+  "batch_size": 16,
+  "elapsed_seconds": 4.483998584997607,
+  "examples_per_second": 37.46655954845482,
+  "label_filter": "PPSN",
+  "overall": {
+    "precision": 0.0,
+    "recall": 0.0,
+    "f1": 0.0,
+    "tp": 0,
+    "fp": 0,
+    "fn": 84
+  },
+  "by_domain": {
+    "citizen_chat": {
+      "precision": 0.0,
+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 0,
+      "fn": 28
+    },
+    "gov_data": {
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+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 0,
+      "fn": 28
+    },
+    "hse_medical": {
+      "precision": 0.0,
+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 0,
+      "fn": 28
+    }
+  },
+  "by_language": {
+    "de": {
+      "precision": 0.0,
+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 0,
+      "fn": 6
+    },
+    "en": {
+      "precision": 0.0,
+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 0,
+      "fn": 6
+    },
+    "es": {
+      "precision": 0.0,
+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 0,
+      "fn": 6
+    },
+    "fr": {
+      "precision": 0.0,
+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 0,
+      "fn": 6
+    },
+    "ga": {
+      "precision": 0.0,
+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 0,
+      "fn": 6
+    },
+    "it": {
+      "precision": 0.0,
+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 0,
+      "fn": 6
+    },
+    "ja": {
+      "precision": 0.0,
+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 0,
+      "fn": 6
+    },
+    "nl": {
+      "precision": 0.0,
+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 0,
+      "fn": 6
+    },
+    "pl": {
+      "precision": 0.0,
+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 0,
+      "fn": 6
+    },
+    "pt": {
+      "precision": 0.0,
+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 0,
+      "fn": 6
+    },
+    "ro": {
+      "precision": 0.0,
+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 0,
+      "fn": 6
+    },
+    "ru": {
+      "precision": 0.0,
+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 0,
+      "fn": 6
+    },
+    "uk": {
+      "precision": 0.0,
+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 0,
+      "fn": 6
+    },
+    "zh": {
+      "precision": 0.0,
+      "recall": 0.0,
+      "f1": 0.0,
+      "tp": 0,
+      "fp": 0,
+      "fn": 6
+    }
+  }
+}

eval/benchmark_summary.json

@@ -0,0 +1,90 @@
+{
+  "release": "OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc8",
+  "repo_id": "temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc8",
+  "raw_only": true,
+  "scanner_free": true,
+  "validator_free": true,
+  "base_model": "OpenMed/OpenMed-PII-mLiteClinical-Base-135M-v1",
+  "previous_public_release": "temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc7",
+  "full": {
+    "min_score": 0.5,
+    "irish_core_manual_f1": 0.9736842105263158,
+    "irish_edge_f1": 1.0,
+    "finance_suite_f1": 1.0,
+    "finance_boundary_f1": 1.0,
+    "user_raw_ppsn_f1": 1.0,
+    "gaelic_weak_ppsn_f1": 1.0,
+    "multilingual_ppsn_f1": 0.9176470588235294,
+    "core_examples_per_second": 2.2964689405250116,
+    "multilingual_examples_per_second": 6.309541168268772
+  },
+  "onnx_q8": {
+    "min_score": 0.5,
+    "irish_core_manual_f1": 0.9736842105263158,
+    "irish_edge_f1": 1.0,
+    "finance_suite_f1": 1.0,
+    "finance_boundary_f1": 1.0,
+    "user_raw_ppsn_f1": 1.0,
+    "gaelic_weak_ppsn_f1": 1.0,
+    "multilingual_ppsn_f1": 0.9176470588235294,
+    "core_examples_per_second": 46.14201741375802,
+    "multilingual_examples_per_second": 99.71655616732895
+  },
+  "comparison": {
+    "base_openmed": {
+      "irish_core_manual_f1": 0.37433155080213903,
+      "irish_edge_f1": 0.05555555555555555,
+      "multilingual_ppsn_f1": 0.0,
+      "core_examples_per_second": 15.181093105847472,
+      "multilingual_examples_per_second": 37.46655954845482
+    },
+    "previous_public_release_rc7": {
+      "release": "OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc7",
+      "based_on_release": "temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc6",
+      "weights_changed": false,
+      "artifacts_changed": false,
+      "scanner_spec_changed": true,
+      "candidate_extraction_regex_free": true,
+      "full": {
+        "other_min_score": 0.5,
+        "ppsn_min_score": 0.55,
+        "irish_core_manual_f1": 1.0,
+        "finance_suite_f1": 1.0,
+        "finance_boundary_f1": 1.0,
+        "gaelic_weak_ppsn_f1": 1.0
+      },
+      "onnx_q8": {
+        "other_min_score": 0.5,
+        "ppsn_min_score": 0.55,
+        "irish_core_manual_f1": 0.9933774834437086,
+        "finance_suite_f1": 1.0,
+        "finance_boundary_f1": 1.0,
+        "gaelic_weak_ppsn_f1": 1.0
+      },
+      "core_label_breakdown": {
+        "full": {
+          "PPSN": 1.0,
+          "PHONE_NUMBER": 1.0,
+          "POSTCODE": 1.0,
+          "PASSPORT_NUMBER": 1.0,
+          "ACCOUNT_NUMBER": 1.0,
+          "BANK_ROUTING_NUMBER": 1.0,
+          "EMAIL": 1.0,
+          "FIRST_NAME": 1.0,
+          "LAST_NAME": 1.0
+        },
+        "onnx_q8": {
+          "PPSN": 1.0,
+          "PHONE_NUMBER": 1.0,
+          "POSTCODE": 0.8571428571428571,
+          "PASSPORT_NUMBER": 1.0,
+          "ACCOUNT_NUMBER": 1.0,
+          "BANK_ROUTING_NUMBER": 1.0,
+          "EMAIL": 1.0,
+          "FIRST_NAME": 1.0,
+          "LAST_NAME": 1.0
+        }
+      }
+    }
+  }
+}

