LOCUS Scorers
Collection
Models that score laws and ordinances along particular legal dimensions. • 4 items • Updated
Text Classification
Transformers
Safetensors
modernbert
regression
legal
locus
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("LocalLaws/LOCUS-Enforcement-Discretion")
model = AutoModelForSequenceClassification.from_pretrained("LocalLaws/LOCUS-Enforcement-Discretion")Quick Links
LocalLaws/LOCUS-Enforcement-Discretion
A ModernBERT regression model that scores local-ordinance text along the Enforcement Discretion axis of the LOCUS (Local Ordinances Corpus, United States) dataset.
Fine-tuned from answerdotai/ModernBERT-base. The
target is a TrueSkill mu distilled from pairwise LLM comparisons on the
enforcement discretion axis, then z-score normalized across the training corpus.
Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
tok = AutoTokenizer.from_pretrained("LocalLaws/LOCUS-Enforcement-Discretion")
model = AutoModelForSequenceClassification.from_pretrained("LocalLaws/LOCUS-Enforcement-Discretion")
model.eval()
text = "No person shall keep any swine within the city limits."
enc = tok(text, return_tensors="pt", truncation=True, max_length=2048)
with torch.no_grad():
score = model(**enc).logits.squeeze(-1).item()
print(score)
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Model tree for LocalLaws/LOCUS-Enforcement-Discretion
Base model
answerdotai/ModernBERT-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LocalLaws/LOCUS-Enforcement-Discretion")