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OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc8

Token Classification
Transformers
ONNX
Safetensors
English
Irish
distilbert
pii
de-identification
ireland
irish
gaelic
raw-only
ppsn
eircode
passport
phone-number
int8
dynamic-quantization
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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:

How To Use It

Full checkpoint:

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:

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.

Portfolio Comparison

Updated: 2026-03-15.

Use this section for the fastest public comparison across the temsa PII masking portfolio.

  • The first core table only includes public checkpoints that ship both comparable q8 accuracy and q8 CPU throughput.
  • The first PPSN table only includes public artifacts that ship comparable PPSN accuracy and CPU throughput.
  • Missing cells in the archive tables mean the older release did not ship that metric in its public bundle.
  • DiffMask rows use the reconciled clean_single_pass harness that matches the deployed runtime.
  • GlobalPointer rows use the public raw-only span-matrix release bundle and its packaged q8 ONNX artifact.
  • The same content is shipped as PORTFOLIO_COMPARISON.md inside each public model repo.

Irish Core PII: Comparable Public Checkpoints

Repo Stack Full Core F1 Q8 Core F1 Q8 Multilingual PPSN F1 Q8 Core ex/s
temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc23 GlobalPointer raw-only + context labels 1.0000 1.0000 0.9333 237.6
temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc22 GlobalPointer raw-only + context labels 1.0000 1.0000 0.9333 106.8
temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc21 GlobalPointer raw-only + context labels 1.0000 1.0000 0.9333 150.8
temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc20 GlobalPointer raw-only + context labels 1.0000 1.0000 0.9333 181.9
temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc19 GlobalPointer raw-only + context labels 1.0000 1.0000 0.9333 73.1
temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc18 GlobalPointer raw-only + context labels 1.0000 1.0000 0.9333 126.2
temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc17 GlobalPointer raw-only + context labels 1.0000 1.0000 0.9333 125.5
temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc16 GlobalPointer raw-only + context labels 1.0000 1.0000 0.9333 125.5
temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc15 GlobalPointer raw-only + context labels 1.0000 1.0000 0.9333 125.5
temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc14 GlobalPointer raw-only + context labels 1.0000 1.0000 0.9333 119.2
temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc13 GlobalPointer raw-only + context labels 1.0000 1.0000 0.9333 126.1
temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc12 GlobalPointer raw-only + context labels 1.0000 1.0000 0.9333 73.6
temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc11 GlobalPointer raw-only + context labels 1.0000 1.0000 0.9333 94.1
temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc10 GlobalPointer raw-only + context labels 1.0000 1.0000 0.9333 125.8
temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc9 GlobalPointer raw-only + context labels 1.0000 1.0000 0.9333 119.8
temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc8 GlobalPointer raw-only + context labels 1.0000 1.0000 0.9333 128.9
temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc7 GlobalPointer raw-only + context labels 1.0000 1.0000 0.9333 89.0
temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc6 GlobalPointer raw-only + context labels 1.0000 1.0000 0.9333 89.0
temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc5 GlobalPointer raw-only + context labels 1.0000 1.0000 0.9333 84.5
temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc4 GlobalPointer raw-only + context labels 0.9935 0.9935 0.9333 61.5
temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc3 GlobalPointer raw-only + context labels 0.9935 0.9935 0.9333 61.5
temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc2 GlobalPointer raw-only + context labels 0.9935 0.9935 0.9222 61.5
temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc1 GlobalPointer raw-only + context labels 0.9935 0.9935 0.9222 61.5
temsa/IrishCore-GlobalPointer-135M-v1-rc4 GlobalPointer raw-only span-matrix 1.0000 1.0000 0.9333 221.6
temsa/IrishCore-GlobalPointer-135M-v1-rc3 GlobalPointer raw-only span-matrix 1.0000 1.0000 0.9213 204.9
temsa/IrishCore-GlobalPointer-135M-v1-rc2 GlobalPointer raw-only span-matrix 0.9934 0.9934 0.9326 231.2
temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc8 Raw-only token-span 0.9737 0.9737 0.9176 46.1
temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc7 Hybrid classifier + generated scanner spec 1.0000 0.9934 1.0000 30.0
temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc6 Hybrid classifier + repair decoders 1.0000 0.9934 1.0000 29.5
temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc5 Hybrid classifier + repair decoders 0.9737 0.9669 0.9333 34.4
temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc4 Hybrid classifier + repair decoders 0.9870 0.9740 0.9600 114.2
temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc3 Hybrid classifier + repair decoders 0.9806 0.9677 0.9333 44.9
temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc2 Hybrid classifier + repair decoders 0.9554 0.9615 0.7887 119.1
temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v1 Hybrid classifier baseline 0.9530 0.9333 0.9882 103.3
temsa/IrishCore-DiffMask-135M-v1-rc6 DiffMask token-span, scanner-free 0.9801 0.9733 0.9274 130.3
temsa/IrishCore-DiffMask-135M-v1-rc5 DiffMask token-span, scanner-free 0.9733 0.9733 0.9379 249.2
temsa/IrishCore-DiffMask-135M-v1-rc4 DiffMask token-span, scanner-free 0.9733 0.9733 0.9371 29.5
temsa/IrishCore-DiffMask-135M-v1-rc3 DiffMask token-span, scanner-free 0.9664 0.9664 0.9591 30.0
temsa/IrishCore-DiffMask-135M-v1-rc2 DiffMask token-span, scanner-free 0.9664 0.9664 0.9212 247.1
temsa/IrishCore-DiffMask-135M-v1-rc1 DiffMask token-span, scanner-free 0.9801 0.9934 0.9412 251.2