eval/benchmark_summary.md

@@ -0,0 +1,40 @@
+# Benchmark Summary
+
+## Release
+
+- Release: `OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc8`
+- Repo: `temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc8`
+- Raw-only: `true`
+- Scanner free: `true`
+- Validator free: `true`
+- Default `min_score`: `0.5`
+
+## Main Comparison
+
+| Model | Irish core F1 | Edge F1 | Finance F1 | Finance-boundary F1 | User PPSN F1 | GA weak PPSN F1 | Multilingual PPSN F1 | Core CPU ex/s | Multilingual CPU ex/s |
+|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
+| Base OpenMed | 0.3743 | 0.0556 | - | - | - | - | 0.0000 | 15.1811 | 37.4666 |
+| Previous public `rc7` full | 1.0000 | - | 1.0000 | 1.0000 | - | 1.0000 | - | 3.5394 | - |
+| Previous public `rc7` ONNX q8 | 0.9934 | - | 1.0000 | 1.0000 | - | 1.0000 | - | 12.1653 | - |
+| `rc8` full | 0.9737 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 0.9176 | 2.2965 | 6.3095 |
+| `rc8` ONNX q8 | 0.9737 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 0.9176 | 46.1420 | 99.7166 |
+
+## Irish Core Breakdown
+
+| Label | `rc8` full | `rc8` ONNX q8 |
+|---|---:|---:|
+| PPSN | 1.0000 | 1.0000 |
+| PHONE_NUMBER | 0.9565 | 0.9565 |
+| POSTCODE | 1.0000 | 1.0000 |
+| ACCOUNT_NUMBER | 0.8000 | 0.8000 |
+| PASSPORT_NUMBER | 1.0000 | 1.0000 |
+| EMAIL | 1.0000 | 1.0000 |
+| FIRST_NAME | 0.9744 | 0.9744 |
+| LAST_NAME | 0.9744 | 0.9744 |
+
+## Reading The Numbers
+
+- `rc8` is the first public raw-only release in this line: no scanner, no regex extraction, no checksum validator layer.
+- `rc7` is still stronger on the broad manual Irish core suite because it uses a bundled scanner/validator inference stack.
+- `rc8` is simpler to embed and its ONNX q8 artifact stays very close to the full checkpoint on the release-gating suites.
+- For CPU use, the ONNX q8 artifact is the default recommendation.

eval/rc8h_cal3_core_t050_cpu.json

@@ -0,0 +1,108 @@
+{
+  "model": "models/openmed-mliteclinical-irish-core-multitask-rc8h_cal3",
+  "input": "eval/irish_core_pii_v1.jsonl",
+  "loader_type": "token_span_pt",
+  "examples": 37,
+  "min_score": 0.5,
+  "iou_threshold": 0.5,
+  "elapsed_seconds": 16.111691887956113,
+  "examples_per_second": 2.2964689405250116,
+  "overall": {
+    "precision": 0.9736842105263158,
+    "recall": 0.9736842105263158,
+    "f1": 0.9736842105263158,
+    "tp": 74,
+    "fp": 2,
+    "fn": 2
+  },
+  "by_label": {
+    "ACCOUNT_NUMBER": {
+      "precision": 1.0,
+      "recall": 0.6666666666666666,
+      "f1": 0.8,
+      "tp": 2,
+      "fp": 0,
+      "fn": 1
+    },
+    "BANK_ROUTING_NUMBER": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 1,
+      "fp": 0,
+      "fn": 0
+    },
+    "CREDIT_DEBIT_CARD": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 2,
+      "fp": 0,
+      "fn": 0
+    },
+    "EMAIL": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 6,
+      "fp": 0,
+      "fn": 0
+    },
+    "FIRST_NAME": {
+      "precision": 0.95,
+      "recall": 1.0,
+      "f1": 0.9743589743589743,
+      "tp": 19,
+      "fp": 1,
+      "fn": 0
+    },
+    "LAST_NAME": {
+      "precision": 0.95,
+      "recall": 1.0,
+      "f1": 0.9743589743589743,
+      "tp": 19,
+      "fp": 1,
+      "fn": 0
+    },
+    "PASSPORT_NUMBER": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 2,
+      "fp": 0,
+      "fn": 0
+    },
+    "PHONE_NUMBER": {
+      "precision": 1.0,
+      "recall": 0.9166666666666666,
+      "f1": 0.9565217391304348,
+      "tp": 11,
+      "fp": 0,
+      "fn": 1
+    },
+    "POSTCODE": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 4,
+      "fp": 0,
+      "fn": 0
+    },
+    "PPSN": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 6,
+      "fp": 0,
+      "fn": 0
+    },
+    "SWIFT_BIC": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 2,
+      "fp": 0,
+      "fn": 0
+    }
+  }
+}

eval/rc8h_cal3_edge_t050_cpu.json

@@ -0,0 +1,36 @@
+{
+  "model": "models/openmed-mliteclinical-irish-core-multitask-rc8h_cal3",
+  "input": "eval/irish_ppsn_phone_edge_v1.jsonl",
+  "loader_type": "token_span_pt",
+  "examples": 22,
+  "min_score": 0.5,
+  "iou_threshold": 0.5,
+  "elapsed_seconds": 15.53119330003392,
+  "examples_per_second": 1.4165041651984303,
+  "overall": {
+    "precision": 1.0,
+    "recall": 1.0,
+    "f1": 1.0,
+    "tp": 19,
+    "fp": 0,
+    "fn": 0
+  },
+  "by_label": {
+    "PHONE_NUMBER": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 13,
+      "fp": 0,
+      "fn": 0
+    },
+    "PPSN": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 6,
+      "fp": 0,
+      "fn": 0
+    }
+  }
+}

