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
| """Synthetic NERGAL tests. Invented strings only; no corpus text or real identifiers.""" | |
| import hashlib | |
| import json | |
| import unittest | |
| from pathlib import Path | |
| HERE = Path(__file__).resolve().parent | |
| RULES_SHA = '3016ae5bd403ff997458f9dd74bad8c6ed1388eb83dadc1b31cdb182f9ed607f' | |
| class NergalTests(unittest.TestCase): | |
| def test_card_and_rules_hash(self): | |
| from nergal import GAP_IDS, GAPS, HUB_ID, RULES_SHA as PINNED, THRESHOLD, VERSION | |
| card = json.loads((HERE / 'hybrid.json').read_text()) | |
| self.assertEqual(HUB_ID, 'SlayerLab/NERGAL') | |
| self.assertEqual(VERSION, '1.0.2') | |
| self.assertEqual(card['version'], VERSION) | |
| self.assertEqual(card['eval']['union_fp'], 123) | |
| self.assertEqual(card['eval']['rules_fp'], 98) | |
| self.assertEqual(GAPS, card['gaps']) | |
| self.assertEqual(GAP_IDS, card['gap_ids']) | |
| self.assertEqual(THRESHOLD, card['threshold']) | |
| self.assertEqual(PINNED, RULES_SHA) | |
| digest = hashlib.sha256((HERE / 'scrub_pii.py').read_bytes()).hexdigest() | |
| self.assertEqual(digest, RULES_SHA) | |
| def test_real_tokenizer_preserves_batch_and_unit_alignment(self): | |
| from transformers import AutoTokenizer | |
| from nergal import Encoding | |
| tokenizer = AutoTokenizer.from_pretrained(str(HERE), local_files_only=True, fix_mistral_regex=False) | |
| encoding = Encoding(tokenizer) | |
| words = ['A', '[PII_SPACE]', '1'] | |
| encoded, first = encoding.encode(words) | |
| self.assertIsInstance(encoded['input_ids'][0], list) | |
| self.assertEqual(len(first), len(words)) | |
| self.assertEqual([encoded.word_ids(0)[i] for i in first], [0, 1, 2]) | |
| def test_union_keeps_regex_and_adds_model_spans(self): | |
| from nergal import apply_union, scrub_spans | |
| text = 'Ring 000000000 then extra.' | |
| rules = [{'start': 5, 'end': 14, 'label': 'phone', 'score': 1.0}] | |
| model = [ | |
| {'start': 5, 'end': 14, 'label': 'phone', 'score': 0.99}, | |
| {'start': 20, 'end': 25, 'label': 'pii', 'score': 0.97}, | |
| ] | |
| masked, counts = scrub_spans(text, rules, model, threshold=0.95) | |
| self.assertIn('[Telefon]', masked) | |
| self.assertIn('[PII]', masked) | |
| self.assertGreater(counts['union_placeholder_chars'], counts['rules_placeholder_chars']) | |
| self.assertEqual(counts['model_extra_spans'], 1) | |
| _, rules_chars, _, _ = apply_union(text, rules) | |
| self.assertEqual(counts['rules_placeholder_chars'], rules_chars) | |
| self.assertNotIn('000000000', masked) | |
| self.assertNotIn('extra', masked) | |
| if __name__ == '__main__': | |
| unittest.main() | |