OpenMed-mLiteClinical-IrishPPSN-135M-v1

• B-PPSN
• I-PPSN

Deployment Note

• CPU-first deployment, low batch / short text: temsa/OpenMed-mLiteClinical-IrishPPSN-135M-v1-fp16 is a good option
• Why: on the current word_aligned single-example path it preserved PPSN quality and improved end-to-end CPU throughput on the multilingual suite from about 31.27 to 45.80 examples/s while halving model size
• Important caveat: in a batched CPU forward matrix, fp16 became slower than fp32 once sequence length and batch size grew; see the fp16 repo for the runtime matrix
• If your CPU deployment batches aggressively, keep this fp32 repo or use the q8 repo if you need maximum throughput and can accept lower PPSN quality

• short text: about <= 32 tokenizer tokens
• 33-63 tokens: gray zone, benchmark your workload
• >= 64 tokens: not short for this recommendation
• low batch: batch_size = 1
• batch_size = 2-4: gray zone, benchmark your workload
• batch_size >= 8: not low batch for this recommendation

• Prefer fp16 for batch_size = 1 and about <= 32 tokens
• Prefer this fp32 repo once batch size or sequence length grows materially

What This Model Is

• A full transformers checkpoint, not a LoRA adapter
• A derivative of the OpenMed mLiteClinical 135M PII model
• Tuned specifically to add PPSN coverage while preserving the base model's other PII behavior
• Best used with the bundled word_aligned PPSN decoder

What This Model Is Not

• Not a general Irish-PII upgrade across all Irish-specific identifiers
• Not optimized for plain pipeline(..., aggregation_strategy="simple") PPSN extraction
• Not a replacement for downstream policy logic or deterministic validation when your application requires checksum validation

Recommended Inference

• PPSNs are short and often tokenized into fragments
• the custom word_aligned decoder reconstructs cleaner full-span PPSN outputs
• the default token-classification aggregation path can produce partial spans

Intended Use

• De-identification / masking of PPSNs in English or Irish Gaelic text
• Triage and QA for citizen-to-government or healthcare text flows
• A lightweight base for further Irish identifier fine-tuning if you need more than PPSN

Limitations

• This release is strongest on PPSN, not on other Irish-specific identifiers such as Eircode, Irish phone numbers, or Irish IBAN/BIC handling
• Lowercase / weak-context PPSN cases improved versus the base model, but this is still not a checksum-aware system
• You should benchmark on your own traffic before production rollout

Key Results

• User raw PPSN regression F1: 0.8000
• QA PPSN regression v6 validated F1: 0.6667
• QA PPSN regression v8 F1: 0.7385
• Irish PPSN regression F1: 0.8000
• Irish large PPSN F1: 0.8979
• Multilingual gov + citizen + HSE PPSN suite F1: 0.9704
• Non-PPSN agreement vs base mLiteClinical: 1.0000

Files

• Core model:
  • model.safetensors
  • config.json
  • tokenizer.json
  • tokenizer_config.json
  • special_tokens_map.json
  • label_meta.json
  • vocab.txt
• Inference / QA helpers:
  • inference_word_aligned.py
  • qa_config.json
  • pyproject.toml
• Evaluation reports:
  • eval/

Base Model

• Upstream: OpenMed/OpenMed-PII-mLiteClinical-Base-135M-v1
• Upstream license: Apache-2.0

License

Portfolio Comparison

• 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

Irish Core PII: Other Public Checkpoints

PPSN-Only: Comparable Public Artifacts

PPSN-Only: Historical Public Checkpoints