eval/rc8h_cal3_finance_boundary_t050_cpu.json

@@ -0,0 +1,76 @@
+{
+  "model": "models/openmed-mliteclinical-irish-core-multitask-rc8h_cal3",
+  "input": "eval/irish_finance_boundary_repair_v1.jsonl",
+  "loader_type": "token_span_pt",
+  "examples": 12,
+  "min_score": 0.5,
+  "iou_threshold": 0.5,
+  "elapsed_seconds": 17.771310269017704,
+  "examples_per_second": 0.675245652591,
+  "overall": {
+    "precision": 1.0,
+    "recall": 1.0,
+    "f1": 1.0,
+    "tp": 18,
+    "fp": 0,
+    "fn": 0
+  },
+  "by_label": {
+    "ACCOUNT_NUMBER": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 2,
+      "fp": 0,
+      "fn": 0
+    },
+    "BANK_ROUTING_NUMBER": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 2,
+      "fp": 0,
+      "fn": 0
+    },
+    "CREDIT_DEBIT_CARD": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 2,
+      "fp": 0,
+      "fn": 0
+    },
+    "PASSPORT_NUMBER": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 4,
+      "fp": 0,
+      "fn": 0
+    },
+    "PHONE_NUMBER": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 4,
+      "fp": 0,
+      "fn": 0
+    },
+    "PPSN": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 2,
+      "fp": 0,
+      "fn": 0
+    },
+    "SWIFT_BIC": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 2,
+      "fp": 0,
+      "fn": 0
+    }
+  }
+}

eval/rc8h_cal3_finance_t050_cpu.json

@@ -0,0 +1,76 @@
+{
+  "model": "models/openmed-mliteclinical-irish-core-multitask-rc8h_cal3",
+  "input": "eval/irish_phone_passport_finance_v1.jsonl",
+  "loader_type": "token_span_pt",
+  "examples": 20,
+  "min_score": 0.5,
+  "iou_threshold": 0.5,
+  "elapsed_seconds": 24.535578383947723,
+  "examples_per_second": 0.8151427974114888,
+  "overall": {
+    "precision": 1.0,
+    "recall": 1.0,
+    "f1": 1.0,
+    "tp": 25,
+    "fp": 0,
+    "fn": 0
+  },
+  "by_label": {
+    "ACCOUNT_NUMBER": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 2,
+      "fp": 0,
+      "fn": 0
+    },
+    "BANK_ROUTING_NUMBER": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 5,
+      "fp": 0,
+      "fn": 0
+    },
+    "CREDIT_DEBIT_CARD": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 2,
+      "fp": 0,
+      "fn": 0
+    },
+    "PASSPORT_NUMBER": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 6,
+      "fp": 0,
+      "fn": 0
+    },
+    "PHONE_NUMBER": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 6,
+      "fp": 0,
+      "fn": 0
+    },
+    "PPSN": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 2,
+      "fp": 0,
+      "fn": 0
+    },
+    "SWIFT_BIC": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 2,
+      "fp": 0,
+      "fn": 0
+    }
+  }
+}

eval/rc8h_cal3_gaweak_t050_cpu.json

@@ -0,0 +1,28 @@
+{
+  "model": "models/openmed-mliteclinical-irish-core-multitask-rc8h_cal3",
+  "input": "eval/qa_feedback_ga_ppsn_weakctx_v1.jsonl",
+  "loader_type": "token_span_pt",
+  "examples": 2,
+  "min_score": 0.5,
+  "iou_threshold": 0.5,
+  "elapsed_seconds": 4.148581088054925,
+  "examples_per_second": 0.48209254141340796,
+  "overall": {
+    "precision": 1.0,
+    "recall": 1.0,
+    "f1": 1.0,
+    "tp": 2,
+    "fp": 0,
+    "fn": 0
+  },
+  "by_label": {
+    "PPSN": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 2,
+      "fp": 0,
+      "fn": 0
+    }
+  }
+}

eval/rc8h_cal3_multilingual_t050_cpu.json

@@ -0,0 +1,28 @@
+{
+  "model": "models/openmed-mliteclinical-irish-core-multitask-rc8h_cal3",
+  "input": "eval/multilingual_ppsn_v1_all.jsonl",
+  "loader_type": "token_span_pt",
+  "examples": 168,
+  "min_score": 0.5,
+  "iou_threshold": 0.5,
+  "elapsed_seconds": 26.62634184001945,
+  "examples_per_second": 6.309541168268772,
+  "overall": {
+    "precision": 0.9069767441860465,
+    "recall": 0.9285714285714286,
+    "f1": 0.9176470588235294,
+    "tp": 78,
+    "fp": 8,
+    "fn": 6
+  },
+  "by_label": {
+    "PPSN": {
+      "precision": 0.975,
+      "recall": 0.9285714285714286,
+      "f1": 0.951219512195122,
+      "tp": 78,
+      "fp": 2,
+      "fn": 6
+    }
+  }
+}