Irish Core PII: Other Public Checkpoints

Repo Stack Full Core F1 Q8 Core F1 Q8 Multilingual PPSN F1 Notes
temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc1 Hybrid classifier prototype 0.9487 — — Predates the public q8 artifact.

Finance-boundary q8 F1 is 1.0000 for OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc6, OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc7, OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc8, and all public IrishCore-DiffMask releases from rc1 to rc6. OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc5 ships 0.8750 on that public q8 suite.

PPSN-Only: Comparable Public Artifacts

Repo Artifact Irish Large F1 Multilingual PPSN F1 User Raw F1 QA v8 F1 CPU ex/s
temsa/OpenMed-mLiteClinical-IrishPPSN-135M-v1 fp32 canonical checkpoint 0.8979 0.9704 0.8000 0.7385 57.4
temsa/OpenMed-mLiteClinical-IrishPPSN-135M-v1-fp16 fp16 CPU/GPU artifact — 0.9704 0.8000 0.7385 45.8
temsa/OpenMed-mLiteClinical-IrishPPSN-135M-v1-q8 dynamic int8 CPU artifact — 0.9040 — — 132.1

PPSN-Only: Historical Public Checkpoints

Repo Main Published Metrics Notes
temsa/OpenMed-PPSN-mLiteClinical-v1 same as canonical fp32 repo: multilingual 0.9704, user raw 0.8000 Legacy alias; prefer temsa/OpenMed-mLiteClinical-IrishPPSN-135M-v1.
temsa/OpenMed-PPSN-v6-raw-rc2 irish_reg_v5 0.8750; user_raw 0.8000; qa_v8 0.7385 Raw PPSN-only research checkpoint; no packaged multilingual CPU benchmark row.
temsa/OpenMed-PPSN-v5_1 irish_large_v2 raw 0.9285; qa_v6 hybrid strict 1.0000 Hybrid PPSN-only checkpoint; predates the canonical multilingual suite packaging.
temsa/OpenMed-PPSN-v5 irish_reg_v5 raw 0.8235; irish_reg_v5 hybrid strict 1.0000 Hybrid PPSN-only checkpoint; predates the canonical multilingual suite packaging.
temsa/OpenMed-PPSN-v4 synthetic non-PPSN drift check only Predates the current PPSN eval suite; no packaged apples-to-apples multilingual CPU row.

If you need the strongest current raw-only Irish core model, start with IrishCore-GlobalPointer-135M-v1-rc4. If you need the fastest CPU-first raw-only line, compare it against IrishCore-DiffMask-135M-v1-rc6. If you need a PPSN-only artifact, compare the canonical fp32, fp16, and q8 variants of OpenMed-mLiteClinical-IrishPPSN-135M-v1 directly in the table above.

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