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shashankmc/gysbert-citation-ner-v2b
gysbert-citation-ner-v2b is a token classification model from shashankmc. Use it when you need labels on individual words, such as names.
Fine-tuned emanjavacas/GysBERT (historical Dutch, 1500-1950) for detecting Roman-law citation spans (label LEG, BIO-2 scheme) embedded in 17th/18th-century Dutch court-register prose. Trained entirely on synthetic Dut…
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From the Hugging Face model README
Fine-tuned emanjavacas/GysBERT (historical Dutch, 1500-1950)
for detecting Roman-law citation spans (label LEG, BIO-2 scheme) embedded in 17th/18th-century Dutch
court-register prose. Trained entirely on synthetic Dutch-context sentences with injected citation spans and
hard negatives — see notebooks/finetune_gysbert.ipynb
in the roman-law-converter project.
from transformers import pipeline
pipe = pipeline(
"token-classification",
model="shashankmc/gysbert-citation-ner-v2b",
aggregation_strategy="simple",
)
pipe("sulcx als te sien is in L 1 § 2 ff de minoribus conform L 4 D de instru:")
Held-out agreement against the project's regex-grammar reference method on
16585_Deel_2.txt (a manuscript excluded from synthetic-data generation), at
a 512-subword serving window:
Recommended inference threshold: min_score=0.5. This checkpoint's score
distribution is less sharply separated than the prior v2 checkpoint, which
was calibrated for a 0.9 threshold — using 0.9 here suppresses valid
detections. See docs/experiments.md in the source repo for the full
experiment log (v1/v2/v2b comparisons, window sweeps, error analysis).
BIO-2: O, B-LEG, I-LEG — LEG marks a full Roman-law citation span
(e.g. L 1 § 2 ff de minoribus).