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
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Download names/NOTICE.md from SlayerLab/NERGAL: direct link, hf CLI and curl.
- Browser
- Download file 2.32 kB
-
https://huggingface.co/SlayerLab/NERGAL/resolve/main/names/NOTICE.md
- Command line
-
hf download hf://SlayerLab/NERGAL/names/NOTICE.md
-
curl -L -o NOTICE.md https://huggingface.co/SlayerLab/NERGAL/resolve/main/names/NOTICE.md
2.32 kB
names/: FastPDN NER — Polish PII
- Source: ArkadiuszPawlak/fastpdn-ner-polish-pii,
revision
636c57fe77b0afad8f065e7c76243dc051437035. - License: CC-BY-4.0.
- Attribution: "FastPDN NER — Polish PII (ONNX)" by ArkadiuszPawlak, fine-tuned from
clarin-pl/FastPDN on filtered KPWr
(clarin-pl/kpwr-ner) plus LLM-synthetic data.
Upstream model card README.md sha256
9ca776b1157e5dedfb9d404ee17a584ebfc80f4dc426beb68f69200a52522c6f, kept unchanged asUPSTREAM_README.mdbecause the upstream repo is no longer publicly available (checked 2026-10-30).
Changes
config.jsonandtokenizer.jsonunchanged.model.safetensorsis upstreammodel.onnx(FP32, sha25611924bfd20eb478ee614e5e1184536825db4747753d341af7c04b940efa84645) converted byconvert.py(sha256a698bc993b253fc70d960aa14af37a5088afb79c77dae70557c7056ce394333e): 199 tensors copied, 73 linear weights transposed from ONNX [in, out] to torch [out, in], no value changed.convert.pyis included: it reads the upstream snapshot at that revision from the default Hugging Face cache and is create-only, so run a copy outsidenames/. A rerun under anothersafetensorsversion 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 |