eval/rc8h_cal3_q8_core_t050_cpu.json

@@ -0,0 +1,108 @@
+{
+  "model": "models/openmed-mliteclinical-irish-core-multitask-rc8h_cal3_onnx_q8",
+  "input": "eval/irish_core_pii_v1.jsonl",
+  "loader_type": "token_span_onnx_q8",
+  "examples": 37,
+  "min_score": 0.5,
+  "iou_threshold": 0.5,
+  "elapsed_seconds": 0.8018721779808402,
+  "examples_per_second": 46.14201741375802,
+  "overall": {
+    "precision": 0.9736842105263158,
+    "recall": 0.9736842105263158,
+    "f1": 0.9736842105263158,
+    "tp": 74,
+    "fp": 2,
+    "fn": 2
+  },
+  "by_label": {
+    "ACCOUNT_NUMBER": {
+      "precision": 1.0,
+      "recall": 0.6666666666666666,
+      "f1": 0.8,
+      "tp": 2,
+      "fp": 0,
+      "fn": 1
+    },
+    "BANK_ROUTING_NUMBER": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 1,
+      "fp": 0,
+      "fn": 0
+    },
+    "CREDIT_DEBIT_CARD": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 2,
+      "fp": 0,
+      "fn": 0
+    },
+    "EMAIL": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 6,
+      "fp": 0,
+      "fn": 0
+    },
+    "FIRST_NAME": {
+      "precision": 0.95,
+      "recall": 1.0,
+      "f1": 0.9743589743589743,
+      "tp": 19,
+      "fp": 1,
+      "fn": 0
+    },
+    "LAST_NAME": {
+      "precision": 0.95,
+      "recall": 1.0,
+      "f1": 0.9743589743589743,
+      "tp": 19,
+      "fp": 1,
+      "fn": 0
+    },
+    "PASSPORT_NUMBER": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 2,
+      "fp": 0,
+      "fn": 0
+    },
+    "PHONE_NUMBER": {
+      "precision": 1.0,
+      "recall": 0.9166666666666666,
+      "f1": 0.9565217391304348,
+      "tp": 11,
+      "fp": 0,
+      "fn": 1
+    },
+    "POSTCODE": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 4,
+      "fp": 0,
+      "fn": 0
+    },
+    "PPSN": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 6,
+      "fp": 0,
+      "fn": 0
+    },
+    "SWIFT_BIC": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 2,
+      "fp": 0,
+      "fn": 0
+    }
+  }
+}

eval/rc8h_cal3_q8_edge_t050_cpu.json

@@ -0,0 +1,36 @@
+{
+  "model": "models/openmed-mliteclinical-irish-core-multitask-rc8h_cal3_onnx_q8",
+  "input": "eval/irish_ppsn_phone_edge_v1.jsonl",
+  "loader_type": "token_span_onnx_q8",
+  "examples": 22,
+  "min_score": 0.5,
+  "iou_threshold": 0.5,
+  "elapsed_seconds": 0.4215048130135983,
+  "examples_per_second": 52.193947306813435,
+  "overall": {
+    "precision": 1.0,
+    "recall": 1.0,
+    "f1": 1.0,
+    "tp": 19,
+    "fp": 0,
+    "fn": 0
+  },
+  "by_label": {
+    "PHONE_NUMBER": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 13,
+      "fp": 0,
+      "fn": 0
+    },
+    "PPSN": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 6,
+      "fp": 0,
+      "fn": 0
+    }
+  }
+}

eval/rc8h_cal3_q8_finance_boundary_t050_cpu.json

@@ -0,0 +1,76 @@
+{
+  "model": "models/openmed-mliteclinical-irish-core-multitask-rc8h_cal3_onnx_q8",
+  "input": "eval/irish_finance_boundary_repair_v1.jsonl",
+  "loader_type": "token_span_onnx_q8",
+  "examples": 12,
+  "min_score": 0.5,
+  "iou_threshold": 0.5,
+  "elapsed_seconds": 0.22588503907900304,
+  "examples_per_second": 53.12436825797486,
+  "overall": {
+    "precision": 1.0,
+    "recall": 1.0,
+    "f1": 1.0,
+    "tp": 18,
+    "fp": 0,
+    "fn": 0
+  },
+  "by_label": {
+    "ACCOUNT_NUMBER": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 2,
+      "fp": 0,
+      "fn": 0
+    },
+    "BANK_ROUTING_NUMBER": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 2,
+      "fp": 0,
+      "fn": 0
+    },
+    "CREDIT_DEBIT_CARD": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 2,
+      "fp": 0,
+      "fn": 0
+    },
+    "PASSPORT_NUMBER": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 4,
+      "fp": 0,
+      "fn": 0
+    },
+    "PHONE_NUMBER": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 4,
+      "fp": 0,
+      "fn": 0
+    },
+    "PPSN": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 2,
+      "fp": 0,
+      "fn": 0
+    },
+    "SWIFT_BIC": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 2,
+      "fp": 0,
+      "fn": 0
+    }
+  }
+}

eval/rc8h_cal3_q8_finance_t050_cpu.json

@@ -0,0 +1,76 @@
+{
+  "model": "models/openmed-mliteclinical-irish-core-multitask-rc8h_cal3_onnx_q8",
+  "input": "eval/irish_phone_passport_finance_v1.jsonl",
+  "loader_type": "token_span_onnx_q8",
+  "examples": 20,
+  "min_score": 0.5,
+  "iou_threshold": 0.5,
+  "elapsed_seconds": 0.5003277519717813,
+  "examples_per_second": 39.97379701841526,
+  "overall": {
+    "precision": 1.0,
+    "recall": 1.0,
+    "f1": 1.0,
+    "tp": 25,
+    "fp": 0,
+    "fn": 0
+  },
+  "by_label": {
+    "ACCOUNT_NUMBER": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 2,
+      "fp": 0,
+      "fn": 0
+    },
+    "BANK_ROUTING_NUMBER": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 5,
+      "fp": 0,
+      "fn": 0
+    },
+    "CREDIT_DEBIT_CARD": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 2,
+      "fp": 0,
+      "fn": 0
+    },
+    "PASSPORT_NUMBER": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 6,
+      "fp": 0,
+      "fn": 0
+    },
+    "PHONE_NUMBER": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 6,
+      "fp": 0,
+      "fn": 0
+    },
+    "PPSN": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 2,
+      "fp": 0,
+      "fn": 0
+    },
+    "SWIFT_BIC": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 2,
+      "fp": 0,
+      "fn": 0
+    }
+  }
+}

eval/rc8h_cal3_q8_gaweak_t050_cpu.json

@@ -0,0 +1,28 @@
+{
+  "model": "models/openmed-mliteclinical-irish-core-multitask-rc8h_cal3_onnx_q8",
+  "input": "eval/qa_feedback_ga_ppsn_weakctx_v1.jsonl",
+  "loader_type": "token_span_onnx_q8",
+  "examples": 2,
+  "min_score": 0.5,
+  "iou_threshold": 0.5,
+  "elapsed_seconds": 0.1290204740362242,
+  "examples_per_second": 15.50141568568779,
+  "overall": {
+    "precision": 1.0,
+    "recall": 1.0,
+    "f1": 1.0,
+    "tp": 2,
+    "fp": 0,
+    "fn": 0
+  },
+  "by_label": {
+    "PPSN": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 2,
+      "fp": 0,
+      "fn": 0
+    }
+  }
+}

