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
29 days ago
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Prepare 1.1.0: batch API (predict_many, scrub_many), opt-in float16
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>fdc79fc -
1.0.3: registry and label-note rules, placeholder and card fixes (#1)
- Prepare 1.0.3: registry and label-note rules, placeholder and card fixes (6eee92ef79115a4a40eedb615aacf457bdd54868)2bf5c25
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Fix public tokenizer batch shape before 1.0.2 release
510b295 -
Release 1.0.2: conservative labelled country-area phone fix
15eb2b4 -
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