davanstrien / dhd-demo

dhd-demo: ModernBERT Jim Crow law classifier

Fine-tuned answerdotai/ModernBERT-base on biglam/on_the_books to classify North Carolina session-law sections (1866–1967) as Jim Crow laws or not.

Built as a live demo for the Digital Humanities & Discovery webinar (2026-05-05) showing end-to-end fine-tuning via hf jobs .

Labels

• 0 = no_jim_crow
• 1 = jim_crow

Training data

biglam/on_the_books — 1,785 expert-labeled chapter/section pairs from NC session laws, 512 positive / 1,273 negative. Split 90/10 (stratified) for train/eval. Class imbalance handled with inverse-frequency cross-entropy weights.

Training setup

Evaluation (held-out 10% split, n=179)

Per-epoch results

Usage

from transformers import pipeline

clf = pipeline("text-classification", model="davanstrien/dhd-demo")
clf("All schools for the white and colored races shall be kept separate.")

Limitations

• Trained on North Carolina laws, 1866–1967. Will not transfer cleanly to other jurisdictions or modern legal language.
• The training labels reflect what named expert sources / project staff flagged. The negative class is "not flagged," not "verified non-discriminatory."
• OCR noise from period scans is present in training and will be present at inference time on similar corpora.
• Eval set is small (n=179); treat the high metrics as encouraging but bounded by sample size.

See the dataset card for full context, including the Algorithms of Resistance framing of the original On the Books project at UNC Chapel Hill Libraries.

Citation

Please cite the original project:

On the Books: Jim Crow and Algorithms of Resistance. University of North Carolina at Chapel Hill Libraries. https://onthebooks.lib.unc.edu — DOI: https://doi.org/10.17615/5c4g-sd44

Framework versions

• Transformers 5.7.0
• PyTorch 2.11.0+cu130
• Datasets 4.8.5
• Tokenizers 0.22.2