eval/rc8h_cal3_q8_multilingual_t050_cpu.json

@@ -0,0 +1,28 @@
+{
+  "model": "models/openmed-mliteclinical-irish-core-multitask-rc8h_cal3_onnx_q8",
+  "input": "eval/multilingual_ppsn_v1_all.jsonl",
+  "loader_type": "token_span_onnx_q8",
+  "examples": 168,
+  "min_score": 0.5,
+  "iou_threshold": 0.5,
+  "elapsed_seconds": 1.6847753919428214,
+  "examples_per_second": 99.71655616732895,
+  "overall": {
+    "precision": 0.9069767441860465,
+    "recall": 0.9285714285714286,
+    "f1": 0.9176470588235294,
+    "tp": 78,
+    "fp": 8,
+    "fn": 6
+  },
+  "by_label": {
+    "PPSN": {
+      "precision": 0.975,
+      "recall": 0.9285714285714286,
+      "f1": 0.951219512195122,
+      "tp": 78,
+      "fp": 2,
+      "fn": 6
+    }
+  }
+}

eval/rc8h_cal3_q8_user_t050_cpu.json

@@ -0,0 +1,28 @@
+{
+  "model": "models/openmed-mliteclinical-irish-core-multitask-rc8h_cal3_onnx_q8",
+  "input": "eval/user_raw_regression_cases_v1.jsonl",
+  "loader_type": "token_span_onnx_q8",
+  "examples": 7,
+  "min_score": 0.5,
+  "iou_threshold": 0.5,
+  "elapsed_seconds": 0.17300033092033118,
+  "examples_per_second": 40.462350347893775,
+  "overall": {
+    "precision": 1.0,
+    "recall": 1.0,
+    "f1": 1.0,
+    "tp": 3,
+    "fp": 0,
+    "fn": 0
+  },
+  "by_label": {
+    "PPSN": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 3,
+      "fp": 0,
+      "fn": 0
+    }
+  }
+}

eval/rc8h_cal3_user_t050_cpu.json

@@ -0,0 +1,28 @@
+{
+  "model": "models/openmed-mliteclinical-irish-core-multitask-rc8h_cal3",
+  "input": "eval/user_raw_regression_cases_v1.jsonl",
+  "loader_type": "token_span_pt",
+  "examples": 7,
+  "min_score": 0.5,
+  "iou_threshold": 0.5,
+  "elapsed_seconds": 11.713831512955949,
+  "examples_per_second": 0.5975841459097078,
+  "overall": {
+    "precision": 1.0,
+    "recall": 1.0,
+    "f1": 1.0,
+    "tp": 3,
+    "fp": 0,
+    "fn": 0
+  },
+  "by_label": {
+    "PPSN": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1": 1.0,
+      "tp": 3,
+      "fp": 0,
+      "fn": 0
+    }
+  }
+}

