Instructions to use SlayerLab/NERGAL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SlayerLab/NERGAL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="SlayerLab/NERGAL")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("SlayerLab/NERGAL") model = AutoModelForTokenClassification.from_pretrained("SlayerLab/NERGAL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,321 Bytes
dc40291 53cf067 dc40291 be6377b dc40291 be6377b 53cf067 dc40291 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | # names/: FastPDN NER — Polish PII
- Source: [ArkadiuszPawlak/fastpdn-ner-polish-pii](https://huggingface.co/ArkadiuszPawlak/fastpdn-ner-polish-pii),
revision `636c57fe77b0afad8f065e7c76243dc051437035`.
- License: [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/).
- Attribution: "FastPDN NER — Polish PII (ONNX)" by ArkadiuszPawlak, fine-tuned from
[clarin-pl/FastPDN](https://huggingface.co/clarin-pl/FastPDN) on filtered KPWr
([clarin-pl/kpwr-ner](https://huggingface.co/datasets/clarin-pl/kpwr-ner)) plus LLM-synthetic data.
Upstream model card README.md sha256 `9ca776b1157e5dedfb9d404ee17a584ebfc80f4dc426beb68f69200a52522c6f`, kept
unchanged as `UPSTREAM_README.md` because the upstream repo is no longer publicly available (checked 2026-10-30).
## Changes
- `config.json` and `tokenizer.json` unchanged.
- `model.safetensors` is upstream `model.onnx` (FP32, sha256
`11924bfd20eb478ee614e5e1184536825db4747753d341af7c04b940efa84645`) converted by `convert.py` (sha256
`a698bc993b253fc70d960aa14af37a5088afb79c77dae70557c7056ce394333e`): 199 tensors copied, 73 linear weights
transposed from ONNX [in, out] to torch [out, in], no value changed. `convert.py` is included: it reads the upstream
snapshot at that revision from the default Hugging Face cache and is create-only, so run a copy outside `names/`. A
rerun under another `safetensors` version can give a different file sha256 with every tensor identical: only the
`__metadata__` key order differs.
- Review Gate 14, on 1,000 documents (1,714 windows, 614,466 tokens): torch FP32 against ONNX Runtime FP32, max
absolute logit difference 3.4e-4 (within 1e-3), every token's argmax identical, every person range identical.
- NERGAL uses only the PERSON, PERSON_F and PERSON_L tags, masked as `[PERSON]`.
## Files
| File | sha256 |
|---|---|
| `config.json` | `3cef51cd7d184478adbdec9f68730db279386fe145cb32473a0622edea923578` |
| `model.safetensors` | `2fa84ec6abd0c1b12ba0ba1548e89c9fca36fc58f03e66074db423b0e98ec8e6` |
| `tokenizer.json` | `3a690c2d605076ad3901d946d4c0145fbfb19fef2f01370c7388430aa9f31edf` |
| `convert.py` | `a698bc993b253fc70d960aa14af37a5088afb79c77dae70557c7056ce394333e` |
| `UPSTREAM_README.md` | `9ca776b1157e5dedfb9d404ee17a584ebfc80f4dc426beb68f69200a52522c6f` |
| `NOTICE.md` | this file |
|