README.md

---
language:
- en
- ga
license: apache-2.0
library_name: transformers
pipeline_tag: token-classification
tags:
- pii
- de-identification
- token-classification
- ireland
- irish
- gaelic
- diffusion-style
- denoising
- ppsn
- eircode
- onnx
- int8
- dynamic-quantization
- cpu
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
model-index:
- name: IrishCore-DiffMask-135M-v1-rc3
  results:
  - task:
      type: token-classification
      name: Irish core PII masking
    dataset:
      type: custom
      name: irish_core_pii_v1
    metrics:
    - type: f1
      name: Overall F1
      value: 0.9664
  - task:
      type: token-classification
      name: Multilingual PPSN masking
    dataset:
      type: custom
      name: multilingual_ppsn_v1_all
    metrics:
    - type: f1
      name: Overall F1
      value: 0.9591
  - task:
      type: token-classification
      name: Hardening exact suite
    dataset:
      type: custom
      name: irish_dllm_hardening_exact_v1
    metrics:
    - type: f1
      name: Overall F1
      value: 1.0000
  - task:
      type: token-classification
      name: UAT replay exact suite
    dataset:
      type: custom
      name: diffmask_gap_uat_exact_v1
    metrics:
    - type: f1
      name: Overall F1
      value: 0.9032
---

# IrishCore-DiffMask-135M-v1-rc3

`IrishCore-DiffMask-135M-v1-rc3` is a raw-only Irish PII masking model derived from `OpenMed/OpenMed-PII-mLiteClinical-Base-135M-v1`.

It is a small, scanner-free span extractor tuned for:

- `PPSN`
- `ACCOUNT_NUMBER`
- `BANK_ROUTING_NUMBER`
- `CREDIT_DEBIT_CARD`
- `PASSPORT_NUMBER`
- `POSTCODE`
- `PHONE_NUMBER`
- `EMAIL`
- `FIRST_NAME`
- `LAST_NAME`
- `SWIFT_BIC`

The main target is English plus Irish Gaelic text in citizen-support, public-sector, and HSE-style flows. The repo ships both the full `transformers` checkpoint and a dynamic q8 ONNX artifact for CPU deployment.

## What "DiffMask" Means Here

This release is not a generative diffusion language model. It is a compact discriminative token-span model trained with a diffusion-style denoising schedule.

The short version:

- **Base OpenMed**: plain BIO token classification
- **DiffMask**: token-span extraction with token-presence and boundary heads
- **DiffMask training**: repeated masked denoising over the same sentence
- **DiffMask inference**: one forward pass, no iterative refinement, no text generation

Concretely:

- The encoder starts from the DistilBERT-family weights inside `OpenMed/OpenMed-PII-mLiteClinical-Base-135M-v1`.
- The model adds three task heads over the encoder hidden states:
  - a per-label token-presence head
  - a typed start-boundary head
  - a typed end-boundary head
- During training, each input sentence is corrupted multiple times by replacing a random fraction of visible tokens with `[MASK]`.
- The corruption level follows a short noise schedule from heavy masking to light masking.
- The same gold spans are learned at every noise level, and the losses are averaged across the denoising passes.
- At inference time there is no diffusion loop and no rewrite step: the model runs once and a score-only span decoder reconstructs spans from token scores plus typed boundaries.

So the "DLLM" aspect here is the training recipe: repeated masked denoising over text, not autoregressive generation.

## What It Is Not

This model is **not** a full discrete diffusion language model in the LLaDA sense.

A true DLLM would usually have:

- timestep or noise conditioning inside the model
- iterative denoising at inference time
- multi-step sequence refinement at runtime
- text generation or full-sequence reconstruction as a first-class objective

This release does **not** do that.

Instead, it uses the diffusion idea only as a **training-time robustness trick**:

- corrupt the sentence with `[MASK]` at several noise levels
- train on the same target spans each time
- average those losses

At runtime, it behaves like a normal fast discriminative extractor.

## Architecture

- Encoder: DistilBERT-size encoder from the OpenMed mLiteClinical 135M base
- Heads:
  - token presence per released label
  - typed start boundary per released label
  - typed end boundary per released label
- Decoder:
  - score-only span decoding from offsets, token continuity, label-specific thresholds, and typed boundaries
  - no regex candidate extractor
  - no checksum validator
  - no scanner layer

The release behavior is fully defined by the weights plus the bundled decoder in `common.py`.

## Training And Inference Flow

Training:

1. tokenize a sentence with gold BIO spans
2. convert spans into:
   - token-presence targets
   - typed start targets
   - typed end targets
3. create several noised copies of the same tokenized sentence by masking random visible tokens
4. run the same encoder+heads on each noised copy
5. average the losses across those denoising passes

Inference:

1. tokenize the raw text once
2. run a single forward pass
3. predict:
   - which labels are present on each token
   - where each labeled span starts
   - where each labeled span ends
4. decode spans with label-aware thresholds and boundary rules
5. replace the detected spans with placeholders such as `[PII:PPSN]`

There is no multi-step refinement loop in deployment.