inference_mask.py

@@ -0,0 +1,104 @@
+#!/usr/bin/env python3
+from __future__ import annotations
+
+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 AutoConfig
+
+from common import (
+    boundary_label_thresholds_from_config,
+    decode_token_presence_segments,
+    label_max_span_tokens_from_config,
+    label_min_nonspace_chars_from_config,
+    label_names_from_config,
+    safe_auto_tokenizer,
+    token_extend_thresholds_from_config,
+    token_label_thresholds_from_config,
+)
+from multitask_model import IrishCoreTokenSpanModel
+
+
+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 predict(text: str, model, tokenizer, min_score: float):
+    encoded = tokenizer(text, return_offsets_mapping=True, return_tensors="pt", truncation=True)
+    offsets = [tuple(item) for item in encoded.pop("offset_mapping")[0].tolist()]
+    device = next(model.parameters()).device
+    encoded = {key: value.to(device) for key, value in encoded.items()}
+    with torch.no_grad():
+        output = model(**encoded)
+    token_scores = torch.sigmoid(output.token_logits[0]).cpu().numpy()
+    start_scores = torch.sigmoid(output.start_logits[0]).cpu().numpy()
+    end_scores = torch.sigmoid(output.end_logits[0]).cpu().numpy()
+    label_names = label_names_from_config(model.config)
+    thresholds = token_label_thresholds_from_config(model.config, min_score)
+    extend_thresholds = token_extend_thresholds_from_config(model.config)
+    max_span_tokens = label_max_span_tokens_from_config(model.config)
+    min_nonspace_chars = label_min_nonspace_chars_from_config(model.config)
+    boundary_thresholds = boundary_label_thresholds_from_config(model.config)
+    return decode_token_presence_segments(
+        text,
+        offsets,
+        token_scores,
+        label_names,
+        min_score,
+        thresholds,
+        extend_thresholds,
+        max_span_tokens,
+        min_nonspace_chars,
+        boundary_thresholds,
+        start_scores=start_scores,
+        end_scores=end_scores,
+    )
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model", required=True)
+    parser.add_argument("--text", required=True)
+    parser.add_argument("--min-score", type=float, default=0.5)
+    parser.add_argument("--device", choices=["auto", "cpu", "cuda"], default="auto")
+    parser.add_argument("--json", action="store_true")
+    args = parser.parse_args()
+
+    tokenizer = safe_auto_tokenizer(args.model)
+    config = AutoConfig.from_pretrained(args.model)
+    model = IrishCoreTokenSpanModel.from_pretrained(args.model, config=config)
+    if args.device == "auto":
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+    else:
+        device = args.device
+    model.to(device)
+    model.eval()
+
+    spans = predict(args.text, model, tokenizer, args.min_score)
+    result = {
+        "model": args.model,
+        "backend": "transformers_token_span",
+        "min_score": args.min_score,
+        "spans": spans,
+        "masked_text": mask_text(args.text, spans),
+    }
+    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 +1,90 @@
+#!/usr/bin/env python3
+from __future__ import annotations
+
+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 numpy as np
+
+from common import (
+    boundary_label_thresholds_from_config,
+    decode_token_presence_segments,
+    label_max_span_tokens_from_config,
+    label_min_nonspace_chars_from_config,
+    label_names_from_config,
+    load_onnx_session,
+    run_onnx_all,
+    token_extend_thresholds_from_config,
+    token_label_thresholds_from_config,
+)
+
+
+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 predict(text: str, session, tokenizer, config, min_score: float):
+    encoded = tokenizer(text, return_offsets_mapping=True, return_tensors="np", truncation=True)
+    offsets = [tuple(item) for item in encoded["offset_mapping"][0].tolist()]
+    token_logits, start_logits, end_logits = run_onnx_all(session, encoded)
+    token_scores = 1.0 / (1.0 + np.exp(-token_logits[0]))
+    start_scores = 1.0 / (1.0 + np.exp(-start_logits[0]))
+    end_scores = 1.0 / (1.0 + np.exp(-end_logits[0]))
+    label_names = label_names_from_config(config)
+    thresholds = token_label_thresholds_from_config(config, min_score)
+    extend_thresholds = token_extend_thresholds_from_config(config)
+    max_span_tokens = label_max_span_tokens_from_config(config)
+    min_nonspace_chars = label_min_nonspace_chars_from_config(config)
+    boundary_thresholds = boundary_label_thresholds_from_config(config)
+    return decode_token_presence_segments(
+        text,
+        offsets,
+        token_scores,
+        label_names,
+        min_score,
+        thresholds,
+        extend_thresholds,
+        max_span_tokens,
+        min_nonspace_chars,
+        boundary_thresholds,
+        start_scores=start_scores,
+        end_scores=end_scores,
+    )
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model", required=True)
+    parser.add_argument("--text", required=True)
+    parser.add_argument("--min-score", type=float, default=0.5)
+    parser.add_argument("--json", action="store_true")
+    args = parser.parse_args()
+
+    session, tokenizer, config = load_onnx_session(args.model, onnx_file="model_quantized.onnx", onnx_subfolder="onnx")
+    spans = predict(args.text, session, tokenizer, config, args.min_score)
+    result = {
+        "model": args.model,
+        "backend": "onnx_token_span_q8",
+        "min_score": args.min_score,
+        "spans": spans,
+        "masked_text": mask_text(args.text, spans),
+    }
+    if args.json:
+        print(json.dumps(result, indent=2, ensure_ascii=False))
+    else:
+        print(result["masked_text"])
+
+
+if __name__ == "__main__":
+    main()

model.py

@@ -0,0 +1,75 @@
+#!/usr/bin/env python3
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+import torch.nn as nn
+from transformers import AutoConfig, AutoModel, PreTrainedModel
+from transformers.utils import ModelOutput
+
+
+def hidden_size_from_config(config) -> int:
+    return int(getattr(config, "hidden_size", getattr(config, "dim")))
+
+
+@dataclass
+class MultilabelSpanOutput(ModelOutput):
+    loss: Optional[torch.Tensor] = None
+    start_logits: Optional[torch.Tensor] = None
+    end_logits: Optional[torch.Tensor] = None
+
+
+class IrishCoreSpanHeadModel(PreTrainedModel):
+    config_class = AutoConfig
+    base_model_prefix = "encoder"
+
+    def __init__(self, config):
+        super().__init__(config)
+        num_span_labels = int(getattr(config, "num_span_labels"))
+        self.encoder = AutoModel.from_config(config)
+        hidden_size = hidden_size_from_config(config)
+        dropout = float(getattr(config, "seq_classif_dropout", getattr(config, "dropout", 0.1)))
+        self.dropout = nn.Dropout(dropout)
+        self.start_classifier = nn.Linear(hidden_size, num_span_labels)
+        self.end_classifier = nn.Linear(hidden_size, num_span_labels)
+        pos_weight = float(getattr(config, "span_positive_weight", 6.0))
+        self.register_buffer("loss_pos_weight", torch.full((num_span_labels,), pos_weight), persistent=False)
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        start_positions=None,
+        end_positions=None,
+        token_mask=None,
+        **kwargs,
+    ) -> MultilabelSpanOutput:
+        encoder_kwargs = {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            **kwargs,
+        }
+        if token_type_ids is not None and getattr(self.config, "model_type", "") not in {"distilbert", "roberta"}:
+            encoder_kwargs["token_type_ids"] = token_type_ids
+        outputs = self.encoder(**encoder_kwargs)
+        hidden = self.dropout(outputs.last_hidden_state)
+        start_logits = self.start_classifier(hidden)
+        end_logits = self.end_classifier(hidden)
+
+        loss = None
+        if start_positions is not None and end_positions is not None:
+            if token_mask is None:
+                token_mask = attention_mask
+            mask = token_mask.float().unsqueeze(-1)
+            pos_weight = self.loss_pos_weight.to(start_logits.device)
+            bce = nn.BCEWithLogitsLoss(reduction="none", pos_weight=pos_weight)
+            start_loss = bce(start_logits, start_positions.float()) * mask
+            end_loss = bce(end_logits, end_positions.float()) * mask
+            denom = mask.sum().clamp_min(1.0) * start_logits.shape[-1]
+            loss = (start_loss.sum() + end_loss.sum()) / (2.0 * denom)
+
+        return MultilabelSpanOutput(loss=loss, start_logits=start_logits, end_logits=end_logits)

