Token Classification
Transformers
ONNX
Safetensors
English
Irish
distilbert
pii
de-identification
ireland
irish
gaelic
ppsn
eircode
passport
phone-number
iban
int8
Instructions to use temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc7")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc7") model = AutoModelForTokenClassification.from_pretrained("temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc7", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Benchmark Summary
What Changed
rc7 keeps the same checkpoint weights and the same ONNX q8 artifact as temsa/OpenMed-mLiteClinical-IrishCorePII-135M-v2-rc6.
The change is implementation transparency and maintainability:
- scanner candidate extraction is defined in
scanner_spec.yaml - runtime scanner data is generated into
irish_core_generated_scanner_spec.py - semantic validation remains explicit and deterministic in code
- release Python files no longer depend on
regex - no regex-based candidate extraction is used in the repair layer
Full Checkpoint
| Suite | rc6 | rc7 |
|---|---|---|
| Irish core manual | 1.0000 | 1.0000 |
| Phone / passport / finance | 1.0000 | 1.0000 |
| Finance boundary repair | 1.0000 | 1.0000 |
| Gaelic weak-context PPSN | 1.0000 | 1.0000 |
ONNX q8
| Suite | rc6 | rc7 |
|---|---|---|
| Irish core manual | 0.9934 | 0.9934 |
| Phone / passport / finance | 1.0000 | 1.0000 |
| Finance boundary repair | 1.0000 | 1.0000 |
| Gaelic weak-context PPSN | 1.0000 | 1.0000 |