## How It Differs From The Original OpenMed Model

The original `OpenMed/OpenMed-PII-mLiteClinical-Base-135M-v1` is a standard `DistilBertForTokenClassification` model:

- one encoder
- one token-classification head
- BIO labels such as `B-email`, `I-email`, `B-phone_number`
- generic token aggregation to recover spans

DiffMask changes two things:

1. **Different supervision**
   - base OpenMed learns only BIO token labels
   - DiffMask learns token presence plus typed span boundaries

2. **Different training recipe**
   - base OpenMed is trained as a standard token classifier
   - DiffMask is trained on multiple masked-noised views of the same sentence

That makes DiffMask better suited to structured Irish identifiers and mixed PII masking, while still keeping a small encoder and a fast CPU path.

## How It Differs From `rc5` And `rc8`

| Model | Core idea | External scanner/validator | Runtime shape |
|---|---|---|---|
| `rc5` | token classifier + repair logic | yes | heavier, decoder-assisted |
| `rc8` | raw-only token-span model | no | one pass + span decoder |
| `DiffMask` | raw-only token-span model + denoising training | no | one pass + span decoder |

So DiffMask is closest to `rc8` operationally, but it uses a stronger training recipe.

## Why This Exists

The older `rc5` release still depended on a repair-oriented decoder stack. The public `rc8` release removed that external logic, but it regressed on several structured Irish identifiers. This release keeps the raw-only deployment shape while re-hardening the model on Irish numeric and mixed-PII cases.

`rc3` is the next candidate after `rc2`. It keeps the stronger `focusv3` checkpoint selected during local iteration, then applies a small decoder-profile retune for the published config:

- lower `EMAIL` token extend threshold to keep contiguous mailbox fragments together
- lower `PASSPORT_NUMBER` q8 threshold slightly to recover a mixed-message passport miss after dynamic quantization

The weights remain raw-only and scanner-free. The `rc3` change is the checkpoint plus a stricter release-time decoder profile in `config.json`.

## References

Direct implementation references:

- Devlin et al., *BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding*  
  https://arxiv.org/abs/1810.04805
- Sanh et al., *DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter*  
  https://arxiv.org/abs/1910.01108
- Fu et al., *Boundary Smoothing for Named Entity Recognition*  
  https://aclanthology.org/2022.acl-long.490/
- Wang et al., *SPANNER: Named Entity Re-/Recognition as Span Prediction*  
  https://aclanthology.org/2021.acl-long.558/

Conceptual diffusion-style training references:

- Nie et al., *LLaDA 2.0: Scaling Up Diffusion Language Models to 100B*  
  https://arxiv.org/abs/2512.15745
- Gong et al., *Scaling Diffusion Language Models via Adaptation from Autoregressive Models*  
  https://arxiv.org/abs/2410.17891

These diffusion papers were used as architectural inspiration for the masked noising schedule. This release does **not** implement a generative text diffusion runtime.

## Included Artifacts

- Full `transformers` checkpoint in the repo root
- Dynamic q8 ONNX export in `onnx/model_quantized.onnx`
- Unquantized ONNX export in `onnx/model.onnx`
- `inference_mask.py` for the full checkpoint
- `inference_mask_onnx.py` for the ONNX q8 path
- `common.py`, `model.py`, and `multitask_model.py` implementing the release decoder
- benchmark files in `eval/`

Artifact sizes:

- Full checkpoint: `514 MB` (`model.safetensors`)
- Dynamic q8 ONNX: `393 MB` (`onnx/model_quantized.onnx`)

## How To Use It

Full checkpoint:

```bash
uv run python inference_mask.py \
  --model temsa/IrishCore-DiffMask-135M-v1-rc3 \
  --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/IrishCore-DiffMask-135M-v1-rc3 \
  --min-score 0.5 \
  --text "Please provide your passport NN5123456 and call me on 0851234567." \
  --json
```

Both scripts emit explicit placeholders like `[PII:PPSN]` in `masked_text`.

## Q8 Comparison

Deployment-relevant comparison on CPU:

| Model | Core F1 | Edge F1 | Finance F1 | Finance-boundary F1 | User PPSN F1 | GA weak PPSN F1 | Multilingual PPSN F1 | Hardening F1 |
|---|---:|---:|---:|---:|---:|---:|---:|---:|
| `rc5` ONNX q8 | 0.9669 | 0.9744 | 0.9362 | 0.8750 | 1.0000 | 1.0000 | 0.9333 | - |
| `rc8` ONNX q8 | 0.9737 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 0.9176 | 0.7059 |
| `IrishCore-DiffMask-135M-v1-rc3` ONNX q8 | 0.9664 | 1.0000 | 1.0000 | 1.0000 | 0.8571 | 1.0000 | 0.9591 | 1.0000 |

UAT replay exact suite used for the recent hardening pass:

| Model | UAT replay exact F1 | Precision | Recall |
|---|---:|---:|---:|
| `IrishCore-DiffMask-135M-v1-rc1` ONNX q8 | 0.4545 | 1.0000 | 0.2941 |
| `IrishCore-DiffMask-135M-v1-rc2` ONNX q8 | 0.8276 | 1.0000 | 0.7059 |
| `rc8` ONNX q8 | 0.3636 | 0.3750 | 0.3529 |
| `IrishCore-DiffMask-135M-v1-rc3` ONNX q8 | 0.9032 | 1.0000 | 0.8235 |