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3c7908e59662702202602a82808406bc724e1b8fedb0c7771992914411ea0e32
+size 539050284

multitask_head_meta.json

@@ -0,0 +1,43 @@
+{
+  "base_model": "models/openmed-mliteclinical-irish-core-span-rc8f_gapunct_cal",
+  "init_span_model": "models/openmed-mliteclinical-irish-core-span-rc8f_gapunct_cal",
+  "init_token_model": "release/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc7",
+  "label_names": [
+    "ACCOUNT_NUMBER",
+    "BANK_ROUTING_NUMBER",
+    "CREDIT_DEBIT_CARD",
+    "EMAIL",
+    "FIRST_NAME",
+    "LAST_NAME",
+    "PASSPORT_NUMBER",
+    "PHONE_NUMBER",
+    "POSTCODE",
+    "PPSN",
+    "SWIFT_BIC"
+  ],
+  "max_length": 128,
+  "task": "Irish core PII token-presence plus start/end span extraction",
+  "freeze_layers": 2,
+  "span_positive_weight": 6.0,
+  "token_positive_weight": 4.0,
+  "token_presence_weight": 1.0,
+  "boundary_loss_weight": 1.0,
+  "boundary_smoothing": 0.15,
+  "fitted_label_max_span_tokens": {
+    "ACCOUNT_NUMBER": 19,
+    "BANK_ROUTING_NUMBER": 6,
+    "CREDIT_DEBIT_CARD": 13,
+    "EMAIL": 16,
+    "FIRST_NAME": 5,
+    "LAST_NAME": 8,
+    "PASSPORT_NUMBER": 9,
+    "PHONE_NUMBER": 10,
+    "POSTCODE": 8,
+    "PPSN": 9,
+    "SWIFT_BIC": 8
+  },
+  "train_examples": 30000,
+  "valid_examples": 3750,
+  "test_examples": 3750,
+  "data": "data/irish_core_multitask_rc8_mix_v1"
+}

multitask_model.py

@@ -0,0 +1,91 @@
+#!/usr/bin/env python3
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+import torch.nn as nn
+from transformers import AutoConfig, AutoModel, PreTrainedModel
+from transformers.utils import ModelOutput
+
+from model import hidden_size_from_config
+
+
+@dataclass
+class MultitaskSpanOutput(ModelOutput):
+    loss: Optional[torch.Tensor] = None
+    token_logits: Optional[torch.Tensor] = None
+    start_logits: Optional[torch.Tensor] = None
+    end_logits: Optional[torch.Tensor] = None
+
+
+class IrishCoreTokenSpanModel(PreTrainedModel):
+    config_class = AutoConfig
+    base_model_prefix = "encoder"
+
+    def __init__(self, config):
+        super().__init__(config)
+        num_span_labels = int(getattr(config, "num_span_labels"))
+        self.encoder = AutoModel.from_config(config)
+        hidden_size = hidden_size_from_config(config)
+        dropout = float(getattr(config, "seq_classif_dropout", getattr(config, "dropout", 0.1)))
+        self.dropout = nn.Dropout(dropout)
+        self.token_classifier = nn.Linear(hidden_size, num_span_labels)
+        self.start_classifier = nn.Linear(hidden_size, num_span_labels)
+        self.end_classifier = nn.Linear(hidden_size, num_span_labels)
+        boundary_pos_weight = float(getattr(config, "span_positive_weight", 6.0))
+        presence_pos_weight = float(getattr(config, "token_positive_weight", 4.0))
+        self.register_buffer("boundary_pos_weight", torch.full((num_span_labels,), boundary_pos_weight), persistent=False)
+        self.register_buffer("presence_pos_weight", torch.full((num_span_labels,), presence_pos_weight), persistent=False)
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        token_labels=None,
+        start_positions=None,
+        end_positions=None,
+        token_mask=None,
+        **kwargs,
+    ) -> MultitaskSpanOutput:
+        encoder_kwargs = {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            **kwargs,
+        }
+        if token_type_ids is not None and getattr(self.config, "model_type", "") not in {"distilbert", "roberta"}:
+            encoder_kwargs["token_type_ids"] = token_type_ids
+        outputs = self.encoder(**encoder_kwargs)
+        hidden = self.dropout(outputs.last_hidden_state)
+        token_logits = self.token_classifier(hidden)
+        start_logits = self.start_classifier(hidden)
+        end_logits = self.end_classifier(hidden)
+
+        loss = None
+        if token_labels is not None and start_positions is not None and end_positions is not None:
+            if token_mask is None:
+                token_mask = attention_mask
+            mask = token_mask.float().unsqueeze(-1)
+            boundary_pos_weight = self.boundary_pos_weight.to(token_logits.device)
+            presence_pos_weight = self.presence_pos_weight.to(token_logits.device)
+            bce_boundary = nn.BCEWithLogitsLoss(reduction="none", pos_weight=boundary_pos_weight)
+            bce_presence = nn.BCEWithLogitsLoss(reduction="none", pos_weight=presence_pos_weight)
+            token_loss = bce_presence(token_logits, token_labels.float()) * mask
+            start_loss = bce_boundary(start_logits, start_positions.float()) * mask
+            end_loss = bce_boundary(end_logits, end_positions.float()) * mask
+            denom = mask.sum().clamp_min(1.0) * token_logits.shape[-1]
+            token_loss = token_loss.sum() / denom
+            boundary_loss = (start_loss.sum() + end_loss.sum()) / (2.0 * denom)
+            token_weight = float(getattr(self.config, "token_presence_weight", 1.0))
+            boundary_weight = float(getattr(self.config, "boundary_loss_weight", 1.0))
+            loss = token_weight * token_loss + boundary_weight * boundary_loss
+
+        return MultitaskSpanOutput(
+            loss=loss,
+            token_logits=token_logits,
+            start_logits=start_logits,
+            end_logits=end_logits,
+        )

onnx/model.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7810664f9e5918086c9128de9647bcf4f587248f35e3460e398c0e1238bc06cf
+size 539089958

onnx/model.preprocessed.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:83191025f2ccd7b2f970a8fd3b8f22120bfdb5497f6f8be4df4ccc165e037a1b
+size 539100128

