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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
- 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-rc6
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: 0.9733
- task:
type: token-classification
name: Multilingual PPSN masking
dataset:
type: custom
name: multilingual_ppsn_v1_all
metrics:
- type: f1
name: Overall F1
value: 0.9274
- task:
type: token-classification
name: Hardening exact suite
dataset:
type: custom
name: irish_dllm_hardening_exact_v1
metrics:
- type: f1
name: Overall F1
value: 0.9756
---
# IrishCore-DiffMask-135M-v1-rc6
`IrishCore-DiffMask-135M-v1-rc6` 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.
## Evaluation Harness
Release gating for this checkpoint uses the deployment-aligned clean single-pass token-span path.
- `experiments/irish_core_span_raw_only/benchmark_multitask.py`
- `scripts/eval_dllm_release.py --inference-mode clean_single_pass`
These two paths now match exactly on the checked suites.
The older diffusion-style eval path is retained only as a training diagnostic and is not used for release scoring.
For this release, the best local raw checkpoint is `models/irishcore-diffmask-135m-v1-rc6k-blend-0p30`, and the bundled CPU artifact is `onnx/model_quantized.onnx`.
## 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.
## 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-rc6 \
--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-rc6 \
--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-rc6` ONNX q8 | 0.9733 | 0.9500 | 0.9804 | 1.0000 | 0.7500 | 1.0000 | 0.9274 | 0.9756 |
CPU throughput references:
| Suite | `rc5` q8 | `rc8` q8 | `IrishCore-DiffMask-135M-v1-rc6` q8 |
|---|---:|---:|---:|
| Irish core short-text path | 33.6193 ex/s | 257.3756 ex/s | 130.3415 ex/s |
| Multilingual PPSN short-text path | 35.5561 ex/s | 230.5181 ex/s | 200.2041 ex/s |
| Runtime profile source | 23.8338 ex/s | 179.4708 ex/s | 167.1461 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-rc6` 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.
## 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`.
## 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.
## Release Note
`rc6` is the first DiffMask package selected after reconciling the evaluation harness. Under the aligned harness, it matches the prior best candidate on fresh holdout, QA feedback exact, UAT exact, multilingual PPSN, edge, finance, and hardening, while improving Irish core on the full checkpoint.
<!-- 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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