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")# 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
1.1.0 card: throughput in TL;DR
Browse filesCo-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
README.md
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@@ -26,6 +26,7 @@ Python rules do the identifiers they can prove. A transformer NER head adds phon
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- **Version:** `1.1.0` (`hybrid.json`, `CHANGELOG.md`)
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- **Ground:** `scrub_pii` regex (SHA256 `f32d5c54…`)
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- **Additive labels:** XLM-RoBERTa-large token classifier, BIO tags `phone` / `pii`, threshold 0.95
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- **This snapshot:** seed `202609160`, **epoch 5** of a seven-epoch schedule
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## What NERGAL detects — and what it does not
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- **Version:** `1.1.0` (`hybrid.json`, `CHANGELOG.md`)
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- **Ground:** `scrub_pii` regex (SHA256 `f32d5c54…`)
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- **Additive labels:** XLM-RoBERTa-large token classifier, BIO tags `phone` / `pii`, threshold 0.95
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- **Throughput:** about 80k chars/s on one RTX 4090 with `scrub_many` + `dtype="float16"` and 3 processes (1.0.3: 23k)
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- **This snapshot:** seed `202609160`, **epoch 5** of a seven-epoch schedule
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## What NERGAL detects — and what it does not
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