onnx/model_quantized.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3d6bec2bc83956535708b57f7524f03cc4ed53a8b41ddf0aefd2bf10b099592b
+size 411883593

onnx/onnx_export.json

@@ -0,0 +1,12 @@
+{
+  "source_model": "models/openmed-mliteclinical-irish-core-multitask-rc8g_cal2",
+  "onnx_path": "models/openmed-mliteclinical-irish-core-multitask-rc8g_cal2_onnx/model.onnx",
+  "task": "multitask-token-span-extraction",
+  "opset": 18,
+  "max_length": 256,
+  "output_names": [
+    "token_logits",
+    "start_logits",
+    "end_logits"
+  ]
+}

onnx/quantization.json

@@ -0,0 +1,26 @@
+{
+  "source_dir": "models/openmed-mliteclinical-irish-core-multitask-rc8g_cal2_onnx",
+  "input_model": "models/openmed-mliteclinical-irish-core-multitask-rc8g_cal2_onnx_q8/model.onnx",
+  "preprocessed_input_model": "models/openmed-mliteclinical-irish-core-multitask-rc8g_cal2_onnx_q8/model.preprocessed.onnx",
+  "output_model": "models/openmed-mliteclinical-irish-core-multitask-rc8g_cal2_onnx_q8/model_quantized.onnx",
+  "weight_type": "qint8",
+  "per_channel": true,
+  "reduce_range": false,
+  "preprocess_applied": true,
+  "op_types": [
+    "MatMul",
+    "Gemm",
+    "Attention"
+  ],
+  "copied_assets": [
+    "models/openmed-mliteclinical-irish-core-multitask-rc8g_cal2_onnx/model.onnx",
+    "onnx_export.json",
+    "config.json",
+    "special_tokens_map.json",
+    "tokenizer.json",
+    "tokenizer_config.json",
+    "vocab.txt"
+  ],
+  "format": "onnx_dynamic_quantized",
+  "task": "token-classification"
+}

pyproject.toml

@@ -0,0 +1,17 @@
+[project]
+name = "openmed-mliteclinical-irish-core-pii-rawonly"
+version = "0.1.0"
+description = "Raw-only Irish core PII release for OpenMed mLiteClinical"
+requires-python = ">=3.10"
+readme = "README.md"
+license = { text = "Apache-2.0" }
+dependencies = [
+  "transformers>=4.41.0",
+  "torch",
+  "numpy>=1.26.0",
+  "onnxruntime>=1.20.0",
+  "huggingface_hub>=0.36.0",
+]
+
+[tool.uv]
+package = false

qa_config.json

@@ -0,0 +1,14 @@
+{
+  "release": "OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc8",
+  "repo_id": "temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc8",
+  "decoder": "raw_only_multitask_token_span",
+  "min_score": 0.5,
+  "recommended_backend": "onnx_q8_cpu",
+  "onnx_file": "onnx/model_quantized.onnx",
+  "full_example": "My PPSN is 1234567TW, my Eircode is D02 X285, and my phone is 087 123 4567.",
+  "notes": [
+    "No scanner or validator layer is required for release behavior.",
+    "Use the bundled inference scripts or import common.decode_token_presence_segments.",
+    "ONNX q8 is the recommended CPU path."
+  ]
+}

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 +1,73 @@
+{
+  "release": "OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc8",
+  "base_model": "OpenMed/OpenMed-PII-mLiteClinical-Base-135M-v1",
+  "public_baseline_reference": "temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc7",
+  "task": "Irish core PII detection and masking in English and Irish Gaelic",
+  "coverage": [
+    "PPSN",
+    "ACCOUNT_NUMBER",
+    "BANK_ROUTING_NUMBER",
+    "CREDIT_DEBIT_CARD",
+    "PASSPORT_NUMBER",
+    "POSTCODE",
+    "PHONE_NUMBER",
+    "EMAIL",
+    "FIRST_NAME",
+    "LAST_NAME",
+    "SWIFT_BIC"
+  ],
+  "architecture": {
+    "encoder_family": "DistilBERT-size token encoder from OpenMed mLiteClinical 135M",
+    "heads": [
+      "token_presence_head",
+      "typed_start_head",
+      "typed_end_head"
+    ],
+    "decoder": "score-only token+boundary decoder with continuity and minimum-length priors",
+    "scanner_free": true,
+    "validator_free": true
+  },
+  "training_data": {
+    "published": [
+      "temsa/OpenMed-Irish-CorePII-TrainMix-v1",
+      "temsa/OpenMed-Irish-PPSN-Eircode-Spec-v1",
+      "joelniklaus/mapa",
+      "gretelai/synthetic_pii_finance_multilingual"
+    ],
+    "local_component_sets": [
+      "irish_ppsn_weakctx_exact_v1",
+      "irish_ppsn_weakctx_permissive_v1",
+      "irish_ppsn_ga_punctless_v1",
+      "irish_ppsn_tfamily_v1",
+      "ppsn_recover_v4_mix"
+    ]
+  },
+  "training": {
+    "epochs": 1.5,
+    "learning_rate": 3e-05,
+    "freeze_layers": 2,
+    "token_presence_weight": 1.0,
+    "boundary_loss_weight": 1.0,
+    "token_positive_weight": 4.0,
+    "span_positive_weight": 6.0,
+    "boundary_smoothing": 0.15
+  },
+  "papers": [
+    {
+      "title": "Split-NER",
+      "url": "https://aclanthology.org/2023.acl-short.36/"
+    },
+    {
+      "title": "SpanNER",
+      "url": "https://aclanthology.org/2021.acl-long.558/"
+    },
+    {
+      "title": "Boundary Smoothing for Named Entity Recognition",
+      "url": "https://aclanthology.org/2022.acl-long.490/"
+    },
+    {
+      "title": "TinyBERT",
+      "url": "https://aclanthology.org/2020.findings-emnlp.372/"
+    }
+  ]
+}