CPU throughput references:

| Suite | `rc5` q8 | `rc8` q8 | `IrishCore-DiffMask-135M-v1-rc3` q8 |
|---|---:|---:|---:|
| Irish core short-text path | 33.6193 ex/s | 257.3756 ex/s | 29.9676 ex/s |
| Multilingual PPSN short-text path | 35.5561 ex/s | 230.5181 ex/s | 54.2219 ex/s |
| Runtime profile source | 23.8338 ex/s | 179.4708 ex/s | 46.1519 ex/s |

Notes:

- The `rc5` speed references come from its published q8 end-to-end inference stack, which includes its older repair decoder.
- The `rc8` and `IrishCore-DiffMask-135M-v1-rc3` numbers use the same raw-only token-span ONNX path.
- A weight-only q4 ONNX experiment was also tried during development, but it was slower than q8 on this CPU and is not shipped.
- The `user_raw_regression_cases_v1` suite is a legacy PPSN-only regression set. In `rc3`, the single counted false positive is `0871234567`, which is now intentionally masked as `PHONE_NUMBER` rather than misread as `PPSN`.

## Additional Training Data Used For This RC

Published training sources:

- `temsa/OpenMed-Irish-CorePII-TrainMix-v1`
- `temsa/OpenMed-Irish-PPSN-Eircode-Spec-v1`
- `joelniklaus/mapa`
- `gretelai/synthetic_pii_finance_multilingual`

Additional local synthetic hardening and replay sets used during checkpoint selection:

- `irish_core_diffmask_v5_mix`
- `dllm_uat_replay_v1`
- `dllm_gap_patch_v4`
- `dllm_uat_patch_v3`
- `irish_core_diffmask_focus_v3`

`rc3` is based on the locally selected `focusv3` checkpoint and then retuned with a narrower decoder profile for the public config.

## Limits

- This is still a compact model. The hardest remaining errors are multilingual PPSN near-miss cases rather than Irish core numeric formats.
- The release path is intentionally scanner-free. If you need deterministic validation of individual identifier types, add that in your application layer.
- If you rely on release behavior, use the bundled inference scripts or import `decode_token_presence_segments` from `common.py`.
- Known remaining misses on the current UAT replay suite are the second phone number in the long Client Identity Services sentence (`071 967 2616`), `R93 EC57` inside the longer allocation-centre block, and `EPStamp4@enterprise.gov.ie`.

## License And Attribution

- Release license: Apache-2.0
- Base model: `OpenMed/OpenMed-PII-mLiteClinical-Base-135M-v1`
- The derivative release remains subject to the attribution terms of the upstream datasets listed above.
- See `NOTICE`, `training_sources.json`, and `eval/benchmark_summary.json` for provenance and benchmark details.

<!-- portfolio-comparison:start -->
## Portfolio Comparison

Updated: `2026-03-16`.

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-4L-122M-v1-rc4`](https://huggingface.co/temsa/IrishCore-GlobalPointer-ContextPII-4L-122M-v1-rc4) | 4-layer GlobalPointer distilled fast student | 1.0000 | 1.0000 | 0.9333 | 299.0 |
| [`temsa/IrishCore-GlobalPointer-ContextPII-4L-122M-v1-rc3`](https://huggingface.co/temsa/IrishCore-GlobalPointer-ContextPII-4L-122M-v1-rc3) | 4-layer GlobalPointer distilled fast student | 1.0000 | 1.0000 | 0.9333 | 317.9 |
| [`temsa/IrishCore-GlobalPointer-ContextPII-4L-122M-v1-rc2`](https://huggingface.co/temsa/IrishCore-GlobalPointer-ContextPII-4L-122M-v1-rc2) | 4-layer GlobalPointer distilled fast student | 1.0000 | 1.0000 | 0.9333 | 292.5 |
| [`temsa/IrishCore-GlobalPointer-ContextPII-4L-122M-v1-rc1`](https://huggingface.co/temsa/IrishCore-GlobalPointer-ContextPII-4L-122M-v1-rc1) | 4-layer GlobalPointer distilled fast student | 1.0000 | 1.0000 | 0.9333 | 337.3 |
| [`temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc27`](https://huggingface.co/temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc27) | GlobalPointer raw-only + context labels | 1.0000 | 1.0000 | 0.9333 | 270.0 |
| [`temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc25`](https://huggingface.co/temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc25) | GlobalPointer raw-only + context labels | 1.0000 | 1.0000 | 0.9333 | 212.1 |
| [`temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc24`](https://huggingface.co/temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc24) | GlobalPointer raw-only + context labels | 1.0000 | 1.0000 | 0.9333 | 278.9 |
| [`temsa/IrishCore-GlobalPointer-ContextPII-135M-v1-rc23`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v1) | Hybrid classifier baseline | 0.9530 | 0.9333 | 0.9882 | 103.3 |
| [`temsa/IrishCore-DiffMask-135M-v1-rc6`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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`](https://huggingface.co/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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