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
Commit History
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Release 1.0.2: conservative labelled country-area phone fix
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Release 1.0.1: prefix-only glued-email trim.
Same epoch-5 weights; 841-dev union FP 133→123 with no gold loss. Co-authored-by: Cursor <cursoragent@cursor.com>7ce967b
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Document synthetic test_nergal.py
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Add synthetic NERGAL unit tests
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Add 841-dev naked vs hybrid comparison with GLiNER and GLiNER 2.5
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Add hybrid PII scripts: nergal.py and frozen scrub_pii.py
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Add hybrid PII scripts: nergal.py and frozen scrub_pii.py
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Add hybrid PII scripts: nergal.py and frozen scrub_pii.py
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Add NERGAL epoch-5 hybrid PII snapshot
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initial commit
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